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title: 'Research trends in transient receptor potential vanilloid in cardiovascular
disease: Bibliometric analysis and visualization'
authors:
- Lingfeng Zhang
- Yantao Xu
- Yingxu Ma
- Tianjian Xie
- Chan Liu
- Qiming Liu
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC9992894
doi: 10.3389/fcvm.2023.1071198
license: CC BY 4.0
---
# Research trends in transient receptor potential vanilloid in cardiovascular disease: Bibliometric analysis and visualization
## Abstract
### Background
Transient receptor potential vanilloid (TRPV) is one of the transient receptor potential protein groups; cardiovascular system disease is a crucial cause of mortality among people globally.
### Objective
This article is intended to accomplish a bibliometric analysis of the trends and public interest since TRPV was reported for the first time.
### Methods
The article summarized the Web of Science (WOS) Core Collection on the relationship between TRPV and cardiovascular system disease each year from 2000 to 2021. Data extraction and visualization were completed by R package bibliometrix. Keyword citation burst and co-citation networks were generated and produced by CiteSpace. The map evaluating the distribution of country and region was painted in GunnMap 2 (lert.co.nz). The ranking was performed using the Standard Competition Ranking method. Co-authorship and co-occurrence were analyzed with VOSviewer.
### Results
After removing duplicated data, books, conference proceedings, and articles of uncertain age, 493 were included, and 17 were excluded. The pattern of publication years showed that the number of publications increased rapidly from 2008 to 2021 with no peak in the number of publications until 2021. The geographical distribution pattern revealed a considerable gap in the number of publications between the United States, China, and other countries, with East Asian institutions leading the world in this area. The pattern of co-authorship showed that 77 institutions were divided into 19 clusters, each covering one country or region.
These results suggest that intercontinental cooperation among institutions should be strengthened. The core authors section displayed the change in the most published authors. Keyword analysis listed six burst keywords. Co-citation analysis of references from 2011 to 2021 showed the number and centrality of citations to leading articles.
### Conclusion
Our findings reveal trends and public interest in transient receptor potential vanilloid for cardiovascular disease. These findings suggest that the field has experienced significant growth since 2008, with the United States and China in dominant positions. Our findings also suggest that intercontinental cooperation should be strengthened, and that future research hotspots may focus on pharmacological mechanisms and in-depth exploration of drug clinical trials and new clinical disease application areas such as hypertension, diabetes, and cardiac arrhythmias, which could serve as a foundation for further research.
## Introduction
Cardiovascular disease (CVD) has been a global threat to allage groups, mainly middle-aged and elderly individuals, for several decades. In 2019, an estimated 17.9 million CVD deaths occurred worldwide [1]. The number of fatalities has also continued to rise over the past 30 years, reaching 12.3 million in 1990. Fortunately, most cardiovascular diseases can be attributed to behavioral risk factors such as tobacco and alcohol abuse, unhealthy diet and obesity, and physical inactivity, and it is estimated that up to $90\%$ of these diseases can be prevented [2]. Transient receptor potential vanilloid (TRPV) is a members of the transient receptor potential protein group that regulates calcium ions and senses heat and inflammation by activating vanilloid receptors. TRPV was first discovered in Caenorhabditis elegans in 1997 [3]. As research has progressed, it has been found that TRPV plays a role in cardiovascular diseases, such as cardiac failure, arrhythmogenesis, and pulmonary arterial hypertension [4].
## Data sources and search strategy
Bibliographic data were obtained from the Web of Science Core Collection (WOScc). The search strategy designed is that: (TS = (Cardiovascular) OR TI = (Cardiovascular) OR AB = (Cardiovascular)) AND (TS = (TRP channels) OR TI = (TRP channels)) OR (AB = (TRP channels) OR TS = (transient receptor potential channels) OR TI = (transient receptor potential channels) OR AB = (transient receptor potential channels)) OR TS = (transient receptor potential vanilloid) OR TI = (transient receptor potential vanilloid) OR AB = (transient receptor potential vanilloid) OR (TS = (TRPV) OR TI = (TRPV) OR AB = (TRPV)) (TS = Topic; TI = Title; AB = Abstract). Limitations were English, original research, and review, and all the documents were filtered between January 01, 2000 and December 31, 2021.
Four hundred seventy articles were extracted from the Web of Science Core Collection. Book chapters, meeting abstracts, proceeding papers, editorial material, and early access were eliminated, leaving 453 documents for the bibliometric analysis and visualization. A flowchart presented more details. The search was completed on August 14th, 2022 (Figure 1).
**Figure 1:** *Flowchart showing the steps to identify and filter papers. Publication years were limited from 2000 to 2021. The language was limited to English.*
## Data extraction and analysis
The following characteristics were included in the retrieval publication year, country and region, origin (including organization and institution), publication source, core authors, keywords, and primary references. A detailed search strategy was provided in Multimedia Appendix 1. Bibliometric analysis and visualization were performed using VOSviewer (version 1.6.18, Leiden University), bibliometrix package in R (version 4.2.1, R Foundation), and CiteSpace. SCImago Graphica (version Beta 1.0.23) was used to create a graph illustrating the number of posts in different regions. GunnMap 2 (lert.co.nz) was used to assess geographical differences in distribution. The ranking was determined using the standard competition ranking method. Co-authorship analysis, co-occurrence analysis, and visualization were conducted using VOSviewer.
## Distribution of publications
The bar chart showed the chronological distribution (Figure 2A). From 2000 to 2006, the number of publications per year increased steadily, with a near-exponential growth trend, but did not reach a peak yet. A dramatic increase followed this in the number of publications per year from 2007 to 2008 and 2009 to 2010. Figure 2B graphically showed the total number of cumulative publications. Chronologically, the growth of publications on the specific topic shows a relatively slow growth rate in the cumulative number of publications from 2000 to 2006, and a sharp increase from 2006 to 2008. A peak briefly occurred in 2008 and then did not occur until 2021.Overall, the number of publications and their growth rate continued to grow steadily from 2000 to 2021. The annual percentage growth rate of publications is 14.7.
**Figure 2:** *Distribution of chronological publication (A). The number of publications accumulated and the number per year (B).*
According to the geographical distribution pattern, 453 articles were published from 50 countries and regions. A heatmap depicted the 50 countries and regions that published articles with green representing a value to 1, and red representing a value close to 154 (Figure 3). The 10 countries with the most publications were listed in Table 1. Overall, the United States had the highest number of publications at 154 out of 453, or $34.00\%$, far surpassing China with 119 out of 453 publications, or $26.27\%$. England and Japan both had 41 out of 453 publications, or $9.05\%$. In terms of citations, the United States and China were far ahead. Interestingly, the United States and China had the highest number of publications and also had a larger percentage of citations than the sum of the 3rd to 15th countries, comprising $60.26\%$ of all publications and $41.19\%$ of all citations, respectively (Figure 4).
**Figure 3:** *Geographical distribution of 50 countries is depicted, with the color bar on the left being linear.* TABLE_PLACEHOLDER:Table 1 **Figure 4:** *The 15 most prolific countries and regions were listed with the number of publications and citations.*
## Analysis of leading organizations and public sources
The publishing organization information was analyzed with VOSviewer. There were 453 articles contributed by 660 institutions. After merging duplicate organizations and excluding irrelevant ones, 21 organizations that met the inclusion threshold were visualized. The 10 organizations with the most published articles were listed in Table 1. The 10 organizations with the most published articles are listed in Table 1. The most influential organization was the Third Military Medical University with 14 out of 453 publications, or $3.09\%$, followed by the Fourth Military Medical University with 10 out of 453 publications, or $2.21\%$, and Kyushu University with 10 out of 453 publications, or $2.21\%$. Among the most prolific organizations, 4 out of 10 were from China, and 3 out of 10 were from Japan. In total, 7 out of 10 were from East Asian organizations, which was far more than the 2 from the United States that published the most documents. A co-authorship analysis of the organizations was also conducted (Figure 5). It revealed that all 21 most published institutions were grouped into five clusters, each roughly representing the core organization from an East Asian country. The size of the nodes in the graph indicates the frequency of occurrence. We selected some of the most frequent institutions, which indicates that these institutions have a strong presence in the cluster and are representative. The red, yellow, green, blue, and purple clusters included Third Military Medical University and Forth Military Medical University in China, Kyushu University in Japan, Katholieke University from Taiwan, China, Wuhan University in China, and Kyoto University in Japan, in, respectively. The National Institute of Environmental Health Sciences (NIEHS) and the University of Leeds in the green cluster were two of the few influential institutions from western countries. This result showed that it was evident that East Asian organizations were the dominant leader in this topic and that intercontinental cooperation among various institutions should be strengthened, especially for institutions in the United States, England, and Germany, as the United States had the most significant number of publications but had a mismatched number of core institutions.
**Figure 5:** *Co-authorship analysis of organizations. Co-authorship analysis of organizations showed by plots. It was normalized in the fractionalization method and weighted by the number of publications. The thickness of the lines indicates the strength of co-authorship relationships. Different clusters were painted in different colors.*
After analyzing published materials, the 10 most published journals, along with their impact factor (IF) in 2020 and 2021 for research on the role of TRPV in the cardiovascular disease were extracted (Table 2). The British Journal of Pharmacology (impact factor 9.473), with 19 documents, was the most prolific journal, ranking first. The American Journal of Physiology-Heart and Circulatory Physiology (18 publications, IF 5.125) and the International Journal of Molecular Sciences (13 publications, IF 6.208) followed as the second and third most published journals, respectively. The impact factor of the 10 journals ranged from 3.493 for Channels to 13.081 for Cardiovascular Research. These findings suggest that Cardiovascular Research may be the most influential journal in in the field of TRPV and cardiovascular disease.
**Table 2**
| Rank | Journal | Publications | Impact factor (2020) | Impact factor (2021) |
| --- | --- | --- | --- | --- |
| 1 | British Journal of Pharmacology | 19 | 8.74 | 9.473 |
| 2 | American Journal of Physiology-Heart and Circulatory Physiology | 18 | 4.733 | 5.125 |
| 3 | International Journal of Molecular Sciences | 13 | 5.924 | 6.208 |
| 4 | Cardiovascular Research | 12 | 10.787 | 13.081 |
| 5 | European Journal of Pharmacology | 12 | 4.432 | 5.195 |
| 6 | Frontiers in Physiology | 12 | 4.566 | 4.755 |
| 7 | Hypertension | 10 | 10.19 | 9.897 |
| 8 | Channels | 8 | 2.581 | 3.493 |
| 9 | Cell Calcium | 7 | 6.817 | 4.69 |
| 10 | Journal of Biological Chemistry | 7 | 5.157 | 5.486 |
## Analysis of co-authorship and core authors
The data of co-authors was analyzed using VOSviewer. 2,510 authors contributed to a total of 453 publications.
The most important evaluation criteria for core authors included the number of publications, total citations, and H-index. Based on these criteria, the most productive authors were identified and visualized (as shown in Table 3; Figure 6). Zhu Zhiming, director and professor of Cardiology and Endocrinology at Third Military Medical University and Hypertension and Endocrinology at the Center for Hypertension and Metabolic Diseases at Daping Hospital, Army Medical University, was the most productive author in this field, having published 11 articles and received a total of 564 citations. Liu Daoyang, professor of the department of Hypertension and Endocrinology at the Center for Hypertension and Metabolic Diseases at Daping Hospital, Army Medical University, was the second most productive author with 10 publications and 504 total citations. Rhian M. Touyz is the Executive Director and Chief Scientific Officer of the Research Institute of the McGill University Health Centre. Three of the 10 most cited authors were from Hypertension and Endocrinology at the Center for Hypertension and Metabolic Diseases at Daping Hospital, Army Medical University (formerly known as Third Military Medical University before 2017): Zhu Zhiming, Liu Daoyan, and Gao Peng. It is clear that Hypertension and Endocrinology at the Center for Hypertension and Metabolic Diseases at Daping Hospital, Army Medical University (formerly known as Third Military Medical University before 2017) was a dominant player in this research field.
The results showed that the top 10 most prolific authors came from East Asia, Europe, and the United States. It was unusual for East Asian researchers, particularly those from Third Military Medical University, to have the same level of influence as researchers from Europe and the United States. It is possible that the smaller number of data may have skewed the results. Figure 7 is an overlay visualization of the co-authorship relationship among 2,510 authors, 270 of which met the inclusion threshold. The figure showed that the most influential authors, such as Zhu, Zhiming, and Liu, Daoyan had close collaborations. The average year of publication, which represents the average of all relevant publications by these authors, indicates the time period in which the authors were most active on the topic. The figure showed that the average year of publication ranged from 2016 to 2018. Some other authors, such as Moccia Francesco, published more actively after 2020 and were likely to become leaders in the future.
**Figure 7:** *Overlay visualization of co-authorship analyzed in the method of Association strength, weighted by citations, scored by average year of publication. The strength of the relationships is indicated by the thickness of the lines. The average year of publication is represented by the color of the circle.*
## Analysis of keywords and burst terms
All 453 documents were analyzed to extract 1,339 keywords. The co-occurrence of these keywords was analyzed using overlay visualization and a network of author-given keywords after duplicates were removed. Of the 1,339 keywords, the 113 most frequently occurring ones that met the inclusion threshold were divided into 2 clusters (shown in Figure 8) and grouped by publication date (between 2012 and 2018; shown in Figure 9). The range of publication dates corresponds to the high-growth phase of publication seen in Figure 2. The keywords were distinguished into five main clusters for the network. The green cluster included keywords related to transient receptor potential channels activated by vanilloid chemicals and metabolism dysfunction in cells or the body, such as “TRPV1,” “capsaicin,” “obesity,” “diabetes,” “apoptosis,” “oxidative stress,” “TRPA1,” “cardiomyocytes,” and “metabolic syndrome.” This may suggest that TRPV1 affects metabolism through apoptosis. The yellow cluster included terms related to vascular dysfunction and inflammation, such as “hypertension,” “blood pressure,” “endothelium,” “TRPV4,” and “inflammation,” which may indicate that TRPV4 is associated with vascular dysfunction and inflammation. The blue cluster included terms related to vascular and heart remodeling and related disease and TRPC, such as “calcium,” “heart failure,” “arrhythmia,” “hypertrophy,” “TRPC,” “vascular remodeling,” “atherosclerosis.” This may suggest that these vascular and heart remodeling processes have more links with TRPC. The purple cluster included terms r cannabinoid substances and proliferation, such as “anandamide,” “calcium channels,” “proliferation,” “endocannabinoid,” and “vascular muscle cells.” The red cluster included terms related to TRP channels and factors influencing cardiovascular disease in cells or the body, such as “TRP channels,” “ion channel,” “cardiovascular disease,” “endothelial cells,” “endothelial dysfunction,” “hydrogen peroxide,” “nitric oxide.”
**Figure 8:** *The co-occurrence analysis of keywords was shown by the co-occurrence of keywords normalized in the Linlog/modularity method, weighted by an occurrence for each plot.* **Figure 9:** *These keywords were grouped by year of publication, and the color of the circles represented the average year.*
Burst keywords analyzed with CiteSpace. Figure 10 showed six keywords with the highest frequency burst of change, indicating a significant keyword change in a short period. In these six words, “smooth muscle cell” and “dysfunction” were either histologic or pathologic level keywords with the most muscular strength. For hot years, “smooth muscle cell” continued from 2007 to 2012 and “dysfunction” continued from 2018 to 2021, suggesting possible pathological mechanisms. The order of “smooth muscle cell,” “smooth muscle,” “in vivo,” and “cardiovascular disease” might reveal the research process from the cellular to the system level. “ Up regulation” and “dysfunction” were sustained research hot spots for the near 5 years; both were future research hotspots in this field.
**Figure 10:** *The 6 most vital frequency bursts keywords. Many frequency bursts indicate that a variable has changed significantly in a short period. The red bars indicate the duration of the bursts.*
## Citation analysis
The citation counts of publications were mainly extracted using bibliometrix package. The 10 most cited publications were listed in Table 4. In total, the number of citations ranged from 198 to 314. The article published in the Pharmacological Reviews: “Transient receptor potential channels as drug targets: from the science of basic research to the art of medicine,” ranked first and had 314 total citations. Only two of the 10 most cited articles were original articles (“H2S and NO cooperatively regulate vascular tone by activating a neuroendocrine HNO-TRPA1-CGRP signaling pathway,” published in Nature Communications and “Activation of TRPV1 By Dietary Capsaicin Improve Endothelium-Dependent” published in Cell Metabolism). These two high-quality articles were published in sub-publications of Cell or Nature, demonstrating an extraordinary research level. The rest eight articles were all reviews. Undoubtedly, reviews were cited more often because of their comprehensive feature. Five of the eight reviews were published in journals of the pharmacologic category, meaning that TRPV research is mainly in the field of pharmacology. Although “H2S and NO cooperatively regulate vascular tone by activating a neuroendocrine HNO-TRPA1-CGRP signaling pathway” was the latest publication of 10, it had forth ranked citations, which also proves that it is a high-quality paper.
**Table 4**
| Rank | Title | DOIa | Source | Publication date | Total citationsb |
| --- | --- | --- | --- | --- | --- |
| 1 | Transient receptor potential channels as drug targets: from the science of basic research to the art of medicine (13) | 10.1124/pr.113.008268 | Pharmacological Reviews | Jul 2014 | 314 |
| 2 | Transient Receptor Potential Channels in Cardiovascular Function and Disease (14) | 10.1161/01.RES.0000233356.10630.8a | Circulation Research | Jul 2006 | 297 |
| 3 | Recent advances in the study of capsaicinoids and capsinoids (15) | 10.1016/j.ejphar.2010.09.074 | European Journal of Pharmacology | Jan 2011 | 285 |
| d4 | H2S and NO cooperatively regulate vascular tone by activating a neuroendocrine HNO-TRPA1-CGRP signaling pathway (16) | 10.1038/ncomms5381 | Nature Communications | Oct 2019 | 268 |
| 5 | Physiology and pathophysiology of canonical transient receptor potential channels (17) | 10.1096/fj.08-119495 | Faseb Journal | Jul 2014 | 252 |
| 6 | Transient receptor potential (TRP) channels: a clinical perspective (18) | 10.1111/bph.12414 | British Journal of Pharmacology | May 2014 | 227 |
| 7 | Activation of TRPV1 by dietary capsaicin improves endothelium-dependent vasorelaxation and prevents hypertension (19) | 10.1016/j.cmet.2010.05.015 | Cell Metabolism | Apr 2017 | 224 |
| 8 | Recent developments in vascular endothelial cell transient receptor potential channels (20) | 10.1161/01.RES.0000187473.85419.3e | Circulation Research | Oct 2005 | 207 |
| 9 | Systemic activation of the transient receptor potential vanilloid subtype 4 channel causes endothelial failure and circulatory collapse: Part 2 (21) | 10.1124/jpet.107.134551 | Journal of Pharmacology and Experimental Therapeutics | Aug 2008 | 200 |
| 10 | Unraveling the mystery of capsaicin: a tool to understand and treat pain (22) | 10.1124/pr.112.006163 | Pharmacological Reviews | Oct 2012 | 198 |
Co-citation references from 2011 to 2021 were analyzed and visualized by CiteSpace (version 5.8R3; Figure 11). In Figure 11, the size of a circle represents the number of citations, and the purple area of the circle depicts the centrality. The analysis showed that there was no dominant centrality but a few inferior centralities, such as Eder [5], Kuwahara [6], Mathar [7], Harada [8], Watanabe [9], Venkatachalam [10], Sonkusare [11], and Earley [12]. The co-citations were largely dispersed.
**Figure 11:** *Co-citation analysis of references. Using VOSviewer, depict a co-citation analysis of references from 2010 to 2020. In VOSviewer, the size of a circle indicates number of citations. The purple area of the circle indicates the centrality of a document.*
## Principal findings
This study presents the most recent systematic information about the role of TRPV in cardiovascular disease. It provides an overview of this research area and identifies potential future areas of focus for interested researchers. The study included a comprehensive search of the Web of Science Core Collection for published literature on this topic that was published before December 31, 2021. A total of 453 bibliographies were retrieved and analyzed using bibliometric techniques. The analysis of chronology showed that from 2000 to 2006, the annual number of publications increased slowly, with no clear research trends. However, there was a sharp increase in the annual number of publications from 2007 to 2008 and 2009 to 2010, likely due to the discovery and study of several members of the TRPV channel family between 1998 and 2008 (23–28). As the study of TRPV channels continues to grow, especially following the recognition of this topic with a Nobel Prize, it is likely that we will see even more research in this field in the future.
The United *States is* the leading country in terms of the number of publications on TRPV and cardiovascular disease, but most of this literature is produced in collaboration with institutions in East Asia. China is the second most productive country, with East Asian countries leading the field overall. Other countries and regions, such as the United Kingdom, Japan, and European countries, have also made significant contributions. Despite the high number of publications from the United States, East Asian institutions and authors are more productive in this field. Of the 10 most productive organizations, seven are from East Asian countries (four from China and three from Japan), and five of the 10 most productive authors are from East Asia (three from China and two from Japan). The Third Military Medical University in *China is* particularly dominant, with three of the 10 most cited authors affiliated with the institution. Cardiovascular *Research is* the most influential journal on this topic in terms of the number of publications and impact factor (IF). In 2021, it published two TRPV-related papers, one of which was about how omega-3 fatty acids improve flow-induced vasodilation by enhancing TRPV4 in arteries from diet-induced obese mice. The other was an editorial about the first paper. The IF of the top 10 core journals has generally increased, with the exception of Channels. However, it is difficult to conclude that the impact of all journals has increased due to the overall increase in IF of journals in recent years due to the COVID-19 pandemic.
The results of the network analysis showed that most collaborations among authors occurred within the same continent, particularly within the same institution or in East Asia. There was a low level of collaboration between continents. These findings indicate that there is a need to improve intercontinental cooperation. In addition, the analysis of keywords and burst keywords revealed significant changes in research focus. There was a shift from studying action potentials, calcium channels, and anandamide to focusing on cardiomyocytes, endothelium, arrhythmias, diabetes, and oxidative stress. This suggests that research has moved from examining basic molecular mechanisms to studying the physiological and pathological mechanisms of specific diseases, and from focusing on smooth muscle and smooth muscle cells to examining upregulation and dysfunction. This indicates that theories are becoming more advanced and that there is increasing interest in exploring the role of TRPV in cardiovascular disease. The discovery of the TRPV channel family, including TRPV1, in 1998 marked the beginning of research in this field. However, it wasn’t until the subtypes and mechanisms of these channels were fully understood in the years following 2008 that research on TRPV channels began to accelerate. Currently, the focus of research has shifted from understanding the mechanisms of action of these channels to exploring their pharmacological mechanisms, with small molecule antagonists of channels like TRPV1, TRPV3, and TRPA1 entering clinical trials [18]. As the study of TRPV channels continues to grow, especially following the recognition of this topic with a Nobel Prize, it is likely that we will see even more research in this field in the future.
## Strengths and limitations
This analysis provided more comprehensive and intuitive information than a literature review due to its use of quantitative statistical analysis and visualization. But it also has some limitations: [1] some minor subtopics have not been addressed or reflected due to the breadth of the TRPV field; [2] newly published articles are not cited in a timely manner; [3] our analysis focused on English manuscripts and data from non-English sources were ignored; [4] in addition to the quality of the articles, the number of citations can be influenced by a number of factors, including post-merger deletions, the tendency not to cite competitors or conflicting results, attitudes toward citing high IF core journals and attitudes toward citing review articles rather than original research, and country or language preferences; [5] this analysis included fewer studies, which may prone to statistical error and bias; and [6] only the citations and abstracts of the literature were analyzed and may have missed some essential information in the main text, such as the authors’ perspectives, outlook on the field, and their forward-looking opinions on the field.
## Conclusion
This paper focuses on the role of TRPV in the cardiovascular system and analyzes the most recent articles in the field of TRPV over the last two decades, including their publication years, regional distribution, publishing institutions, publishing journals, authors, keywords, and burst keywords and co-citation analysis. Our work provides a comprehensive list of landmark publications in TRPV and cardiovascular disease-related research and recognizes the contributions of critical authors and institutions. In addition, we summarize the trends and research directions in TRPV and cardiovascular disease-related research. Currently, calcium channels, metabolism, inflammation, myocardial remodeling, endothelial cell dysfunction, and the TRPV channel family remain the main research directions in this field. Studies on TRPV channel expression upregulation and dysfunction will be a hot topic in the future. Given the increasing burden of cardiovascular disease worldwide and the depth of human research on the TRPV channel, the interest in TRPV channels will continue to grow. Research on the TRPV channel will ultimately change how more clinical treatments are administered.
Regarding collaboration between authors, it shows that most collaboration occurs within continents, while cooperation between continents is rare. The same phenomenon is revealed when analyzing joint authors of organizations, where clusters offer intricate links within countries and fewer links between clusters. These results suggest that cooperation should be strengthened between continents.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.
## Author contributions
LZ: conceptualization, methodology, data curation, investigation, formal analysis, and writing – original draft. YX: data curation, software, and writing – original draft. YM: data curation and visualization. TX: visualization. CL: supervision, validation, and writing – review and editing. QL: conceptualization, funding acquisition, resources, supervision, and writing – review and editing. All authors contributed to the article and approved the submitted version
## Funding
This study was supported by the Hunan Province Innovative Project (no. 2020SK1013), the National Natural Science Foundation of China (no. 82070356), and the Natural Science Foundation Hunan Province China (nos. 2021JJ30033 and 2021JJ40870).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2023.1071198/full#supplementary-material
## References
1. Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP. **Heart disease and stroke statistics update: a report from the American Heart Association**. *Circulation* (2019) **139** e56-e528. DOI: 10.1161/CIR.0000000000000659
2. O'donnell MJ, Chin SL, Rangarajan S, Xavier D, Liu L, Zhang H. **Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study**. *Lancet* (2016) **388** 761-75. DOI: 10.1016/S0140-6736(16)30506-2
3. Colbert HA, Smith TL, Bargmann CI. **OSM-9, a novel protein with structural similarity to channels, is required for olfaction, mechanosensation, and olfactory adaptation in Caenorhabditis elegans**. *J Neurosci* (1997) **17** 8259-69. DOI: 10.1523/JNEUROSCI.17-21-08259.1997
4. Singh K, Luo N, Rosenberg P, Szallasi A. **Chapter 20 – TRP channels in cardiovascular disease**. *TRP Channels as Therapeutic Targets* (2015) 365-83
5. Earley S, Brayden JE. **Transient receptor potential channels in the vasculature**. *Physiological Reviews* (2015) **95** 645-90. PMID: 25834234
6. Eder P, Molkentin JD. **TRPC channels as effectors of cardiac hypertrophy**. *Circulation Research* (2011) **108** 265-72. PMID: 21252153
7. Venkatachalam K, Montell C. *TRP Channels. Annual Review of Biochemistry* (2007) **76** 387-417
8. Harada M, Luo X, Qi XY, Tadevosyan A, Maguy A, Ordog B. **Transient Receptor Potential Canonical-3 Channel–Dependent Fibroblast Regulation in Atrial Fibrillation**. *Circulation* (2012) **126** 2051-64. PMID: 22992321
9. Kuwahara K, Wang Y, Mcanally J, Richardson JA, Bassel-Duby R, Hill JA. **TRPC6 fulfills a calcineurin signaling circuit during pathologic cardiac remodeling**. *The Journal of Clinical Investigation* (2006) **116** 3114-26. PMID: 17099778
10. Mathar I, Vennekens R, Meissner M, Kees F, Van Der Mieren G, Camacho Londoño JE. **Increased catecholamine secretion contributes to hypertension in TRPM4-deficient mice**. *The Journal of Clinical Investigation* (2010) **120** 3267-79. PMID: 20679729
11. Sonkusare SK, Dalsgaard T, Bonev AD, Nelson MT. **Inward rectifier potassium (Kir2.1) channels as end-stage boosters of endothelium-dependent vasodilators**. *J Physiol* (2016) **594** 3271-85. PMID: 26840527
12. Watanabe H, Vriens J, Prenen J, Droogmans G, Voets T, Nilius B. **Anandamide and arachidonic acid use epoxyeicosatrienoic acids to activate TRPV4 channels**. *Nature* (2003) **424** 434-8. PMID: 12879072
13. Nilius B, Szallasi A. **Transient receptor potential channels as drug targets: from the science of basic research to the art of medicine**. *Pharmacol Rev* (2014) **66** 676-814. DOI: 10.1124/pr.113.008268
14. Inoue R, Jensen LJ, Shi J, Morita H, Nishida M, Honda A. **Transient receptor potential channels in cardiovascular function and disease**. *Circ Res* (2006) **99** 119-31. DOI: 10.1161/01.RES.0000233356.10630.8a
15. Luo XJ, Peng J, Li YJ. **Recent advances in the study on capsaicinoids and capsinoids**. *Eur J Pharmacol* (2011) **650** 1-7. DOI: 10.1016/j.ejphar.2010.09.074
16. Eberhardt M, Dux M, Namer B, Miljkovic J, Cordasic N, Will C. **H2S and NO cooperatively regulate vascular tone by activating a neuroendocrine HNO-TRPA1-CGRP signalling pathway**. *Nat Commun* (2014) **5** 17. DOI: 10.1038/ncomms5381
17. Abramowitz J, Birnbaumer L. **Physiology and pathophysiology of canonical transient receptor potential channels**. *FASEB J* (2009) **23** 297-328. DOI: 10.1096/fj.08-119495
18. Kaneko Y, Szallasi A. **Transient receptor potential (TRP) channels: a clinical perspective**. *Br J Pharmacol* (2014) **171** 2474-507. DOI: 10.1111/bph.12414
19. Yang DC, Luo ZD, Ma ST, Wong WT, Ma LQ, Zhong JA. **Activation of TRPV1 by dietary capsaicin improves endothelium-dependent Vasorelaxation and prevents hypertension**. *Cell Metab* (2010) **12** 130-41. DOI: 10.1016/j.cmet.2010.05.015
20. Yao XQ, Garland CJ. **Recent developments in vascular endothelial cell transient receptor potential channels**. *Circ Res* (2005) **97** 853-63. DOI: 10.1161/01.RES.0000187473.85419.3e
21. Willette RN, Bao WK, Nerurkar S, Yue TL, Doe CP, Stankus G. **Systemic activation of the transient receptor potential vanilloid subtype 4 channel causes endothelial failure and circulatory collapse: part 2**. *J Pharmacol Exp Ther* (2008) **326** 443-52. DOI: 10.1124/jpet.107.134551
22. O'neill J, Brock C, Olesen AE, Andresen T, Nilsson M, Dickenson AH. **Unravelling the mystery of capsaicin: a tool to understand and treat pain**. *Pharmacol Rev* (2012) **64** 939-71. DOI: 10.1124/pr.112.006163
23. Caterina MJ, Rosen TA, Tominaga M, Brake AJ, Julius D. **A capsaicin-receptor homologue with a high threshold for noxious heat**. *Nature* (1999) **398** 436-41. DOI: 10.1038/18906
24. Caterina MJ, Schumacher MA, Tominaga M, Rosen TA, Levine JD, Julius D. **The capsaicin receptor: a heat-activated ion channel in the pain pathway**. *Nature* (1997) **389** 816-24. DOI: 10.1038/39807
25. Clapham DE, Julius D, Montell C, Schultz G. **International Union of Pharmacology. XLIX. Nomenclature and structure-function relationships of transient receptor potential channels**. *Pharmacol Rev* (2005) **57** 427-50. DOI: 10.1124/pr.57.4.6
26. Peng JB, Chen XZ, Berger UV, Vassilev PM, Tsukaguchi H, Brown EM. **Molecular cloning and characterization of a channel-like transporter mediating intestinal calcium absorption**. *J Biol Chem* (1999) **274** 22739-46. DOI: 10.1074/jbc.274.32.22739
27. Smith GD, Gunthorpe MJ, Kelsell RE, Hayes PD, Reilly P, Facer P. **TRPV3 is a temperature-sensitive vanilloid receptor-like protein**. *Nature* (2002) **418** 186-90. DOI: 10.1038/nature00894
28. Strotmann R, Harteneck C, Nunnenmacher K, Schultz G, Plant TD. **OTRPC4, a nonselective cation channel that confers sensitivity to extracellular osmolarity**. *Nat Cell Biol* (2000) **2** 695-702. DOI: 10.1038/35036318
|
---
title: A cross-sectional study of relationships between social risks and prevalence
and severity of pediatric chronic conditions
authors:
- Annelise Brochier
- Emily Messmer
- Mikayla Gordon Wexler
- Stephen Rogers
- Erika Cottrell
- Yorghos Tripodis
- Arvin Garg
journal: BMC Pediatrics
year: 2023
pmcid: PMC9992899
doi: 10.1186/s12887-023-03894-6
license: CC BY 4.0
---
# A cross-sectional study of relationships between social risks and prevalence and severity of pediatric chronic conditions
## Abstract
### Background
To examine the differential relationships between seven social risk factors (individually and cumulatively) with the prevalence and severity of asthma, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and overweight/obesity in children.
### Methods
Using the 2017–2018 National Survey of Children's Health, we examined associations between social risk factors (caregiver education, caregiver underemployment, discrimination, food insecurity, insurance coverage, neighborhood support, and neighborhood safety) and the prevalence and severity of asthma, ADHD, ASD, and overweight/obesity. We used multivariable logistic regression to assess the relationship between individual and cumulative risk factors with each pediatric chronic condition, controlling for child sex and age.
### Results
Although each social risk factor was significantly associated with increased prevalence and/or severity of at least one of the pediatric chronic conditions we investigated, food insecurity was significantly associated with higher disease prevalence and severity for all four conditions. Caregiver underemployment, low social support, and discrimination were significantly associated with higher disease prevalence across all conditions. For each additional social risk factor a child was exposed to, their odds of having each condition increased: overweight/obesity (aOR: 1.2, $95\%$ CI: [1.2, 1.3]), asthma (aOR: 1.3, $95\%$ CI: [1.2, 1.3], ADHD (aOR: 1.2, $95\%$ CI: [1.2, 1.3]), and ASD (aOR: 1.4, $95\%$ CI: [1.3, 1.5]).
### Conclusions
This study elucidates differential relationships between several social risk factors and the prevalence and severity of common pediatric chronic conditions. While more research is needed, our results suggest that social risks, particularly food insecurity, are potential factors in the development of pediatric chronic conditions.
## Background
In the United States (US), more than $40\%$ of children have a chronic condition such as asthma, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD) or obesity [1]. Social determinants of health (SDOH), the social circumstances in which people are born, live, work, and age, are key drivers of health and disease—especially for children, who are particularly vulnerable to adverse SDOH, also termed social risk factors, such as poor neighborhood conditions, housing instability, and food insecurity (FI) [2–5]. Yet, it remains unclear how multiple social risks differentially impact the prevalence and severity of common childhood chronic diseases. It is hypothesized, and demonstrated in animal studies, that social risks can act as toxic stressors that disrupt the hypothalamic-pituitary-adrenocortical axis and cause epigenetic changes, leading to the development of chronic diseases [6–8]. The cumulative risk model posits that having multiple risk factors increases the likelihood of developing pediatric disease and developmental and behavioral problems [9]. Additionally, longitudinal studies demonstrate that increased time spent living in poverty in childhood is associated with elevated cortisol and dysregulated cardiovascular response, mediated by cumulative risk exposure; however, little is known about the relationship between specific adverse SDOH and common pediatric conditions [9, 10].
Previous studies have demonstrated that FI is associated with worse general health as well as acute and chronic health conditions in children and adults [11]. Increased asthma morbidity in children is associated with poverty, poor housing, less neighborhood safety, and low social and community support [12]. Furthermore, an analysis of the 2003–2004 National Survey of Children’s Health (NSCH) dataset found that Black, Latinx, and low-income families reported lower prevalence of ASD than White and middle-to-high-income families, yet reported higher ASD severity [13]. A systematic review of socioeconomic variables and chronic conditions found that parental socioeconomic status was a determinant of health-related quality of life for children with a chronic condition [14]. Understanding the differential association of various social risk factors with common pediatric chronic conditions may inform management strategies for chronic diseases (e.g., screening for social risk factors at healthcare visits for children with asthma), and inform social and health policies.
To advance our understanding of SDOH and their influence on the development of childhood chronic conditions, we used nationally representative data to assess the associations between multiple social risks—individually and cumulatively—with prevalence and severity of asthma, ADHD, ASD, and overweight/obesity.
## Data set
We conducted a secondary data analysis of the 2017–2018 NSCH—a nationally representative survey conducted annually by the Maternal and Child Health Bureau and the US Census Bureau [15]. The NSCH is administered via web-based and mail instruments. US households are randomly contacted by mail to identify those with one or more children under 18 years old. In each eligible household, one child is randomly selected as the subject of the survey and the respondent is any adult in the household who has knowledge of the child’s health and health care; of note, we refer to these respondents as caregivers hereafter. The NSCH collects data on children’s physical and mental health; access and quality of health care; social risks including family, neighborhood, and school characteristics; as well as adverse childhood experiences. The 2017–2018 two-year combined NSCH dataset comprises 52,129 completed surveys, and includes 4,041 children with asthma, 4,540 children with ADHD, 1,321 children with ASD, and 7,255 children with overweight/obesity.
## Independent variables
The US Department of Health and Human Services’ Healthy People 2030 initiative classifies SDOH by five different domains: economic stability; education access and quality; health care access and quality; neighborhood and built environment; and social and community context [16]. We selected seven social risk factors (caregiver education less than high school, caregiver underemployment, child’s experience of discrimination, household FI, gap in child’s insurance coverage, neighborhood social support, and safety of child’s neighborhood) assessed through the below NSCH questions that map to relevant Healthy People 2030 SDOH domains (Fig. 1). Consistent with prior studies, we used NSCH-established scoring criteria to define each social risk factor and categorize each child’s exposure [17–19].Fig. 1Social risks from the National Survey of Children’s Health (NSCH) mapped to Healthy People 2030’s social determinants of health (SDoH) domains.
## Economic stability
The two social risk factors we mapped to economic stability were “caregiver underemployment” and “food insecurity”. Children who lived in households where none of the adult primary caregivers were employed at least 50 of the last 52 weeks were categorized as having “caregiver underemployment.” Respondents were also asked to describe their “ability to afford the food the child’s family needed in the past 12 months”. If the caregiver responded with “We could always afford enough to eat but not always the kinds of food we should eat”. “ Sometimes we could not afford enough to eat,” or “Often we could not afford enough to eat,” the child was categorized “food insecure”.
## Education access and quality
To measure education access and quality, we examined caregivers’ highest level of educational attainment. We categorized children whose caregivers’ education level was “Less than high school” as having the “caregiver education” social risk factor.
Children whose caregivers’ highest level of educational attainment was less than high school had significantly increased odds of being overweight/obese (aOR: 1.8; $95\%$ CI: 1.3, 2.3) and of moderate/severe ASD (aOR: 3.3; $95\%$ CI: 1.1, 10.4).
## Health and healthcare
Children whose caregivers indicated that the child did not have consistent health insurance coverage during the preceding 12 months were categorized as having the “insurance coverage gap” risk factor, which we mapped to the health and healthcare SDOH domain.
Lack of consistent health insurance was significantly associated with higher odds of overweight/obesity prevalence (aOR 1.3; $95\%$ CI: 1.1, 1.7) and ADHD severity (aOR: 2.0; $95\%$ CI: 1.2, 3.2).
## Neighborhood and built environment
When caregivers indicated that they “somewhat disagree” or “definitely disagree” with the statement “the child is safe in our neighborhood,” we considered the child to have the “unsafe neighborhood” social risk factor within the neighborhood and built environment SDOH domain.
Living in an unsafe neighborhood was significantly associated with higher odds of asthma (aOR: 1.9; $95\%$ CI: 1.3, 2.7), ADHD (aOR: 1.5; $95\%$ CI: 1.1, 2.0), and ASD (aOR: 2.1; $95\%$ CI: 1.1, 4.1), as well as increased odds of moderate/severe asthma (aOR: 2.7; $95\%$ CI: 1.4, 5.3).
## Social and community context
We mapped two social risk factors to the social and community context domain: low social support and discrimination. The NSCH measures neighborhood support based on responses to three statements: “People in the neighborhood help each other out”; “We watch out for each other’s children in this neighborhood”; and “When we encounter difficulties, we know where to go for help in our community.” Only surveys with valid responses to all three questions were included in the denominator for the “neighborhood support” variable. If the caregiver did not respond “definitely agree” to at least one of the items and “somewhat agree” or “definitely agree” to the other two items, we categorized the child as having low social support.
The NSCH also inquires about 9 adverse childhood experiences, one of which is “the child was treated or judged unfairly because of his/her race or ethnic group.” If the caregiver responded “Yes” to this specific adverse childhood experience, we considered the child to have experienced discrimination.
Low social support was significantly associated with all four chronic conditions; children with low social support had higher odds of being overweight/obese (aOR: 1.3; $95\%$ CI: 1.1–1.4) and of having asthma (aOR: 1.3; $95\%$ CI: 1.2–1.6), ADHD (aOR: 1.4; $95\%$ CI: 1.2–1.6), or ASD (aOR: 1.8; $95\%$ CI: 1.4–2.4). Low social support was significantly associated with increased odds of moderate/severe ADHD (aOR: 1.4; $95\%$ CI: 1.1, 1.8) or ASD (aOR: 1.7; $95\%$ CI: 1.0, 2.7).
Though discrimination was not significantly associated with increased severity of any of the four conditions, children who experienced discrimination had significantly higher odds of being overweight/obese (aOR: 1.4; $95\%$ CI: 1.1, 1.8), and of having asthma (aOR: 2.2; $95\%$ CI: 1.7, 3.0), ADHD (aOR: 1.8; $95\%$ CI: 1.3, 2.8), or ASD (aOR: 1.9; $95\%$ CI: 1.5–3.0).
## Dependent variables
We assessed the prevalence and severity of the four most prevalent chronic childhood conditions available in the NSCH dataset: asthma, ADHD, ASD and overweight/obesity. Respondents were asked if the child currently has asthma, ADHD, or ASD, and if so, whether the severity of each condition was “mild” or “moderate/severe”.
Overweight/obesity was assessed using the child’s body mass index (BMI) percentile, which was calculated from the respondent’s report of child height, weight, and age. The child was considered overweight if their BMI was in the 85th to 94th percentile, and obese if their BMI was at or above the 95th percentile. The NSCH only calculates BMI for children ages 10 through 17, therefore our denominator for the overweight/obesity prevalence and severity variables only included these children. In our analyses, the “severity” of overweight/obesity was considered “mild” if the child was overweight, and “moderate/severe” if the child was obese.
## Statistical analysis
We examined sociodemographic characteristics and social risk factors via descriptive statistics. We used multivariable logistic regression to assess the relationship between individual and cumulative social risk factors with the prevalence of each of the four chronic conditions, and repeated that analysis with each social risk factor and the severity of each condition. We adjusted for child’s sex and age in our analysis; we purposefully did not adjust for other sociodemographic characteristics such as household income and race/ethnicity due to their collinearity with social risk factors, which would have led to over-adjustment of our models.
We performed all statistical analyses using the SAS software version 9.4 (SAS Institute, Inc., Cary, NC) using survey-specific SAS procedures for weighting, clustering, and stratification in the survey design (PROC SURVEYMEANS, PROC SURVEYFREQ and PROC SURVEYLOGISTIC). Adjusted odds ratios (aORs), $95\%$ confidence intervals (CIs), and P values were calculated for each model. Statistical significance was defined as $p \leq 0.05.$
## Sociodemographic characteristics
Overall, for the 2017–2018 combined NSCH dataset, a total of 52,129 surveys were completed with overall weighted response rates of $37.4\%$ in 2017 and $43.1\%$ 2018. The majority of respondents identified their children as male ($51.1\%$), non-Hispanic White ($50.7\%$), and between the ages of 12–17 years ($34.0\%$). Over half (adjusted percentage: $63\%$) of the children had at least one of the seven risk factors studied. Overall, $38.2\%$ of children lived in an environment with low social support, $26.1\%$ were food insecure, $5.8\%$ had inconsistent healthcare insurance coverage over the preceding 12 months, $3.1\%$ experienced discrimination, and $3.1\%$ lived in an unsafe neighborhood. Additionally, $7.3\%$ of children had no caregivers who were employed fulltime, and $2.3\%$ of children had no caregivers with at least a high school diploma or equivalent.
The most prevalent chronic condition in our sample was overweight/obesity ($30.8\%$), followed by ADHD ($8.7\%$), asthma ($7.6\%$), and ASD ($2.9\%$). Table 1 includes the number and weighted percentages of children’s sociodemographic characteristics by chronic condition. Table 1Sociodemographic characteristics and social risk factors, by chronic conditionAsthman, % (SE)Attention Deficit Hyperactivity Disordern, % (SE)Autism Spectrum Disordern, % (SE)Overweight/ Obesityn, % (SE)Total4041, 7.64540, 8.71321, 2.97255, 30.8Sex Female1766, 44.5 (1.7)1444, 32.4 (1.6)280, 22.1 (2.9)3150, 47.4 (1.3) Male2275, 55.5 (1.7)30,096, 67.6 (1.6)1041, 77.9 (2.9)4105, 52.6 (1.3)Age 0–5527, 17.1 (1.3)118, 3.5 (0.6)140, 10,3 (1.5)N/A 6–111390, 39.0 (1.7)1743, 45.0 (1.6)465, 46.2 (3.6)1725, 26.8 (1.2) 12–172124, 43.9 (1.7)2679, 51.5 (1.6)716, 43.5 (3.5)5530, 73.2 (1.2)Race Asian, non-Hispanic114, 3.0 (0.7)71, 0.9 (0.2)55, 3.7 (0.9)249, 2.9 (0.4) Black, non-Hispanic518, 24.4 (1.5)334, 16.8 (1.4)88, 14.4 (2.3)673, 17.8 (1.1) Hispanic526, 23.3 (1.8)451, 19.9 (1.6)173, 34.0 (4.0)1005, 31.2 (1.4) Multi-racial/Other, non-Hispanic382, 7.0 (0.7)366, 6.2 (0.6)117, 4.8 (0.7)551, 5.8 (0.5) White, non-Hispanic2501, 23.3 (1.8)3318, 56.2 (1.6)888, 43.1 (3.0)4777, 42.2 (1.2)Social Risk Factors Caregiver Education (< high school)114, 9.5 (1.5)115, 9.2 (1.5)34, 12.2 (3.7)303, 15.8 (1.4) Caregiver Underemployment494, 17.7 (1.4)556, 16.4 (1.3)192, 18.1 (2.4)722, 14.0 (1.0) Discrimination244, 8.5 (1.1)238, 8.0 (1.0)69, 8.1 (1.6)386, 7.6 (0.7) Food Insecurity1438, 42.1 (1.7)1603, 42.6 (1.6)502, 49.1 (3.6)2556, 41.0 (1.4) Insurance Coverage Gap264, 8.9 (1.0)256, 7.7 (0.8)74, 8.3 (2.1)506, 11.0 (0.9) Low Social Support1691, 50.1 (1.7)2001, 49.9 (1.6)677, 56.8 (3.4)3041, 46.2 (1.4) Unsafe Neighborhood186, 8.1 (1.3)206, 6.5 (0.8)80, 9.0 (2.9)255, 5.7 (0.6)Total Number of Social Risk Factors 0 Risk factors1478, 28.3 (1.4)1625, 28.2 (1.2)400, 23.6 (2.4)2622, 27.7 (1.1) 1 Risk factor1305, 29.6 (1.5)1519, 32.1 (1.4)441, 23.5 (2.1)2447, 31.1 (1.2) 2 Risk factors802, 23.2 (1.6)899, 21.8 (1.3)307, 28.7 (3.1)1448, 24.1 (1.3) 3 Risk factors331, 12.6 (1.4)367, 12.3 (1.4)127, 19.3 (4.1)559, 11.4 (0.9) 4 Risk factors102, 5.5 (1.0)98, 3.9 (0.6)39, 4.2 (1.1)151, 4.7 (0.7) 5 Risk factors17, 0.5 (0.2)27, 1.5 (0.4)6, 0.6 (0.4)23, 0.8 (0.3) > 6 Risk factors6, 0.4 (0.2)5, 0.3 (0.2)1, 0.0 (0.0)5, 0.1 (0.1) Figure 2 plots the aORs and $95\%$ CIs of condition prevalence and severity, grouped by social risk factor. Overall, within each individual social risk domain, differences in associations between the social risk and relative prevalence or severity of any of the chronic conditions were insignificant. For example, within “caregiver education,” the odds of severe asthma was not statistically significantly different from the odds of severe ADHD, ASD, or overweight/obesity. Fig. 2Adjusted odds ratios of condition prevalence and severity, by social risk factor, adjusted for child age and sex
## Economic Stability
Children whose caregivers were underemployed had significantly increased odds of being overweight/obese (aOR: 1.7; $95\%$ CI: 1.4–2.1) and of having asthma (aOR: 1.7; $95\%$ CI: 1.4–2.2), ADHD (aOR: 1.8; $95\%$ CI: 1.4–2.2), or ASD (aOR: 1.9; $95\%$ CI: 1.3–2.6) compared to children whose caregivers were employed. Caregivers’ underemployment was significantly associated with higher severity of every condition except overweight/obesity.
Compared to children who were not experiencing FI, children who were food insecure had increased odds of being overweight/obese (aOR: 1.7; $95\%$ CI: 1.5–2.0) and of having asthma (aOR: 1.6; $5\%$ CI: 1.4–1.8), ADHD (aOR: 1.6; $95\%$ CI: 1.4–1.9), or ASD (aOR: 2.1; $95\%$ CI: 1.5–2.7). Additionally, FI was associated with higher severity for all four chronic conditions.
## Cumulative impact
On average, for each additional social risk factor a child was exposed to, their odds of having a chronic condition increased; specifically, each additional risk factor was associated with increased odds of overweight/obesity (aOR: 1.2, $95\%$ CI: 1.2, 1.3), asthma (aOR: 1.3, $95\%$ CI: 1.2, 1.3), ADHD (aOR:1.2, $95\%$ CI: 1.2, 1.3), and ASD (aOR: 1.4, $95\%$ CI: 1.3, 1.5). Looking more granularly, the odds of overweight/obesity, asthma, and ADHD significantly increased from 0 to 1 risk factor and odds of overweight/obesity, asthma, and ASD significantly increased from 1 to 2 risk factors (Table 2). Among children with ADHD, their odds of moderate/severe ADHD significantly increased from 0 to 1 risk factors (aOR: 1.3, $95\%$ CI: 1.0, 1.8) and from 2 to 3 risk factors (aOR: 2.0, $95\%$ CI: 1.2, 3.5). Among children with ASD, those with 3 social risk factors had 5.7 times the odds of moderate/severe ASD compared to those with 2 social risk factors ($95\%$ CI: 2.3, 15.5).Table 2Adjusted odds ratios of chronic condition prevalence and severity by N social risk factors compared to N-1 social risk factorsNumber of social risksHealth outcomesAdjusted odds ratio of condition prevalence[$95\%$ confidence interval]Adjusted odds ratio of moderate/severe[$95\%$ confidence interval]1Asthma1.23 [1.05, 1.44]*1.33 [0.94, 1.89]ADHD†1.37 [1.18, 1.58]*1.33 [1.01, 1.75]*ASD‡1.17 [0.91, 1.52]0.82 [0.49, 1.36]Overweight/Obesity1.46 [1.27, 1.68]*1.19 [0.93, 1.51]2Asthma1.28 [1.05, 1.56]*1.11 [0.72, 1.72]ADHD1.08 [0.89, 1.29]1.29 [0.91, 1.85]ASD1.97 [1.46, 2.67]*1.19 [0.64, 2.18]Overweight/Obesity1.37 [1.44, 1.64]*1.20 [0.90, 1.62]3Asthma1.26 [0.92, 1.73]1.56 [0.89, 2.72]ADHD1.32 [0.96, 1.81]2.03 [1.18, 3.49]*ASD1.55 [0.88, 1.54]5.72 [2.26, 15.49]*Overweight/Obesity1.0 [0.8, 1.31]1.03 [0.68, 1.56] > 4Asthma1.46 [0.94, 2.25]1.42 [0.61 3.28]ADHD1.26 [0.84, 1.90]0.73 [0.36, 1.47]ASD0.64[0.81, 2.08]0.53 [0.23, 1.22]Overweight/Obesity1.43 [0.93, 2.21]0.72 [0.40, 1.29]†ADHD Attention deficit hyperactivity disorder.‡ASD *Autism spectrum* disorder.*$p \leq 0.05.$
## Discussion
In this nationally representative sample of US children, we found that several social risk factors (namely, caregiver underemployment, FI, low social support, and discrimination) were significantly associated with each pediatric chronic condition. Additionally, we found that, consistent with the cumulative risk model, children with multiple social risk factors (i.e., 3 vs. 1) had increased odds of each condition (and of worse condition severity) with each additional social risk. These results demonstrate a strong association between social risk factors and increased prevalence and severity of chronic conditions broadly, as well as differential associations across various SDOH domains and chronic conditions. For example, we found that social risk factors related to economic stability and social and community context were more strongly associated with condition prevalence than were social risks in the health care and education domains. Our findings align with and expand upon research demonstrating associations between individual and cumulative social risks with adult chronic conditions, including hypertension, diabetes, asthma, and depression [20, 21].
FI was the only social risk factor associated with increased prevalence and severity of all four chronic conditions. We classified households as having FI for three of four possible response options, encompassing a spectrum of food security from marginal (i.e., anxiety over food sufficiency) to very low (i.e., disrupted eating patterns and reduced food intake) [22]. The associations we found between FI with pediatric chronic conditions are consistent with a growing body of evidence linking even marginal FI with child health (e.g., asthma, anemia, obesity), health care utilization (e.g., forgone medical care), and adverse psychosocial and developmental outcomes [10, 23–25]. Researchers suggest these outcomes may be attributable to FI disrupting physiological stress-response systems by increasing children’s allostatic load; however, a systemic, pathophysiological framework to explain these results remains a gap in the literature [26, 27].
Caregiver unemployment—our second measure of economic instability—was also strongly associated with prevalence of all four chronic conditions, and severity of all except overweight/obesity. The strong associations between FI and caregiver unemployment with disease outcomes indicate that economic instability may have pernicious, short- and long-term harmful effects on child health [28, 29]. This is especially salient in the context of the COVID-19 pandemic, which has had severe economic ramifications for children and families, precipitating increased rates of FI. A recent study found that that the child tax credit, in addition to providing more economic stability for many low-income families, also reduced FI by $26\%$ for families; if social policies can reduce FI (which, as suggested by our findings may be associated with increased prevalence and severity of chronic conditions), these same social policies may have the potential to reduce the burden of chronic conditions among children [30].
Our results also highlight the role of social and community risk factors (e.g., discrimination and neighborhood social support) on the prevalence and severity of selected chronic conditions. Experiencing discrimination was significantly associated with increased prevalence of each of the four chronic conditions but not with condition severity. Prior studies found strong evidence of racial disparities in ASD screening practices and in rates of connection to intervention services, and that Black and Hispanic children have lower rates of ASD but higher severity compared to White children [31]; our findings suggest that discrimination based on race and ethnicity may also play a role in determining severity of chronic conditions. Programs and intervention services for children with chronic conditions should be cognizant that racial and ethnic minority groups are more likely to experience discrimination, and such programs should incorporate trauma-informed and anti-racist care into their clinical practice [32]. Further research is needed to investigate how the concerted development and implementation of anti-racist approaches as best pediatric practice might enhance equity in pediatric health care, mitigate providers’ implicit biases, and—as our findings suggest—potentially reduce the prevalence and severity of asthma, ADHD, ASD, and overweight/obesity among children. Since 2020, the majority of US children are Black, Indigenous, or People of Color, making research that addresses race- and ethnicity-based discrimination a vital public health priority [33].
Prior studies have found that early environmental and epigenetic factors can lead to a variety of complex diseases and conditions in children, including the four conditions studied here [34, 35]. As such, addressing social risk factors in early childhood, prenatal care, and preconception care might prevent the development or reduce the severity of these childhood chronic conditions. Social risk factors are root causes of chronic disease, and disproportionately impact historically marginalized populations in the US. Investing in effective interdisciplinary SDOH interventions—especially those that promote economic stability and improve social, community, and neighborhood-level factors—may also reduce the prevalence and severity of asthma, ADHD, ASD, and overweight/obesity in childhood, though more translational research is needed to investigate this.
This study has several limitations. First, because we used cross-sectional data from the NSCH, we cannot infer causality, directionality, or temporality of the relationships. The results herein may be explained by the theory of social causation (i.e., poverty and social risks result in disease) and/or social selection (i.e., the challenges associated with managing a child’s chronic condition may lead to poverty and social risks) [36]; however, given the multitude of longitudinal studies and randomized controlled trials demonstrating temporal relationships between social risks and adverse health outcomes, we conceptualized our cross-sectional findings according to the social causation theory. Secondly, health information in the NSCH is based on caregiver recollection and is not independently verified, which may make these data susceptible to recall and selection bias. Additionally, children may be at genetic risk of one or more of the chronic conditions explored in this manuscript, though detailed data from NSCH respondents regarding genetic risks was not available in this dataset. Further, the cumulative impact of social risk factors on caregivers’ stress may have influenced their perception of severity of their child’s illness, exaggerating the relationships between social risks and severity of chronic conditions. Also, while we used insurance coverage as a measure for healthcare access, it is not a precise measure of actual healthcare use; the NSCH datasets analyzed herein changed items related to healthcare access and quality between 2017 and 2018, so these indicators could not be used in the analysis of the combined dataset. Future studies should consider exploring the influence of other measures of healthcare access and quality on the prevalence and severity of chronic conditions. Finally, the relationships presented herein are complex, and while our seven chosen risk factors represent the SDOH domains outlined in Healthy People 2030, there are many social risks not measured through NSCH and therefore not included in our analysis.
## Conclusions
This study elucidates relationships between several social risk factors and the prevalence and severity of common pediatric chronic conditions. Further research is needed to evaluate the causality of these relationships and to assess the role of protective factors and resiliency on the prevalence and severity of asthma, ADHD ASD, and overweight/obesity. Our findings suggest that mitigating social risks in childhood via social and health policies, pediatrics-based interventions, and integrated health-social care clinics may reduce the development and severity of chronic conditions among children.
## References
1. 1.Managing chronic health conditions in schools. Centers for disease control and prevention. Updates October 20, 2021. https://www.cdc.gov/healthyschools/chronicconditions.htm. Accessed 19 January, 2022.
2. 2.Solar O, Irin A. A conceptual framework for action on the social determinants of health. Social Determinants of Health Discussion Paper 2 (Policy and Practice). World Health Organization. 2010. http://apps.who.int/iris/bitstream/10665/44489/1/9789241500852_eng.pdf. Accessed 30 July, 2020.
3. Aber JL, Bennett NG, Conley DC, Li J. **The effects of poverty on child health and development**. *Annu Rev Public Health* (1997.0) **18** 463-483. DOI: 10.1146/annurev.publhealth.18.1.463
4. Gortmaker SL. **Poverty and infant mortality in the United States**. *Am Sociol Rev* (1979.0) **44** 280-297. DOI: 10.2307/2094510
5. Larson K, Halfon N. **Family income gradients in the health and health care access of US children**. *Matern Child Health J* (2010.0) **14** 332-342. DOI: 10.1007/s10995-009-0477-y
6. Furman D, Campisi J, Verdin E. **Chronic inflammation in the etiology of disease across the life span**. *Nat Med* (2019.0) **25** 1822-1832. DOI: 10.1038/s41591-019-0675-0
7. Bucci M, Marques SS, Oh D, Harris NB. **Toxic stress in children and adolescents**. *Adv Pediatr* (2016.0) **63** 403-428. DOI: 10.1016/j.yapd.2016.04.002
8. Koss KJ, Gunnar MR. **Annual research review: early adversity, the hypothalamic–pituitary–adrenocortical axis, and child psychopathology**. *J Child Psychol Psychiatry* (2018.0) **59** 327-346. DOI: 10.1111/jcpp.12784
9. Sameroff AJ, Seifer R, Barocas R, Zax M, Greenspan S. **Intelligence quotient scores of 4-year-old children: social-environmental risk factors**. *Pediatrics* (1987.0) **79** 343-350. DOI: 10.1542/peds.79.3.343
10. Thomas MMC, Miller DP, Morrissey TW. **Food insecurity and child health**. *Pediatrics* (2019.0) **144** e20190397. DOI: 10.1542/peds.2019-0397
11. Evans GW, English K. **The environment of poverty: multiple stressor exposure, psychophysiological stress, and socioemotional adjustment**. *Child Dev* (2002.0) **73** 1238-1248. DOI: 10.1111/1467-8624.00469
12. Keet CA, Matsui EC, McCormack MC, Peng RD. **Urban residence, neighborhood poverty, race/ethnicity and asthma morbidity among children in medicaid**. *J Allergy Clin Immunol* (2017.0) **140** 822-827. DOI: 10.1016/j.jaci.2017.01.036
13. Liptak GS, Benzoni LB, Mruzek DW. **Disparities in diagnosis and access to health services for children with autism: data from the national survey of children’s health**. *J Dev Behav Pediatr* (2008.0) **29** 152-160. DOI: 10.1097/DBP.0b013e318165c7a0
14. Didsbury MS, Kim S, Medway MM. **Socio-economic status and quality of life in children with chronic disease: a systematic review**. *J Paediatr Child Health* (2016.0) **52** 1062-1069. DOI: 10.1111/jpc.13407
15. 15.Child and Adolescent Health Measurement Initiative. The National Survey of Children's Health. In. Vol 2020. Data Resource Center for Child and Adolescent Health supported by the U.S. Department of Health and Human Services, Health Resources and Services Administration (HRSA), Maternal and Child Health Bureau (MCHB). 2017–2018. Retrieved 06/2020 from from www.childhealthdata.org.
16. 16.Office of disease prevention and health promotion. healthy people 2030 framework. published online 2020. https://www.healthypeople.gov/2020/About-Healthy-People/Development-Healthy-People-2030/Framework.
17. Kabani F, Lykens K, Tak HJ. **Exploring the relationship between adverse childhood experiences and oral health-related quality of life**. *J Public Health Dent* (2018.0) **78** 313-320. DOI: 10.1111/jphd.12274
18. Jackson DB, Testa A. **Household food insecurity and children’s oral health: findings from the 2016–2018 National Survey of Children’s Health**. *J Public Health Dent* (2021.0) **81** 150-161. DOI: 10.1111/jphd.12431
19. Kogan MD, Schuster MA, Yu SM. **Routine assessment of family and community health risks: parent views and what they receive**. *Pediatr* (2004.0) **113** 1934-1943. DOI: 10.1542/peds.113.S5.1934
20. Heller CG, Rehm CD, Parsons AH, Chambers EC, Hollingsworth NH, Fiori KP. **The association between social needs and chronic conditions in a large, urban primary care population**. *Prev Med* (2021.0) **153** 106752. DOI: 10.1016/j.ypmed.2021.106752
21. Pantell MS, Prather AA, Downing JM, Gordon NP, Adler NE. **Association of social and behavioral risk factors with earlier onset of adult hypertension and diabetes**. *JAMA Netw Open* (2019.0) **2** e193933. DOI: 10.1001/jamanetworkopen.2019.3933
22. 22.Coleman-Jensen A, Rabbit MP, Gregory CA, Singh A. Household food security in the United States in 2019. Washington, DC: US Department of Agriculture Economic Research Service; 2020. ERR-275. Retrieved from: https://www.ers.usda.gov/webdocs/publications/99282/err-275.pdf?v=8858.4.
23. Peltz A, Garg A. **Food insecurity and health care use**. *Pediatr* (2019.0) **144** e20190347. DOI: 10.1542/peds.2019-0347
24. Shankar P, Chung R, Frank DA. **Association of food insecurity with children’s behavioral, emotional, and academic outcomes: a systematic review**. *J Dev Behav Pediatr* (2017.0) **38** 135-150. DOI: 10.1097/DBP.0000000000000383
25. Grineski SE, Morales DX, Collins TW, Rubio R. **Transitional dynamics of household food insecurity impact children’s developmental outcomes**. *J Dev Behav Pediatr* (2018.0) **39** 715-725. DOI: 10.1097/DBP.0000000000000598
26. McEwen BS. **Stress, adaptation, and disease allostasis and allostatic load**. *Ann N Y Acad Sci* (1998.0) **840** 33-44. DOI: 10.1111/j.1749-6632.1998.tb09546.x
27. Wurster B, Garner J. **P018 the pathophysiology of food insecurity a narrative review and system map**. *J Nutri Edu Behav* (2022.0) **54** S26. DOI: 10.1016/j.jneb.2022.04.058
28. Black MM, Drennen C, Gallego N, Coleman S, Frank DA. **Household food insecurity is associated with children’s health and developmental risks, but not with age-specific obesity and underweight**. *FASEB J* (2017.0) **31** 791.17-791.17. DOI: 10.1096/fasebj.31.1_supplement.791.17
29. Frank DA, Casey PH, Black MM. **Cumulative hardship and wellness of low-income, young children: multisite surveillance study**. *Pediatr* (2010.0) **125** e1115-1123. DOI: 10.1542/peds.2009-1078
30. Shafer PR, Gutiérrez KM, Ettinger de Cuba S, Bovell-Ammon A, Raifman J. **Association of the implementation of child tax credit advance payments with food insufficiency in US households**. *JAMA Netw Open* (2022.0) **5** e2143296. DOI: 10.1001/jamanetworkopen.2021.43296
31. Rea KE, Armstrong-Brine M, Ramirez L, Stancin T. **Ethnic disparities in autism spectrum disorder screening and referral: implications for pediatric practice**. *Journal of Developmental Behavioral Pediatrics* (2019.0) **40** 493-500. DOI: 10.1097/DBP.0000000000000691
32. Trent M, Dooley DG, Dougé J. **Pediatrics council on community pediatrics, committee on adolescence the impact of racism on child and adolescent health**. *Pediatr* (2019.0) **144** e20191765. DOI: 10.1542/peds.2019-1765
33. 33.United States census bureau. Population projections. Census.gov. https://www.census.gov/programs-surveys/popproj.html Accessed 18 Jan 2022.
34. Wright RO, Teitelbaum S, Thompson C, Balshaw D. **The child health exposure analysis resource (chear) as a vehicle to measure environment in the environmental influences on child health outcomes (ECHO) Program**. *Curr Opin Pediatr* (2018.0) **30** 285-291. DOI: 10.1097/MOP.0000000000000601
35. McGowan PO. **Epigenomic mechanisms of early adversity and HPA dysfunction: considerations for PTSD research**. *Front Psychiatry* (2013.0) **4** 110. DOI: 10.3389/fpsyt.2013.00110
36. 36.Mossakowski KN. Social causation and social selection. In: Cockerham WC, Dingwall R and Quah S, eds. the Wiley blackwell encyclopedia of health, illness, behavior, and society. John Wiley & Sons Ltd; 2014 10.1002/9781118410868.wbehibs262.
|
---
title: Seroprevalence and socioeconomic impact of the first SARS-CoV-2 infection wave
in a small town in Navarre, Spain
authors:
- Marta Ribes
- Júlia Montañà
- Marta Vidal
- Ruth Aguilar
- Patricia Nicolás
- Uxue Alfonso
- Natalia Rodrigo
- Carlo Carolis
- Carlota Dobaño
- Gemma Moncunill
- Carlos Chaccour
journal: Scientific Reports
year: 2023
pmcid: PMC9992915
doi: 10.1038/s41598-023-30542-x
license: CC BY 4.0
---
# Seroprevalence and socioeconomic impact of the first SARS-CoV-2 infection wave in a small town in Navarre, Spain
## Abstract
The characterization of the antibody response to SARS-CoV-2 and its determinants are key for the understanding of COVID-19. The identification of vulnerable populations to the infection and to its socioeconomic impact is indispensable for inclusive policies. We conducted an age-stratified cross-sectional community-based seroprevalence survey between June 12th and 19th 2020—during the easing of lockdown—in Cizur, Spain. We quantified IgG, IgM and IgA levels against SARS-CoV-2 spike and its receptor-binding domain in a sample of 728 randomly selected, voluntarily registered inhabitants. We estimated a $7.9\%$ seroprevalence in the general population, with the lowest seroprevalence among children under ten ($$n = 3$$/142, $2.1\%$) and the highest among adolescents (11–20 years old, $$n = 18$$/159, $11.3\%$). We found a heterogeneous immune-response profile across participants regarding isotype/antigen-specific seropositivity, although levels generally correlated. Those with technical education level were the most financially affected. Fifty-five percent had visited a supermarket and $43\%$ a sanitary centre since mid-February 2020. When comparing by gender, men had left the household more frequently. In conclusion, few days after strict lockdown, the burden of SARS-CoV-2 infection was the lowest in children under 10. The findings also suggest that a wider isotype-antigen panel confers higher sensitivity. Finally, the economic impact biases should be considered when designing public health measures.
## Introduction
The COVID-19 pandemic has caused—as of July 21st 2022—564,126,546 confirmed cases and 6,371,354 confirmed deaths worldwide1, although the actual number of deaths has been estimated to be three times higher2. Vaccination has resulted in a decrease in mortality and the number of severe COVID-19 cases, and in a relaxation of non-pharmacological public health preventive measures. However, the lack of vaccines preventing infection, the inequitable access to them3, and the appearance of immune-evasive viral variants with higher transmission capacity has led to sustained viral transmission in many settings. Seroprevalence studies are essential to estimate the burden of infection and identify vulnerable populations, but also to characterize the antibody response, critical for surveillance and diagnostics.
In Spain, the cumulative effects of an initial prolonged lockdown and high burden of disease particularly hit an economy that relies heavily on tourism and the service sector. On the 31st of January the first COVID-19 confirmed case in Spain was diagnosed in a tourist in La Gomera. On the 25th of February, the first three locally transmitted cases were detected in Madrid, Barcelona and Castelló. The first case reported in Navarre dated from the 28th of February4. Lockdown in Spain started on the 14th of March 2020 with the only exception of acquiring essential goods, receiving healthcare, returning to the usual place of residence or not having the possibility of teleworking. From the 26th of April, children under 14 were allowed to play for one hour in the street. In Navarre, from the 11th of May, people were allowed to gather in groups of 10 in open spaces and terraces, practice sports in open spaces and without physical contact, visit museums, shops and theaters and places of worship. From the 25th of May people were allowed to gather in groups of 15, and in groups of 20 in outdoor activities, and restaurants reopened indoor spaces with capacity restrictions. The 21st June normality was restored with some public health measures remaining such as the use of face mask in public spaces and the promotion of telework5,6. Estimates show a cumulative increase of $11.9\%$ in the rate of unemployment in the service sector during 2020. In the Autonomous Community of Navarre, this proportion was estimated at $10\%$. Remarkably, no specific association has been found between COVID-19 incidence and economic downturn measured as reduction in gross domestic product7. Bárcena-Martín and Cantó identified youngest individuals to be the most vulnerable to the impact of the economic crisis, whereas individuals with a higher educational level had greater income stability during the crisis caused by the pandemic8.
During the first epidemic wave from March until June 2020, quantitative real-time reverse transcription polymerase chain reaction (RT-qPCR) testing in Spain was restricted to severe patients and/or high-risk groups (mainly health care workers), leading to many undetected cases4. Other factors limited the representability of reported cases: some individuals might not seek medical care if symptoms were mild, variable testing availability depending on the setting, or incomplete case reporting to public health authorities. In the absence of widespread RT-qPCR or rapid diagnostic tests (RDT), serologic assays—by which antibodies against the virus are measured—allowed a more comprehensive identification of those individuals who had been formerly infected, including those who experienced mild disease or sub-clinical infections. Former infections identified from serologic assays are thus not biased by health care–seeking behavior or testing availability9. This information was essential to estimate the true proportion of the population that had been infected by the virus and thus improve disease burden estimates, know the proportion of asymptomatic or mildly symptomatic cases, and inform policy decisions related to restrictive measures. In Navarre, up to the 28th of June, 10,358 cases had been declared ($1.57\%$ of the population)4. In Spain, the ENE-COVID, a nationwide seroprevalence survey study, estimated a seroprevalence of $5.2\%$ ($95\%$ CI 4.9–$5.5\%$) nationwide between the 8th and 20th June 2020, and of $6.6\%$ ($95\%$ CI 5.1–$8.5\%$) in Navarre. No differences were found between sex, and the lowest prevalence was found in babies and children younger than 10 years10.
A robust body of evidence has confirmed that the vast majority of SARS-CoV-2 infected patients develop an antibody response, which is stronger in symptomatic and severe COVID-19 cases11. Immunoglobulins (Ig) type M, G and A peak around 20–30 days post symptoms-onset, after which IgM levels start to decrease while IgG levels—and to a lesser extent IgA—are more persistent12,13. IgGs have been detected up to 16 months after infection14. Although this long maintenance of IgGs sheds light in the end of the pandemic, the threshold of antibody levels conferring protection against infection or severe disease is still not known. Despite antibodies protecting against infection to a certain degree12, re-infections are recurrent but—comfortingly—risk of disease severity is significantly decreased after infection or vaccination13.
Here we present a seroprevalence study in a town of 3,925 inhabitants in Spain conducted in June 2020. Three isotypes (IgG, IgM and IgA) were measured against two SARS-CoV-2 antigens (the spike [S] and its Receptor Binding Domain [RBD]) in a random subsample drawn from voluntarily registered inhabitants.
## Study design, study area and selection of participants
This study was a cross-sectional age-stratified community-based seroprevalence survey that took place between the 12th and the 19th of June 2020 based on a voluntary response sample. The study area was the Cendea de Cizur, a Spanish municipality in the Autonomous Community of Navarre, located at 7 km from the capital of the autonomous community, Pamplona, and belonging to its metropolitan area. The municipality has a surface of 46.5 km2 and had 3,925 inhabitants as of April 202015. It is divided in eight councils: Astráin, Cizur Menor—which is the most populated—, Gazólaz, Larraya, Muru-Astráin, Paternáin, Undiano and Zariquiegui. Due to its proximity to the capital of the region, many inhabitants commute regularly for work, educational or recreational purposes. The town has the highest gross salary and one of the lowest risks of poverty and Gini index ($9.23\%$ and $24.7\%$ respectively) in Navarre16.
Due to the impossibility of accessing personal data of the inhabitants of the town, the sampling frame used was a voluntary registry of the municipality inhabitants. The study was announced on the town hall official accounts on social media and was sent via mail post to all residents. To participate, they had to register filling out an online form, by phone or in paper in the town hall where their contact information was gathered. Using the municipal register as sampling frame, participants were then selected by simple randomization stratified by age group in five age strata. Weighted sampling was done according to the following five age groups: 0–10, 11–20, 21–50, 51–65, > 65 years old.
The inclusion criterion was being a resident of the municipality of Cendea de Cizur who signed-up for the survey. Exclusion criteria were: (i) being absent from the household at the time of the visit, (ii) not answering the phone after five attempts in at least two different days at different daytimes. Assuming a $5\%$ prevalence of SARS-CoV-2 seropositivity at the time of the survey, a total sample of 775 participants was aimed to estimate the seroprevalence within a $3\%$ margin of error and $95\%$ confidence.
## Household visit procedures
Out of the total volunteers registered, a random sample was selected according to the size needed per strata. Randomly selected individuals from this list were approached telephonically to schedule a household appointment and enrolled upon review of eligibility criteria. When an individual was not eligible for recruitment, a substitute was retrieved randomly without replacement from the rest of volunteers in the register.
Informed consent was obtained from each patient before recruitment after explaining the study's objectives. In the case of minors, parents had to provide consent for them, all adolescents between 12 and 17 years of age were further required to sign an informed assent.
Afterwards, a brief epidemiological questionnaire was administered to all survey participants. This covered the following information: participant´s demographic characteristics, targeted medical history, current and past health status (with special emphasis on possible symptoms and signs of COVID-19), exposure to COVID-19 cases or contacts of cases (confirmed or not), other possible sources of infection, socioeconomic status and COVID-19 impact on it, and prevention measures taken in the context of the COVID-19 epidemic. When asked for COVID-19 related-symptoms and behaviours, the period between mid-February and the interview day was considered since the first COVID-19 locally transmitted confirmed case in Spain dated from the 25th of February. Data were collected using standardized electronic questionnaires through smartphones and tablets.
The fieldworker collected a capillary blood sample in an EDTA tube via finger-prick for the determination of antibodies. Samples, and laboratory registers were identified with a unique participant identification number.
## Laboratory procedures
Blood samples were transported from the field to the biobank in the University of Navarra by cold chain using coolers. At the end of each day, blood was centrifuged, and the plasma separated and kept at -80ºC in the laboratory until the shipment to ISGlobal for serological analysis.
We measured the levels of three antibody isotypes (IgG, IgM and IgA) against the SARS-CoV-2 S glycoprotein, produced at the Centre de Regulació Genòmica (CRG), and RBD, kindly donated by the Krammer lab (Mount Sinai, New York)17 using a previously validated method based on quantitative suspension array technology (xMAP®, Luminex®)18. This method, using IgG, IgM and IgA isotypes and RBD and S antigens yielded a sensitivity of $83\%$ and a specificity of $95\%$19.
Plasma samples were incubated with MagPlex® Microspheres coated with the S and RBD antigens. After wash, beads were incubated with anti-human Ig labelled with fluorescent phycoerythrin and resuspended with an assay buffer and read in a Luminex® $\frac{100}{200}$ equipment for quantification of bound IgG, IgM and IgA. Levels of antibodies were expressed in median fluorescence intensity (MFI). Seropositivity was determined based on a threshold calculated as 10 to the mean plus 3 standard deviations of log10-transformed MFIs of 71 negative controls (pre-pandemic samples from adults ranging 20–60 years of age). All methods were carried out in accordance with relevant guidelines and regulations20.
At the time of protocol development, the S antigen from the Wuhan strain was chosen because it was the leading vaccine candidate target and one of the most immunogenic. RBD—the fragment of S protein that mediates binding of the virus to the host receptor ACE2 in the lung cells—was also analyzed because IgG levels to RBD correlated with the levels of neutralizing antibodies that had been associated with protection21.
## Statistical analysis
Seroprevalence of SARS-CoV-2 was estimated overall and stratified by isotype and age group. The seroprevalence for the whole population was estimated considering the weights of the sampling per age group. Seroprevalence estimates were also corrected for the finite population correction factor, used when sampling without replacement from more than $5\%$ of a finite population. Although the study did not use household cluster sampling, the target population was small and the sample included $25\%$ of the entire population that was large enough to include multiple members of the same household. This makes the variance inter-participants unequal due to probable household clustering. To account for this, we used robust variance estimates.
Study participants’ characteristics were described via mean and standard deviation (quantitative variables) and percentages (categorical variables). Chi-square or Fisher’s exact test (for categorical variables) and T-test (for continuous quantitative variables) were used to test the association between certain variables of interest.
Venn Diagrams were created to illustrate the overlap between antigens and between the three isotypes. We assessed the correlations between log10-transformed MFIs with Spearman’s rank test. Locally estimated scatterplot smoothing (LOESS) was used to visualize the non-parametric correlation.
Univariable and multivariable logistic regression models were run to evaluate factors associated with being seropositive and with being seropositive specifically for each isotype among the seropositive overall. Variables that had a p-value < 0.2 in the univariable analysis were included in the multivariable models. Categorical variables with less than six observations in one of the categories were discarded as well as observations with missing data. Explanatory variables included were sex, age, town, body mass index (BMI), blood group, Bacillus Calmette–Guérin (BCG) vaccination, flu vaccination, having had flu or a cold in last winter season, having any comorbidity, allergy, reporting COVID-19 compatible symptoms since mid-February 2020, having had a close COVID-19 contact, COVID-19 diagnosis, number of members in the household, highest schooling level and exposure to gatherings. A stepwise selection model by AIC was used. Beta coefficients were transformed to Odds Ratios.
Univariable and multivariable linear regression models were run among the seropositive participants to evaluate factors associated with isotype-specific antibody levels against SARS-CoV-2. Variables that had a p-value < 0.2 in the univariable analysis for any of the antigens (RBD, S or their sum) were included in the isotype-specific multivariable model. The days post symptoms onset (pso) were also included to account for time passed since infection. Categorical variables with less than six observations in any of the categories were discarded as well as observations with missing data. Explanatory variables included were sex, age, BMI, days post symptom onset, body ache/fatigue, upper respiratory symptoms, allergy, fever, smoker, anosmia/ageusia, lower respiratory symptoms, gastrointestinal symptoms, having had a cold in last winter season. Beta coefficients were transformed to percentages to be more easily interpreted.
A p-value of ≤ 0.05 was considered statistically significant and $95\%$ CIs were calculated for all estimates. We performed the statistical analysis with Stata v14.2 (College Station, TX:StataCorp LLC) and with R version 4.0.3 (packages tidyverse22, ggplot223, st24, sjPlot25).
The protocol was reviewed and approved by the ethics committee Comité de Ética de la Investigación con medicamentos (PI_$\frac{2020}{54}$).
## Characteristics of the study participants
Out of the total population censed in Cendea de Cizur, 1,218 inhabitants voluntarily registered for the study. Altogether, 814 randomly selected individuals were approached by phone call, of which 769 were eligible. Among the reasons for not being eligible: (i) 13 individuals approached were not residents of the municipality of Cendea de Cizur, (ii) in 12 an error had been made in the register and the individual was unlocalizable, (iii) 20 did not answer the phone after a minimum of five phone calls in at least two different days and different times of the day. Out of the 769 individuals who were eligible, 733 were recruited, yielding a participation rate of $95.3\%$, which was expected as individuals had already expressed their willingness to participate. Five participants were further excluded from the analysis because of insufficient sample collected or because of incomplete questionnaire (Fig. 1).Figure 1Study participants’ flowchart.
Sufficient participants were recruited for each age strata except for the group over 65 years of age, since only 97 people older than 65 registered for the study, of which 93 were recruited while the sample size needed was 130 (Supplementary Table 1). There was a large overlap between the age distribution of the population and the sample obtained.
Half of participants were female ($\frac{365}{728}$, $50.1\%$) (Table 1 and Supplementary Table 2). Thirty-three percent across all ages had had flu symptoms or a cold during last transmission season up to mid-February (the week with the highest number of flu diagnosis was the 4th of January). Twenty percent had received the flu vaccine, $65.6\%$ in those over 65 years (in Navarre these figures were $18.6\%$ and $61\%$ respectively)4. Forty-six percent declared having comorbidities, some known to be risks factors for severe COVID-19 disease (Supplementary Table 10). Regarding COVID-19 related characteristics, $54\%$ of participants declared having had any compatible symptoms (fever, chills, fatigue, myalgia, sore throat, anosmia, ageusia, cough, rhinorrhea, dyspnea, wheezing, chest pain, headache, abdominal pain, diarrhea, sputum) from mid-February 2020 until date of interview (mid-June 2020); and $33.6\%$ had had a close contact with a suspected or confirmed case of COVID-19. Ten participants had been previously diagnosed with COVID-19, seven of them only by clinical evaluation, one via RT-qPCR and two via a serology test. Seventy-seven percent had been to a group gathering by having visited a health center, grocery shop, bank, church, hairdresser, or another city from mid-February until the end of March 2020.Table 1Characteristics of study participants. CharacteristicsTotal ($$n = 728$$)Sexa Male363($49.9\%$) Female365 ($50.1\%$)Age, yearsb36.4 (23.8)Towna Cizur Menor483 ($66.4\%$) Astráin41 ($5.6\%$) Gazólaz21 ($2.9\%$) Larraya20 ($2.8\%$) Muru Astráin11 ($1.5\%$) Paternáin59 ($8.1\%$) Sagüés6 ($0.8\%$) Undiano50 ($6.9\%$) Zariquiegui37 ($5.1\%$)Body mass indexb22.4 (5.1)BCG vaccine immunization statusaα Not vaccinated421 ($57.8\%$) Vaccinated246 ($33.8\%$) Unknown61 ($8.4\%$)Having received flu vaccine in this transmission seasonaβ No573 ($78.7\%$) Yes148 ($20.3\%$) Unknown7 ($1\%$)Having had a flu or a cold in this transmission seasona No472 ($64.8\%$) Yes240 ($33\%$) Unknown15 ($2.2\%$)Comorbiditiesa* No391 ($53.7\%$) Yes338 ($46.3\%$)Reporting COVID-19 compatible symptoms since mid-Februarya# No335 ($46\%$) Yes393 ($54\%$)Close contact with COVID-19 confirmed or suspected casea No469 ($64.3\%$) Yes245 ($33.6\%$) Unknown6 ($4.7\%$)Previously diagnosed with COVID-19a No718 ($98.6\%$) Yes10 ($1.4\%$)*Visited a* group gathering settinga& No164 ($22.5\%$) Yes564 ($77.5\%$)Household membersb4.3 (1.9)Highest schooling levela¥ Primary education4 ($0.9\%$) Secondary education104 ($23.3\%$) Technical studies80 ($17.9\%$) University degree/Masters/PhD250 ($56.1\%$) N/A7 ($1.7\%$)Occupation classa¥ Active worker280 ($62.8\%$) Houseworker18 ($4\%$) Permanent or temporary disability1 ($0.2\%$) Retired89 ($20\%$) Student27 ($6.1\%$) Unemployed7 ($1.6\%$) Other1 ($0.2\%$) N/A22 ($4.9\%$)Perceptions about information received from government and authoritiesa$ I get enough useful information146 ($30.7\%$) I get information but I still have some doubts87 ($18.3\%$) I get information but I still have many doubts99 ($22.8\%$) I don’t get enough useful information135 ($28.4\%$) N/A8 ($1.7\%$)an/N (%) where N is the number of people in that age group (or overall, in the case of totals).bArithmetic Mean (SD).*Comorbidities included diabetes, asthma, allergy, tuberculosis, Human Immunodeficiency Virus, Hepatitis A, B and C; depression, anxiety, dementia and other.#Compatible symptoms included fever, chills, fatigue, myalgia, sore throat, anosmia, ageusia, cough, rhinorrhea, dyspnea, wheezing, chest pain, headache, abdominal pain, diarrhea, sputum from mid-February until date of interview.¥Among participants aged 20 years or older.&Exposures included visiting a sanitary center, grocery shop, bank, church, hairdresser, another city from mid-February until the end of March.$Among participants aged 18 years or older.αIn Navarre BCG vaccine is not included in the immunization schedule since 1980.βIn Navarre flu vaccine is recommended to people over 60 years old.
Regarding socioeconomic characteristics, $0.9\%$ among participants over 20 years of age had attended primary school as highest educational level, $23\%$ secondary school, $18\%$ technical studies and $56\%$ had a university/masters or PhD degree ($79.6\%$, $12.4\%$ and $8\%$ respectively). Sixty-three percent were active workers, $4\%$ were homemakers, $0.2\%$ declared permanently or temporary disability, $20\%$ were retired, $6.1\%$ were students and $1.6\%$ were unemployed.
Finally, $30.7\%$ perceived that they were receiving enough useful information by public health authorities and governments, $18.3\%$ felt that they still had some doubts, $22.8\%$ still had many doubts and $28.4\%$ perceived not getting enough useful information.
## COVID-19 occupational and economic impact
We analysed the impact of the pandemic on the occupation and economy of the families (Supplementary Table 3). Among participants with 20 years of age or older, two participants with secondary education lost their job during the pandemic ($1.9\%$), none because of reasons directly or indirectly related to the COVID-19 pandemic. Among the participants with technical education, four lost their job ($5\%$) among which 3 ($75\%$) were due to COVID-19 derived reasons; among those with superior university degrees, 5 ($2\%$) lost their job, among which 2 ($40\%$) were due to COVID-19. These differences did not reach statistical significance.
Twenty-five percent of participants with secondary education level reported being financially affected by the pandemic, the percentage rose to $36.3\%$ of those with technical education and was lower in those with university degrees at $18.4\%$ (46 out of 250). These differences were statistically significant (X2 = 11.045, p-value 0.011).
Among those with school level education, 1 out of 4 ($25\%$) declared having struggles paying their expenses, $\frac{12}{92}$ ($13\%$) in the case of secondary education, 6 out of 75 ($8\%$) in the case of technical education, and 13 out of 234 ($5.7\%$) in the case of university degree level. Of these, $100\%$, $91.7\%$, $100\%$ and $66.7\%$, respectively, declared that the difficulty was related or aggravated by the COVID-19 pandemic. These differences did not reach statistical significance. We found no significant differences in neither economic nor occupational impact between participant sex (data not shown).
## Behaviours during the first wave
Since mid-February 2020 to June 19th 2020, $43\%$ of participants went to a health centre; $32.9\%$ because of non-related COVID-19 symptoms, $7.2\%$ for COVID-19-related symptoms, $23.5\%$ accompanying someone else and $15.9\%$ because of work.
Since mid-February until June 19th, adult participants (≥ 18 years old, $$n = 473$$) declared these exposures: $55.4\%$ went to the supermarket, $29.9\%$ went to a smaller grocery store, $9.1\%$ went to the bank, $9.9\%$ went to the church, $4\%$ to the hairdresser, $24.3\%$ went to Pamplona, $0.6\%$ went to Madrid, $2.1\%$ had been to another place in Spain and $0.4\%$ (or 2 participants) had been to another place beyond Spain (Supplementary Table 4). Of those who went to the supermarket: $61.9\%$ did not follow any preventive measure when bringing the groceries home, $33.7\%$ cleaned them with soap, $20.1\%$ with bleach, $2.6\%$ put them in quarantine for one day, $4.8\%$ for two or three days and $0.7\%$ for four days or more.
A higher proportion of men ($62.9\%$) went to the supermarket than women ($47.9\%$) (X2 = 10.152, p-value < 0.001). Likewise, more men ($29.1\%$) went to Pamplona—the nearest city—than women ($19.5\%$) (X2 = 5.438, p-value 0.02), and to the bank—$12.2\%$ of men versus $6\%$ of women—(X2 = 4.821, p-value 0.028).
## Seroprevalence
Fifty-six participants ($7.7\%$) were seropositive for at least one of the isotype-antigen pairs tested (Table 2). Per age group, the seroprevalence was the lowest at $2.1\%$ ($95\%$ CI 1.5–3) in 0–10 years old; the highest at $11.3\%$ ($95\%$ CI 9.9–13) in 11–20 years; $7.9\%$ ($95\%$ CI 6.7–9.2) in 21–50 years; $8.9\%$ ($95\%$ CI 7.6–9.2) in 51–65 years; $7.5\%$ ($95\%$ CI 6–9.1) in over 65 years. Seroprevalence in women was $8.52\%$ ($95\%$ CI 6.1–11.8) and $7.4\%$ ($95\%$ CI 5.6–10.7) in men. The estimated cumulative seroprevalence for the population was $7.9\%$ ($95\%$ CI 7.3–8.6).Table 2Age-specific seroprevalences. Presence of IgM, IgA and IgG antibodies against S and/or RBD antigens was analyzed. The table presents the cumulative seroprevalence and seroprevalences for each isotype, their proportions and CI.Isotype specificity0–10 years of age ($$n = 142$$)11–20 years of age ($$n = 159$$)21–50 years of age ($$n = 177$$)51–65 years of age ($$n = 157$$) > 65 years of age ($$n = 93$$)Estimated at total population ($$n = 728$$)IgG and/or IgM and/or IgA3/$1422.1\%$(1.5–3)18/$15911.3\%$(9.9–13)14/$1777.9\%$(6.7–9.2)14/$1578.9\%$(7.6–9.2)7/$937.5\%$(6–9.3)56/$7287.9\%$(7.3–8.6)IgM08/$1595.0\%$(4.1–6.2)7/$1774.0\%$(3.1–5)5/$1573.2\%$(2.4–4.2)2/$932.2\%$(1.4–3.3)22/$7283.3\%$(2.9–3.8)IgA2/$1421.4\%$(0.9–2.2)8/$1595.0\%$(4.7–7)6/$1773.4\%$(2.6–4.3)9/$1575.7\%$(4.7–7)7/$937.5\%$(6–9.4)32/$7284.3\%$(3.8–4.8)IgG3/$1422.1\%$(1.5–3)8/$1595.0\%$(4–6.2)2/$1771.1\%$(0.7–1.7)2/$1571.3\%$(0.8–2)1/$931.1\%$(0.6–2)16/$7282.0\%$(1.7–2.4) The estimated seroprevalence of IgG in the population was $2.0\%$ ($95\%$ CI 1.7–2.4), $4.3\%$ for IgA ($95\%$ CI 3.8–4.8), and $3.3\%$ ($95\%$ CI 2.9–3.8) for IgM. Sixteen participants were only positive for IgG, 32 were only positive for IgA and 22 were only positive for IgM (Supplementary Fig. 1). Only two participants were seropositive for all three isotypes. Noteworthy, no participant with 0–10 years of age was found to be seropositive for IgM. Regarding antigen specificity, 17 were only positive for RBD, 27 only for S and 12 for both (at any of the three isotypes).
## Symptoms
Out of the 56 participants who were seropositive for any of the isotype-antigen pairs, $71.43\%$ reported symptoms compatible with COVID-19 since mid-February vs $52.6\%$ in seronegative participants. The most frequent reported symptoms among the seropositive were headache ($35.7\%$), sore throat ($35.7\%$), cough ($32.1\%$) and rhinorrhoea ($32.1\%$) (Supplementary Table 5). There were no differences in categories of symptoms reported across seropositive nor across seronegative between participant sex (data not shown).
Ten participants had been previously diagnosed with COVID-19, seven of them were diagnosed only based on symptoms and signs (during the first wave of the pandemic RT-qPCR testing was restricted to severe patients and/or high-risk groups), one via RT-qPCR and two via a serology test. All of them declared having had COVID-19 related symptoms. We did not detect antibodies in five previously diagnosed participants, however, four of them had only received a clinical diagnosis. The remaining had had a positive serology test.
Since mid-February until June, the highest frequency of onset of COVID-19 compatible symptoms was reported in the second week of March, right before the start of lockdown in Spain, and surprisingly in both seropositive and the seronegative participants (Fig. 2).Figure 2Frequency of dates of start of reported symptoms compatible with COVID-19 across time among participants. Purple bars represent seronegative participants and green bars seropositive participants.
## Correlations between isotypes
We computed Spearman correlations between isotype-antigen pairs and found that, among the seropositive participants ($$n = 56$$), RBD-IgM levels highly correlated with S-IgM levels ($r = 0.75$), and S-IgG with RBD-IgG levels ($r = 0.85$) but this was not observed for IgA. Besides, S-IgA moderately correlated with S-IgG ($r = 0.57$) and with RBD-IgG ($r = 0.45$). IgM levels did not correlate with IgG nor IgA levels. Among the seronegative participants, IgGs and IgMs were found to correlate significantly (Fig. 3).Figure 3Scatter plots matrix representing correlations between isotype-antigen pairs. Two-sided spearman test was used to calculate the rs correlation coefficients and p-values. Lines represent the fitted curves calculated using the loess method. Shaded areas represent $95\%$ confidence intervals. “***” if the p-value is < 0.001, “**” if the p-value is < 0.01, “*” if the p-value is < 0.05. Purple dots, lines and shades represent seronegative participants and green seropositive ones.
## Factors associated with seropositivity and antibody levels
Univariable analysis showed that adolescent age, having had flu symptoms or a cold in the previous transmission season, reporting COVID-19 compatible symptoms and having been diagnosed with COVID-19 were associated with higher odds of being seropositive (Table 3). When adjusting for one another in the multivariable analysis, results showed that the odds of being seropositive were lower in participants aged 0–10 than in all other groups and significantly lower than in adolescents (11–20; p-value < 0.05) and higher in those who had had COVID-19-like symptoms (Table 3).Table 3Univariable and multivariable analysis of factors associated with seropositivity (IgM and/or IgG and/or IgA).CharacteristicsSeropositive ($$n = 56$$)Seronegative ($$n = 672$$)Univariable analysisMultivariable analysis ($$n = 712$$)OR($95\%$ CI)pOR($95\%$ CI)pGendera56672 Female31 ($55.4\%$)334 ($49.7\%$)10.23 Male25 ($44.6\%$)338 ($50.3\%$)0.70(0.40; 1.25)Age, yearsa56672 0–103 ($5.4\%$)139 ($20.7\%$)10.0651 11–2018($32.1\%$)141 ($21\%$)5.91(1.80; 19.38)5.03(1.64; 21.94)0.012 21–5014 ($25\%$)163 ($24.3\%$)3.98(1.20; 12.25)3.24(1.02; 14.39)0.071 51–6514 ($25\%$)143 ($21.3\%$)4.54(1.27; 16.09)3.34(1.02; 14.97)0.068 > 657 ($12.5\%$)86 ($12.8\%$)3.77(0.95; 14.83)3.89(1.04; 18.55)0.056Towna56672 Cizur Menor38 ($67.9\%$)445 ($66.2\%$)10.507 Atráin3 ($5.4\%$)38 ($5.6\%$)0.88(0.25; 3.09) Gazólaz3 ($5.4\%$)18 ($2.7\%$)1.56(0.44; 5.50) Larraya020 ($3\%$)- Muru Astráin1 ($1.8\%$)10 ($1.5\%$)0.95(0.13; 6.92) Paternáin4 ($7.1\%$)55 ($8.2\%$)1.10(0.37; 3.33) Sagüés2 ($3.6\%$)4 ($0.6\%$)5.76(0.94; 35.34) Undiano2 ($3.6\%$)48 ($7.1\%$)0.36(0.08; 1.60) Zariquiegui3 ($5.4\%$)34 ($5.1\%$)0.82(0.17; 3.91)Body mass index b22 (4.3)22.5 (5.2)0.97(0.92; 1.03)0.73Blood group a24219 A9 ($37.6\%$)130 ($44.7\%$)10.94 AB1 ($4.1\%$)6 ($2.1\%$)2.21(0.32; 15.07) B2 ($8.3\%$)21 ($7.2\%$)1.34(0.31; 5.83) O12 ($50\%$)134 ($46\%$)1.27(0.55; 2.92)BCG vaccine immunization statusa49617 Not vaccinated32 ($65.3\%$)388 ($62.9\%$)10.74 Vaccinated17 ($34.7\%$)229 ($37.1\%$)0.91(0.51; 1.60)Having received flu vaccine in this transmission season a56666 No45 ($88.3\%$)527 ($79.1\%$)10.46 Yes9 ($16.7\%$)139 ($20.9\%$)0.77(0.39; 1.54)Having had the flu or a cold in this transmission season a55611 No33 ($60\%$)446 ($67.5\%$)10.0151 Yes22 ($40\%$)165 ($32.5\%$)1.89(1.13; 3.15)1.740.94–3.160.073Comorbiditiesa*56672 No28 ($50\%$)309 ($46\%$)10.94 Yes28 ($50\%$)363 ($54\%$)0.98(0.55; 1.75)Reporting COVID-19 compatible symptoms a#56671 No16 ($28.6\%$)322 ($48\%$)10.0051 Yes40 ($71.4\%$)349 ($52\%$)2.17(1.24; 3.81)1.991.07–3.100.035Previously diagnosed with COVID-19 a No51 ($91.1\%$)665 ($99.3\%$)10.0003 Yes5 ($8.93\%$)5 ($0.7\%$)17.18(3.74; 78.85)Having had a COVID-19 close contact No33 ($60\%$)435 ($66.2\%$)10.7441 Yes, confirmed16 ($29\%$)154 ($23.4\%$)1.37(0.72;2.52)1.250.64–2.370.493 Yes, suspected6 ($11\%$)68 ($10.4\%$)1.16(0.43–2.70)1.060.38–2.530.902Highest schooling levela¥ University degree/Master/PhD29 ($19.8\%$)383 ($61.8\%$)10.58 Technical studies10 ($19.2\%$)111 ($17.9\%$)1.17(0.73; 2.33) Secondary education13 ($25\%$)122 ($19.7\%$)1.37(0.73; 2.55) Primary education0 ($0\%$)4 ($0.6\%$)0Exposure to gatheringsa£ No14 ($25\%$)203 ($30.3\%$)10.49 Yes42 ($75\%$)468 ($69.7\%$)1.28(0.71; 2.29)an/N (%) where N is the total number of participants who are seropositive or seronegative.bArithmetic Mean (SD).#Compatible symptoms included fever, chills, fatigue, myalgia, sore throat, anosmia, ageusia, cough, rhinorrhea, dyspnea, wheezing, chest pain, headache, abdominal pain, diarrhea, sputum from mid-February until date of interview.¥For participants over 20 years of age.£Exposures included visiting a sanitary center, grocery shop, bank, church, hairdresser, another city from mid-February until the end of march. Significant values are in bold.
To determine the factors associated with being seropositive for antigen and/or isotype specific when infected, antigen and isotype-specific multivariable models including only seropositive participants were performed. One-unit increase in BMI decreased by 0.82 ($95\%$ CI 0.68–0.96) the odds of being RBD seropositive (any isotype). Likewise, a unit increase in age was associated with an increase by 1.04 ($95\%$ CI 1.01–1.07) in the odds of being IgA seropositive. Finally, being male and a unit increase in age were associated with an increase by 4.83 ($95\%$ CI 1.22–22.91) and a decrease by 0.96 ($95\%$ CI 0.93–1.28) in the odds of being IgG seropositive, respectively (Supplementary Table 6).
None of the collected variables were uniquely associated with (i) IgG, (ii) IgM, (iii) IgA levels against (a) S, (b) RBD in univariable and multivariable linear regression models, in seropositive participants (for any isotype-antigen pair) only, except for levels of IgG against RBD which were $107\%$ higher in male participants ($95\%$ CI 7.97–298.76). However, the models only included 36 observations which correspond to the seropositive participants for which we knew the date of onset of symptoms to be able to adjust the models for time since infection (Supplementary Tables 7–9).
## Discussion
We found an overall estimated seroprevalence of $7.9\%$ ($95\%$ CI 7.3–8.6) in an age-stratified seroprevalence survey in a sample of 728 randomly selected, voluntarily registered inhabitants from a municipality of 3,925 inhabitants in Spain, conducted between June 12th and 19th 2020, during lockdown easing in Navarre. Participants showed a very heterogeneous immune response in terms of isotypes and antigens seropositivity, thus by analyzing IgA, IgM and IgG levels against S and RBD SARS-CoV-2 antigens we increased the sensitivity to detect seropositive responses. As expected, we found a higher seroprevalence than the proportion of diagnosed population ($1.57\%$)4. ENE-COVID, a government large seroprevalence study performed in June 2020, estimated a seroprevalence of $5.2\%$ ($95\%$ CI 4.9–$5.5\%$) in the Autonomous Community of Navarre measured by a point-of-care rapid antigen test with $82.1\%$ sensitivity and $100\%$ specificity26, while our study estimated a seroprevalence of $7.9\%$ ($95\%$ CI 1.7–2.4). The difference could be explained by the fact that we were assessing a larger panel of isotypes and antigens. When stratifying by age, taking into consideration all isotypes, we found a higher seroprevalence in aged 0–20 than ENE-COVID ($7\%$ vs $4.7\%$) and aged 21–50 ($7.9\%$ vs $5.6\%$); and a lower seroprevalence for aged 51–65 ($8.9\%$ vs $9.3\%$) and over 65 years ($7.5\%$ vs $8.2\%$)4, although the age distribution is consistent.
On the socioeconomic aspect, we have seen that four out of 728 participants lost their job for a pandemic-related reason, those with technical education were the most affected financially by the pandemic ($36.25\%$ of them) and those with school level education were the ones declaring having the most struggles playing their expenses ($25\%$ of them). Although our sample size was small and is extracted from a high-income town relative to its surroundings, these results are consistent with other reports predicting an aggravation of already existing inequalities across socioeconomic strata27. Oxfam estimated an increase in net income inequality of 1.0 points in 2020 in Navarre, measured in terms of the Gini index28.
Remarkably, we found that a higher proportion of men went to the supermarket, the bank, and the nearest city, than women. To our knowledge, this has not been reported scientifically, although informal communications corroborate it29. We hypothesize that this could be related to women tending to be more risk-averse than men30 or to women staying at home to take care of children, people with disabilities and elderly31.
We observed that most IgG seropositive participants were seronegative for IgA and vice versa. Recent publications show that IgG and IgA antibody levels are still stable at 12.532, 1333, 1614 and up to 20.5 months after infection34, thus, seroreversion would not explain their seronegativity, but rather that the immune response profile mounted upon infection may be heterogeneous across individuals. The same variability was observed in epitope specificity: most participants had seropositive levels just for one of the two antigens assessed. In a previous paper, we had already shown this variability35. To our knowledge, this heterogeneity in antibody profiles has been seldom studied since results for each isotype-antigen pair are usually aggregated and not contrasted at individual level. Markmann et al. found that $65\%$ of their seropositive participants had IgG antibodies against both RBD and N36. However, many studies show positive correlations between different isotype-antigen pair levels36–38, as we do. Anti-S and anti-RBD levels correlated for IgM and IgG, but not for IgA. Besides, S-IgA correlated with S-IgG and with RBD-IgG. This suggests that, albeit the correlations, panels assessing a wider range of antigens and evaluating both IgA and IgG might have higher sensitivity in determining seropositivity.
Chronologically, the week prior to lockdown witnessed the peak in the number of seropositive participants reporting onset of COVID-19 compatible symptoms. Assuming those seropositive individuals were experiencing COVID-19 at the time of COVID-19 compatible symptoms, it is surprising that the peak occurred before the start of lockdown. We would have expected the peak to occur one week after the start of lockdown (stopping the potential transmission chains), since the incubation period was around 6 days for the original SARS-CoV-2 variant39. Indeed, the peak of diagnosis in Navarre occurred the week of the 23rd to 29th March4. The small sample size could explain this shift as well as a recall bias, since the start of the state of alarm was used as a reference of time by fieldworkers. Furthermore, the frequency of seronegative participants declaring compatible symptoms peaked the same week. This leads us to reinforce the belief that some recall bias might have distorted the reported dates. Moreover, we hypothesize that—to some extent—a nocebo effect might partially explain the parallel frequency of symptoms in seropositive and seronegative participants. The nocebo effect—as opposed to the placebo effect—occurs when a preconceived belief or expectation leads to a negative effect, in this case developing or worsening of symptoms, and has been observed in severe COVID-1940 and in vaccination side-effects41.
We found that children between 0 and 10 years of age had less odds of being seropositive than those aged 11–20. A review reported this decreased risk of infection in children aged < 10 years42. Some observed that their adaptive immune response was less robust, and proposed that they might have benefited from a more efficient innate response43–45. Others have suggested that adults, because they have very likely been in contact with other coronaviruses, might be subjected to immune imprinting, where the immune system approaches the new virus as an exposure to an old one and hence mounts a less efficient response46. Besides, we also hypothesize that this might be explained by the differential measures that children underwent during the lockdown in Spain, as they were not allowed to leave their homes at all for almost a two-month period. Among the seropositive participants, we also found associations between higher age and higher odds of being specifically seropositive for IgA. While many studies are congruent with these findings45,47,48, other studies have found inverse association of age and antibody levels49.
Because of the heterogeneity in antigen-isotype responses across participants, we explored factors that might be associated with being seropositive for an antigen and/or isotype specifically once infected. We found an association between higher BMI and lower odds of RBD seropositivity for any isotype (0.82, $95\%$ CI 0.68–0.96), which was described before by Frasca et al.50 and explained by the known influence of obesity in impairing the functioning of immune cells. Finally, being male was associated with higher odds of IgG seropositivity (4.83, $95\%$ CI: 1.22–22.91) and higher levels of IgG-RBD. Male bias in COVID-19 disease severity has been widely reported and might be explained by differences in the immune response against the virus, as it has been described before for other viral infections, like influenza viruses, HIV or hepatitis viruses51. Klein et al. found higher antibody titers against S, S1 and RBD antigens in male47. Others have seen higher titers of neutralizing antibodies36,48 or higher levels of pro-inflammatory chemokines and cytokines52. However, some studies point towards a more robust response in females52,53.
When the outcomes were levels of a specific isotype-antigen pair, models did not provide any specific associated variable, except for levels of IgG against RBD which were $107\%$ higher in male participants. However, we had a sample size of only 36 which probably reduces the statistical power of the model. A robust body of literature has associated severity with higher antibody levels38,54, unfortunately we did not collect degree of severity but only presence of symptoms.
The biggest limitation of the study is the low number of seropositive participants that restricts the power of regression models. Secondly, only $30.7\%$ of the total population registered for the study and this may imply some self-selection bias and therefore would make the sample not fully representative of the population: people who had had symptoms were more likely to be interested, thus overestimating the seroprevalence, people with comorbidities might have had more interest to know their serostatus, while people already diagnosed where more likely to not register and, as a result, underestimate the true seroprevalence. Inhabitants over 65 years of age were under-represented in our sample and as a result, seroprevalence of this age group might be less representative of the population. In addition, we did not assess whether people had teleworked during lockdown, which should have been introduced in the regression models as a confounder. Finally, even if some degree of correlation is observed, levels of antibodies do not represent directly protection against infection and are just a piece of a complex immune response implicating many more actors.
Two years into the pandemic, $40\%$ of the global population is believed to have been infected, $75\%$ in Spain according to the IHME55. Knowledge has been created at an unprecedented speed, but key questions such as threshold for protection against infection, or duration of natural or acquired immunity against severe disease remain unknown. Seroprevalence studies like the one here presented helped in the early stages of the pandemic in determining the penetration of the infection at the population level to react accordingly. Now with the high vaccination coverage and lower rates of testing they shed light on the determinants and characteristics of the immune response to the vaccines and the virus, their duration and their efficacy against de variants of concern. Furthermore, household questionnaires inform on the socioeconomic aspects of the pandemic and when taken together with the serological data they elucidate populations that are more vulnerable to the infection and to its socioeconomic impact.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-30542-x.
## References
1. 1.WHO Coronavirus Disease (COVID-19) Dashboard | WHO Coronavirus Disease (COVID-19) Dashboard. https://covid19.who.int/.
2. Wang H. **Estimating excess mortality due to the COVID-19 pandemic: A systematic analysis of COVID-19-related mortality, 2020**. *Lancet* (2022.0) **399** 1513-1536. DOI: 10.1016/S0140-6736(21)02796-3
3. Mukaigawara M. **An equitable roadmap for ending the COVID-19 pandemic**. *Nat. Med.* (2022.0) **2022** 1-4. DOI: 10.1038/s41591-022-01787-2
4. 4.Instituto de Salud Pública y Laboral de Navarra. PRIMERA ONDA PANDÉMICA DE COVID-19 EN NAVARRA MARZO A JUNIO DE 2020. (2020).
5. 5.Gobierno de Navarra. Plan y Normas para la Transición. https://gobiernoabierto.navarra.es/es/participacion/transitando-hacia-normalidad/plan-para-transicion (2020).
6. 6.Gobierno de España. ANEXO II.- PREVISIÓN ORIENTATIVA PARA EL LEVANTAMIENTO DE LAS LIMITACIONES DE ÁMBITO NACIONAL ESTABLECIDAS EN EL ESTADO DE ALARMA, EN FUNCIÓN DE LAS FASES DE TRANSICIÓN A UNA NUEVA NORMALIDAD. https://www.lamoncloa.gob.es/consejodeministros/resumenes/Documents/2020/28042020_Anexo%20II%20FASES.pdf (2020).
7. Pinilla J. **The economic impact of the SARS-COV-2 (COVID-19) pandemic in Spain**. *Int. J. Environ. Res. Public Health* (2021.0) **18** 4708. DOI: 10.3390/ijerph18094708
8. 8.Bosch, N., Esteller-Moré, A. & Sorribas-Navarro, P. El IEB Report 4/20 analiza el impacto de la COVID-19 en la pobreza: IEB. https://ieb.ub.edu/el-ieb-report-4-20-analiza-el-impacto-de-la-covid-19-en-la-pobreza/(2020).
9. Cheng MP. **Serodiagnostics for severe acute respiratory syndrome-related coronavirus-2**. *Ann. Intern. Med.* (2020.0). DOI: 10.7326/m20-2854
10. 10.Instituto de Salud Carlos III. Ministerio de Ciencia e Innovación. Gobierno de España. ESTUDIO ENE-COVID: INFORME FINAL ESTUDIO NACIONAL DE SERO-EPIDEMIOLOGÍA DE LA INFECCIÓN POR SARS-COV-2 EN ESPAÑA. http://www.thelancet.com/journals/lancet/article/PIIS0140-6736 (2020).
11. Sette A, Crotty S. **Immunological memory to SARS-CoV-2 infection and COVID-19 vaccines**. *Immunol. Rev.* (2022.0). DOI: 10.1111/imr.13089
12. Arkhipova-Jenkins I. **Antibody response after SARS-CoV-2 infection and implications for immunity a rapid living review background: The clinical significance of the antibody response**. *Ann. Intern. Med.* (2021.0) **174** 811-821. DOI: 10.7326/M20-7547
13. Altawalah H, De Francesco MA. **Antibody responses to natural SARS-CoV-2 infection or after COVID-19 vaccination**. *Vaccines* (2021.0). DOI: 10.3390/vaccines9080910
14. Yang Y. **Longitudinal analysis of antibody dynamics in COVID-19 convalescents reveals neutralizing responses up to 16 months after infection**. *Nat. Microbiol.* (2022.0) **7** 423-433. DOI: 10.1038/s41564-021-01051-2
15. 15.Ayuntamiento de la Cendea de Cizur. Datos de Interés. https://cendeadecizur.es/nuestra-cendea/datos-de-interes/.
16. 16.Infografias - Nastat - navarra.es. https://nastat.navarra.es/es/listado_infografias.
17. Stadlbauer D. **SARS-CoV-2 seroconversion in humans: A detailed protocol for a serological assay, antigen production, and test setup**. *Curr. Protoc. Microbiol.* (2020.0) **57** e100. DOI: 10.1002/cpmc.100
18. Dobaño C. **Highly sensitive and specific multiplex antibody assays to quantify immunoglobulins M, A, and G against SARS-CoV-2 antigens**. *J. Clin. Microbiol.* (2021.0) **59** e01731. DOI: 10.1128/JCM.01731-20
19. Santano R. **Agreement between commercially available ELISA and in-house Luminex SARS-CoV-2 antibody immunoassays**. *Sci. Reports* (2021.0) **11** 1-7
20. 20.Jefatura del Estado. LEY 14/2007, de 3 de julio, de Investigación biomédica. 159 (BOE, 2007).
21. 21.Premkumar, L. et al. The RBD of the spike protein of SARS-group coronaviruses is a highly specific target of SARS-CoV-2 antibodies but not other pathogenic human and animal coronavirus Antibodies. medRxiv Prepr. Serv. Heal. Sci. 2020.05.06.20093377 (2020) 10.1101/2020.05.06.20093377.
22. Wickham H. **Welcome to tidyverse**. *J. Open Source Softw.* (2019.0) **4** 1686. DOI: 10.21105/joss.01686
23. Wickham H. *ggplot2: Elegant Graphics for Data Analysis* (2016.0)
24. Pebesma E. **Simple features for R: Standardized support for spatial vector data**. *R J.* (2018.0) **10** 439-446. DOI: 10.32614/RJ-2018-009
25. 25.Lüdecke, D. sjPlot: Data Visualization for Statistics in Social Science. https://cran.r-project.org/web/packages/sjPlot/citation.html (2021).
26. 26.Estudio Nacional de Sero-epidemiología de la Infección por SARS-CoV-2 en España. (2020).
27. 27.Observatorio de la Realidad Social. VI Informe sobre la pobreza y la desigualdad social en Navarra (2023). (2022).
28. 28.Oxfam Intermon. Informe Pobreza y Desigualdad. https://oxfam.app.box.com/s/90ijkmfp0tlfzd1q7hb64mka4q29xfvt (2020).
29. 29.¿Quién se encarga de las tareas domésticas durante el confinamiento? Covid-19, mercado de trabajo y uso del tiempo en el hogar—Nada es Gratis. https://nadaesgratis.es/admin/quien-se-encarga-de-las-tareas-domesticas.
30. Byrnes JP, Miller DC, Schafer WD. **Sex differences in risk taking: A meta-analysis**. *Psychol. Bull.* (1999.0) **125** 367-383. DOI: 10.1037/0033-2909.125.3.367
31. 31.Instituto Navarro para la Igualdad. El Confinamiento En Primera Persona: Vivencias y Testimonios. El Impacto y las Consecuencias del Confinamiento en Mujeres de Navarra. (2020).
32. Dobaño C. **Persistence and baseline determinants of seropositivity and reinfection rates in health care workers up to 12.5 months after COVID-19**. *BMC Med.* (2021.0). DOI: 10.1186/s12916-021-02032-2
33. Gallais F. **Evolution of antibody responses up to 13 months after SARS-CoV-2 infection and risk of reinfection**. *EBioMedicine* (2021.0) **71** 103561. DOI: 10.1016/j.ebiom.2021.103561
34. Dobaño C. **Sustained seropositivity up to 20.5 months after COVID-19**. *BMC Med.* (2022.0). DOI: 10.1186/s12916-022-02570-3
35. Ortega N. **Seven-month kinetics of SARS-CoV-2 antibodies and role of pre-existing antibodies to human coronaviruses**. *Nat. Commun.* (2021.0) **12** 1-10. DOI: 10.1038/s41467-021-24979-9
36. Markmann AJ. **Sex disparities and neutralizing-antibody durability to SARS-CoV-2 infection in convalescent individuals**. *mSphere.* (2021.0). DOI: 10.1128/mSphere.00275-21
37. Ma J. **COVID-19 patients in earlier stages exhaled millions of SARS-CoV-2 per hour**. *Clin. Infect. Dis.* (2020.0). DOI: 10.1093/cid/ciaa1283
38. Wang H. **Clinical and antibody characteristics reveal diverse signatures of severe and non-severe SARS-CoV-2 patients**. *Infect. Dis. Poverty* (2022.0) **11** 1-15. DOI: 10.1186/s40249-022-00940-w
39. Wassie GT, Azene AG, Bantie GM, Dessie G, Aragaw AM. **Incubation period of severe acute respiratory syndrome novel coronavirus 2 that causes coronavirus disease 2019: A systematic review and meta-analysis**. *Curr. Ther. Res. Clin. Exp.* (2020.0) **93** 100607. DOI: 10.1016/j.curtheres.2020.100607
40. Daniali H, Flaten MA. **Experiencing COVID-19 symptoms without the disease: The role of nocebo in reporting of symptoms**. *Scand. J. Public Health* (2022.0) **50** 61-69. DOI: 10.1177/14034948211018385
41. Sever PP. **Nocebo affects after COVID-19 vaccination**. *Lancet Reg. Heal. Eur.* (2022.0) **12** 100273. DOI: 10.1016/j.lanepe.2021.100273
42. Irfan O, Li J, Tang K, Wang Z, Bhutta ZA. **Correspondence to: Risk of infection and transmission of SARS-CoV-2 among children and adolescents in households, communities and educational settings: A systematic review and meta-analysis**. *J Glob Health.* (2021.0) **11** 5013. DOI: 10.7189/jogh.11.05013
43. Cohen CA. **SARS-CoV-2 specific T cell responses are lower in children and increase with age and time after infection**. *Nat. Commun.* (2021.0) **12** 1-14. DOI: 10.1038/s41467-021-24938-4
44. Pierce CA. **Immune responses to SARS-CoV-2 infection in hospitalized pediatric and adult patients**. *Sci. Transl. Med* (2020.0) **12** 5487. DOI: 10.1126/scitranslmed.abd5487
45. Weisberg SP. **Distinct antibody responses to SARS-CoV-2 in children and adults across the COVID-19 clinical spectrum**. *Nat. Immunol.* (2020.0) **22** 25-31. DOI: 10.1038/s41590-020-00826-9
46. 46.Stephenson, T. et al. Long COVID: The physical and mental health of children and non-hospitalised young people 3 months after SARS-CoV-2 infection; a national matched cohort study (The CLoCk) Study. BMJ Open11, (2021).
47. Klein SL. **Sex, age, and hospitalization drive antibody responses in a COVID-19 convalescent plasma donor population**. *J. Clin. Invest.* (2020.0) **130** 6141-6150. DOI: 10.1172/JCI142004
48. Mehew J, Johnson R, Roberts D, Harvala H. **Convalescent plasma for COVID-19: Male gender, older age and hospitalisation associated with high neutralising antibody levels, England, 22 April to 12 May 2020**. *Eurosurveillance* (2020.0) **25** 2001754. DOI: 10.2807/1560-7917.ES.2020.25.45.2001754
49. Yang HS. **Association of age with SARS-CoV-2 antibody response**. *JAMA Netw. Open* (2021.0) **4** e214302. DOI: 10.1001/jamanetworkopen.2021.4302
50. Frasca D. **Influence of obesity on serum levels of SARS-CoV-2-specific antibodies in COVID-19 patients**. *PLoS ONE* (2021.0) **16** e0245424. DOI: 10.1371/journal.pone.0245424
51. Jacobsen H, Klein SL. **Sex differences in immunity to viral infections**. *Front. Immunol.* (2021.0) **12** 3483. DOI: 10.3389/fimmu.2021.720952
52. Takahashi T. **Sex differences in immune responses that underlie COVID-19 disease outcomes Overview of the study design**. *Nature* (2020.0) **588** 315. DOI: 10.1038/s41586-020-2700-3
53. Schlickeiser S. **Disease severity, fever, age, and sex correlate with SARS-CoV-2 neutralizing antibody responses**. *Front. Immunol.* (2021.0) **11** 3888. DOI: 10.3389/fimmu.2020.628971
54. Yan X. **Anti-SARS-CoV-2 IgG levels in relation to disease severity of COVID-19**. *J. Med. Virol.* (2022.0) **94** 380-383. DOI: 10.1002/jmv.27274
55. 55.IHME. Estimation of total mortality due to COVID-19|Institute for Health Metrics and Evaluation. http://www.healthdata.org/special-analysis/estimation-excess-mortality-due-covid-19-and-scalars-reported-covid-19-deaths.
|
---
title: 'Pregnancy outcomes among Indian women: increased prevalence of miscarriage
and stillbirth during 2015–2021'
authors:
- Periyasamy Kuppusamy
- Ranjan K Prusty
- Itta K Chaaithanya
- Rahul K Gajbhiye
- Geetanjali Sachdeva
journal: BMC Pregnancy and Childbirth
year: 2023
pmcid: PMC9992916
doi: 10.1186/s12884-023-05470-3
license: CC BY 4.0
---
# Pregnancy outcomes among Indian women: increased prevalence of miscarriage and stillbirth during 2015–2021
## Abstract
### Background
Pregnancy outcome is an important health indicator of the quality of maternal health. Adverse pregnancy outcomes is a major public health problem, which can lead to poor maternal and neonatal outcomes. This study investigates the trends in pregnancy outcomes prevalent during 2015–2021 in Indian women.
### Methods
The study analysed the data presented in the fourth [2015-16] and fifth [2019-21] rounds of National Family Health Survey (NFHS). The absolute and relative changes in the birth outcomes of last pregnancy during the five years preceding the surveys were estimated using data collected from 195,470 women in NFHS-4 and from 255,549 women in NFHS-5.
### Results
Livebirth decreased by 1.3 points ($90.2\%$ vs. $88.9\%$), and nearly half of the Indian states/UTs ($$n = 17$$/36) had lower than the national average of livebirth ($88.9\%$) reported during 2019-21. A higher proportion of pregnancy loss was noted, particularly miscarriages increased in both urban ($6.4\%$ vs. $8.5\%$) and rural areas ($5.3\%$ vs. $6.9\%$), and stillbirth increased by $28.6\%$ ($0.7\%$ vs. $0.9\%$). The number of abortions decreased ($3.4\%$ vs. $2.9\%$) among Indian women. Nearly half of the abortions were due to unplanned pregnancies ($47.6\%$) and more than one-fourth ($26.9\%$) of abortions were performed by self. Abortions among adolescent women in Telangana was eleven times higher during 2019-21 as compared to 2015-16 ($8.0\%$ vs. $0.7\%$).
### Conclusion
Our study presents evidence of a decrease in the livebirth and an increase in the frequency of miscarriage and stillbirth among Indian women during 2015–2021. This study emphasises that there is a need of regional-specific, comprehensive and quality maternal healthcare programs for improving livebirth among Indian women.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12884-023-05470-3.
## Background
Better maternal health and pregnancy outcomes are significant public health priorities. Adverse pregnancy outcomes such as miscarriage, stillbirth and abortion reflect poor maternal health indicators. Antenatal care (ANC) and institutional delivery are the most important strategies to reduce the higher risk of maternal and fetal complications and deaths. The risk of maternal and neonatal deaths due to complications of pregnancy and childbirth is higher in the low-middle income countries (LMICs) [1]. In India too, approximately 44,000 women die from pregnancy-related complications every year [2]. To improve pregnant women’s health and pregnancy outcomes, the Government of India (GoI) has initiated various programs like Janani Suraksha Yojana-2005 [3], Dakshata implementation package-2015 [4], Pradhan Mantri Surakshit Matritva Abhiyan-2016 [2], Pradhan Mantri Matru Vandana Yojna-2017 [5], and LaQshya-2017 [6] to provide a quality of free antenatal check-ups and care during delivery, identify high-risk pregnancies and provide cash incentives.
Miscarriage and stillbirth are the most common natural pregnancy losses, which affects the mother’s physical and psychosocial well-being [7]. Maternal age, abnormal parental genetic makeup, infections, hormonal imbalances, uterine dysfunctions, comorbidities, and lifestyle factors are the attributable risk for higher pregnancy loss, however the cause of miscarriage remains unknown [8]. Patki et al. reported higher prevalence ($32\%$) of spontaneous miscarriages among Indian women in 2016 [9].
India is one of six countries that share half of the global burden of stillbirth [10]. Almost one-third of stillbirth remain unexplained, and two-thirds of cases are reported to be caused by infection in the placenta or umbilical cord, high blood pressure, birth defects, or poor nutrition [8]. To reduce the existing stillbirth rate to 10 per 1000 births by 2030, the Indian New-born Action Plan was implemented in 2014 [11]. There was a substantial reduction in stillbirth rate from 29.6 to 13.9 per 1000 total births during 2000–2019 [10]. The prevalence of stillbirth (4.2 to 14.8) was reported to be widely variable across the Indian states [12]. In recently published study by McClure et al., the major causes of stillbirth were hypertensive diseases ($36\%$), followed by severe anaemia ($11\%$) in Indian and Pakistani population [13]. The authors also reported the maternal and fetal vascular malperfusion in $47\%$ stillbirth as primary placental causes. Intrauterine hypoxia was reported in $72\%$ stillbirth as primary fetal cause of stillbirth [13]. While there has been an improvement in reducing the burden of stillbirth, the pace of this reduction has been slow. This may be partly attributed to the less priority given to stillbirth reduction in national programs. Limited availability of accurate, complete, and actionable information on stillbirth, particularly in high-burden areas, also contributes to slow progress in reducing stillbirth [14, 15].
Unplanned pregnancies are the main reason for seeking abortions [16]. A study estimated that around 15.6 million abortions occurred in India in 2015 [17], and unsafe abortions contribute to 10 to $13\%$ of maternal mortality [18]. Nearly half of the unintended pregnancies ended with abortions and mostly were unsafe [17]. Several factors including socio-cultural barriers contribute to women opting for abortions at outside the healthcare settings. Considering the present needs, the Medical Termination of Pregnancy (Amendment) Act – 2021 allows universal access to reproductive health services, providing comprehensive abortion care and increasing the upper gestation time limit up to 20 weeks [19]. The study assessed the trends and patterns of pregnancy outcomes across different Indian states and union territories (UTs), considering that several initiatives have been undertaken by the GoI in the last decade to improve maternal health and pregnancy outcomes. It was envisaged that this analysis will directly reflect the impact of various initiatives and also highlight the areas that warrant more efforts towards better maternal healthcare.
## Data source and study population
This study was conducted using the nationally representative households survey data of the National Family Health Survey (NFHS) round fourth [2015-16] and fifth [2019-21]. Both surveys used probability proportionate sampling, and the methods and data collection tools were published elsewhere [16, 20]. Birth outcomes of last pregnancies among women aged 15–49 years during the five years preceding the survey were considered for analysis. NFHS-4 provides pregnancy outcome data of 1,95,470 women conducted in 29 Indian states and 6 UTs. Recently published NFHS-5 provides pregnancy outcome data of 2,55,549 women conducted in 28 states and 8 UTs. Jammu & Kashmir (J&K) state was divided into two UTs J&K and Ladakh in 2019. The NFHS-4 data for J&K represents both Jammu & Kashmir and Ladakh UTs. Similarly, data of Dadra & Nagar Haveli, and Daman & Diu were reported as one UT during NFHS-5, so we calculated the proportion using the sample size and given proportion of both UTs of pregnancy outcomes in NFHS-4.
## Pregnancy outcome measures
Livebirth is defined as a child born alive. Pregnancy loss refers to pregnancy ending in a non-livebirth due to miscarriage, stillbirth, or abortion. Miscarriage is defined as a pregnancy ended early and involuntarily. Spontaneous abortion or miscarriage refers to fetal death in the womb before 20 weeks of gestation. Stillbirth is defined as birth of a child with no signs of life or fetal demise occurring at the gestation of 28 weeks or later. Abortion is defined as voluntary termination of pregnancy [16].
## Data analysis
Data were extracted from the national and state/UTs- reports of NFHS-4 and NFHS-5. Data of pregnancy outcome measures such as livebirth, pregnancy loss, miscarriage, stillbirth and abortions were reported as proportion (%). The absolute and relative changes were computed. Absolute change refers to the change in the indicator in percentage points i.e. the value of the indicator in NFHS-5 minus that in NFHS-4. Relative change is the absolute change as a percentage of the value of NFHS-4. As per the data available in the report of states and UTs in both surveys, the sub-group analysis was carried out to understand trends in sociodemographic characteristics level. The state-wise map based on the prevalence of the proportion of pregnancy loss was created through ArcGIS 10.1 software packages.
## Livebirth and pregnancy loss
The proportion of livebirth among Indian women was $90.2\%$ ($$n = 195$$,470) in 2015-16 and $88.9\%$ ($$n = 255$$,549) in 2019-21 (Table 1). Nearly half of the Indian states/UTs ($$n = 17$$/36) had lower than the national proportion of livebirth ($88.9\%$) during 2019-21. A trend towards higher pregnancy loss ($9.8\%$ vs. $11.1\%$) was observed during 2015-21 (Fig. 1). The highest proportion of pregnancy loss (8.5 points) was reported in the UT of Puducherry during 2015-21. Among the Indian states/UTs, the lowest prevalence of livebirth ($78.9\%$ vs. $76.8\%$) and highest pregnancy loss ($21.2\%$ vs. $23.1\%$) was reported in Manipur. Meghalaya had the lowest proportion of pregnancy loss ($5.9\%$). However, livebirth in Uttar Pradesh increased by 2.4 points ($84.9\%$ vs. $87.3\%$) and a higher reduction in the pregnancy loss ($15.1\%$ vs. $12.7\%$) was recorded during 2015-21 (Table 1). About 20.9 points ($11.0\%$ vs. $31.9\%$) increased proportion of pregnancy loss was found among teenage women in Punjab during 2015-21 (Additional file 1).
Table 1Trends in proportion of livebirth and pregnancy loss across the Indian states/UTs during 2015-21 *States/UTsNumber of pregnanciesLivebirthPregnancy lossNFHS-4NFHS-5NFHS-4NFHS-5ACRCStates/UTSNFHS-4NFHS-5ACRCPuducherry (PY)NANA93.084.6-8.4-9.0PY7.015.58.5121.4Andaman & Nicobar Islands (AN)NANA94.387.0-7.3-7.7AN5.713.07.3128.1Goa (GA)37636591.284.3-6.9-7.6GA8.815.76.978.4Haryana (HR)6060550490.886.0-4.8-5.3TN7.712.54.862.3Tamil Nadu (TN)6406550492.287.5-4.7-5.1HR9.313.94.649.5Dadra & Nagar Haveli and Daman & Diu (DD)NANA92.588.3-4.2-4.5DD7.611.74.153.9Andhra Pradesh (AP)2324218793.189.0-4.1-4.4AP7.010.93.955.7Himachal Pradesh (HP)2403225590.286.6-3.6-4.0HP9.813.43.636.7Bihar (BR)17,49914,42793.290.6-2.6-2.8BR6.89.42.638.2Odisha (OD)9699765387.785.1-2.6-3.0TS7.810.42.633.3Punjab (PB)4449486590.587.9-2.6-2.9PB9.512.12.627.4Sikkim (SK)94755193.390.8-2.5-2.7OD12.414.92.520.2Maharashtra (MH)7379787990.988.4-2.5-2.8MH9.111.62.527.5Karnataka (KA)6137652894.592.0-2.5-2.6KA5.58.02.545.5Telangana (TS)1882576892.289.7-2.5-2.7SK6.79.22.537.3Manipur (MN)4875269578.976.8-2.1-2.7WB10.812.82.018.5Madhya Pradesh (MP)18,02112,02893.591.6-1.9-2.0MN21.223.11.99.0West Bengal (WB)4782523389.287.3-1.9-2.1MP6.68.31.725.8Uttarakhand (UK)4617338588.787.1-1.6-1.8MZ6.07.71.728.3Mizoram (MZ)3516179693.992.3-1.6-1.7UK11.412.91.513.2Gujarat (GJ)6022793292.091.1-0.9-1.0GJ8.08.90.911.3Assam (AS)8995992289.688.7-0.9-1.0JH9.210.00.88.7Jharkhand (JH)9477773090.790.0-0.7-0.8AS10.411.20.87.7Nagaland (NL)3218207293.492.7-0.7-0.7NL6.67.30.710.6Meghalaya (ML)3189451194.794.1-0.6-0.6ML5.35.90.611.3Rajasthan (RJ)12,59011,28490.890.2-0.6-0.7RJ9.29.70.55.4Tripura (TR)1263193986.686.3-0.3-0.3TR13.313.70.43.0Delhi (DL)NA267681.881.6-0.2-0.2DL18.118.40.31.7Kerala (KL)2267249690.490.40.00.0KL9.69.60.00.0Lakshadweep (LD)NANA93.693.60.00.0LD6.46.3-0.1-1.6Chandigarh (CH)NANA84.885.10.30.4CH15.114.9-0.2-1.3Ladakh (LA)NANA89.389.80.50.6LA10.610.2-0.4-3.8Chhattisgarh (CG)7160667991.192.31.21.3CG8.87.6-1.2-13.6Jammu & Kashmir (JK)6313504189.391.01.71.9JK10.69.0-1.6-15.1Arunachal Pradesh (AR)4121495791.093.22.22.4AR9.06.9-2.1-23.3Uttar Pradesh (UP)31,07926,94784.987.32.42.8UP15.112.7-2.4-15.9 India 195,470 255,549 90.2 88.9 -1.3 -1.4 India 9.8 11.1 1.3 13.3 *Proportion of birth outcomes of the last pregnancy in the five years preceding the survey of women age 15–49; Pregnancy loss includes miscarriage, stillbirth and abortion; AC Absolute changes, RC Relative changes; NA Not available Fig. 1Prevalence of pregnancy loss among Indian women during 2015-21. ( Note: Proportion of birth outcomes of the last pregnancy in the five years preceding the survey of women age 15–49; Pregnancy loss includes miscarriage, stillbirth and abortion)
## Miscarriage
The prevalence of miscarriage among Indian women was $7.3\%$ and higher in Manipur ($12.3\%$) in 2019-21. Miscarriage increased in both urban ($6.4\%$ vs. $8.5\%$) and rural ($5.3\%$ vs. $6.9\%$) women during 2015-21. An increasing trend in miscarriage was observed in Puducherry ($3.4\%$ vs. $9.9\%$) (Table 2). Among teenage women, miscarriage was higher in Punjab ($23.2\%$) during 2019-21. Further, miscarriage increased by 4.3 points proportion ($2.7\%$ vs. $7.0\%$) in the scheduled caste (SC) category in Andhra Pradesh followed by 9.6 points proportion ($3.4\%$ vs. $13.0\%$) in the scheduled tribes (ST) category in Tamil Nadu and 2.8 points proportion ($6.7\%$ vs. $9.5\%$) in other backward class (OBC) category in Haryana during 2015-21 (Additional file 2).
Table 2Trends in proportion of pregnancy loss across the Indian States/UTs during 2015-16 and 2019-21 *MiscarriageStillbirthAbortionStates/ UTsNFHS-4NFHS-5ACRCStates/ UTsNFHS-4NFHS-5ACRCStates/ UTsNFHS-4NFHS-5ACRCPY3.49.96.5191.2AN0.71.81.1157.1TR5.17.01.937.3GA5.510.95.498.2LA0.81.50.787.5GA3.34.81.545.5AN2.16.94.8228.6TR0.51.10.6120.0PY3.65.11.541.7HR6.610.33.756.1HP0.10.60.5500.0AN2.94.31.448.3TN3.87.53.797.4OD0.71.20.571.4AP2.94.01.137.9WB4.98.43.571.4ML0.51.00.5100.0SK1.12.00.981.8DD6.09.43.456.7PY0.00.50.5NCHR1.92.70.842.1CH6.49.73.351.6AS0.50.90.480.0TN3.64.40.822.2AP3.46.43.088.2DL0.50.80.360.0TS3.34.10.824.2HP7.210.02.838.9WB0.50.80.360.0KA1.82.40.633.3PB6.18.42.337.7DD0.00.30.3NCDD1.52.00.533.3MN10.012.32.323.0TN0.30.60.3100.0PB2.73.10.414.8MH4.97.12.244.9UK0.91.10.222.2BR1.31.70.430.8MZ5.37.42.139.6MP0.60.80.233.3HP2.52.80.312.0BR4.66.62.043.5BR0.91.10.222.2MH3.84.00.25.3OD7.09.02.028.6JH1.01.20.220.0LD1.71.90.211.8KA3.25.11.959.4TS0.40.60.250.0UK3.33.40.13.0MP4.46.21.840.9HR0.80.90.112.5OD4.74.70.00.0TS4.15.71.639.0RJ0.60.70.116.7MZ0.20.20.00.0SK4.86.31.531.3SK0.80.90.112.5JH2.62.4-0.2-7.7KL4.76.21.531.9GJ0.50.60.120.0GJ2.22.0-0.2-9.1DL10.511.91.413.3MH0.40.50.125.0LA3.53.2-0.3-8.6UK7.28.41.216.7JK0.80.80.00.0MP1.61.3-0.3-18.8NL4.15.21.126.8CG1.01.00.00.0NL2.01.7-0.3-15.0ML3.64.71.130.6MN0.40.40.00.0MN10.810.4-0.4-3.7AS4.45.51.125.0GA0.00.00.0NCRJ2.01.5-0.5-25.0GJ5.36.31.018.9KA0.50.50.00.0CG2.41.7-0.7-29.2RJ6.67.50.913.6PB0.70.6-0.1-14.3AS5.54.8-0.7-12.7JH5.66.40.814.3NL0.50.4-0.1-20.0ML1.20.2-1.0-83.3LD3.34.10.824.2KL0.30.2-0.1-33.3JK3.52.3-1.2-34.3UP8.68.5-0.1-1.2AR0.60.4-0.2-33.3AR4.02.7-1.3-32.5JK6.35.9-0.4-6.3AP0.70.5-0.2-28.6DL7.15.7-1.4-19.7CG5.44.9-0.5-9.3UP1.41.1-0.3-21.4KL4.63.2-1.4-30.4AR4.43.8-0.6-13.6CH1.41.0-0.4-28.6WB5.43.6-1.8-33.3LA6.35.5-0.8-12.7MZ0.50.1-0.4-80.0UP5.13.1-2.0-39.2TR7.75.6-2.1-27.3LD1.40.3-1.1-78.6CH7.34.2-3.1-42.5 India 5.7 7.3 1.6 28.1 India 0.7 0.9 0.2 28.6 India 3.4 2.9 -0.5 -14.7 *Proportion of birth outcomes of the last pregnancy in the five years preceding the survey of women age 15–49; AC-Absolute changes, RC-Relative changes; NC-Not calculated
## Stillbirth
The prevalence of stillbirth in India was $0.9\%$ during 2019-21 and it relatively increased by $28.6\%$. About 1.1 points proportion ($0.7\%$ vs. $1.8\%$) of stillbirth increased in Andaman & Nicobar Islands during 2015-21 (Table 2). Among different age groups, there was a higher prevalence of stillbirth noted among 15–19 years in Madhya Pradesh ($2.0\%$). Stillbirth prevalence increased by $2.1\%$ in the age group of women in 20–39 years in Sikkim and $5.2\%$ in 40–49 years-old women in West Bengal, as compared with other Indian states/UTs during 2019-21. The prevalence of stillbirth was higher among women in rural than urban ($0.9\%$ vs. $0.7\%$), women with no education than highly educated ($1.1\%$ vs. $0.6\%$) and in women belonging to SC than ST and OBC ($1.0\%$ vs. $0.8\%$) categories observed during 2019-21 (Additional file 3).
## Abortion
Overall, the frequency of abortions declined up to $15\%$ (relative changes) among Indian women during 2015-21. The prevalence was higher than the national average ($2.9\%$) in Manipur ($10.4\%$) during 2019-21. On the other hand, Meghalaya and Mizoram ($0.2\%$ each) had the lowest proportion of abortions in 2019-21. Abortions increased by 1.9 points proportion ($5.1\%$ vs. $7.0\%$) in Tripura and the highest decline was observed in Chandigarh ($7.3\%$ vs. $4.2\%$) during 2015-21 (Table 2). However, an eleven-fold increase in abortion was noted among teenage pregnancies in Telangana ($0.7\%$ vs. $8.0\%$) during 2015-21. More abortions were reported in urban women than in rural ($4.0\%$ vs. $2.5\%$) during 2019-21. ( Additional file 4).
Women undergoing abortion at public health hospitals in Kerala ($20.9\%$ vs. $48.5\%$) and private health facilities in Himachal Pradesh sharply increased during 2015-21. In India, more than half of the abortions were performed at private health sector ($52.4\%$ vs. $52.9\%$) than in public health sector ($20.2\%$ vs. $20.3\%$) during 2015-21. Women performing abortions at home increased by 21 points proportion ($13\%$ vs. $34\%$) in Punjab and 19.9 points proportion ($18.7\%$ vs. $38.6\%$) in Rajasthan during 2015-21. About more than half ($55.7\%$) of women in Odisha and one-quarter ($26.2\%$) of women in India aborted their foetuses at home during 2019-21 (Additional file 5). Half of the abortions ($54.8\%$) were performed by doctors followed by $13.5\%$ by nurses or auxiliary nurse midwives or lady health visitors and $26.9\%$ by self in India during 2019-21. However, a higher proportion of self-abortions was noted in Odisha ($54.1\%$) and lower proportion in Telangana ($4.8\%$) during 2019-21 (Additional file 6). Among various reasons for seeking an abortion, the most commonly reported were unplanned pregnancies ($47.6\%$), health did not permit ($11.3\%$), the last child being too young ($9.7\%$), and pregnancy complications ($9.1\%$) during 2019-21. Abortions due to unplanned pregnancy ($73.5\%$ in Delhi), last child being too young ($24.9\%$ in Chhattisgarh), complications in pregnancy ($26.2\%$ in Punjab), and congenital abnormalities ($16\%$ in Kerala) were higher (Additional file 7).
## Discussion
The present study highlights a trend towards decrease in the proportion of livebirth during 2015–2021. The proportion of livebirth was $88.9\%$ during 2019-21 among Indian women, which is comparatively lower than reported in other low-middle income countries like Ghana ($95.1\%$), Democratic Republic of the Congo ($97.1\%$), Zambia ($99.2\%$), and Kenya ($99.9\%$) [1] and higher than the Ethiopian population ($84.1\%$) [21]. The data from the last two rounds of NFHS showed that livebirth declined by 1.3 point percent from 2015-16 to 2019-21. Livebirth proportion was lower in seventeen Indian states/UTs as compared to the national level in 2019-21. Despite launch of various programs and schemes by the GoI for improving maternal health and outcomes, a trend towards the reduction in livebirth proportion was observed in many states [2–6]. Age at conception, mode of conception and psychological well-being during pregnancy are the major determinants of a livebirth [22]. In addition, other factors such as anemia, infection, hypertension, hyperglycemia, spousal violence, and environmental pollution also contribute to high pregnancy losses [23, 24]. Further inequality of socioeconomic status in urban and rural areas among the Indian states/UTs might be one of the factors for the reduction of livebirth rates during 2019–21 [25].
The highest reduction of livebirth among teenage women was observed in Punjab. A report shows $2.6\%$ of teenage girls in Punjab became pregnant [20] and also a higher rate of pregnancy loss was reported in vulnerable populations [26]. Child marriage could result in teenage pregnancy due to social pressure, low education, and lack of knowledge about sexual and reproductive health. Teenage pregnancies pose a serious health risk to both mother and fetus and were also higher risks for miscarriage, preterm birth, low birth weight, and intrauterine growth retardation [27]. The Prohibition of Child Marriage (Amendment) Act, 2021 enables raising women’s marriage age from 18 to 21 years, which could bring down the incidence of teenage pregnancies [28]. Reduction of teenage pregnancy through community awareness not only improves women’s reproductive health but also lowers the incidence of miscarriage and stillbirth.
The proportion of pregnancy loss increased by 1.3 points proportion among Indian women. The pregnancy loss reported in our study ($11.1\%$) during 2019-21, was higher than Malawi ($0.6\%$), South Africa ($2.5\%$), Uganda ($1.4\%$) and Zimbabwe ($1\%$) [29]. Higher proportion of pregnancy loss was noted in some of the states like Manipur, Odisha, Haryana, Himachal Pradesh, and Tripura during 2019-21. Further, there are states/UTs like Puducherry, Andaman and Nicobar Islands, Goa, and larger states like Tamil Nadu and Haryana where pregnancy loss increased during 2019-21 as compared to 2015-16. Pregnancy loss can be prevented by increasing access to high-quality healthcare services in the public health sector. Although, the WHO recommends at least eight ANC visits during pregnancy, only $58\%$ of Indian mothers received 4 ANC visits during their last childbirth [16]. Higher frequency of ANC visits is associated with lower chance of pregnancy loss [12]. Indian women who had maternal hypertension, antepartum haemorrhage, short gestation age, and asphyxia during labor are reported to experience pregnancy loss [30]. The other risk factors for pregnancy loss were poor nutrition [31] and spousal violence, which may cause anxiety and depression [16]. The prevalence of miscarriage was 73 per 1000 pregnancies and relatively increased by $28.1\%$ among Indian women. This prevalence was higher than that reported in neighbouring countries like Pakistan ($3.6\%$) and Bangladesh ($5.8\%$) [1]. It was higher particularly in teenage, older, highly educated, and urban women.
Miscarriage leads to a physical risk of bleeding or infections and a psychological risk of anxiety, depression, and post-traumatic stress [7]. The known predisposing risk factors were low body mass index, anemia, overweight and obesity, hypertension, and diabetes [32] and it was higher among advanced maternal-aged women [33] and also in educated urban women [34]. The prevalence of stillbirth increased from 0.7 to $0.9\%$ among Indian women, however it was lower than reported in Malawi ($2.3\%$) and Uganda ($3.0\%$) [29]. Apart from medical factors, stillbirth is also associated with various social factors such as vulnerability based on place of residence [35], and low socioeconomic status [12].
Overall, the reported incidence of abortion decreased by $15\%$. It could be the impact of the COVID-19 pandemic, which resulted in the disruption of abortion care services [36]. However, eleven fold higher proportion of abortion was noted among adolescent women in Telangana. Several abortions are driven by socioeconomic vulnerability and demographic determinants including wealth quintiles, maternal age, education, and lack of awareness on the use of contraceptive methods [18, 37]. A higher prevalence of self-abortion was recorded in Odisha, Tripura, Arunachal Pradesh, Chhattisgarh and Bihar, where women with socioeconomic vulnerability, hard-to-reach healthcare settings and social stigma together pose women at higher risk for unsafe abortion [18]. In India, two-thirds of the population lives in rural settings, due to inadequate health care and transport facilities, more abortion-related deaths are reported rural areas of India [38]. A recent study shows that around $10\%$ of maternal deaths in India were due to abortions [18].
Our study has few limitations. This study is the compilation of the reports of national and states/UTs, the unit-level data is not utilized for this study. Some data for UTs were not available in the report and the cross-sectional study doesn’t give any causal relationship. Further, there were some UTs boundary change during the study period, we have used aggregate proportions for comparison.
## Policy recommendations
Increase prevalence of miscarriage in both urban and rural areas is a matter of concern. The government may focus in improving the health infrastructure in rural and underprivileged areas.
The stillbirth rate is an indicator of the quality and equity of health care. Increased stillbirth leads to the heavy burden of psychosocial and economic costs to the family as well as to the country [39]. The government should ensure respectful maternity services including bereavement care as majority of the women experience various psychological symptoms after the death of their baby. The health system improvement may reduce the incidence of preventable stillbirth, therefore high quality antenatal and intrapartum care should be provided through public health care system in India.
Our study reported nearly half of abortions were due to unplanned pregnancies. These findings strongly suggest the need for more research on interventions that improve the knowledge and practice of providing medical abortion in India. A priority should be given for improving policies and practices at national and state level to increase access to comprehensive abortion care and quality contraceptive services for preventing unplanned pregnancies.
## Conclusion
This study found that the prevalence of miscarriage and stillbirth increased in many Indian states/UTs during 2015-21. Antenatal health check-ups have gone up but still, there is evidence of low livebirth in some states/UTs. Hence, the quality of ANC check-ups needs greater attention in the health mission programs. Miscarriage contributes a major share of pregnancy loss. Controllable factors such as maternal weight, hypertension, anemia and blood sugar should be paid a greater attention in prevent miscarriage and stillbirth in India. Although, abortion rate is low, the major concern is around half of the abortions are not done by qualified medical professionals. Prevention of unsafe abortion practices in some of states requires the highest priority.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1
## References
1. 1.Alliance for Maternal and Newborn Health Improvement (AMANHI) mortality study group. Population-ba2sed rates, timing, and causes of maternal deaths, stillbirths, and neonatal deaths in south Asia and sub-saharan Africa: a multi-country prospective cohort study. Lancet Glob Health. 2018;6(12):e1297–308.
2. 2.Pradhan Mantri Surakshit Matritva Abhiyan (PMSMA)-2016., Ministry of Health and Family Welfare, Government of India. https://pmsma.nhp.gov.in/wp-content/uploads/2016/09/Pradhan_Mantri_Surakshit_Matritva_Abhiyan-2.pdf, accessed on 8 November 2022.
3. 3.Janani Suraksha Yojana – 2005. National Health Mission. Available from: https://nhm.gov.in/index1.php?lang=1&level=3&lid=309&sublinkid=841, accessed on January 29, 2023.
4. 4.DAKSHATA Implementation Package-2015. National Health Mission. Available from: https://nhm.gov.in/index1.php?lang=1&level=3&sublinkid=838&lid=449, accessed on January 29, 2023.
5. 5.Pradhan Mantri Matru Vandana Yojana (PMMVY). Scheme Implemetation Guidelines 2017.https://wcd.nic.in/sites/default/files/PMMVY%20Scheme%20Implemetation%20Guidelines%20._0.pdf, accessed on 8 November 2022.
6. 6.LaQshya-Guidelines.pdf. Available from: https://nhm.gov.in/New_Updates_2018/NHM_Components/RMNCH_MH_Guidelines/LaQshya-Guidelines.pdf, accessed on January 29, 2023.
7. Shaohua L, Shorey S. **Psychosocial interventions on psychological outcomes of parents with perinatal loss: a systematic review and meta-analysis**. *Int J Nurs Stud* (2021.0) **117** 103871. DOI: 10.1016/j.ijnurstu.2021.103871
8. 8.Standard Treatment Guidelines. Management of Recurrent Spontaneous Abortion-2017. http://clinicalestablishments.gov.in/WriteReadData/3361.pdf, accessed on 8 November 2022.
9. Patki A, Chauhan N. **An Epidemiology Study to determine the prevalence and risk factors Associated with recurrent spontaneous miscarriage in India**. *J Obstet Gynaecol India* (2016.0) **66** 310-5. DOI: 10.1007/s13224-015-0682-0
10. 10.A neglected tragedy. The global burden of stillbirths 2020. https://www.unicef.org/media/84851/file/UN-IGME-the-global-burden-of-stillbirths-2020.pdf, accessed on 8 November 2022.
11. 11.India N. Action Plan – 2014, Ministry of Heath & Family Welfare, Government of Indian. http://nhm.gov.in/images/pdf/programmes/inap-final.pdf, aceessed on 8 November 2022.
12. Altijani N, Carson C, Choudhury SS, Rani A, Sarma UC, Knight M. **Stillbirth among women in nine states in India: rate and risk factors in study of 886,505 women from the annual health survey**. *BMJ Open* (2018.0) **8** e022583. DOI: 10.1136/bmjopen-2018-022583
13. 13.McClure EM, Saleem S, Goudar SS, Tikmani SS, Dhaded SM, Hwang K et al. The causes of stillbirths in south Asia: results from a prospective study in India and Pakistan (PURPOSe). Lancet Glob Health. 2022;10(7):e970-e977.
14. 14.de Bernis L, Kinney MV, Stones W, Ten Hoope-Bender P, Vivio D, Leisher SH et al. Stillbirths: ending preventable deaths by 2030. Lancet. 2016;387(10019):703–16.
15. 15.Kiguli J, Kirunda R, Nabaliisa J, Nalwadda CK. Inadequate evidence on stillbirths: rethinking public health. Lancet. 2021;398(10302):727–29.
16. 16.National Family Health Survey (NFHS-5). - India Report. https://dhsprogram.com/pubs/pdf/FR375/FR375.pdf, accessed on 8 November 2022.
17. Singh S, Shekhar C, Acharya R, Moore AM, Stillman M, Pradhan MR. **The incidence of abortion and unintended pregnancy in India, 2015**. *Lancet Glob Health* (2018.0) **6** e111-20. DOI: 10.1016/S2214-109X(17)30453-9
18. Yokoe R, Rowe R, Choudhury SS, Rani A, Zahir F, Nair M. **Unsafe abortion and abortion-related death among 1.8 million women in India**. *BMJ Glob Health* (2019.0) **4** e001491. DOI: 10.1136/bmjgh-2019-001491
19. 19.The Medical Termination of Pregnancy. (Amendment) Act – 2021. https://egazette.nic.in/WriteReadData/2021/226130.pdf, accessed on 8 November 2022.
20. 20.National Family Health Survey (NFHS-4). - India Report. https://dhsprogram.com/pubs/pdf/FR339/FR339.pdf, accessed on 8 November 2022.
21. 21.Tadese M, Dagne K, Wubetu AD, Abeway S, Bekele A, Misganaw Kebede W, Baye Mulu G. Assessment of the adverse pregnancy outcomes and its associated factors among deliveries at Debre Berhan Comprehensive Specialized Hospital, Northeast Ethiopia. PLoS One. 2022;17(7):e0271287.
22. McMahon CA, Boivin J, Gibson FL, Hammarberg K, Wynter K, Saunders D. **Age at first birth, mode of conception and psychological wellbeing in pregnancy: findings from the parental age and transition to parenthood Australia (PATPA) study**. *Hum Reprod* (2011.0) **26** 1389-98. DOI: 10.1093/humrep/der076
23. Xue T, Guan T, Geng G, Zhang Q, Zhao Y, Zhu T. **Estimation of pregnancy losses attributable to exposure to ambient fine particles in south Asia: an epidemiological case-control study**. *Lancet Planet Health* (2021.0) **5** e15-24. DOI: 10.1016/S2542-5196(20)30268-0
24. Padhi BK, Baker KK, Dutta A, Cumming O, Freeman MC, Satpathy R. **Risk of adverse pregnancy outcomes among women practicing poor sanitation in rural India: a Population-Based prospective cohort study**. *PLOS Med* (2015.0) **12** e1001851. DOI: 10.1371/journal.pmed.1001851
25. Asaria M, Mazumdar S, Chowdhury S, Mazumdar P, Mukhopadhyay A, Gupta I. **Socioeconomic inequality in life expectancy in India**. *BMJ Glob Health* (2019.0) **4** e001445. DOI: 10.1136/bmjgh-2019-001445
26. 26.Verma P, Swain PK, Singh KK, Khetan M. Count data regression modeling: an application to spontaneous abortion. Reprod Health. 2020;17(1):106.
27. 27.Bas EK, Bulbul A, Uslu S, Bas V, Elitok GK, Zubarioglu U. Maternal Characteristics and Obstetric and Neonatal Outcomes of Singleton Pregnancies Among Adolescents. Med Sci Monit. 2020;26:e919922-1-e919922-9.
28. 28.The Prohibition Of Child Marriage. (Amendment)Bill, 2021. https://prsindia.org/files/bills_acts/bills_parliament/2021/The%20Prohibition%20Of%20Child%20Marriage%20(Amendment)%20Bill,%202021.pdf, accessed on 8 November 2022.
29. Lokken EM, Mathur A, Bunge KE, Fairlie L, Makanani B, Beigi R. **Pooled prevalence of adverse pregnancy and neonatal outcomes in Malawi, South Africa, Uganda, and Zimbabwe: results from a systematic review and Meta-analyses to inform trials of Novel HIV Prevention Interventions during pregnancy**. *Front Reprod Health* (2021.0) **3** 672446. DOI: 10.3389/frph.2021.672446
30. Goldenberg R, Saleem S, Goudar S, Silver R, Tikmani S, Guruprasad G. **Preventable stillbirths in India and Pakistan: a prospective, observational study**. *BJOG* (2021.0) **128** 1762-73. DOI: 10.1111/1471-0528.16820
31. Marshall NE, Abrams B, Barbour LA, Catalano P, Christian P, Friedman JE. **The importance of nutrition in pregnancy and lactation: lifelong consequences**. *Am J Obstet Gynecol* (2022.0) **226** 607-32. DOI: 10.1016/j.ajog.2021.12.035
32. 32.India State-Level Disease Burden Initiative Malnutrition Collaborators. The burden of child and maternal malnutrition and trends in its indicators in the states of India: the global burden of Disease Study 1990–2017. Lancet Child Adolesc Health. 2019;3(12):855–70.
33. Vennu V, Abdulrahman TA, Bindawas SM. **The prevalence of overweight, obesity, hypertension, and diabetes in India: analysis of the 2015–2016 National Family Health Survey**. *Int J Environ Res Public Health* (2019.0) **16** 3987. DOI: 10.3390/ijerph16203987
34. Gouda J, Prusty RK. **Overweight and obesity among women by Economic Stratum in Urban India**. *J Health Popul Nutr* (2014.0) **32** 79-88. PMID: 24847596
35. Busch SLP, Houweling TAJ, Pradhan H, Gope R, Rath S, Kumar A. **Socioeconomic inequalities in stillbirth and neonatal mortality rates: evidence on particularly vulnerable tribal groups in eastern India**. *Int J Equity Health* (2022.0) **21** 61. DOI: 10.1186/s12939-022-01655-y
36. Cousins S. **COVID-19 has “devastating” effect on women and girls**. *Lancet* (2020.0) **396** 301-2. DOI: 10.1016/S0140-6736(20)31679-2
37. 37.Nabhi VRM, Madhuri M, Vijayalakshmi G, Vasantha K, Kavitha K, Uma B. Awareness, attitude and practice of contraception amongst post-partum women in a rural tertiary care hospital, Telangana, India. Int J Reprod Contracept Obstet Gynecol 2019;9.
38. Banerjee SK, Andersen K. **Exploring the pathways of unsafe abortion in Madhya Pradesh, India**. *Glob Public Health* (2012.0) **7** 882-96. DOI: 10.1080/17441692.2012.702777
39. 39.Ending preventable stillbirths: an executive summary for the lancet’s series. Jan. 2016.https://www.thelancet.com/pb/assets/raw/Lancet/stories/series/stillbirths2016-exec-summ.pdf, accessed on 27 January 2023.
|
---
title: Metabolic reprogramming and lipid droplets are involved in Zika virus replication
in neural cells
authors:
- Suelen Silva Gomes Dias
- Tamires Cunha-Fernandes
- Luciana Souza-Moreira
- Vinicius Cardoso Soares
- Giselle Barbosa Lima
- Isaclaudia G. Azevedo-Quintanilha
- Julia Santos
- Filipe Pereira-Dutra
- Caroline Freitas
- Patricia A. Reis
- Stevens Kastrup Rehen
- Fernando A. Bozza
- Thiago M. Lopes Souza
- Cecilia J. G. de Almeida
- Patricia T. Bozza
journal: Journal of Neuroinflammation
year: 2023
pmcid: PMC9992922
doi: 10.1186/s12974-023-02736-7
license: CC BY 4.0
---
# Metabolic reprogramming and lipid droplets are involved in Zika virus replication in neural cells
## Abstract
Zika virus (ZIKV) infection is a global public health concern linked to adult neurological disorders and congenital diseases in newborns. Host lipid metabolism, including lipid droplet (LD) biogenesis, has been associated with viral replication and pathogenesis of different viruses. However, the mechanisms of LD formation and their roles in ZIKV infection in neural cells are still unclear. Here, we demonstrate that ZIKV regulates the expression of pathways associated with lipid metabolism, including the upregulation and activation of lipogenesis-associated transcription factors and decreased expression of lipolysis-associated proteins, leading to significant LD accumulation in human neuroblastoma SH-SY5Y cells and in neural stem cells (NSCs). Pharmacological inhibition of DGAT-1 decreased LD accumulation and ZIKV replication in vitro in human cells and in an in vivo mouse model of infection. In accordance with the role of LDs in the regulation of inflammation and innate immunity, we show that blocking LD formation has major roles in inflammatory cytokine production in the brain. Moreover, we observed that inhibition of DGAT-1 inhibited the weight loss and mortality induced by ZIKV infection in vivo. Our results reveal that LD biogenesis triggered by ZIKV infection is a crucial step for ZIKV replication and pathogenesis in neural cells. Therefore, targeting lipid metabolism and LD biogenesis may represent potential strategies for anti-ZIKV treatment development.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12974-023-02736-7.
## Introduction
The outbreak of ZIKV infection caused great alarm worldwide, as the relationship between infection with ZIKV and cases of microcephaly in neonates was demonstrated [1, 2] and associated with Guillain–Barré Syndrome in adults [3, 4]. ZIKV shows a remarkable tropism for neural cells and prevents brain development, leading to irreversible damage [5–7]. ZIKV is an arbovirus (arthropod-borne virus) and belongs to the family Flaviviridae, genus Flavivirus [8]. Its genome is a single-stranded ribonucleic acid (RNA) with positive-sense (+ RNA) and encodes a single polyprotein that undergoes cleavage generating three structural and seven nonstructural proteins [9–11].
During many stages of the viral replication cycle in the cytoplasm, + RNA viruses interact with host proteins and alter cell homeostasis to benefit viral replication and assembly [12, 13]. Members of the family Flaviviridae manipulate host lipid metabolism and induce lipid droplet (LD) biogenesis, as observed in *Dengue virus* (DENV)- [14] and Hepatitis C virus (HCV)-infected cells [15]. LDs are dynamic organelles consisting of a core rich in neutral lipids surrounded by a phospholipid monolayer and structural proteins of the Perilipin family (PLIN1-5) [16, 17]. LDs play an essential role in cellular lipid storage and homeostasis [18], intracellular transport, and inflammatory processes [19]. Moreover, different pathogens, including bacteria, viruses, and parasite infection, demonstrate the ability to modulate the lipid metabolism of the host cell, favoring its survival and replication [20–22]. In this context, multiple + RNA viruses use the host lipid machinery to facilitate their replication and assembly [23].
Pharmacological interference in lipid metabolism and LD inhibition affects viral replication of different viruses. Inhibition of fatty acid synthase (FASN) or acyl-CoA:diacylglycerol acyltransferase-1 (DGAT-1) impairs the replication of HCV, DENV and SARS-CoV-2 [14, 24, 25]. DGAT-1 is a key endoplasmic membrane-bound enzyme for triacylglycerol (TAG) synthesis and LD formation. Therefore, these data demonstrate the essential role of lipid metabolism and LDs during + RNA viral replication. However, the role of LDs during ZIKV infection is still poorly explored.
Recent studies have suggested functions for LDs in the central nervous system [26]. Indeed, cells of the nervous system exhibit LDs in the context of development, obesity, and neurodegenerative pathologies [26–28], but to our knowledge, no studies have addressed the functions of CNS LDs in the context of infection.
We hypothesize that LDs may contribute to ZIKV infection-induced neuropathology. Here, we demonstrate that ZIKV infection alters cellular lipid metabolism in human neural cells, decreasing lipogenesis and increasing de novo lipid synthesis and remodeling. As a result, LD organelles accumulate in the cytoplasm, providing a favorable environment for viral replication. In addition, preventing LD biogenesis with a pharmacological inhibitor of DGAT-1 impairs viral replication in vitro and in vivo. Accordingly, the DGAT-1 inhibitor decreases inflammatory cytokine production, weight loss and mouse mortality induced by ZIKV infection. Our results demonstrate that ZIKV virus infection alters host cell lipid metabolism to benefit ZIKV replication and may impact the development of therapies to combat ZIKV infection.
## ZIKV infection alters lipid metabolism in human neuroblastoma cells
Flaviviridae members strictly depend on host resources and induce metabolic alterations, modulating lipid metabolism to support viral replication and assembly [29–32]. Here, we first confirmed the ability of neuroblastoma SH-SY5Y cells to produce ZIKV infectious particles. This cell line has been previously described as a model to study the infection of neural cells by different viruses [33–36], including ZIKV [37, 38]. SH-SY5Y cells were infected with African ZIKV strain MR766 for 24 h and 48 h at a multiplicity of infection (MOI) of 1. As shown in Fig. 1A, there was an increase in viral replication after infection in a time-dependent manner. Similar to other + RNA viruses, ZIKV produces double-stranded RNA (dsRNA) as an intermediate during viral replication into host cells. Thus, we used an antibody against dsRNA to confirm ZIKV infection and replication. At 48 h post-infection (hpi), SH-SY5Y-infected cells showed increased expression of dsRNA compared to uninfected cells (Mock) (Fig. 1B).Fig. 1ZIKV infection alters lipid metabolism in human neuroblastoma cells. A Viral replication was analyzed by PFU assay in SH-SY5Y cells after 24 h and 48 h of ZIKV infection at an MOI of 1. B Immunofluorescence analyses of SH-SY5Y cells after ZIKV infection at an MOI of 1 for 48 h. The double strain RNA was detected by indirect immunofluorescence with a J2 antibody (red), and the nuclei were stained with DAPI (blue). The scale bar represents 20 µm in range. C Images of SH-SY5Y cells infected with ZIKV at MOIs of 0.1, 1, and 5 after 48 h. The cells were stained with Oil Red O (Red) and DAPI (blue) for nuclei. The scale bar represents 10 µm in range. D Quantification of the fluorescence area per cell in SH-SY5Y cells. E Real-time PCR for proteins related to lipid metabolism after 24 h of ZIKV infection. F Representative western blot analysis and densitometry data set of PPAR-gamma (G) and the lipolytic enzymes ATGL and HSL in SH-SY5Y lysates 48 h post ZIKV infection. Data information: In A, D–F, data are presented as the means ± SEMs of three independent experiments. * $P \leq 0.05$ mock- versus ZIKV-infected cells Several viruses trigger LD formation, which regulates host metabolism and lipid storage. This organelle supports viral replication and is central in the pathogenesis of various infections [14, 39, 40]. Indeed, ZIKV infection increased the biogenesis of LDs in SH-SY5Y cells in an MOI-dependent manner (Fig. 1C, D). To further gain insights into the mechanisms involved in LD formation induced by ZIKV infection in neuroblastoma cells, we analyzed the expression of regulatory factors associated with lipid metabolism. Interestingly, compared with uninfected cells, ZIKV-infected cells exhibited upregulated gene expression of PLIN-2, DGAT-1, and FASN (Fig. 1E). Additionally, SH-SY5Y cells infected with ZIKV showed increased expression of the lipogenesis transcription factor peroxisome proliferator-activated receptor-γ (PPAR-γ) (Fig. 1F). In contrast, significantly decreased expression of two lipolysis enzymes, adipose triglyceride lipase (ATGL) and hormone-sensitive lipase (HSL), was observed 48 h after infection (Fig. 1G).
## ZIKV infection modulates lipid metabolism in human neural stem cells
Next, we asked whether ZIKV infection alters lipid metabolism proteins and increases LD accumulation in NSCs derived from iPSCs. We observed that the production of ZIKV infective particles increased in a time-dependent manner in NSCs, as shown in Fig. 2A. Similar to the results with SH-SY5Y cells, ZIKV infection in NSCs induced the expressive accumulation of LDs compared with mock-treated NSCs (Fig. 2B, C).Fig. 2ZIKV infection modulates lipid metabolism in human neural stem cells. A Viral replication was analyzed by PFU assay in NSCs after 24 h and 48 h of ZIKV infection at an MOI of 1. B Images of NSCs infected with ZIKV at MOIs of 0.1, 1, and 5 after 48 h. The cells were stained with Oil Red O (Red) and DAPI (blue) for nuclei. The scale bar represents 10 µm in range. C Quantification of the fluorescence area per cell in NSCs. D Representative western blot analysis of PPAR-gamma, (E) precursor and mature SREBP-1, (F) ATGL and HSL in NSC lysates 48 h post-ZIKV infection at an MOI of 1. Data information: In A, C-D, data are presented as the means ± SEMs of three independent experiments. * $P \leq 0.05$ mock- versus ZIKV-infected cells Furthermore, ZIKV infection showed a trend to increase the expression of PPAR-γ compared with the uninfected cells CTR and Mock-treated NSCs (Fig. 2D). ZIKV infection reduced the precursor form of sterol regulatory element-binding protein 1 (SREBP-1) while significantly increasing the mature/active form of SREBP-1 at 48 hpi compared to that in uninfected cells (Fig. 2E). As observed in the neuroblastoma cell line, ZIKV infection in NSCs decreased the expression of ATGL and HSL compared with uninfected cells (Fig. 2F).
These data suggest that modulation of host lipid metabolism is critical during ZIKV infection in neuroblastoma cells and NSCs, as shown by the effects of increased lipogenesis and decreased lipolysis regulatory factors in neural cells favoring lipogenesis and LD accumulation, occurring in parallel with virus replication.
## Inhibition of LD accumulation decreases ZIKV replication
Pharmacological inhibition of enzymes involved in lipid metabolism and LD biogenesis impacts viral replication, as observed in DENV [14], HCV [41], rotavirus [42] and SARS-CoV-2 [25] infection models. To advance the understanding of the role of lipid accumulation during ZIKV infection in neural cells, we analyzed the pharmacological inhibition of the DGAT-1 enzyme, which catalyzes the terminal step in TAG synthesis. Compared with vehicle treatment, treatment with a DGAT-1 inhibitor (iDGAT-1, A922500) markedly decreased LD accumulation triggered by ZIKV infection in a concentration-dependent manner in SH-SY5Y cells (Fig. 3A, B; Additional file 1: Fig. S1A).Fig. 3Inhibition of lipid droplet accumulation induced by ZIKV decreases viral replication in neuroblastoma cells. A Representative images of SH-SY5Y cells 48 h after ZIKV infection treated with 50 µM DGAT-1 inhibitor (A922500) and stained with Oil Red O (red) and DAPI (blue) for nuclei. The scale bar represents 10 µm in range. B Quantification of the fluorescent area per cell in each group. C Real-time PCR for PLIN-2 after 24 h of ZIKV infection and treatment with a DGAT-1 inhibitor. D Effect of the DGAT-1 inhibitor on ZIKV replication at 48 hpi. E Images of SH-SY5Y cells after 48 h of ZIKV infection and the ds-RNA stained with J2 antibody (green), the lipid droplets were stained with LipidTox (Red) and nuclei with DAPI (blue). The scale bar represents 10 µm in range. F Quantification of LD pixels per cell. G Quantification of ds-RNA pixels per cell. Data information: In B–D, F, G), data are presented as the means ± SEMs of three independent experiments. * $P \leq 0.05$ mock- versus ZIKV-infected cells. # $P \leq 0.05$ ZIKV-infected cells versus A922500 treatments Corroborating these data, treatment with iDGAT-1 decreased the mRNA expression of PLIN-2, a structural LD protein induced by ZIKV infection (Fig. 3C). Moreover, iDGAT-1 treatment decreased ZIKV replication in SH-SY5Y cells (Fig. 3D). This observation is supported by the reduction in dsRNA labeling and LD accumulation observed by J2 clone antibody and LipidTox staining, respectively, after iDGAT-1 treatment (Fig. 3E). Further image analysis and quantification of dsRNA and LDs confirmed that treatment with iDGAT-1 significantly reduced LD production (Fig. 3F) and ZIKV replication (Fig. 3G).
Moreover, we evaluated whether the upregulation of LDs could affect ZIKV replication. SH-SY5Y cells were treated with oleic acid (40 µM) 1 h prior to ZIKV infection at an MOI of 1 and maintained after 48 h of infection. Interestingly, supplementation with 40 µM oleic acid increased LD accumulation and dsRNA labeling, as observed by LipidTox staining and J2 clone antibody, respectively (Fig. 4A–C, and ZIKV replication (Fig. 4D). To evaluate whether oleic acid treatment could rescue ZIKV replication during DGAT-1 inhibition, the cells were treated with oleic acid in the presence of a DGAT-1 inhibitor or vehicle during 48 h of ZIKV infection. We observed that oleic acid treatment was able to partially restore the inhibitory effect of DGAT-1 inhibition on LD accumulation and ZIKV replication (Fig. 4).Fig. 4Upregulation of LD biogenesis increased ZIKV replication in SH-SY5Y cells. A Representative images of SH-SY5Y cells treated with 40 µM oleic acid and 50 µM DGAT-1 inhibitor (A922500) 48 h after ZIKV infection. The ds-RNA was stained with a J2 antibody (green), the lipid droplets were stained with LipidTox (red), and the nuclei were stained with DAPI (blue). The scale bar represents 10 µm in range. B Quantification of LD pixels per cell. C Quantification of ds-RNA pixels per cell. D Effect of the DGAT-1 inhibitor on ZIKV replication at 48 hpi. Data information: In A–C, data are presented as the means ± SEMs of three independent experiments, and in D, data are presented as the means ± SEMs of four independent experiments. * $P \leq 0.05$ Veh alone versus Veh with oleic acid-treated cells. # $P \leq 0.05$ A922500 alone versus A922500 with oleic acid-treated cells Furthermore, treatment with iDGAT-1 expressively reduced LD accumulation by ZIKV in NSCs, even when a lower concentration was used (Fig. 5A, B. Along with the observation in SH-SY5Y cells, treatment with iDGAT-1 significantly reduced ZIKV replication at 48 hpi in NSCs (Fig. 5C). Additionally, A922500 treatment alone did not affect the viability of uninfected cells, with a $50\%$ cytotoxic concentration (CC50) value of 88.14 μM for SH-SY5Y cells and higher than 250 μM for NSCs (Additional file 2: Fig S2).Fig. 5Effect of iDGAT-1 treatment on lipid droplet biogenesis and ZIKV replication in human neural stem cells. A Representative images of NSCs 48 h after ZIKV infection at an MOI of 1 treated with 50 µM DGAT-1 inhibitor (A922500) and stained with Oil Red O (red) and DAPI (blue) for nuclei. The scale bar represents 10 µm in range. B Quantification of the fluorescent area per cell in each group. C Effect of the DGAT-1 inhibitor on ZIKV replication at 48 hpi in NSCs. Data information: In B, C, data are presented as the means ± SEMs of three independent experiments. * $P \leq 0.05$ mock- versus ZIKV-infected cells. # $P \leq 0.05$ ZIKV-infected cells versus A922500 treatments These findings suggest that TAG metabolism and LD accumulation may play a crucial role during the ZIKV cycle, as the inhibition of DGAT decreases LD accumulation and significantly impacts virus replication in both SH-SY5Y cells and NSCs.
## DGAT-1 treatment improves the survival of ZIKV-infected mice
To further demonstrate the relevant role of TAG accumulation during ZIKV infection, we established an acute in vivo ZIKV infection model. We treated newborn (P2) to P9 mice with an intraperitoneal injection of the A922500 DGAT-1 inhibitor daily and infected them afterwards with a dose of 2 × 107 PFU of Brazilian ZIKV strain to induce an acute infection according to previous studies [43, 44] (Fig. 6A).Fig. 6Treatment with DGAT-1 inhibitor (A922500, iDGAT) reduces viral loads in the brain during acute infection, increases survival, and inhibits weight loss in ZIKV-infected mice. A Three-day-old Swiss mice were infected with Brazilian ZIKV (2 × 104 PFU) and treated with iDGAT-1 one day before infection for 7 days. On the indicated days after infection, animals were euthanized. B ZIKV RNA levels were measured in the brain. C Weight variation (D) and survival were assessed during treatment. E Inflammatory mediators were measured in mouse brain extracts by ELISA: TNF, IL-1β and MCP-1. Data information: In B, the viral loads are displayed as the mean ± SEM of seven ZIKV-infected mice, and twelve ZIKV-infected mice per day were assayed. Student's t test was used to compare the viral levels from ZIKV-infected vs. ZIKV-treated mice. * $p \leq 0.05.$ In C, differences in weight are displayed as the mean ± SEM, and two-way ANOVA for each day was used to assess the significance. In D, survival was statistically assessed by the log-rank (Mantel‒Cox) test. Independent experiments were performed with ten mice/group ($$n = 30$$). * $P \leq 0.05.$ In E, the inflammatory mediators are presented as the mean ± SEM of 14 mock mice, nine mock-treated, 13 ZIKV-infected mice and 27 ZIKV-treated mice. * $P \leq 0.05$ mock- versus ZIKV-infected mice. # $P \leq 0.05$ ZIKV-infected mice versus A922500 treatment Since iDGAT inhibited ZIKV replication in vitro, we evaluated the magnitude of the inhibition of virus replication in vivo. Therefore, we measured the viral load in the brain tissue on day 13 after infection. We observed that iDGAT treatment reduced mouse viremia by over threefold compared to untreated mice (Fig. 6B).
In addition, we evaluated the weight gain of the animals during the time course of the assay. ZIKV-infected animals stopped gaining weight from day 12 onward, whereas the weight gain of iDGAT-1-treated ZIKV-infected mice was indistinguishable compared to the animals from the control group (Fig. 6C). Most importantly, iDGAT-1-treated mice showed enhanced survival compared to nontreated mice ($75\%$ vs. $25\%$, respectively) (Fig. 6D).
Therefore, we observed that mice infected with ZIKV exhibited increased production of proinflammatory cytokines (TNF and IL-1β) and chemokines (CCL-2/MCP-1) in the brain tissue in comparison with mock-infected mice (Fig. 6E). Additionally, mice treated with the DGAT-1 inhibitor A922500 exhibited reduced inflammatory mediator production compared to nontreated mice during ZIKV infection. Our results showed that DGAT-1 inhibition reduced the ZIKV particle load and inflammatory profile and significantly protected against ZIKV-induced mortality in mice.
In summary, our results demonstrate that ZIKV modulates host lipid-related pathways leading to LD accumulation and suggest that this regulation (increased lipogenesis and decreased lipolysis) mainly depends on triglyceride metabolism. Therefore, the pharmacological targeting of LD formation to inhibit ZIKV replication is a potential strategy for antiviral development (Fig. 7).Fig. 7Conclusion. ZIKV is able to modulate the expression of important genes in lipid metabolism pathways, leading to increased levels of PPAR-γ and activation of SREBP-1. ZIKV infection increases the expression of FASN, which plays a role in fatty acid synthesis and is regulated by PPAR-γ. In addition, ZIKV decreases the levels of ATGL and HSL, important lipolytic enzymes. Therefore, these lipid metabolism regulations possibly contribute to the increase in LDs observed during ZIKV infection, and the biogenesis of this organelle is dependent on the DGAT-1 enzyme during infections. Treatment with the pharmacological inhibitor of DGAT-1, A922500, reduces the biogenesis of LDs and reduces the replication of ZIKV, contributing to the decrease in the production of inflammatory mediators. Altogether, our data suggest that LDs are important for the replication of ZIKV, participating in ZIKV pathogenesis
## Discussion
The rapid spread of ZIKV, mainly in the Americas, led to a global alert state and intensified studies on this virus. However, to date, there is no specific treatment for ZIKV infection, and a complete cellular mechanism regulated by ZIKV to allow its replication in host cells is still unclear. Therefore, it is necessary to elucidate which dysfunctions are caused by the virus in neural cells, which somehow contribute to the pathogenesis of viral infection. Hence, in this work, we demonstrated that ZIKV upregulates lipogenesis pathways and downregulates lipolysis factors, leading to increased LD accumulation in human neural cells. In addition, the inhibition of DGAT-1 blocked LD biogenesis, reducing virus replication, inflammatory mediators and the viral load in the brain and improving mouse survival.
A variety of pathogens, including viruses, modulate cellular signaling and lipid metabolism to provide a more favorable environment for obtaining energy and replication [12, 31]; this feature has been well described in members of the family Flaviviridae, such as HCV and DENV [32, 45–47]. In this context, LD biogenesis plays a crucial role in maintaining intracellular energy storage and lipid homeostasis. Several enzymes, such as DGAT-1 and -2, tightly regulate the production of this organelle by controlling lipid synthesis [18]. These enzymes are essential in the biosynthesis of TAG, the main lipid constituent of LDs, and catalyze the final step in converting diacylglycerol (DAG) and fatty acids into TAG [48]. Lipolytic enzymes, such as HSL and ATGL, catabolize TAG stored in cellular LDs for lipid mobilization [49].
Our results demonstrate that ZIKV induces the expression and/or activation of transcription factors, such as PPAR-γ, SREBP-1 and FASN, and the production of LDs in human neural cells, suggesting cellular switching to a lipogenic phenotype. PPARγ is a transcription factor activated by lipid ligands and promotes multiple metabolic regulations, the expression of proteins involved in lipid homeostasis, and LD biogenesis [50, 51]. In consonance, the increase in this nuclear receptor has been associated with hepatic steatosis characterized by lipid accumulation during HCV infection [52, 53]. The activation of SREBP-1 occurs by the cleavage of the precursor SREBP-1 protein that converts it into the mature form, which regulates several metabolic genes acting on lipogenesis transcriptionally [54]. Accordingly, lipid accumulation induced by the HCV core in vitro depends on the activation of PPAR-γ and SREBP-1 [55, 56]. Indeed, inhibition of PPAR and SREBP-linked pathways is associated with HCV antiviral effects [57, 58].
Many stages of virus replication occur in the cytoplasm, altering host metabolism and interacting with structures to establish replication and assembly, as observed in LD organelles. Thus, Park et al. and Xiang et al. demonstrated that HCV nonstructural protein 4B (NS4B) [59] and nonstructural protein 5A (NS5A) induce SREBP-1 activation, contributing to lipid accumulation [60], respectively. Recently, it has been endorsed that AMP-activated protein kinase (AMPK) activation reverts hepatic lipid accumulation induced by the hepatitis virus [61]. In parallel, increased expression of FASN was also observed in virus-infected hepatocyte cell lines and mouse livers [59, 61, 62]. FASN is a key enzyme catalyzing the de novo synthesis of fatty acids and plays an essential role in lipogenesis. Moreover, recent results demonstrated lipid remodeling through SREBPs and increased FASN expression in human dendritic cells during ZIKV infection [63].
In addition to revealing significant expression of lipogenic factors induced by ZIKV, we observed that ZIKV decreases the expression of ATGL and HSL enzymes in neural cells. Under nutrient deprivation, for example, lipids are mobilized via the activation of lipolytic pathways. Perilipins found in LDs, which play a regulatory role in lipolysis, are degraded and allow the action of critical lipolytic enzymes such as ATGL and HSL involved in the intracellular degradation of TAGs [64]. Therefore, our results suggest that ZIKV fine-tunes LD biogenesis by tightening lipid metabolism pathways; the upregulation of transcription factors associated with lipogenesis and reduced lipolytic enzymes culminates in LD accumulation.
Furthermore, we identified that DGAT-1 expression increases after ZIKV infection; this enzyme catalyzes the last step in triglyceride synthesis. Interestingly, the DGAT-1 enzyme plays an essential role in the replication and assembly of the HCV virus since it allows the transport of the NS5 protein to the surface of the LDs [41]. Several strategies of inhibiting enzymes involved in lipid metabolism impact the biogenesis of LDs [14, 65]. The pharmacological inhibition of DGAT-1 reduced hepatic lipid accumulation and serum triglyceride concentrations in rodent models of postprandial hyperlipidemia [66, 67]. In the virus context, for instance, the pharmacological inhibitor of the DGAT-1 enzyme (A922500) reduced LD accumulation and decreased HCV replication [68]. Likewise, we demonstrated that ZIKV-induced DGAT-1 and the DGAT-1 inhibitor decreased the structural component of LDs, PLIN-2, which is necessary for the formation and maintenance of this organelle. Corroborating these observations, blocking DGAT-1 reduced the biogenesis of LDs induced by ZIKV infection and decreased viral replication in neural cells. Additionally, our results showed that oleic acid supplementation increased LD accumulation and ZIKV replication. These results are in agreement with recent findings demonstrating that oleic acid increased ZIKV replication in different cells [69]. Moreover, oleic acid treatment partially restored the inhibitory effect of A922500 on LD accumulation and ZIKV replication. Further supporting a role for LD on ZIKV replication in neural cells.
LDs are emerging as important organelles in the brain since LDs are present in different cells of the CNS under healthy and pathological conditions drawing attention to the potential functions of LDs during development, aging, and neurodegenerative diseases [26]. However, little is known about the regulation and functions of CNS LDs in the context of infection. To gain insight on the functions of LDs during brain infection, animals were pretreated with the DGAT-1 inhibitor prior to ZIKV infection. Indeed, the DGAT-1 inhibitor reduced not only the production of viral RNA, but also the proinflammatory mediators in mouse brains infected with ZIKV, such as TNF, IL-1β and MCP-1, suggesting an important role for CNS LDs in ZIKV replication in the brain and neuroinflammation. Moreover, our data revealed that treatment with a DGAT-1 inhibitor also decreased mouse mortality induced by ZIKV infection. These results indicate a role of the DGAT-1 enzyme and infection-induced LDs in ZIKV replication and neuropathology. Future studies will be necessary to characterize the involvement of LDs in other neuroinfections.
## Conclusion
ZIKV modulates the lipid metabolism of host cells by increasing the expression of important lipogenic proteins and decreasing lipolytic enzymes, contributing to a significant increase in LD accumulation in human neural cells. ZIKV-induced LD modulation provides a favorable environment for virus replication in cells susceptible to infection. Additionally, based on the data obtained, LDs participate in the ZIKV replicative cycle and neuroinflammation, and blocking DGAT-1 showed a protective effect in mice infected with ZIKV. Further studies are necessary to deeply understand the molecular mechanisms that regulate LD biogenesis and the contribution of this organelle to ZIKV pathogenesis. However, our results indicate that inhibiting DGAT-1 or other enzymes associated with lipid metabolism and LD accumulation could be a promising therapeutic strategy to control ZIKV.
## Cells and reagents
Human neuroblastoma cells (SH-SY5Y, ATCC CRL-2266) were cultured in Dulbecco's Modified Essential (DMEM) and F12 medium (GIBCO, supplemented with $10\%$ fetal bovine serum (FSB, HyClone, Logan, Utah) and 100 U/mL penicillin‒streptomycin (P/S; GIBCO).
Human neural stem cells (NSCs) derived from iPS cells were prepared as previously described [2]. Human iPS cells were cultured in PSC neural induction medium (GIBCO, USA) containing neurobasal medium and PSC supplement for 7 days. Then, initial neural stem cells (NSCs) were split and expanded on neural induction medium (advanced DMEM/F12 and neurobasal medium (1:1) with neural induction supplement; Gibco).
African green monkey kidney (VERO subtype E6) cells were maintained in high glucose DMEM supplemented with $10\%$ FBS and 100 U/mL P/S. All cell types were maintained at 37 °C in $5\%$ CO2.
## Virus, infection, and titration
The ZIKV African (MR766) and Brazilian (GenBank accession #KX19720513) strains were propagated in VERO subtype E6 cells. These cells were infected at a multiplicity of infection (MOI) of 0.01 for 2 h at 37 °C. After that, the cells were cultured for 3 days in high glucose DMEM supplemented with $2\%$ FBS. Then, cell lysates were obtained by freezing and thawing and centrifuged at 1,500 RPM at 4 °C for 5 min to remove cellular debris. Viral titers were quantified using the $50\%$ tissue culture infectious dose (TCID50/mL) for further studies, and viruses were stored at − 70 °C. In parallel, cultures of noninfected VERO E6 cells were used as a mock control.
For experiments, SH-SY5Y cells were plated in DMEM/F-12 medium supplemented with $5\%$ FSB and NSCs in Neurobasal, Advanced DMEM/F12, 2X NIS. Cells were infected 24 h after plating, upon reaching 80–$90\%$ confluency, using different MOIs of ZIKV and were incubated for 2 h for virus adsorption. After that, the virus inoculum medium was removed, and fresh medium was added. As controls, cells received only culture medium without FBS and VERO mock supernatants. In some experiments, cells were treated with a pharmacological inhibitor of the enzyme DGAT-1 (A922500—Sigma A11737) or dimethyl sulfoxide (DMSO—Sigma) as the vehicle control after infection.
Additionally, SH-SY5Y cells were pretreated or not with 40 μM oleic acid (Cat# O1008 –Sigma-Aldrich) in medium without FBS for 1 h prior to ZIKV infection. After ZIKV infection with an MOI of 1 for 2 h, the virus inoculum medium was removed, and fresh medium with $5\%$ FSB was added with DGAT-1i (50 μM) or DMSO as the vehicle control in the presence or absence of oleic acid (40 μM) and analyzed after 48 h.
Viral titers were determined using the plaque-forming assay in VERO E6 cells seeded in 24-well plates. Cell monolayers were infected with different dilutions of the supernatant containing the virus in a tenfold serial dilution for one hour at 37 °C. After that, the cells were overlaid with high glucose DMEM containing $2\%$ FBS and $2.4\%$ carboxymethylcellulose, and after 3 days, they were fixed with $10\%$ formaldehyde in PBS for 3 h. The cell monolayers were stained with $0.04\%$ crystal violet in $20\%$ ethanol for 1 h. The viral titer was calculated from the count of plaques formed in the wells corresponding to each dilution and expressed as plaque-forming units per mL (PFU/mL).
## Lipid droplet staining
SH-SY5Y cells were seeded on glass coverslips treated with 0.2℅ gelatin, and NSCs were plated on glass coverslips treated with Geltrex matrix (GIBCO) following the manufacturer's protocol. Cells were fixed with $3.7\%$ formaldehyde, and LDs were stained with $0.3\%$ Oil Red O (diluted in $60\%$ isopropanol) for 2 min at room temperature. The coverslips were mounted on slides using antifade mounting medium (VECTASHIELD®). Nuclear recognition was based on DAPI staining (1 μg/mL) for 5 min. Fluorescence was analyzed by fluorescence microscopy with a 100 × objective lens (Olympus, Tokyo, Japan). The numbers of LDs were automatically quantified by ImageJ software analysis from 15 random fields.
## Immunofluorescence staining
SH-SY5Y cells were seeded in coverslips treated with $0.2\%$ gelatin and, after 48 h, were fixed using $3.7\%$ formaldehyde. Cells were rinsed three times with PBS containing 0.1 M CaCl2 and 1 M MgCl2 (PBS/CM) and then permeabilized with $0.1\%$ Triton X-100 plus $0.2\%$ BSA in PBS/CM for 10 min (PBS/CM/TB). The double-stranded RNA (ds-RNA) was labeled with mouse monoclonal antibody J2 clone—Scicons [54] at a 1:500 dilution overnight, followed by mouse anti-IgG-DyLight 550 or 488 at a 1:1000 dilution for one hour. LDs were stained with HCS LipidTOX™ Red Neutral Lipid Stain (Invitrogen) in PBS (Concentration 1:1000) for 30 min. The coverslips were mounted on slides using antifade mounting medium (VECTASHIELDs®). Nuclear recognition was based on DAPI staining (1 μg/mL) for 5 min. Fluorescence microscopy was analyzed with a 100 × objective lens (Olympus, Tokyo, Japan).
## SDS-PAGE and western blot
Forty-eight hours post-infection, cells were harvested using ice-cold lysis buffer pH 8.0 ($1\%$ Triton X-100, $2\%$ SDS, 150 mM NaCl, 10 mM HEPES, 2 mM EDTA containing protease inhibitor cocktail—Roche). Cell lysates were heated at 100 °C for 5 min in Laemmli buffer pH 6.8 ($20\%$ β-mercaptoethanol; 370 mM Tris base; 160 μM bromophenol blue; $6\%$ glycerol; $16\%$ SDS). Thirty micrograms of protein/sample was resolved by electrophoresis on an SDS-containing $12\%$ polyacrylamide gel (SDS-PAGE). After electrophoresis, the separated proteins were transferred to nitrocellulose membranes and incubated in blocking buffer ($5\%$ nonfat milk, 50 mM Tris–HCl, 150 mM NaCl, and $0.1\%$ Tween 20). Membranes were probed overnight with the following antibodies: anti-PPARγ (Santa Cruz Biotechnology, #SC-7196-H100), anti-SREBP-1 (Abcam, Ab-28481), anti-HSL (Cell Signaling Technology, #4107), anti-ATGL (Santa Cruz Technology, # SC-365278), and anti-β-actin (Sigma, #A1978). After washing, they were incubated with IRDye—LICOR or HRP-conjugated secondary antibodies. All antibodies were diluted in blocking buffer. Signal detection was achieved with Supersignal Chemiluminescence (GE Healthcare) or fluorescence imaging using the Odyssey system. Densitometry was performed using Image Studio Lite Ver 5.2 software.
## Quantitative real-time RT-PCR assay
Twenty-four hours post-infection, the total RNA of SH-SY5Y monolayers was extracted and quantified by RT-PCR. The total RNA from each sample was extracted using the SV total RNA isolation system kit according to the manufacturer's protocols (Promega). RNA concentration and purity were determined by measuring absorbance at A260 and A280 nm with a Nanodrop 2000 spectrophotometer. RNA was stored at − 70 °C in nuclease-free water for later use. According to the manufacturer's protocol, 2 μg of total RNA was reverse transcribed in a 20-μl reaction mixture using the High Capacity cDNA Reverse Transcription kit (Applied Biosystems, Foster City, CA). The cDNA was amplified in 10 μl of 1 × TaqMan universal PCR master mix with Predeveloped TaqMan assay primers and probes Perilipin-2 (PLIN-2), Hs00605340_m1; diacylglycerol O-acyltransferase 1 (DGAT-1), Hs01020362_g1; fatty acid synthase (FASN) Hs01005622_m1 and beta-actin (ACTB) Hs99999903_m1 as endogenous controls (Thermo Fisher Scientific) according to the manufacturer's instructions. Quantitative RT-PCR was performed in a StepOne™ Real-Time PCR System (Thermo Fisher Scientific). PCR products were analyzed relative to the endogenous control ACTB (ΔΔCt).
## Cell viability assays
Cells were treated with a range of concentrations of A922500 for 48 h and fixed using $3.7\%$ formaldehyde for 20 min. The cell monolayers were stained with $1\%$ crystal violet in $20\%$ ethanol for 10 min. Next, the cells were washed with water, and the crystal violet was extracted using methanol. The crystal violet was read in a spectrophotometer at a wavelength of 595 nm. Moreover, the cytotoxicity was determined according to the activity of lactate dehydrogenase (LDH) in the culture supernatants using a CytoTox® Kit according to the manufacturer’s instructions (Promega, USA).
## Animals
Swiss mice were supplied by the Institute of Science and Technology in Bio models (ICTB/Fiocruz) at approximately the 14th gestational day. The animals were kept at a constant temperature (25 °C) with free access to chow and water in a 12-h light/dark cycle. Pregnant mice were observed daily until delivery to determine the postnatal day accurately. We established a litter size of 10 animals for all experimental replicates. The Animal Welfare Committee of the Oswaldo Cruz Institute (CEUA/IOC) approved and covered the experiments in this study (license number L-$\frac{001}{2018}$). The procedures described in this study follow the local guidelines and guidelines published in the National Institutes of Health Guide for the Care and Use of Laboratory Animals.
## Experimental infection and treatment
Three-day-old Swiss mice were infected intraperitoneally with 2 × 104 PFU of the Brazilian ZIKV strain. Treatments with iDGAT-1 were carried out at 2.5 mg/kg/day intraperitoneally and started one day before infection (pretreatment). The same dose was given once daily for the subsequent days throughout the experiment. For comparisons, a mock-infected group of animals was used as a control. Animals were monitored daily for survival and weight gain; euthanasia was performed to alleviate animal suffering when necessary. The criteria for humane endpoint were differences in weight between infected and control groups > $25\%$, ataxia or rejection of moribund offspring, characterized by no feeding by the female adult mouse.
## Measurements of inflammatory mediators in mouse brain homogenates
To evaluate the inflammatory process induced by ZIKV infection in the brain, mice were euthanized on day 13 after birth. Brains were perfused with 20 mL of PBS to remove the circulating blood and then collected, pottered, and homogenized in 500 µL of sterile PBS containing the complete EDTA-free protease inhibitor cocktail (Roche Applied Science, Mannheim, Germany) using an Ultra-Turrax Disperser T-10 basic IKA® (Guangzhou, China). Homogenates were stored at − 80 °C for inflammatory mediator measurements. IL-1β, TNF-α and CCL2/MCP1 levels were quantified in brain extracts from infected mice by ELISA, following the manufacturer’s instructions (R&D Systems).
## Molecular detection of virus RNA levels
Brain tissue was collected on day 13 after birth. The brains were lysed with sterile 1 × PBS and homogenized with a homogenizer work center (IKA® T10 basic). Homogenates were cleared by centrifugation, and total RNA was extracted. According to the manufacturer's instructions, total RNA from culture, extract-containing organs in 1 × PBS was extracted using QIAamp Viral RNA (Qiagen®). Quantitative RT-PCR was performed using the TaqMan® Fast Virus 1-Step Master Mix kit (ThermoFisher®) in an ABI PRISM 7300 Sequence Detection System (Applied Biosystems). Amplifications were carried out in 10 µL reaction mixtures containing 2 × reaction mix buffer, 20 µM of each primer, 10 µM of the probe, and 5 µL of RNA template. Primers specific to ZIKV were used: Primer F (5′-CAG CTG GCA TCA AGA AYC-3′) and Primer R (5′-CAC YTG TCC CAT CTT YTT CTCC-3′). The standard curve method was employed for virus quantification. The Ct values for this target were compared for calibration, and the brain weight was used for normalization.
## Statistical analysis
Data are expressed as the mean ± standard error of the mean (SEM) of at least three independent experiments. The paired two-tailed t test was used to evaluate the significance of the comparison between two groups. Survival curves were evaluated using the log-rank (Mantel‒Cox) test. Multiple comparisons among three or more groups were performed with one-way ANOVA followed by Tukey's multiple comparison test using GraphPad Prism software 8.0. P values of 0.05 or less were considered statistically significant when comparing ZIKV infection to the uninfected control (*) group or ZIKV-infected and treated with A922500 group (#).
## Supplementary Information
Additional file 1: Fig S1. Treatment with A922500 decreases LD accumulation at 48 hpi in human neural cells. Representative images of (A) SH-SY5Y cells and (B) NSCs 48 h after ZIKV infection treated with a range of concentrations of the DGAT-1 inhibitor (A922500) and stained with Oil Red O (Red). The scale bar represents 20 µm in range. Data information: In (A-B), the data are presented as the means ± SEMs of three independent experiments. * $P \leq 0.05$ mock- versus ZIKV-infected cells. # $P \leq 0.05$ ZIKV-infected cells versus A922500 treatments. Additional file 2: Fig S2. Cell cytotoxicity after A922500 treatment. Cells were treated with a range of concentrations of A922500 for 48 h. Cell viability using crystal violet staining of uninfected (A) SH-SY5Y cells and (B) NSCs treated with A922500. Cytotoxicity was evaluated by LDH activity in (C) SH-SY5Y cells and (D) NSCs. ( E) CC50, EC50 and SI for SH-SY5Y cells and NSCs treated with A922500. Data information: In (A and C), the data are presented as the means ± SEMs of five independent experiments, and in (B and D), the data are presented as the means ± SEMs of four independent experiments. * $P \leq 0.05$ versus untreated cells.
## References
1. Brasil P, Pereira JP, Moreira ME, Ribeiro Nogueira RM, Damasceno L, Wakimoto M. **Zika virus infection in pregnant women in Rio de Janeiro**. *N Engl J Med* (2016.0) **375** 2321-2334. DOI: 10.1056/NEJMoa1602412
2. Garcez PP, Loiola EC, Madeiro da Costa R, Higa LM, Trindade P, Delvecchio R. **Zika virus impairs growth in human neurospheres and brain organoids**. *Science* (2016.0) **352** 816-818. DOI: 10.1126/science.aaf6116
3. Cao-Lormeau VM, Blake A, Mons S, Lastère S, Roche C, Vanhomwegen J. **Guillain-Barré syndrome outbreak associated with Zika virus infection in French polynesia: a case–control study**. *Lancet* (2016.0) **387** 1531-1539. DOI: 10.1016/S0140-6736(16)00562-6
4. Krauer F, Riesen M, Reveiz L, Oladapo OT, Martínez-Vega R, Porgo TV. **Zika virus infection as a cause of congenital brain abnormalities and Guillain-Barré syndrome: systematic review**. *PLoS Med* (2017.0) **14** e1002203. DOI: 10.1371/journal.pmed.1002203
5. El Costa H, Gouilly J, Mansuy JM, Chen Q, Levy C, Cartron G. **ZIKA virus reveals broad tissue and cell tropism during the first trimester of pregnancy**. *Sci Rep* (2016.0) **6** 35296. DOI: 10.1038/srep35296
6. Reefhuis J, Gilboa SM, Johansson MA, Valencia D, Simeone RM, Hills SL. **Projecting month of birth for at-risk infants after Zika virus disease outbreaks**. *Emerg Infect Dis* (2016.0) **22** 828-832. DOI: 10.3201/eid2205.160290
7. Chibueze EC, Tirado V, Lopes KD, Balogun OO, Takemoto Y, Swa T. **Zika virus infection in pregnancy: a systematic review of disease course and complications**. *Reprod Health* (2017.0) **14** 28. DOI: 10.1186/s12978-017-0285-6
8. Herrero LJ, Zakhary A, Gahan ME, Nelson MA, Herring BL, Hapel AJ. **Dengue virus therapeutic intervention strategies based on viral, vector and host factors involved in disease pathogenesis**. *Pharmacol Ther* (2013.0) **137** 266-282. DOI: 10.1016/j.pharmthera.2012.10.007
9. Hamel R, Dejarnac O, Wichit S, Ekchariyawat P, Neyret A, Luplertlop N. **Biology of Zika virus infection in human skin cells**. *J Virol* (2015.0) **89** 8880-8896. DOI: 10.1128/JVI.00354-15
10. Kuhn RJ, Zhang W, Rossmann MG, Pletnev SV, Corver J, Lenches E. **Structure of dengue virus: implications for flavivirus organization, maturation, and fusion**. *Cell* (2002.0) **108** 717-725. DOI: 10.1016/S0092-8674(02)00660-8
11. Chambers TJ, Hahn CS, Galler R, Rice CM. **Flavivirus genome organization, expression, and replication**. *Annu Rev Microbiol* (1990.0) **44** 649-688. DOI: 10.1146/annurev.mi.44.100190.003245
12. Herker E, Ott M. **Emerging role of lipid droplets in host/pathogen interactions**. *J Biol Chem* (2012.0) **287** 2280-2287. DOI: 10.1074/jbc.R111.300202
13. Fernandez-Garcia MD, Mazzon M, Jacobs M, Amara A. **Pathogenesis of flavivirus infections: using and abusing the host cell**. *Cell Host Microbe* (2009.0) **5** 318-328. DOI: 10.1016/j.chom.2009.04.001
14. Samsa MM, Mondotte JA, Iglesias NG, Assunção-Miranda I, Barbosa-Lima G, Da Poian AT. **Dengue virus capsid protein usurps lipid droplets for viral particle formation**. *PLoS Pathog* (2009.0) **5** e1000632. DOI: 10.1371/journal.ppat.1000632
15. Syed GH, Amako Y, Siddiqui A. **Hepatitis C virus hijacks host lipid metabolism**. *Trends Endocrinol Metab* (2010.0) **21** 33-40. DOI: 10.1016/j.tem.2009.07.005
16. Itabe H, Yamaguchi T, Nimura S, Sasabe N. **Perilipins: a diversity of intracellular lipid droplet proteins**. *Lipids Health Dis* (2017.0) **16** 83. DOI: 10.1186/s12944-017-0473-y
17. Sztalryd C, Brasaemle DL. **The perilipin family of lipid droplet proteins: gatekeepers of intracellular lipolysis**. *Biochim Biophys Acta Mol Cell Biol Lipids.* (2017.0) **1862** 1221-1232. DOI: 10.1016/j.bbalip.2017.07.009
18. Walther TC, Farese RV. **Lipid droplets and cellular lipid metabolism**. *Annu Rev Biochem* (2012.0) **81** 687-714. DOI: 10.1146/annurev-biochem-061009-102430
19. Bozza PT, Bakker-Abreu I, Navarro-Xavier RA, Bandeira-Melo C. **Lipid body function in eicosanoid synthesis: an update**. *Prostaglandins Leukot Essent Fatty Acids* (2011.0) **85** 205-213. DOI: 10.1016/j.plefa.2011.04.020
20. Farese RV, Walther TC. **Lipid droplets finally get a little R-E-S-P-E-C-T**. *Cell* (2009.0) **139** 855-860. DOI: 10.1016/j.cell.2009.11.005
21. D'Avila H, Toledo DA, Melo RC. **Lipid bodies: inflammatory organelles implicated in host–Trypanosoma cruzi interplay during innate immune responses**. *Mediators Inflamm* (2012.0) **2012** 478601. DOI: 10.1155/2012/478601
22. Mattos KA, Lara FA, Oliveira VG, Rodrigues LS, D'Avila H, Melo RC. **Modulation of lipid droplets by**. *Cell Microbiol* (2011.0) **13** 259-273. DOI: 10.1111/j.1462-5822.2010.01533.x
23. Roingeard P, Melo RC. **Lipid droplet hijacking by intracellular pathogens**. *Cell Microbiol* (2017.0) **19** e12688. DOI: 10.1111/cmi.12688
24. Heaton NS, Randall G. **Dengue virus-induced autophagy regulates lipid metabolism**. *Cell Host Microbe* (2010.0) **8** 422-432. DOI: 10.1016/j.chom.2010.10.006
25. Dias SSG, Soares VC, Ferreira AC, Sacramento CQ, Fintelman-Rodrigues N, Temerozo JR. **Lipid droplets fuel SARS-CoV-2 replication and production of inflammatory mediators**. *PLoS Pathog* (2020.0) **16** e1009127. DOI: 10.1371/journal.ppat.1009127
26. 26.Ralhan I, Chang CL, Lippincott-Schwartz J, Ioannou MS. Lipid droplets in the nervous system. J Cell Biol. 2021;220(7).
27. Maya-Monteiro CM, Corrêa-da-Silva F, Hofmann SS, Hesselink MKC, la Fleur SE, Yi CX. **Lipid droplets accumulate in the hypothalamus of mice and humans with and without metabolic diseases**. *Neuroendocrinology* (2021.0) **111** 263-272. DOI: 10.1159/000508735
28. Liu L, Zhang K, Sandoval H, Yamamoto S, Jaiswal M, Sanz E. **Glial lipid droplets and ROS induced by mitochondrial defects promote neurodegeneration**. *Cell* (2015.0) **160** 177-190. DOI: 10.1016/j.cell.2014.12.019
29. Herker E, Ott M. **Unique ties between hepatitis C virus replication and intracellular lipids**. *Trends Endocrinol Metab* (2011.0) **22** 241-248. DOI: 10.1016/j.tem.2011.03.004
30. Negro F, Sanyal AJ. **Hepatitis C virus, steatosis and lipid abnormalities: clinical and pathogenic data**. *Liver Int* (2009.0) **29** 26-37. DOI: 10.1111/j.1478-3231.2008.01950.x
31. Saka HA, Valdivia R. **Emerging roles for lipid droplets in immunity and host–pathogen interactions**. *Annu Rev Cell Dev Biol* (2012.0) **28** 411-437. DOI: 10.1146/annurev-cellbio-092910-153958
32. Villareal VA, Rodgers MA, Costello DA, Yang PL. **Targeting host lipid synthesis and metabolism to inhibit dengue and hepatitis C viruses**. *Antiviral Res* (2015.0) **124** 110-121. DOI: 10.1016/j.antiviral.2015.10.013
33. Kalia M, Khasa R, Sharma M, Nain M, Vrati S. **Japanese encephalitis virus infects neuronal cells through a clathrin-independent endocytic mechanism**. *J Virol* (2013.0) **87** 148-162. DOI: 10.1128/JVI.01399-12
34. Wu S, Yang S, Ou M, Chen J, Huang J, Xiong D. **Transcriptome analysis reveals the role of cellular calcium disorder in varicella zoster virus-induced post-herpetic neuralgia**. *Front Mol Neurosci* (2021.0) **14** 665931. DOI: 10.3389/fnmol.2021.665931
35. Sharma A, Vasanthapuram R, Venkataswamy M, Desai A. **Prohibitin 1/2 mediates Dengue-3 entry into human neuroblastoma (SH-SY5Y) and microglia (CHME-3) cells**. *J Biomed Sci* (2020.0) **27** 55. DOI: 10.1186/s12929-020-00639-w
36. Dhanwani R, Khan M, Bhaskar AS, Singh R, Patro IK, Rao PV. **Characterization of Chikungunya virus infection in human neuroblastoma SH-SY5Y cells: role of apoptosis in neuronal cell death**. *Virus Res* (2012.0) **163** 563-572. DOI: 10.1016/j.virusres.2011.12.009
37. Luplertlop N, Suwanmanee S, Muangkaew W, Ampawong S, Kitisin T, Poovorawan Y. **The impact of Zika virus infection on human neuroblastoma (SH-SY5Y) cell line**. *J Vector Borne Dis* (2017.0) **54** 207-214. DOI: 10.4103/0972-9062.217611
38. Sánchez-San Martín C, Li T, Bouquet J, Streithorst J, Yu G, Paranjpe A. **Differentiation enhances Zika virus infection of neuronal brain cells**. *Sci Rep* (2018.0) **8** 14543. DOI: 10.1038/s41598-018-32400-7
39. Crawford SE, Desselberger U. **Lipid droplets form complexes with viroplasms and are crucial for rotavirus replication**. *Curr Opin Virol* (2016.0) **19** 11-15. DOI: 10.1016/j.coviro.2016.05.008
40. Boulant S, Targett-Adams P, McLauchlan J. **Disrupting the association of hepatitis C virus core protein with lipid droplets correlates with a loss in production of infectious virus**. *J Gen Virol* (2007.0) **88** 2204-2213. DOI: 10.1099/vir.0.82898-0
41. Herker E, Harris C, Hernandez C, Carpentier A, Kaehlcke K, Rosenberg AR. **Efficient hepatitis C virus particle formation requires diacylglycerol acyltransferase-1**. *Nat Med* (2010.0) **16** 1295-1298. DOI: 10.1038/nm.2238
42. Gaunt ER, Cheung W, Richards JE, Lever A, Desselberger U. **Inhibition of rotavirus replication by downregulation of fatty acid synthesis**. *J Gen Virol* (2013.0) **94** 1310-1317. DOI: 10.1099/vir.0.050146-0
43. van den Pol AN, Mao G, Yang Y, Ornaghi S, Davis JN. **Zika virus targeting in the developing brain**. *J Neurosci* (2017.0) **37** 2161-2175. DOI: 10.1523/JNEUROSCI.3124-16.2017
44. Huang WC, Abraham R, Shim BS, Choe H, Page DT. **Zika virus infection during the period of maximal brain growth causes microcephaly and corticospinal neuron apoptosis in wild type mice**. *Sci Rep* (2016.0) **6** 34793. DOI: 10.1038/srep34793
45. Carvalho FA, Carneiro FA, Martins IC, Assunção-Miranda I, Faustino AF, Pereira RM. **Dengue virus capsid protein binding to hepatic lipid droplets (LD) is potassium ion dependent and is mediated by LD surface proteins**. *J Virol* (2012.0) **86** 2096-2108. DOI: 10.1128/JVI.06796-11
46. Filipe A, McLauchlan J. **Hepatitis C virus and lipid droplets: finding a niche**. *Trends Mol Med* (2015.0) **21** 34-42. DOI: 10.1016/j.molmed.2014.11.003
47. Miyanari Y, Atsuzawa K, Usuda N, Watashi K, Hishiki T, Zayas M. **The lipid droplet is an important organelle for hepatitis C virus production**. *Nat Cell Biol* (2007.0) **9** 1089-1097. DOI: 10.1038/ncb1631
48. Kuerschner L, Moessinger C, Thiele C. **Imaging of lipid biosynthesis: how a neutral lipid enters lipid droplets**. *Traffic* (2008.0) **9** 338-352. DOI: 10.1111/j.1600-0854.2007.00689.x
49. Zechner R, Zimmermann R, Eichmann TO, Kohlwein SD, Haemmerle G, Lass A. **FAT SIGNALS–lipases and lipolysis in lipid metabolism and signaling**. *Cell Metab* (2012.0) **15** 279-291. DOI: 10.1016/j.cmet.2011.12.018
50. Lefebvre P, Chinetti G, Fruchart JC, Staels B. **Sorting out the roles of PPAR alpha in energy metabolism and vascular homeostasis**. *J Clin Invest* (2006.0) **116** 571-580. DOI: 10.1172/JCI27989
51. Souza-Moreira L, Soares VC, Dias SDSG, Bozza PT. **Adipose-derived mesenchymal stromal cells modulate lipid metabolism and lipid droplet biogenesis via AKT/mTOR -PPARγ signalling in macrophages**. *Sci Rep* (2019.0) **9** 20304. DOI: 10.1038/s41598-019-56835-8
52. Kim K, Kim KH, Ha E, Park JY, Sakamoto N, Cheong J. **Hepatitis C virus NS5A protein increases hepatic lipid accumulation via induction of activation and expression of PPARgamma**. *FEBS Lett* (2009.0) **583** 2720-2726. DOI: 10.1016/j.febslet.2009.07.034
53. García-Mediavilla MV, Pisonero-Vaquero S, Lima-Cabello E, Benedicto I, Majano PL, Jorquera F. **Liver X receptor α-mediated regulation of lipogenesis by core and NS5A proteins contributes to HCV-induced liver steatosis and HCV replication**. *Lab Invest* (2012.0) **92** 1191-1202. DOI: 10.1038/labinvest.2012.88
54. Brown MS, Goldstein JL. **The SREBP pathway: regulation of cholesterol metabolism by proteolysis of a membrane-bound transcription factor**. *Cell* (1997.0) **89** 331-340. DOI: 10.1016/S0092-8674(00)80213-5
55. Kim KH, Hong SP, Kim K, Park MJ, Kim KJ, Cheong J. **HCV core protein induces hepatic lipid accumulation by activating SREBP1 and PPARgamma**. *Biochem Biophys Res Commun* (2007.0) **355** 883-888. DOI: 10.1016/j.bbrc.2007.02.044
56. Olmstead AD, Knecht W, Lazarov I, Dixit SB, Jean F. **Human subtilase SKI-1/S1P is a master regulator of the HCV Lifecycle and a potential host cell target for developing indirect-acting antiviral agents**. *PLoS Pathog* (2012.0) **8** e1002468. DOI: 10.1371/journal.ppat.1002468
57. Lin YM, Sun HY, Chiu WT, Su HC, Chien YC, Chong LW. **Calcitriol inhibits HCV infection via blockade of activation of PPAR and interference with endoplasmic reticulum-associated degradation**. *Viruses* (2018.0) **10** 57. DOI: 10.3390/v10020057
58. Su AI, Pezacki JP, Wodicka L, Brideau AD, Supekova L, Thimme R. **Genomic analysis of the host response to hepatitis C virus infection**. *Proc Natl Acad Sci USA* (2002.0) **99** 15669-15674. DOI: 10.1073/pnas.202608199
59. Park CY, Jun HJ, Wakita T, Cheong JH, Hwang SB. **Hepatitis C virus nonstructural 4B protein modulates sterol regulatory element-binding protein signaling via the AKT pathway**. *J Biol Chem* (2009.0) **284** 9237-9246. DOI: 10.1074/jbc.M808773200
60. Xiang Z, Qiao L, Zhou Y, Babiuk LA, Liu Q. **Hepatitis C virus nonstructural protein-5A activates sterol regulatory element-binding protein-1c through transcription factor Sp1**. *Biochem Biophys Res Commun* (2010.0) **402** 549-553. DOI: 10.1016/j.bbrc.2010.10.081
61. Meng Z, Liu Q, Sun F, Qiao L. **Hepatitis C virus nonstructural protein 5A perturbs lipid metabolism by modulating AMPK/SREBP-1c signaling**. *Lipids Health Dis* (2019.0) **18** 191. DOI: 10.1186/s12944-019-1136-y
62. Yang W, Hood BL, Chadwick SL, Liu S, Watkins SC, Luo G. **Fatty acid synthase is up-regulated during hepatitis C virus infection and regulates hepatitis C virus entry and production**. *Hepatology* (2008.0) **48** 1396-1403. DOI: 10.1002/hep.22508
63. Branche E, Wang YT, Viramontes KM, Valls Cuevas JM, Xie J, Ana-Sosa-Batiz F. **SREBP2-dependent lipid gene transcription enhances the infection of human dendritic cells by Zika virus**. *Nat Commun* (2022.0) **13** 5341. DOI: 10.1038/s41467-022-33041-1
64. Brasaemle DL. **Thematic review series: adipocyte biology. The perilipin family of structural lipid droplet proteins: stabilization of lipid droplets and control of lipolysis**. *J Lipid Res* (2007.0) **48** 2547-2559. DOI: 10.1194/jlr.R700014-JLR200
65. Fujimoto Y, Onoduka J, Homma KJ, Yamaguchi S, Mori M, Higashi Y. **Long-chain fatty acids induce lipid droplet formation in a cultured human hepatocyte in a manner dependent of Acyl-CoA synthetase**. *Biol Pharm Bull* (2006.0) **29** 2174-2180. DOI: 10.1248/bpb.29.2174
66. Tsuda N, Kumadaki S, Higashi C, Ozawa M, Shinozaki M, Kato Y. **Intestine-targeted DGAT1 inhibition improves obesity and insulin resistance without skin aberrations in mice**. *PLoS ONE* (2014.0) **9** e112027. DOI: 10.1371/journal.pone.0112027
67. King AJ, Segreti JA, Larson KJ, Souers AJ, Kym PR, Reilly RM. **In vivo efficacy of acyl CoA: diacylglycerol acyltransferase (DGAT) 1 inhibition in rodent models of postprandial hyperlipidemia**. *Eur J Pharmacol* (2010.0) **637** 155-161. DOI: 10.1016/j.ejphar.2010.03.056
68. Camus G, Herker E, Modi AA, Haas JT, Ramage HR, Farese RV. **Diacylglycerol acyltransferase-1 localizes hepatitis C virus NS5A protein to lipid droplets and enhances NS5A interaction with the viral capsid core**. *J Biol Chem* (2013.0) **288** 9915-9923. DOI: 10.1074/jbc.M112.434910
69. Raini SK, Takamatsu Y, Dumre SP, Urata S, Mizukami S, Moi ML. **The novel therapeutic target and inhibitory effects of PF-429242 against Zika virus infection**. *Antiviral Res* (2021.0) **192** 105121. DOI: 10.1016/j.antiviral.2021.105121
|
---
title: 'Autoimmune pancreatitis: A bibliometric analysis from 2002 to 2022'
authors:
- Xian-Da Zhang
- Yao Zhang
- Yi-Zhou Zhao
- Chun-Hua Zhou
- Duo-Wu Zou
journal: Frontiers in Immunology
year: 2023
pmcid: PMC9992966
doi: 10.3389/fimmu.2023.1135096
license: CC BY 4.0
---
# Autoimmune pancreatitis: A bibliometric analysis from 2002 to 2022
## Abstract
### Background/Objectives
Autoimmune pancreatitis (AIP) is a distinct form of pancreatic inflammatory disease that responds well to glucocorticoid therapy. Knowledge on AIP has rapidly evolved over the past two decades. Based on bibliometric analysis, this study aimed to assess the research status of AIP over the past two decades and determine the research focus and emerging topics.
### Methods
AIP-related publications published between January 1, 2002, and June 6, 2022, were retrieved from the Web of Science Core Collection. Bibliometric data were analyzed using HisCite, VOSviewer, CiteSpace, and bibliometrix package. Annual output, leading countries/regions, active institutions and authors, core journals and references, and keywords of AIP were evaluated.
### Results
Overall, 1,772 publications were retrieved from 501 journals by 6,767 authors from 63 countries/regions. Japan published articles on AIP the most ($$n = 728$$, $41.1\%$), followed by the United States ($$n = 336$$, $19\%$), Germany ($$n = 147$$, $8.3\%$), China ($$n = 127$$, $7\%$), and Italy ($$n = 107$$, $6\%$). The top three most prolific authors were Terumi Kamisawa from Tokyo Metropolitan Komagome Hospital ($$n = 117$$), Kazuichi Okazaki from Kansai Medical University ($$n = 103$$), and Shigeyuki Kawa from Matsumoto Dental University ($$n = 94$$). Pancreas was the most productive journal regarding AIP research ($$n = 95$$), followed by the Journal of Gastroenterology ($$n = 67$$), Internal Medicine ($$n = 66$$), Pancreatology ($$n = 63$$), and World Journal of Gastroenterology ($$n = 62$$). “ Diagnosis” was the most mentioned keyword. “ Risk,” “malignancy,” “outcome,” “22-gauge needle,” and “fine-needle aspiration” were recognized as emerging topics.
### Conclusion
Japan was the leading country in AIP research. Research papers were mainly published in specialized journals. Diagnosis was the research focus. Long-term outcomes and pancreatic tissue acquisition were recognized as research frontiers for AIP.
## Introduction
Autoimmune pancreatitis (AIP) is a distinct form of pancreatic inflammatory disease that responds well to glucocorticoid therapy [1]. AIP can be classified into types 1 and 2 based on clinical and pathological findings. Type 1 AIP is the pancreatic manifestation of IgG4-related disease (IgG4-RD), which is characterized by an elevation of serum IgG4 levels and infiltration of IgG4-positive plasmacytes [2, 3], whereas type 2 is more localized in the pancreas, with normal serum IgG4 levels and the presence of neutrophil infiltration [2, 3]. As a relatively newly identified disease, the knowledge on the diagnosis, treatment, and clinical outcomes of AIP has rapidly evolved over the past two decades.
Bibliometric analysis enables the qualitative and quantitative profiling of publications [4] and allows researchers to identify not only the productive countries/regions, institutions, and authors but also the research focus and emerging topics within a specific field [5, 6]. Additionally, bibliometric analysis has been applied in research on autoimmune digestive diseases, including inflammatory bowel disease and primary biliary cholangitis [7, 8]. However, a bibliometric analysis of AIP has not been reported in the literature thus far.
In the current study, bibliometric analysis was utilized to assess the research status of AIP over the past two decades, as well as identify the research focus and emerging topics.
## Search strategy
Literature search was performed in the Web of Science Core Collection (WoSCC) on June 6, 2022, at the Ruijin Hospital affiliated to the Shanghai Jiao Tong University School of Medicine. Thesauruses of AIP were identified in the Medical Subject Headings (MeSH) database (https://www.ncbi.nlm.nih.gov/mesh) and added to the search query, as follows: TI = (“autoimmune pancreatitis” OR “IgG4-related pancreatitis” OR “lymphoplasmacytic sclerosing pancreatitis” OR “idiopathic duct centric pancreatitis”) OR AB = (“autoimmune pancreatitis” OR “IgG4-related pancreatitis” OR “lymphoplasmacytic sclerosing pancreatitis” OR “idiopathic duct centric pancreatitis”) OR AK = (“autoimmune pancreatitis” OR “IgG4-related pancreatitis” OR “lymphoplasmacytic sclerosing pancreatitis” OR “idiopathic duct centric pancreatitis”). According to our search query, articles that mentioned AIP or its synonyms in the title, abstract, or keywords were identified. The date of publications was set between January 1, 2002, and June 6, 2022, and the type of publications was restricted to articles and review articles. Documents published earlier than January 1, 2002, were excluded. Moreover, case reports, meeting abstracts, editorial materials, and other documents types were excluded. No restriction on languages was applied.
## Data collection
Information on the literature identified by search query was downloaded from the WoSCC on June 6, 2022. Details of the literature, including author, title, source, sponsors, times cited count, accession number, abstract, address, document type, and cited references, were downloaded in txt and BibTex formats for further analysis. The H-index of the top 10 most productive authors were collected from Web of Science on June 6, 2022. The 2021 impact factor and 2021 Journal Citation Report category quartile of the top 10 core journals in AIP were collected from Web of Science.
## Statistical analysis
Bibliometric data were analyzed using HisCite (version 12.03.17), VOSviewer (version 1.6.18), CiteSpace (version 6.1.R3), and bibliometrix package (version 3.2.1; https://cran.r-project.org/web/packages/bibliometrix/) based on R language (version 4.1.2). HisCite was used to identify the number of publications and the number of citations for productive countries, institutions, and authors. The top 10 publications with the highest number of citations in AIP research were recognized by HisCite. The annual number of publications was also identified by HisCite and visualized by ggplot2 package (version 3.3.6; https://github.com/tidyverse/ggplot2) based on R language. VOSviewer was used to recognize the top 10 keywords with the highest number of occurrences, as well as the clustering of the top 50 keywords. A list of thesauri was employed for better understanding, which included “serum IgG4 concentrations,” represented by “serum IgG4”; “diagnostic criteria,” represented by “diagnosis”; “carcinoma,” represented by “cancer”; “disease,” represented by “IgG4-related disease”; “clinical feature”; and “characteristics,” represented by “features.” CiteSpace was used to construct a dual-map overlay of the journals related to AIP and to perform a keyword burst detection of the top 25 keywords with the strongest emergent strength. CiteSpace was used to measure the collaborative centrality of countries/regions, institutions, and authors. The setting of CiteSpace was as follows: scale factor $k = 25$, the strength of links measured by cosine, the scope of links measured within slices, and pruning with pathfinder and sliced network. The distribution of publications and collaborations between countries/regions and the annual output of the top 10 most productive authors were visualized using bibliometrix package. Clustering of collaboration among countries/regions, institutions, and authors was also visualized by bibliometrix package. The ratios of original and review articles for each year were measured using bibliometrix package.
## Overview
Overall, 1,772 publications related to AIP published between 2002 and 2022 were found in the WoSCC, including 1,436 original articles and 336 review articles. Figure 1 shows the inclusion and exclusion of publications. Most identified literatures were published in English ($$n = 1$$,689, $95.3\%$), followed by German ($$n = 36$$, $2.0\%$), Spanish ($$n = 18$$, $1.0\%$), French ($$n = 12$$, $0.7\%$), Russian ($$n = 4$$, $0.2\%$), and other 7 languages. Figure 2 presents the annual number of publications on AIP. According to the annual output, we artificially divided this period into two stages: the growing stage (2002–2009) and the mature stage (2010–2022). The yearly number of publications increased from 16 in 2002 to 113 in 2009, with an average increase of 13.9 publications per year. During the mature stage, the annual output stayed at >80 per year, and the highest output was 127 in 2012. The ratios of original and review articles for each year are displayed in Supplementary Table S1. Notably, the ratio of review articles increased from $6.3\%$ in 2002 to $34.8\%$ in 2021. So far, this collection of articles has been cited 55,504 times, with an average of 27.29 citations per article.
**Figure 1:** *The inclusion and exclusion of publications on AIP.* **Figure 2:** *Annual number of publications on AIP.*
## Leading countries/regions
Between 2002 and 2022, 63 countries/regions over 6 continents published articles on AIP, with close collaboration among East Asia, North America, and Western Europe (Figure 3A).
**Figure 3:** *Leading Countries/Regions. (A) Distribution of publications and collaborations between countries/regions and (B) clustering of collaboration among countries/regions.*
The top 10 most productive countries are listed in Table 1. Japan was the most productive country with respect to AIP research ($$n = 728$$, $41.1\%$), followed by the United States ($$n = 336$$, $19\%$), Germany ($$n = 147$$, $8.3\%$), China ($$n = 127$$, $7\%$), and Italy ($$n = 107$$, $6\%$). While articles from Japan received the most total citations (29,705 times), those from the United States showed the highest number of average citations (48.47 times per article). Three clusters of collaboration were identified (Figure 3B). Active collaborations were noted among Japan, the United States, China, and South Korea, whereas Germany had close collaboration with Italy. Collaborative centrality measures the position of a country/institution/author in the network of research collaboration, and a higher level of collaborative centrality reflects a greater number of research connections with partners. The United States showed the highest level of collaborative centrality, followed by Japan and Germany in this study.
**Table 1**
| Rank | Country | Publicationsn (%) | Total citations | Average citations | Collaborative centrality |
| --- | --- | --- | --- | --- | --- |
| 1 | Japan | 728 (41.1%) | 29705 | 40.8 | 0.12 |
| 2 | United States | 336 (19%) | 16285 | 48.47 | 0.3 |
| 3 | Germany | 147 (8.3%) | 4649 | 31.63 | 0.09 |
| 4 | China | 124 (7.0%) | 1996 | 16.1 | 0.05 |
| 5 | Italy | 107 (6.0%) | 4736 | 44.26 | 0.08 |
| 6 | South Korea | 89 (5.0%) | 3755 | 42.19 | 0.01 |
| 7 | United Kingdom | 71 (4.0%) | 2803 | 39.48 | 0.06 |
| 8 | France | 39 (2.2%) | 1024 | 26.26 | 0.03 |
| 9 | Sweden | 39 (2.2%) | 2007 | 51.46 | 0.08 |
| 10 | Netherlands | 34 (1.9%) | 1570 | 46.18 | 0.06 |
## Active institutions and authors
A total of 6,767 authors from 1,617 institutions published articles on AIP. The top 10 most productive institutions are listed in Table 2. Tokyo Metropolitan Komagome Hospital ($$n = 112$$, $6.3\%$) was the leading institution, followed by Kansai Medical University ($$n = 100$$, $5.6\%$), Mayo Clinic ($$n = 95$$, $5.4\%$), Shinshu University ($$n = 95$$, $5.4\%$), and the University of Ulsan ($$n = 57$$, $3.2\%$). Seven out of the ten most productive institutions were located in Japan. Four clusters of collaboration among institutions were identified (Figure 4A). The cluster led by Tokyo Metropolitan Komagome Hospital, Kansai Medical University, Shinshu University, Tohoku University, and Nagoya City University showed the closest cooperation. Mayo Clinic displayed the highest level of collaborative centrality, followed by Tokyo Metropolitan Komagome Hospital and the University of Verona.
The top three most prolific authors were Terumi Kamisawa from Tokyo Metropolitan Komagome Hospital ($$n = 117$$, $6.6\%$), Kazuichi Okazaki from Kansai Medical University ($$n = 103$$, $5.8\%$), and Shigeyuki Kawa from Matsumoto Dental University ($$n = 94$$, $5.3\%$) (Table 3). Eight out of the ten most productive authors came from Japan, one was from Korea, and another was from the United States. Suresh T. Chari of the University of Texas MD Anderson Cancer Center showed the highest H-index of 87. Figure 4B displays the annual output of the top 10 most productive authors. Cooperation among authors was relatively close, with five clusters present (Figure 4C). Terumi Kamisawa showed the highest level of collaborative centrality, followed by Suresh T Chari and Myung-Hwan Kim.
**Table 3**
| Rank | Author | Institution | Country | Publicationsn (%) | Total citations | Average citations | H-index | Collaborative centrality |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | Terumi Kamisawa | Tokyo Metropolitan Komagome Hospital | Japan | 117 (6.6%) | 9720 | 83.08 | 60 | 0.18 |
| 2 | Kazuichi Okazaki | Kansai Medical University | Japan | 103 (5.8%) | 8296 | 80.54 | 60 | 0.04 |
| 3 | Shigeyuki Kawa | Matsumoto Dental University | Japan | 94 (5.3%) | 7773 | 82.69 | 57 | 0.02 |
| 4 | Suresh T Chari | University of Texas MD Anderson Cancer Center | United States | 62 (3.5%) | 6989 | 112.73 | 87 | 0.09 |
| 5 | Kazushige Uchida | Kansai Medical University | Japan | 61 (3.4%) | 2115 | 34.67 | 40 | 0.03 |
| 6 | Hideaki Hamano | Shinshu University | Japan | 59 (3.3%) | 4537 | 76.9 | 40 | 0.01 |
| 7 | Myung-Hwan Kim | University of Ulsan | Korea | 51 (2.9%) | 3093 | 60.65 | 66 | 0.09 |
| 8 | Tetsuhide Ito | Fukuoka Sanno Hospital | Japan | 51 (2.9%) | 3970 | 77.84 | 41 | 0.01 |
| 9 | Tomohiro Watanabe | Kindai University | Japan | 45 (2.5%) | 1181 | 26.24 | 37 | 0.03 |
| 10 | Naoto Egawa | Tokyo Metropolitan Komagome Hospital | Japan | 43 (2.4%) | 3467 | 80.62 | 37 | 0.03 |
## Core journals and references
Overall, 501 journals published studies on AIP. The top 10 most productive journals with respect to AIP research are summarized in Table 4. Pancreas was the most productive journal ($$n = 95$$, $5.4\%$), followed by the Journal of Gastroenterology ($$n = 67$$, $3.8\%$), Internal Medicine ($$n = 66$$, $3.7\%$), Pancreatology ($$n = 63$$, $3.6\%$), and World Journal of Gastroenterology ($$n = 62$$, $3.5\%$). Publications from the American Journal of Gastroenterology had the highest number of average citations (104 times per article). The dual-map overlay revealed multiple inter-domain connections between journals (Figure 5). In Figure 5, the journals on the left are the citing journals, whereas the journals on the right are the cited journals; the lines denote the citation relationship between them [4]. Two main citation paths were identified. Publications in the journals of Health/Nursing/Medicine and Molecular/Biology/Genetics were mostly cited by publications in the journals of Medicine/Medical/Clinical.
The top 10 references with the highest number of citations are presented in Table 5. In 2003, Kamisawa et al. suggested that AIP could be a pancreatic manifestation of a chronic fibroinflammatory condition currently known as IgG4-RD. In 2009, Kamisawa et al. proposed the standard steroid regimen for AIP. In 2011, Shimosegawa et al. developed the International Consensus Diagnostic Criteria (ICDC) for AIP, which is the diagnostic standard most frequently used in clinical practice and categorizes AIP into types 1 and 2. In summary, the top 10 core references mainly focused on the diagnosis and treatment of AIP.
**Table 5**
| Rank | First author | Title | Journal | Year of publication | Total citations |
| --- | --- | --- | --- | --- | --- |
| 1 | Hisanori Umehara | Comprehensive diagnostic criteria for IgG4-related disease (IgG4-RD), 2011 | Modern Rheumatology | 2012 | 1181 |
| 2 | Terumi Kamisawa | A new clinicopathological entity of IgG4-related autoimmune disease | Journal of Gastroenterology | 2003 | 902 |
| 3 | Tooru Shimosegawa | International Consensus Diagnostic Criteria for Autoimmune Pancreatitis Guidelines of the International Association of Pancreatology | Pancreas | 2011 | 850 |
| 4 | Suresh T Chari | Diagnosis of autoimmune pancreatitis: The Mayo Clinic experience | Clinical Gastroenterology and Hepatology | 2006 | 678 |
| 5 | Hisanori Umehara | A novel clinical entity, IgG4-related disease (IgG4RD): general concept and details | Modern Rheumatology | 2012 | 566 |
| 6 | Amaar Ghazale | Immunoglobulin G4-associated cholangitis: Clinical profile and response to therapy | Gastroenterology | 2008 | 557 |
| 7 | Rob C Aalberse | Immunoglobulin G4: an odd antibody | Clinical & Experimental Allergy | 2009 | 529 |
| 8 | Kenji Notohara | Idiopathic chronic pancreatitis with periductal lymphoplasmacytic infiltration: clinicopathologic features of 35 cases | The American Journal of Surgical Pathology | 2003 | 510 |
| 9 | Terumi Kamisawa | Standard steroid treatment for autoimmune pancreatitis | Gut | 2009 | 442 |
| 10 | Giuseppe Zamboni | Histopathological features of diagnostic and clinical relevance in autoimmune pancreatitis: a study on 53 resection specimens and 9 biopsy specimens | Virchows Archive | 2004 | 429 |
## Analysis of keywords
The top 10 keywords with the highest number of occurrences are listed in Table 6. “ Diagnosis” was the most mentioned keyword. Among the top 50 keywords, three clusters were identified according to keyword co-occurrence (i.e., how frequently two keywords appear in the same literature), as shown in Figure 6A. The cluster led by “diagnosis” and “IgG4-related disease” displayed the highest number of occurrences, followed by the cluster led by “cancer” and “features” and then the cluster led by “serum IgG4” and “cholangitis.”
Keyword burst detection is regarded as an indicator of research frontiers or emerging topics in a specific field over time [9, 10]. The top 25 keyword terms with the strongest emergent strength are illustrated in Figure 6B. In Figure 6B, “Year” indicates the year in which the keyword first appeared; “Begin” and “End” indicate the starting and ending years of the keyword as a frontier, respectively; and “Strength” indicates the emergent strength. Figure 6B reflects the research frontiers during different time periods. “ Proposal” was the focus of research in 2007–2011, with 17.21 being the strongest emergent strength. “ Risk,” “malignancy,” “outcome,” “22-gauge needle,” and “fine-needle aspiration” have been the research frontiers in recent years.
## Discussion
Herein, we conducted a bibliometric analysis of AIP-related publications over the last 20 years. The annual number of publications showed an upward trend from 2002 to 2009 and has remained relatively stable since 2010. Leading countries/regions, active institutions and authors, core journals and references, and keywords were evaluated. Some landmark articles were identified (Figure 7). To our knowledge, this is the first bibliometric analysis of AIP reported.
**Figure 7:** *Timeline of some landmark publications of AIP.*
Japan was the leading country with respect to AIP research, contributing over $40\%$ of studies on AIP. Seven out of the ten most productive institutions and eight out of the ten most productive authors were from Japan. Furthermore, six out of the top 10 most cited articles were first authored by Japanese researchers. Collaboration among Tokyo Metropolitan Komagome Hospital, Kansai Medical University, Shinshu University, Tohoku University, and Nagoya City University was relatively close. The Japan Pancreas Society had provided timely updates on the diagnostic criteria for AIP (11–16).
Diagnosis of AIP is currently the research focus. The Japan Pancreas Society proposed the first diagnostic criteria in 2002 [11]. Since then, much progress has been made. Kamisawa et al. [ 17] proposed that AIP might be a pancreatic manifestation of a chronic fibroinflammatory condition currently known as IgG4-RD. Notohara et al. [ 18] and Zamboni et al. [ 19] summarized the histopathological findings of AIP and reported two subtypes—namely, lymphoplasmacytic sclerosing pancreatitis and idiopathic duct-centric pancreatitis. In 2006, Chari et al. [ 20] introduced the HISORt diagnostic criteria, which are based on histology, imaging of the pancreas using computed tomography or magnetic resonance imaging, serum IgG4 levels, other organ involvement, and response to steroid therapy. The integration of histology, radiology, serology, and follow-up formed a pathway for the diagnosis of AIP, which remained in further studies. In 2011, Shimosegawa et al. [ 2] proposed the ICDC, which are the most widely used diagnostic criteria in clinical practice. According to the ICDC, AIP can be subclassified into types 1 and 2 [2]. AIP types 1 and 2 share similar radiological findings such as sausage-like pancreatic enlargement, rim-like enhancement around the lesion, delayed homogenous enhancement in the pancreatic parenchyma, and long or multiple strictures without marked upstream dilatation in the main pancreatic duct (21–23). However, AIP types 1 and 2 differ in terms of serology and histopathology. As the pancreatic manifestation of IgG4-RD, type 1 AIP exhibits elevated serum IgG4 levels [3, 24, 25]. Histopathologically, type 1 AIP corresponds to lymphoplasmacytic sclerosing pancreatitis, with abundant IgG4-positive plasma cell infiltration [2]. Other organ involvement, including sclerosing cholangitis, retroperitoneal fibrosis, and sclerosing sialadenitis, is common in type 1 AIP (26–29), with IgG4-related sclerosing cholangitis being the most common organ involvement, occurring in up to $80\%$ of type 1 AIP cases [30, 31]. In contrast, other organ involvement is less common in type 2 AIP [26]. Type 2 AIP accounts for <$5\%$ of AIP cases in Eastern countries and is more common in Western countries, accounting for up to $10\%$–$20\%$ of AIP cases in Western countries [32, 33]. Approximately $30\%$ of type 2 AIP cases are estimated to be associated with inflammatory bowel disease, particularly ulcerative colitis [2]. Type 2 AIP has an earlier onset (in individuals in their 30s) and a lower incidence of disease relapse after initial induction of remission than type 1 AIP [32]. Histopathologically, type 2 AIP corresponds to idiopathic duct-centric pancreatitis and usually exhibits infiltration of no or very few IgG4-positive plasma cells [2]. Serum IgG4 levels are often within the normal range [3]. Owing to the paucity of reliable serum biomarkers, the diagnosis of type 2 AIP heavily relies on pancreatic histopathology [26]. Moreover, both types of AIP share a dramatic response to glucocorticoid therapy [32].
Much progress has been made in the treatment of AIP. Glucocorticoids are the first-line therapy. Indications for glucocorticoid therapy are symptoms such as obstructive jaundice, abdominal pain, and other organ involvement [34]. As for the induction of remission, Kamisawa et al. [ 35] recommended an initial dose of 0.6 mg/kg/day of oral prednisolone for 2–4 weeks. Symptoms are anticipated to be relieved within days after commencing the treatment [36]. Assessment of the response to initial treatment with biochemical, serological, and radiological work-up at weeks 2–4 is recommended [36]. Subsequently, glucocorticoids should be gradually tapered off, usually 5 mg every 1–2 weeks. When glucocorticoid therapy fails to relieve AIP-related symptoms, a reevaluation of diagnosis should be in order [15, 16]. Clinicians should be particularly cautious about pancreatic cancer misdiagnoses. There are disputes over glucocorticoid maintenance therapy. In Western countries, glucocorticoid therapy is generally limited to the induction of remission without maintenance [37] because prolonged administration may increase the risk of infections, diabetes, osteoporosis, and cataracts [34]. However, Masamune et al. [ 38] conducted the first AIP-related randomized controlled trial, the results of which favored maintenance therapy. Maintenance therapy with prednisolone at a dose of 5–7.5 mg/day was continued for 3 years. Compared with the cessation group, which had withdrawn at 26 weeks since the initial glucocorticoid therapy, the maintenance group achieved better 3-year relapse-free survival ($42.1\%$ vs. $76.7\%$, $$p \leq 0.007$$) [38]. Moreover, no major glucocorticoid-related complications requiring treatment cessation were found in the maintenance group [38]. Thus, the *Japanese consensus* guidelines advocate a 3-year maintenance therapy to prevent disease relapse [16]. Immunosuppressants such as azathioprine, methotrexate, and mycophenolate mofetil may be beneficial for patients with AIP. A recent meta-analysis had suggested that azathioprine was effective in preventing AIP relapse [39]. B-cell depletion therapy has been proposed as a treatment for recurrent type 1 AIP. CD20 is a B-cell surface marker involved in calcium channel activation, cell proliferation, and B-cell differentiation [40]. Rituximab is a monoclonal antibody targeting human CD20. Rituximab can induce complement activation and cell-mediated cytotoxicity, leading to B-cell depletion [41]. Hart et al. [ 42] reported rituximab as a treatment for recurrent AIP. A decrease in serum IgG4 concentration and the extinction of pancreatic hypermetabolic signal on positron emission tomography were achieved in type 1 AIP after rituximab treatment [43]. Other therapies for type 1 AIP, including rilzabrutinib (Bruton tyrosine kinase inhibitor), belimumab (B-cell activating factor inhibitor), and inebilizumab (anti-CD19 monoclonal antibody), are under investigation [44].
Keyword burst detection is capable of tracing research frontiers [9, 10]. Some emerging topics had been recognized, including “risk,” “malignancy,” and “outcome.” Patients with AIP are at a high risk for malignancy [45]. A recent meta-analysis of 17 studies involving 2,746 patients revealed that the overall prevalence of malignancy in patients with AIP was $9.6\%$ [46]. The top 5 most prevalent malignancies in patients with AIP were gastric, colorectal, bladder, prostate, and pancreatic cancers [46]. The majority of pancreatic cancer cases in patients with AIP occurred at no less than 2 years after an AIP diagnosis [47]. Other than malignancy, the long-term outcomes of AIP include diabetes mellitus (DM) and pancreatic exocrine insufficiency (PEI) [48, 49]. Chronic inflammation in patients with AIP may cause damage to the pancreatic β-cells and acinar cells, leading to DM and PEI [50]. By alleviating the inflammation and swelling of pancreatic tissues with glucocorticoid therapy, both endocrine and exocrine functions are supposed to be restored [50, 51]. However, DM and PEI are often described at the time of diagnosis and during follow-up. The pooled prevalence of DM and PEI in patients with AIP at the time of diagnosis is $36.5\%$ and $45.2\%$, respectively [52]. Moreover, the pooled prevalence of DM during follow-up is $40.9\%$ [52]. The prevalence of PEI during follow-up ranged from $23.8\%$ to $72.7\%$ [49, 53, 54]. Studies concerning malignancy, DM, and PEI in patients with AIP are mostly retrospective. More high-quality prospective cohort studies are required to better understand the long-term outcomes of patients with AIP.
In addition to clinical outcomes, pancreatic tissue acquisition is now gaining attention. A “22-gauge needle” and “fine-needle aspiration” have been recognized as research hotspots since the late 2010s by keyword burst analysis. Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) and endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) have been utilized for pancreatic tissue acquisition in patients with AIP. When pathology specimen collection is necessary for diagnosis or when malignancy is suspected, EUS-FNA or EUS-FNB should be taken into consideration [16]. EUS-FNA is designed for the aspiration of cells from the target lesion using a conventional straight needle. Tissue core samples acquired by EUS-FNA are usually limited in size, making the histopathological diagnosis of AIP less than satisfactory. With the help of recently developed core needles, EUS-FNB is capable of obtaining a large amount of tissue core samples with preserved tissue architecture [16, 55]. According to the ICDC, the histological findings of AIP can be categorized into levels 1 and 2 [2]. A recent meta-analysis demonstrated that EUS-FNB had better diagnostic yield than EUS-FNA [56]. The pooled diagnostic yield for level 1 and 2 histological findings was $55.8\%$ for EUS-FNA and $87.2\%$ for EUS-FNB ($$p \leq 0.03$$) [56]. As for the needle size, a 19-gauge needle exhibited a better pooled diagnostic yield for level 1 and 2 histological findings than a 22-gauge needle ($88.9\%$ vs. $60.6\%$, $$p \leq 0.023$$) [56]. However, studies investigating the diagnostic yield of EUS-FNA or EUS-FNB had mainly focused on type 1 AIP, rather than type 2 AIP, possibly because these studies were largely conducted in Eastern countries where type 2 AIP is quite rare (<$5\%$ of total AIP cases) [32, 33]. Moreover, histological diagnosis is more essential for type 2 AIP than for type 1 AIP, as there are no reliable serological markers. Further studies should recruit more patients with type 2 AIP by inviting more European and American centers.
Research cooperation among countries/regions, institutions and authors has been identified by the cooperation network in our study. Active collaborations were noted among Japan, the United States, China, and South Korea. Due to the limited cases of type 2 AIP reported, its genetic predisposition, relationship with inflammatory bowel disease, and long-term outcome have not been characterized in detail [57]. This might be because the current knowledge base of AIP is mainly generated from Eastern-population-driven information, especially in Japan, where type 2 AIP is less common. Therefore, further collaborative global research is essential to understand AIP comprehensively.
This study has some limitations. First, the data were retrieved exclusively from the WoSCC, rather than other databases such as Embase and MEDLINE. The WoSCC is the most commonly applied database in bibliometric analysis because it provides timely and comprehensive updates on citation network. Moreover, software that were applied in our bibliometric analysis had difficulties in integrating data from different resources. Second, the increase in the number of publications on AIP from 2002 to 2009 might have resulted from the overall increase in scientific outputs in the medical field over the past two decades. Finally, some new emerging topics related to AIP might not have been identified because of the sensitivity of algorithms applied in the analysis. Although multiple software/packages have been used in our study, the information provided in our study is still constricted by algorithms applied in bibliometric analysis. Further development in the methodology of bibliometric analysis might be helpful in resolving these limitations.
## Conclusion
Over the past two decades, Japan was the leading country in AIP research, with more than half of the top 10 most productive institutions and top 10 most productive authors being from Japan. Research papers were mainly published in specialized journals. Diagnosis of AIP was the research focus. Long-term outcomes and pancreatic tissue acquisition are recognized as research frontiers for AIP.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Author contributions
X-DZ: conceptualization, methodology, software, formal analysis, resources, data curation, visualization, and writing – original draft. YZ: methodology, software, validation, formal analysis, resources, data curation, visualization, and writing – original draft. Y-ZZ: methodology, software, validation, formal analysis, resources, data curation, visualization, and writing – original draft. C-HZ: conceptualization, supervision, funding acquisition, and writing – review and editing. D-WZ: conceptualization, supervision, project administration, and writing – review and editing. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1135096/full#supplementary-material
## References
1. Uchida K, Okazaki K. **Current status of type 1 (IgG4-related) autoimmune pancreatitis**. *J Gastroenterol* (2022) **57** 695-708. DOI: 10.1007/s00535-022-01891-7
2. Shimosegawa T, Chari ST, Frulloni L, Kamisawa T, Kawa S, Mino-Kenudson M. **International consensus diagnostic criteria for autoimmune pancreatitis: guidelines of the international association of pancreatology**. *Pancreas* (2011) **40**. DOI: 10.1097/MPA.0b013e3182142fd2
3. Hart PA, Zen Y, Chari ST. **Recent advances in autoimmune pancreatitis**. *Gastroenterology* (2015) **149** 39-51. DOI: 10.1053/j.gastro.2015.03.010
4. Chen C. **Science mapping: A systematic review of the literature**. *J Data Inf Sci* (2017) **2** 1-40. DOI: 10.1515/jdis-2017-0006
5. Chen C, Song M. **Visualizing a field of research: A methodology of systematic scientometric reviews**. *PLoS One* (2019) **14**. DOI: 10.1371/journal.pone.0223994
6. Thompson DF, Walker CK. **A descriptive and historical review of bibliometrics with applications to medical sciences**. *Pharmacotherapy* (2015) **35**. DOI: 10.1002/phar.1586
7. Zhao Y, Yin Z, Du H, Huang K, Zhang F, Chen H. **The latest research trends in primary biliary cholangitis: a bibliometric analysis**. *Clin Exp Med* (2022). DOI: 10.1007/s10238-022-00825-0
8. Liu C, Yu R, Zhang J, Wei S, Xue F, Guo Y. **Research hotspot and trend analysis in the diagnosis of inflammatory bowel disease: A machine learning bibliometric analysis from 2012 to 2021**. *Front Immunol* (2022) **13**. DOI: 10.3389/fimmu.2022.972079
9. Li D, Dai FM, Xu JJ, Jiang MD. **Characterizing hotspots and frontier landscapes of diabetes-specific distress from 2000 to 2018: A bibliometric study**. *BioMed Res Int* (2020) **2020**. DOI: 10.1155/2020/8691451
10. Zhou H, Tan W, Qiu Z, Song Y, Gao S. **A bibliometric analysis in gene research of myocardial infarction from 2001 to 2015**. *PeerJ* (2018) **6**. DOI: 10.7717/peerj.4354
11. **Diagnostic criteria for autoimmune pancreatitis by the Japan pancreas society (in Japanese)**. *Suizo* (2002) **17**
12. Okazaki K, Kawa S, Kamisawa T, Naruse S, Tanaka S, Nishimori I. **Clinical diagnostic criteria of autoimmune pancreatitis: revised proposal**. *J Gastroenterol* (2006) **41**. DOI: 10.1007/s00535-006-1868-0
13. Shimosegawa T. **Working group members of the Japan pancreas society, research committee for intractable pancreatic disease by the ministry of labor, health and welfare of japan. the amendment of the clinical diagnostic criteria in Japan (JPS2011) in response to the proposal of the international consensus of diagnostic criteria (ICDC) for autoimmune pancreatitis**. *Pancreas* (2012) **41**. DOI: 10.1097/MPA.0b013e3182706ed5
14. Okazaki K, Kawa S, Kamisawa T, Ito T, Inui K, Irie H. **Amendment of the Japanese consensus guidelines for autoimmune pancreatitis, 2013 i. concept and diagnosis of autoimmune pancreatitis**. *J Gastroenterol* (2014) **49**. DOI: 10.1007/s00535-014-0942-2
15. Kawa S, Kamisawa T, Notohara K, Fujinaga Y, Inoue D, Koyama T. **Japanese Clinical diagnostic criteria for autoimmune pancreatitis, 2018: revision of Japanese clinical diagnostic criteria for autoimmune pancreatitis, 2011**. *Pancreas* (2020) **49**. DOI: 10.1097/MPA.0000000000001443
16. Okazaki K, Kawa S, Kamisawa T, Ikeura T, Itoi T, Ito T. **Amendment of the Japanese consensus guidelines for autoimmune pancreatitis, 2020**. *J Gastroenterol* (2022) **57**. DOI: 10.1007/s00535-022-01857-9
17. Kamisawa T, Funata N, Hayashi Y, Eishi Y, Koike M, Tsuruta K. **A new clinicopathological entity of IgG4-related autoimmune disease**. *J Gastroenterol* (2003) **38**. DOI: 10.1007/s00535-003-1175-y
18. Notohara K, Burgart LJ, Yadav D, Chari S, Smyrk TC. **Idiopathic chronic pancreatitis with periductal lymphoplasmacytic infiltration: clinicopathologic features of 35 cases**. *Am J Surg Pathol* (2003) **27**. DOI: 10.1097/00000478-200308000-00009
19. Zamboni G, Lüttges J, Capelli P, Frulloni L, Cavallini G, Pederzoli P. **Histopathological features of diagnostic and clinical relevance in autoimmune pancreatitis: a study on 53 resection specimens and 9 biopsy specimens**. *Virchows Arch* (2004) **445**. DOI: 10.1007/s00428-004-1140-z
20. Chari ST, Smyrk TC, Levy MJ, Topazian MD, Takahashi N, Zhang L. **Diagnosis of autoimmune pancreatitis: the Mayo clinic experience**. *Clin Gastroenterol Hepatol* (2006) **4**. DOI: 10.1016/j.cgh.2006.05.017
21. Suzuki K, Itoh S, Nagasaka T, Ogawa H, Ota T, Naganawa S. **CT findings in autoimmune pancreatitis: assessment using multiphase contrast-enhanced multisection CT**. *Clin Radiol* (2010) **65**. DOI: 10.1016/j.crad.2010.06.002
22. Kawai Y, Suzuki K, Itoh S, Takada A, Mori Y, Naganawa S. **Autoimmune pancreatitis: assessment of the enhanced duct sign on multiphase contrast-enhanced computed tomography**. *Eur J Radiol* (2012) **81**. DOI: 10.1016/j.ejrad.2012.04.023
23. Tang CSW, Sivarasan N, Griffin N. **Abdominal manifestations of IgG4-related disease: a pictorial review**. *Insights Imaging* (2018) **9**. DOI: 10.1007/s13244-018-0618-1
24. Chari ST, Kloeppel G, Zhang L, Notohara K, Lerch MM, Shimosegawa T. **Histopathologic and clinical subtypes of autoimmune pancreatitis: the Honolulu consensus document**. *Pancreas* (2010) **39**. DOI: 10.1097/MPA.0b013e3181e4d9e5
25. Aalberse RC, Stapel SO, Schuurman J, Rispens T. **Immunoglobulin G4: an odd antibody**. *Clin Exp Allergy* (2009) **39**. DOI: 10.1111/j.1365-2222.2009.03207.x
26. Nagpal SJS, Sharma A, Chari ST. **Autoimmune pancreatitis**. *Am J Gastroenterol* (2018) **113** 1301. DOI: 10.1038/s41395-018-0146-0
27. Ghazale A, Chari ST, Zhang L, Smyrk TC, Takahashi N, Levy MJ. **Immunoglobulin G4-associated cholangitis: clinical profile and response to therapy**. *Gastroenterology* (2008) **134**. DOI: 10.1053/j.gastro.2007.12.009
28. Umehara H, Okazaki K, Masaki Y, Kawano M, Yamamoto M, Saeki T. **A novel clinical entity, IgG4-related disease (IgG4RD): general concept and details**. *Mod Rheumatol* (2012) **22** 1-14. DOI: 10.1007/s10165-011-0508-6
29. Umehara H, Okazaki K, Masaki Y, Kawano M, Yamamoto M, Saeki T. **Comprehensive diagnostic criteria for IgG4-related disease (IgG4-RD), 2011**. *Mod Rheumatol* (2012) **22** 21-30. DOI: 10.1007/s10165-011-0571-z
30. Tanaka A, Tazuma S, Okazaki K, Nakazawa T, Inui K, Chiba T. **Clinical features, response to treatment, and outcomes of IgG4-related sclerosing cholangitis**. *Clin Gastroenterol Hepatol* (2017) **15** 920-26.e3. DOI: 10.1016/j.cgh.2016.12.038
31. Naitoh I, Kamisawa T, Tanaka A, Nakazawa T, Kubota K, Takikawa H. **Clinical characteristics of immunoglobulin IgG4-related sclerosing cholangitis: comparison of cases with and without autoimmune pancreatitis in a large cohort**. *Dig Liver Dis* (2021) **53**. DOI: 10.1016/j.dld.2021.02.009
32. de Pretis N, De Marchi G, Frulloni L. **Diagnosis and treatment of autoimmune pancreatitis**. *Curr Opin Gastroenterol* (2018) **34**. DOI: 10.1097/MOG.0000000000000454
33. Hart PA, Kamisawa T, Brugge WR, Chung JB, Culver EL, Czakó L. **Long-term outcomes of autoimmune pancreatitis: a multicentre, international analysis**. *Gut* (2013) **62**. DOI: 10.1136/gutjnl-2012-303617
34. Matsubayashi H, Ishiwatari H, Imai K, Kishida Y, Ito S, Hotta K. **Steroid therapy and steroid response in autoimmune pancreatitis**. *Int J Mol Sci* (2019) **21**. DOI: 10.3390/ijms21010257
35. Kamisawa T, Shimosegawa T, Okazaki K, Nishino T, Watanabe H, Kanno A. **Standard steroid treatment for autoimmune pancreatitis**. *Gut* (2009) **58**. DOI: 10.1136/gut.2008.172908
36. Löhr JM, Beuers U, Vujasinovic M, Alvaro D, Frøkjær JB, Buttgereit F. **European Guideline on IgG4-related digestive disease - UEG and SGF evidence-based recommendations**. *U Eur Gastroenterol J* (2020) **8**. DOI: 10.1177/2050640620934911
37. Ghazale A, Chari ST. **Optimising corticosteroid treatment for autoimmune pancreatitis**. *Gut* (2007) **56**. DOI: 10.1136/gut.2007.129833
38. Masamune A, Nishimori I, Kikuta K, Tsuji I, Mizuno N, Iiyama T. **Randomised controlled trial of long-term maintenance corticosteroid therapy in patients with autoimmune pancreatitis**. *Gut* (2017) **66**. DOI: 10.1136/gutjnl-2016-312049
39. Masaki Y, Nakase H, Tsuji Y, Nojima M, Shimizu K, Mizuno N. **The clinical efficacy of azathioprine as maintenance treatment for autoimmune pancreatitis: a systematic review and meta-analysis**. *J Gastroenterol* (2021) **56**. DOI: 10.1007/s00535-021-01817-9
40. Pavlasova G, Mraz M. **The regulation and function of CD20: an “enigma” of b-cell biology and targeted therapy**. *Haematologica* (2020) **105**. DOI: 10.3324/haematol.2019.243543
41. Smith MR. **Rituximab (monoclonal anti-CD20 antibody): mechanisms of action and resistance**. *Oncogene* (2003) **22**. DOI: 10.1038/sj.onc.1206939
42. Hart PA, Topazian MD, Witzig TE, Clain JE, Gleeson FC, Klebig RR. **Treatment of relapsing autoimmune pancreatitis with immunomodulators and rituximab: the Mayo clinic experience**. *Gut* (2013) **62**. DOI: 10.1136/gutjnl-2012-302886
43. Le Cosquer G, Ribes D, Faguer S, Jeune M, Alric L, Bournet B. **Long-term follow-up and immunomonitoring of relapsing type 1 autoimmune pancreatitis treated with rituximab**. *Pancreas* (2022) **51**. DOI: 10.1097/MPA.0000000000002048
44. Löhr JM, Vujasinovic M, Rosendahl J, Stone JH, Beuers U. **IgG4-related diseases of the digestive tract**. *Nat Rev Gastroenterol Hepatol* (2022) **19**. DOI: 10.1038/s41575-021-00529-y
45. Shiokawa M, Kodama Y, Yoshimura K, Kawanami C, Mimura J, Yamashita Y. **Risk of cancer in patients with autoimmune pancreatitis**. *Am J Gastroenterol* (2013) **108**. DOI: 10.1038/ajg.2012.465
46. Haghbin H, Chuang J, Fatima R, Zakirkhodjaev N, Lee-Smith W, Aziz M. **Correlation of autoimmune pancreatitis and malignancy: systematic review and meta-analysis**. *Dig Dis Sci* (2022) **67**. DOI: 10.1007/s10620-021-07179-9
47. Macinga P, Bajer L, Del Chiaro M, Chari ST, Dite P, Frulloni L. **Pancreatic cancer in patients with autoimmune pancreatitis: A scoping review**. *Pancreatology* (2021) **21**. DOI: 10.1016/j.pan.2021.03.007
48. Vujasinovic M, Valente R, Maier P, von Beckerath V, Haas SL, Arnelo U. **Diagnosis, treatment and long-term outcome of autoimmune pancreatitis in Sweden**. *Pancreatology* (2018) **18**. DOI: 10.1016/j.pan.2018.09.003
49. Uchida K, Yazumi S, Nishio A, Kusuda T, Koyabu M, Fukata M. **Long-term outcome of autoimmune pancreatitis**. *J Gastroenterol* (2009) **44**. DOI: 10.1007/s00535-009-0049-3
50. Farris AB, Lauwers GY, Deshpande V. **Autoimmune pancreatitis-related diabetes: quantitative analysis of endocrine islet cells and inflammatory infiltrate**. *Virchows Arch* (2010) **457**. DOI: 10.1007/s00428-010-0948-y
51. Frulloni L, Scattolini C, Katsotourchi AM, Amodio A, Gabbrielli A, Zamboni G. **Exocrine and endocrine pancreatic function in 21 patients suffering from autoimmune pancreatitis before and after steroid treatment**. *Pancreatology* (2010) **10**. DOI: 10.1159/000265945
52. Lanzillotta M, Tacelli M, Falconi M, Arcidiacono PG, Capurso G, Della-Torre E. **Incidence of endocrine and exocrine insufficiency in patients with autoimmune pancreatitis at diagnosis and after treatment: a systematic review and meta-analysis**. *Eur J Intern Med* (2022) **100** 83-93. DOI: 10.1016/j.ejim.2022.03.014
53. Huggett MT, Culver EL, Kumar M, Hurst JM, Rodriguez-Justo M, Chapman MH. **Type 1 autoimmune pancreatitis and IgG4-related sclerosing cholangitis is associated with extrapancreatic organ failure, malignancy, and mortality in a prospective UK cohort**. *Am J Gastroenterol* (2014) **109**. DOI: 10.1038/ajg.2014.223
54. Kuraishi Y, Uehara T, Watanabe T, Ashihara N, Ozawa M, Kanai K. **Corticosteroids prevent the progression of autoimmune pancreatitis to chronic pancreatitis**. *Pancreatology* (2020) **20**. DOI: 10.1016/j.pan.2020.07.408
55. de Pretis N, Crinò SF, Frulloni L. **The role of EUS-guided FNA and FNB in autoimmune pancreatitis**. *Diagnostics (Basel)* (2021) **11**. DOI: 10.3390/diagnostics11091653
56. Yoon SB, Moon SH, Song TJ, Kim JH, Kim MH. **Endoscopic ultrasound-guided fine needle aspiration versus biopsy for diagnosis of autoimmune pancreatitis: systematic review and comparative meta-analysis**. *Dig Endosc* (2021) **33**. DOI: 10.1111/den.13866
57. Zen Y. **Type 2 autoimmune pancreatitis: Consensus and controversies**. *Gut Liver* (2022) **16**. DOI: 10.5009/gnl210241
|
---
title: The nonlinear relationship between thyroid function parameters and metabolic
dysfunction-associated fatty liver disease
authors:
- Yingying Hu
- Fan Zhou
- Fang Lei
- Lijin Lin
- Xuewei Huang
- Tao Sun
- Weifang Liu
- Xingyuan Zhang
- Jingjing Cai
- Zhi-Gang She
- Hongliang Li
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9992977
doi: 10.3389/fendo.2023.1115354
license: CC BY 4.0
---
# The nonlinear relationship between thyroid function parameters and metabolic dysfunction-associated fatty liver disease
## Abstract
### Background
The relationship between thyroid function parameters and metabolic dysfunction-associated fatty liver disease (MAFLD) remains controversial. Additionally, little is known about the relationship between thyroid function parameters and MAFLD in the Chinese population.
### Methods
We conducted a retrospective cross-sectional study involving 177,540 individuals with thyroid function tests and MAFLD diagnosis from 2010-2018. The association between thyroid function parameters and MAFLD was evaluated on a continuous scale with restricted cubic spline (RCS) models and by the prior-defined centile categories with multivariable-adjusted logistic regression models. Thyroid function parameters included free triiodothyronine (FT3), free tetra-iodothyronine (FT4), and thyroid stimulating hormone (TSH). Additionally, fully adjusted RCS models stratified by sex, age, and location were studied.
### Results
In the RCS models, the risk of MAFLD increased with higher levels of FT3 when FT3 <5.58pmol/L, while the risk of MAFLD decreased with higher levels of FT3 when FT3 ≥5.58pmol/L (P nonlinearity <0.05). While RCS analysis suggested that the FT4 levels had a negative association with MAFLD (P nonlinearity <0.05), indicating an increase in FT4 levels was associated with a decreased risk of MAFLD. RCS analysis suggested an overall positive association between the concentration of TSH and MAFLD risk (P nonlinearity <0.05). The rising slope was sharper when the TSH concentration was less than 1.79uIU/mL, which indicated the association between TSH and MAFLD risk was tightly interrelated within this range. The multivariable logistic regression showed that populations in the 81st-95th centile had the highest risk of MAFLD among all centiles of FT3/TSH, with the 1st-5th centile as the reference category.
### Conclusions
Our study suggested nonlinear relationships between thyroid function parameters and MAFLD. Thyroid function parameters could be additional modifiable risk factors apart from the proven risk factors to steer new avenues regarding MAFLD prevention and treatment.
## Introduction
Metabolic dysfunction-associated fatty liver disease (MAFLD) is a more inclusive term than nonalcoholic fatty liver disease (NAFLD) for the patient with associated metabolic dysfunction of hepatic steatosis [1]. Although there is a substantial overlap between the two populations defined by MAFLD and NAFLD, the considerable differences in the two populations should not be neglected. Moreover, those populations diagnosed with MAFLD would have more comorbidities and worse prognoses than those with NAFLD [2, 3]. Due to its clinically occult symptoms in the early stages, MAFLD often leads to severe outcomes, such as steatohepatitis, liver fibrosis, and even cirrhosis and hepatic carcinoma [4]. Therefore, improving the awareness and management of its related risk factors could promote early screening to achieve early interventions.
The increasing prevalence and associated burden of MAFLD cannot be fully explained by traditional risk factors, such as age, gender, smoking, body mass index (BMI), hypertension, hyperlipidemia, and diabetes mellitus. Accumulating evidence has suggested that thyroid function parameters may also be associated with MAFLD [5]. Physiologically, thyroid hormone (TH) powerfully influences metabolic processes through multiple pathways for human beings, with profound effects on lipid metabolism and energy expenditure [6]. Their imbalanced levels may also lead to various unfavorable effects and contribute to the development and progression of multiple diseases, including MAFLD [7]. Their importance promotes researchers exploring the relationship between thyroid function parameters and MAFLD. The conclusions from studies regarding the relationship between thyroid function parameters and NAFLD/MAFLD are inconsistent, varying from a solid association to no association (8–12). There are limited studies aiming to assess the association of thyroid function parameters with MAFLD in the large-scale population in China. In addition, the relationship between thyroid function parameters and MAFLD risk may not be linear, and prior studies have been relatively underpowered to assess nonlinear relationships. Therefore, we conducted a large retrospective study to evaluate the potential connection between the level of each thyroid function parameter and MAFLD risk and to further explore these relationships in different sex, age, and location groups.
## Study population
This study was designed as a multicenter retrospective study comprising 180,582 adults from 5 health check-up centers from 2010 to 2018. All individuals had access to serum FT3, FT4, and TSH concentrations, as well as MAFLD diagnosis. Moreover, there were 3 centers in the north of China and 2 in the south of China. Participants who 1) had previously been diagnosed with liver cancer, liver cirrhosis, or had a history of liver surgery ($$n = 227$$); 2) had the administration of drugs influencing serum TH levels, such as methimazole, propylthiouracil, levothyroxine, and amiodarone ($$n = 24$$); 3) had a severe medical illness, such as acute infection, acute heart failure, acute coronary syndrome, stroke, severe kidney diseases, and malignancy ($$n = 2791$$) were excluded. Finally, 177540 adults were enrolled in our multicentered retrospective study. The flow chart of participant selection is shown in Figure 1A.
**Figure 1:** *The flow chart of participant selection. (A) main study. (B) sensitivity analysis 2. ECLIA, electrochemiluminescence immunoassay; MAFLD, metabolic dysfunction-associated fatty liver disease; CLIA, chemiluminescence analysis.*
This study was approved by the ethical review committee of Renmin Hospital of Wuhan University and followed by acceptance by the ethics center in each collaborating hospital. The ethics committees granted a waiver of the requirement for documentation of informed consent for just analyzing existing data after anonymization without individual identification.
## Anthropometric and laboratory data
The medical histories were collected face-to-face and recorded by professional physicians, from which the demographic information of participants was extracted, including sex, age, smoking status, alcohol consumption, medications, medical history, and so on. All participants had undergone comprehensive anthropometric measurements and clinical examinations.
Anthropometric measurements, including height, weight, and waist circumference (WC), and physiological parameters, such as systolic and diastolic blood pressure (SBP and DBP), and heart rates, were performed by well-trained physicians according to standard protocols. BMI was calculated as weight divided by the square of height (kg/m2). After overnight fasting, the participants underwent clinical examinations, including routine blood examinations, biochemical tests, and liver ultrasounds. Biochemical tests included liver function tests, renal function tests, fasting blood glucose (FBG) levels, lipid contents examinations, and thyroid function tests. The estimated glomerular filtration rate (eGFR) was calculated by the Modification of Diet in Renal Disease equations [13].
All check-up centers included in our main study applied identical methodology, namely, the electrochemiluminescence immunoassay (ECLIA) method, to complete thyroid function tests. According to the ECLIA method, the overall ranges of thyroid function parameters were as follows: FT3 ranged from 0.39 to 76pmol/L (reference range, 3.43-6.5pmol/L), FT4 ranged from 0.3 to 294.2pmol/L (reference range, 12-22pmol/L), and TSH ranged from 0 to 230uIU/mL (reference range, 0.27-4.2uIU/mL). All imagological diagnoses were performed and evaluated by experienced imaging specialists at medical health check-up centers.
## Diagnostic criteria
MAFLD was defined by evidence of hepatic steatosis on abdominal ultrasound, Computed Tomography, or Magnetic Resonance Imaging with the presence of one of the following three criteria: overweight or obesity (defined as BMI ≥23 kg/m2 in Asians); the presence of type 2 diabetes (T2DM); lean or normal weight (BMI <23 kg/m2) with the presence of or metabolic dysregulation. Metabolic dysregulation was defined by the presence of at least two of the following metabolic risk abnormalities: 1) WC ≥90 cm for men and 80 cm for women; 2) blood pressure ≥$\frac{130}{85}$ mmHg or on specific drug treatment; 3) plasma triglycerides ≥1.70 mmol/L or on specific drug treatment; 4) plasma high-density lipoprotein cholesterol (HDL-C) <1.0 mmol/L for men and <1.3 mmol/L for women or on specific drug treatment; 5) prediabetes (i.e., FBG was 5.6 to 6.9 mmol/L, or 2 hours postprandial glucose level was 7.8 to 11.0 mmol or glycosylated hemoglobin A1c level was $5.7\%$ to $6.4\%$) [1]. T2DM was diagnosed according to the clinical guidelines for the prevention and treatment of T2DM in the elderly in China (2022 edition) [14]. Hypertension was defined as an SBP ≥140 mmHg and/or DBP ≥90 mmHg, a medical history of hypertension, or the use of antihypertensive agents according to the 2018 Chinese hypertension management guidelines [15]. Metabolic syndrome (MetS) was defined based on the criteria within the CHPSNE (Control Hypertension and Other Risk Factors to Prevent Stroke with Nutrition Education in Urban Area of Northeast China) study [16].
## Statistical analysis
The basic characteristics of participants were presented by descriptive statistics. The Kolmogorov–Smirnov test was used to evaluate the normality of the distribution of the continuous variables. Continuous variables were summarized as mean and standard deviation (SD) if normally distributed and median and interquartile range (IQR) if not normally distributed. Categorical variables were presented as frequencies and percentages. Student’s t-tests (normally distributed) and Wilcoxon rank-sum test (non-normally distributed) for continuous variables, and Fisher’s exact test or chi-square test for categorical variables were used to compare the intergroup differences.
Apart from dividing populations into with or without MAFLD groups, five equally distributed categories were defined by the 20th, 40th, 60th, and 80th centiles of the level of thyroid function parameters, and to evaluate the highest and lowest levels of thyroid function parameters, two additional categories were defined by the 5th and 95th centiles.
We used RCS models fitted for the logistic regression model to assess the potential nonlinear relationships between levels of thyroid function parameters on a continuous scale and MAFLD. To balance best fit and overfitting in the main splines for MAFLD, the number of knots, between three and five, was chosen as the lowest value for the Akaike information criterion, but if within two of each other for different knots, the lowest number of knots was determined. Then, analysis of variance was used to complete nonlinear tests. The concentration of thyroid function parameters associated with the highest risk of MAFLD was the concentration with the highest odds ratio (OR) on the spline curve. Analyses were adjusted for multiple variables. We considered the clinical significance, the baseline difference, and the results of previous studies to determine the adjusted variables (17–20), including age, sex, heart rates, leukocyte counts, red blood cells, platelet counts, hemoglobin, gamma-glutamyl transpeptidase, eGFR, uric acid, total cholesterol, smoking status and alcohol consumption, a history of DM and hypertension. Moreover, the multivariable stratification analysis was conducted according to gender, age, and location. The same number of knots from the main splines was also applied in splines for stratified analysis to allow a direct comparison of overall and stratified analyses. Furthermore, the associations between seven predefined TH categories and MAFLD were examined. Multivariable logistic regression models adjusting for covariates mentioned above were applied to estimate OR and $95\%$ confidence intervals (CI) for MAFLD. The reference category for these analyses was the lowest level of thyroid function parameters.
The nonparametric missing value imputation based on the missForest procedure in R was used to fill in the missing data. The results of the subsequent analysis of the dataset before and after imputation were not significantly different.
R software (version 4.1.0) was used to perform all statistical analyses and create all graphs; a two‐sided $P \leq 0.05$ was considered statistically significant.
## Sensitivity analysis
We further conducted two sensitivity analyses. In our main analysis, BMI was not adjusted because it belongs to one of the diagnostic criteria of MAFLD. While in sensitivity analysis 1, we further adjusted BMI besides covariates in the main analysis, given the possibility that it could be a confounder. In sensitivity analysis 2, populations used the chemiluminescence analysis (CLIA) method to detect thyroid function parameters, which is an entirely different detection method from ECLIA. There is a large difference between the reference ranges of the two methods, and the ECLIA has the characteristics of higher sensitivity, specificity, and selectivity than CLIA. Finally, there were 9560 participants aged ≥18 years enrolled in sensitivity analysis 2 to explore the relationship between thyroid function parameters and MAFLD. They came from 3 health check-up centers covering 3 administrative regions between 2010 and 2017 in China. They all underwent the detection of thyroid function parameters and had the diagnosis of MAFLD. Then based on the exclusion criteria mentioned above, 123 participants were excluded. Finally, 9437 adults were for sensitivity analysis 2. The flow chart of participant selection is shown in Figure 1B. Sensitivity analysis 2 was performed using a statistical method similar to the main analysis.
To validate the robustness of our results, we conducted two sensitivity analyses. The results of sensitivity analyses did not change substantially. In sensitivity analysis 1, in addition to covariates in the main analysis, BMI was regarded as a confounder. The shape with a first rising and then decline trend between FT3 and MAFLD and the shape with a rising trend in a certain range between TSH and MAFLD persisted (P nonlinearity <0.05) (Figures S5A, S5C). In Figure S5B, for FT4, the overall declining trend was observed, although the curve has changed a bit (BMI was regarded as an important confounder) (Figure S5B). In sensitivity analysis 2, the data from various health check-up centers and different detection methods were used to explore the association between thyroid function parameters on a continuous scale and MAFLD. The baseline characteristics of participants in sensitivity analysis 2 are summarized in Table S4. The association between the concentrations of variations in thyroid function and MAFLD was not significantly modified by health check-up centers and detection methods (Figure S6A–C).
## Clinical and laboratory characteristics
The main analysis consisted of 177,540 participants with a median age of 48 (IQR, 42, 54) and $60.19\%$ males. The baseline characteristics of participants are summarized in Table 1. In the overall population, $51.14\%$ of participants had MAFLD. Compared with the participants without MAFLD, those with MAFLD tended to be older, to be male; to have higher BMI, thicker WC, higher SBP and DBP, more leukocyte counts and red blood cells, higher hemoglobin, alanine aminotransferase, aspartate transaminase, gamma-glutamyl transpeptidase, serum creatinine, blood urea nitrogen, uric acid, FBG, total cholesterol, triglycerides, and low-density lipoprotein cholesterol; fewer platelet counts and lower HDL-C; more likely to be smokers and drinkers, and to be more likely have hypertension, diabetes, and dyslipidemia.
**Table 1**
| Characteristics | Total(N = 177540) | Non-MAFLD(N = 86753, 48.86%) | MAFLD(N = 90787, 51.14%) | P-value |
| --- | --- | --- | --- | --- |
| Clinical characteristics | Clinical characteristics | Clinical characteristics | Clinical characteristics | Clinical characteristics |
| Age (years, median [IQR]) | 48.00 [42.00, 54.00] | 46.00 [39.00, 53.00] | 49.00 [43.00, 54.00] | <0.05 |
| Gender, Male, n (%) | 106859 (60.19) | 37607 (43.35) | 69252 (76.28) | <0.05 |
| BMI (kg/m2, median [IQR]) | 24.76 [22.48, 27.04] | 22.47 [20.77, 24.28] | 26.48 [24.81, 28.43] | <0.05 |
| WC (cm, median [IQR]) | 87.00 [79.00, 94.00] | 79.00 [73.00, 86.00] | 93.00 [87.00, 98.00] | <0.05 |
| Self-reported smoking, n (%) | 26119 (14.71) | 8201 (9.45) | 17918 (19.74) | <0.05 |
| Self-reported drinking, n (%) | 39846 (22.44) | 13462 (15.52) | 26384 (29.06) | <0.05 |
| SBP (mmHg, median [IQR]) | 120.00 [109.00, 132.00] | 114.00 [104.00, 126.00] | 126.00 [115.00, 137.00] | <0.05 |
| DBP (mmHg, median [IQR]) | 78.00 [70.00, 87.00] | 74.00 [67.00, 81.00] | 82.00 [75.00, 90.00] | <0.05 |
| Laboratory Examination | Laboratory Examination | Laboratory Examination | Laboratory Examination | Laboratory Examination |
| Heart rates (/min, median [IQR]) | 70.00 [64.00, 76.00] | 70.00 [64.00, 75.00] | 70.00 [65.00, 76.00] | <0.05 |
| LEU (×109/L, median [IQR]) | 5.82 [4.94, 6.88] | 5.56 [4.71, 6.59] | 6.06 [5.20, 7.11] | <0.05 |
| RBC (×1012/L, median [IQR]) | 4.79 [4.46, 5.12] | 4.62 [4.32, 4.96] | 4.93 [4.63, 5.21] | <0.05 |
| PLT (×109/L, median [IQR]) | 220.00 [188.00, 257.00] | 222.00 [189.00, 258.00] | 219.00 [186.00, 255.00] | <0.05 |
| HGB (g/L, median [IQR]) | 147.00 [135.00, 158.00] | 140.00 [130.00, 152.00] | 152.00 [142.00, 161.00] | <0.05 |
| ALT (IU/L, median [IQR]) | 19.40 [13.80, 28.70] | 15.70 [11.70, 22.00] | 23.80 [17.10, 34.40] | <0.05 |
| AST (IU/L, median [IQR]) | 18.40 [15.50, 22.60] | 17.40 [14.90, 21.00] | 19.50 [16.30, 24.20] | <0.05 |
| GGT (IU/L, median [IQR]) | 26.00 [16.00, 46.00] | 17.90 [12.40, 29.00] | 35.10 [23.00, 59.00] | <0.05 |
| Scr (μmol/L, median [IQR]) | 68.00 [57.60, 78.00] | 64.00 [55.00, 75.10] | 71.00 [61.00, 80.00] | <0.05 |
| BUN (mmol/L, median [IQR]) | 4.90 [4.11, 5.72] | 4.70 [3.92, 5.53] | 5.06 [4.30, 5.90] | <0.05 |
| UA (μmol/L, median [IQR]) | 327.00 [264.40, 391.70] | 287.00 [238.00, 349.00] | 362.00 [305.70, 419.50] | <0.05 |
| FBG (mmol/L, median [IQR]) | 5.32 [4.97, 5.82] | 5.12 [4.83, 5.47] | 5.56 [5.17, 6.21] | <0.05 |
| TC (mmol/L, median [IQR]) | 4.72 [4.15, 5.35] | 4.62 [4.06, 5.22] | 4.83 [4.24, 5.46] | <0.05 |
| TG (mmol/L, median [IQR]) | 1.37 [0.94, 2.05] | 1.05 [0.77, 1.48] | 1.76 [1.26, 2.54] | <0.05 |
| LDL-C (mmol/L, median [IQR]) | 3.00 [2.47, 3.55] | 2.87 [2.37, 3.41] | 3.12 [2.58, 3.66] | <0.05 |
| HDL-C (mmol/L, median [IQR]) | 1.24 [1.03, 1.49] | 1.39 [1.17, 1.65] | 1.12 [0.95, 1.31] | <0.05 |
| FT3 (pmol/L, median [IQR]) | 4.90 [4.48, 5.34] | 4.77 [4.36, 5.22] | 5.01 [4.61, 5.44] | <0.05 |
| FT4 (pmol/L, median [IQR]) | 16.24 [14.80, 17.80] | 16.22 [14.77, 17.82] | 16.26 [14.84, 17.78] | <0.05 |
| TSH (uIU/mL, median [IQR]) | 2.07 [1.42, 3.02] | 2.10 [1.42, 3.09] | 2.04 [1.42, 2.96] | <0.05 |
| Comorbidities | Comorbidities | Comorbidities | Comorbidities | Comorbidities |
| Type 2 diabetes, n (%) | 23349 (13.19) | 4151 (4.81) | 19198 (21.16) | <0.05 |
| Hypertension, n (%) | 44234 (26.27) | 11347 (14.42) | 32887 (36.66) | <0.05 |
| MetS, n (%) | 82226 (48.46) | 17423 (21.84) | 64803 (72.07) | <0.05 |
## Association between FT3 levels on a continuous scale and MAFLD
Table S1 shows the baseline characteristics of participants according to prior-defined centile categories of FT3. To assess the nonlinear relationship, the association between FT3 and MAFLD was estimated on a continuous scale using RCS. In the RCS models, the risk of MAFLD increased with the levels of FT3 when FT3 concentration was less than 5.58pmol/L, while the risk of MAFLD decreased with the levels of FT3 when FT3 concentration was more than or equal to 5.58pmol/L (P nonlinearity < 0.05) (Figure 2A). The lowest and highest levels of FT3 were associated with a decreased risk of MAFLD.
**Figure 2:** *Restricted cubic spline analyses with five knots for nonlinear association between FT3 levels and MAFLD on a continuous scale. (A) all population. (B) by sex. (C) by age groups. (D) by location groups. ORs are indicated by solid lines and 95% CIs by shaded areas. Reference point is lowest value for FT3. Analyses were adjusted for age, sex, heart rates, leukocyte counts, red blood cells, platelets, hemoglobin, gamma-glutamyl transpeptidase, estimated glomerular filtration rate, uric acid, total cholesterol, smoking status, alcohol consumption, and a history of diabetes and hypertension. FT3, free triiodothyronine; MAFLD, metabolic dysfunction-associated fatty liver disease; OR, odd ratio; CI, confidence interval.*
To estimate precise OR values, the multivariable logistic regression model was used. When FT3 concentration was less than 5.58pmol/L, the OR per unit higher FT3 was 1.17 ($95\%$ CI, 1.14, 1.20) (Figure 2A), which indicated each unit increase in FT3 concentration was associated with a 0.17-fold increased risk of MAFLD. While when FT3 concentration was more than or equal to 5.58pmol/L, the OR per unit higher FT3 was less than 1 with being 0.90 ($95\%$ CI, 0.88, 0.91) (Figure 2A), suggesting the risk of MAFLD decreased by 0.1-fold for every unit increase of FT3 concentration. Moreover, according to the prior-defined centile categories, we calculated the OR of each centile of FT3 with the 1st-5th centile as a reference group. The highest multivariable-adjusted OR for MAFLD was 1.39 ($95\%$ CI, 1.31, 1.48) for individuals with FT3 concentrations of 5.45-6.05pmol/L (81st-95th centile) among all centiles (Figure 3). The population with the FT3 concentrations of 5.45-6.05pmol/L (81st-95th centile) had the highest risk of MAFLD.
**Figure 3:** *Multivariable-adjusted logistic regression analyses for MAFLD according to FT3 levels by the prior-defined centile categories. Analyses were adjusted for age, sex, heart rates, leukocyte counts, red blood cells, platelets, hemoglobin, gamma-glutamyl transpeptidase, estimated glomerular filtration rate, uric acid, total cholesterol, smoking status, alcohol consumption, and a history of diabetes and hypertension. FT3, free triiodothyronine; MAFLD, metabolic dysfunction-associated fatty liver disease; OR, odd ratio; CI, confidence interval.*
We performed stratified analyses to assess possible effect modification according to sex, age, and location. The nonlinear relationship between FT3 and MAFLD was observed among various sex, age, and location groups (Figures 2B–D). Furthermore, the association was most pronounced in males, individuals over 65 years old, and in populations in Northern China among different subgroups (Figures 2B–D).
## Association between FT4 levels on a continuous scale and MAFLD
We also explored the association between FT4 and MAFLD. Baseline characteristics of participants by prior-defined centile categories of FT4 are summarized in Table S2. RCS analysis suggested the FT4 levels on a continuous scale had a negative association with MAFLD (P nonlinearity <0.05) (Figure S1A), indicating an increase in FT4 levels was associated with a decreased risk of MAFLD.
After adjustment for identified covariates, the multivariable logistic regression model presented that OR per unit higher FT4 was 0.93 ($95\%$ CI, 0.93, 0.93) (Figure S1A), showing every unit increase of FT4 was associated with a 0.07-fold lower risk of MAFLD. In parallel, we found that individuals with concentrations of FT4 more than or equal to 20.43pmol/L (96th-100th centiles) had the lowest MAFLD risk among all centiles, with the 1st -5th centile being a reference group (OR, 0.45 [$95\%$ CI, 0.42, 0.48]) (Figure S2).
Results did not change substantially by further dividing people by sex, age, and location, and the declining relationship between FT4 levels and MAFLD persisted. Additionally, the most pronounced association was observed in males, individuals over 65 years old, and in populations in Northern China among different subgroups (Figure S1B–D).
## Association between TSH levels on a continuous scale and MAFLD
We further explored the relationship between TSH and MAFLD. Table S3 shows the baseline characteristics of participants according to prior-defined centile categories of TSH. RCS analysis suggested an overall positive association between the concentration of TSH and MAFLD risk (P nonlinearity <0.05) (Figure S3A). The rising slope was sharper when the TSH concentration was less than 1.79uIU/mL, which indicated the association between TSH and MAFLD risk was tightly interrelated within this range.
For individuals whose TSH level was less than 1.79uIU/mL, the OR per unit higher TSH was 1.28 ($95\%$ CI, 1.22, 1.33) (Figure S3A), which indicated each unit increase in TSH concentration was associated with a 0.28-fold increased risk of MAFLD. For individuals whose TSH level was more than or equal to 1.79uIU/mL, the $95\%$ confidence interval included the OR of 1.00 (OR, 1.00 [$95\%$ CI, 1.00, 1.00], P value =0.42) (Figure S3A), suggesting the risk of MAFLD slightly increased with each unit increase in TSH concentration, but this trend was not statistically significant. At the same time, with the 1st-5th centile as the reference category, the highest multivariable-adjusted OR for MAFLD was 1.40 ($95\%$ CI, 1.33, 1.48) for individuals with TSH concentrations of 3.33-5.60uIU/mL (81st-95th centile) (Figure S4). The individuals with the TSH concentrations of 3.33-5.60uIU/mL (81st-95th centile) had the highest risk of MAFLD.
The association between TSH concentrations and MAFLD was not significantly modified with respect to sex, age, and location. Moreover, TSH also showed a stronger association with rising shape within a certain range with MAFLD in males, individuals over 65 years old, and individuals in Northern China than in outer subgroups (Figure S3B–D).
## Discussion
This large population-based study among general individuals demonstrated nonlinear relationships between thyroid function parameters and MAFLD. The populations with FT3 in the 81st-95th centile level, FT4 in the 1st-5th centile level, and TSH in the 81st-95th centile level were most likely to develop MAFLD among all centiles. Different stratifications according to gender, age, and location influenced the strength of the nonlinear relationships between thyroid function parameters and MAFLD. Taken together, thyroid function parameters could be additional modifiable risk factors apart from the proven risk factors to steer new avenues regarding MAFLD prevention and treatment.
Because there was a substantial overlap between the MAFLD and NAFLD, our observations between thyroid function parameters and MAFLD could be comparable with the findings between thyroid function parameters and NAFLD. In our results, the population could be divided into two groups based on the relationship between FT3 and MAFLD risk: group one with FT3 less than 5.58pmol/L (within the reference range), where MAFLD risk increased with higher FT3; group two with FT3 more than or equal to 5.58pmol/L, where MAFLD risk decreased with higher FT3. Other studies on thyroid function and NAFLD have concentrated on three subtypes: individuals with hypothyroidism [12, 21], euthyroid subjects (8, 9, 11, 22–25), and individuals with hyperthyroidism [26]. The diagnosis of hypothyroidism does not involve FT3 [27]. Notably, most studies involving euthyroid subjects have concluded that higher FT3 is associated with an increased risk of NAFLD [8, 11, 22, 23]. However, these studies divided the FT3 levels into multiple quartiles and did not investigate its association with MAFLD on a continuous range. However, others did not find a positive association [9, 24, 25]. Comparison with results from various studies is different because of varying population selection and experimental design. In addition, our findings implied that population distribution before and after the change point significantly impacts the study results. In the study of hyperthyroid patients, elevated FT3 is associated with a reduced risk of MAFLD. Therefore, cohort studies are needed to prove the relationship between FT3 and MAFLD on a continuous scale.
FT3 is commonly acknowledged to be more biologically active as a modulator of metabolic processes than FT4 [28]. As a result, from a pathophysiological perspective, the relationship of MAFLD with FT3 rather than with FT4 should be regarded as the most relevant. Our findings could be explained by the significant contribution of TH to hepatic lipid metabolism. Cell studies have shown that TH stimulates lipolysis to generate circulating free fatty acids (FFAs), which are the major source of lipids for the liver. Then, TH promotes FFAs uptake to regulate lipid metabolism [29]. In addition to promoting the uptake of exogenous FFAs, TH can also promote de novel lipogenesis in the liver stimulated by excess glucose directly and indirectly [30]. Moreover, FT3 promotes triacylglycerol stored as lipid droplets in the liver to be hydrolyzed back to FFAs via classic lipases and lipophagy. Afterward, FFAs are broken down, undergoing mitochondrial β-oxidation to produce energy, which also is promoted by FT3 [31]. Taken together, TH maintains the balance between lipid metabolism by stimulation of lipid synthesis and lipid oxidation by direct and indirect actions [5, 32]. Combining this mechanism with the results of our study, it appears that when FT3 is great than 5.58pmol/L (81st-95th centile), more enhanced lipid oxidation than lipid synthesis exists.
Retrospective studies could not explore a causal relationship between elevated FT3 and increased risk of MAFLD. The current studies suggest that hypothyroidism increases the risk of NAFLD [12, 33]. We suggest that the positive relationship between FT3 concentrations and MAFLD risk in group one (FT3 less than 5.58pmol/L) is due to obesity. The relationship between obesity and thyroid hormone levels that obese individuals have higher circulating FT3 has been elaborated in numerous investigations (34–37). In addition, Mendelian randomization research documented that higher BMI/fat mass is a determinant of increasing FT3 levels [36]. The mechanisms responsible for this action are not yet precisely known, and several assumptions exist. Firstly, this action may involve tissue-specific alterations in iodothyronine deiodinase (DIO) expression in relation to obesity [38]. Previous studies have suggested DIO 1 and/or 2 activities in subjects with a relatively higher fat mass and/or a less favorable metabolic profile would change, leading to a higher conversion of FT4 to FT3 (39–41). Secondly, obesity may alter the hypothalamic-pituitary-thyroid axis [42, 43]. Thirdly, observed changes in FT3 may relate partly to excess carbohydrates in the diet of obese individuals [44]. Furthermore, the importance of obesity in the pathogenesis of MAFLD is well established [45]. The cross-sectional design of the present study hampers to establish the causation between FT3 and MAFLD, and the possible interrelationship of obesity with FT3 and the development of MAFLD needs to be prospectively delineated in the future.
Given the key role of TH on hepatic lipid accumulation, much effort has been recently paid to developing a liver-targeted agonist of THRβ, which has been shown to diminish hepatic lipid accumulation in animal studies (46–48). In addition, animal studies have shown that triiodothyronine administration caused a rapid regression of fully established steatosis [49]. However, due to the side effects (especially in the heart, muscle, and bone), none of these drugs has been introduced into clinical practice, which underscores the complexity of TH physiology.
For TSH, inconsistent associations of serum TSH with NAFLD were reported with positive or null results among various populations and research (8, 9, 11, 24, 50–52). We must take the negative feedback loop between TSH and TH into consideration, namely, the production of TSH and TH is regulated by the hypothalamic–pituitary–thyroid axis. Specifically, TSH stimulates the thyroid to synthesize and release TH and in turn, TH acts on the pituitary and hypothalamus to inhibit TSH production. In addition to the interaction with thyroxine, the TSH upregulates the expression of hepatic 3-hydroxy-3-methyl-glutaryl coenzyme A reductase to promote the synthesis of cholesterol to direct effect on lipids and BMI (53–56). In addition, TSH can robustly stimulate the secretion of leptin to affect BMI [57, 58]. In a word, TSH is a complex regulation process, which could explain the variations of the results of TSH.
Several limitations should be noted in our study. Firstly, there are inherent limitations in inferring the causal relationship between TH and MAFLD in our retrospective study. We cannot infer direct causes and effects between TH and MAFLD. Secondly, since MAFLD is mainly diagnosed by ultrasonography, we could not determine the severity of MAFLD-associated hepatitis and might not detect mild steatosis. However, up to now, ultrasonography is still a safe and confirmed reliable noninvasive method, compared with the pathological diagnosis by liver biopsy with higher diagnostic accuracy limited by its invasive, impractical and costly. Thirdly, limited information on smoking and drinking consumption status, medications history, and past medical history may result in bias due to an insufficient adjustment of these confounders in the models.
## Conclusions
Our study suggested nonlinear relationships between thyroid function parameters and MAFLD. The populations with FT3 in the 81st-95th centile level, FT4 in the 1st-5th centile level, and TSH in the 81st-95th centile level were most likely to develop MAFLD among all centiles. Further mechanism research and large prospective studies with long-term follow-up are warranted to provide more definitive evidence to clarify the causal relationship between thyroid function parameters and MAFLD.
## Data availability statement
The datasets presented in this article are not readily available because privacy or ethical restrictions. The data that support the findings of this study are available on request from the corresponding author. Requests to access the datasets should be directed to Hongliang Li, [email protected].
## Ethics statement
The studies involving human participants were reviewed and approved by the ethical review committee of Renmin Hospital of Wuhan University and followed by acceptance by the ethics center in each collaborating hospital. The ethics committees granted a waiver of the requirement for documentation of informed consent for just analyzing existing data after anonymization without individual identification. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
YH and FZ designed the study, collected and analyzed data, and wrote the manuscript. FL, LL and XH collected and reviewed data and contributed to data analysis. TS, WL, XZ, and JC revised the manuscript and provided valuable suggestions for study design and data analysis. Z-GS and HL contributed equally, designed the project, edited the manuscript, and supervised the study. All authors have approved the final version of this paper.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1115354/full#supplementary-material
## References
1. Eslam M, Newsome PN, Sarin SK, Anstee QM, Targher G, Romero-Gomez M. **A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement**. *J Hepatol* (2020) **73**. DOI: 10.1016/j.jhep.2020.03.039
2. Nguyen VH, Le MH, Cheung RC, Nguyen MH. **Differential clinical characteristics and mortality outcomes in persons with nafld and/or mafld**. *Clin Gastroenterol Hepatol* (2021) **19** 2172-81.e6. DOI: 10.1016/j.cgh.2021.05.029
3. Yamamura S, Eslam M, Kawaguchi T, Tsutsumi T, Nakano D, Yoshinaga S. **Mafld identifies patients with significant hepatic fibrosis better than nafld**. *Liver Int* (2020) **40**. DOI: 10.1111/liv.14675
4. Arab JP, Arrese M, Trauner M. **Recent insights into the pathogenesis of nonalcoholic fatty liver disease**. *Annu Rev Pathol* (2018) **13**. DOI: 10.1146/annurev-pathol-020117-043617
5. Sinha RA, Singh BK, Yen PM. **Direct effects of thyroid hormones on hepatic lipid metabolism**. *Nat Rev Endocrinol* (2018) **14**. DOI: 10.1038/nrendo.2018.10
6. Yau WW, Singh BK, Lesmana R, Zhou J, Sinha RA, Wong KA. **Thyroid hormone (T(3)) stimulates brown adipose tissue activation**. *Autophagy* (2019) **15**. DOI: 10.1080/15548627.2018.1511263
7. Sinha RA, Singh BK, Yen PM. **Thyroid hormone regulation of hepatic lipid and carbohydrate metabolism**. *Trends Endocrinol Metab* (2014) **25**. DOI: 10.1016/j.tem.2014.07.001
8. van den Berg EH, van Tienhoven-Wind LJ, Amini M, Schreuder TC, Faber KN, Blokzijl H. **Higher free triiodothyronine is associated with non-alcoholic fatty liver disease in euthyroid subjects: The lifelines cohort study**. *Metabolism* (2017) **67** 62-71. DOI: 10.1016/j.metabol.2016.11.002
9. Xu C, Xu L, Yu C, Miao M, Li Y. **Association between thyroid function and nonalcoholic fatty liver disease in euthyroid elderly Chinese**. *Clin Endocrinol (Oxf)* (2011) **75**. DOI: 10.1111/j.1365-2265.2011.04016.x
10. Bano A, Chaker L, Plompen EP, Hofman A, Dehghan A, Franco OH. **Thyroid function and the risk of nonalcoholic fatty liver disease: The Rotterdam study**. *J Clin Endocrinol Metab* (2016) **101**. DOI: 10.1210/jc.2016-1300
11. Liu G, Zheng X, Guan L, Jiang Z, Lin H, Jiang Q. **Free triiodothyronine levels are positively associated with non-alcoholic fatty liver disease in euthyroid middle-aged subjects**. *Endocr Res* (2015) **40**. DOI: 10.3109/07435800.2014.987399
12. Chung GE, Kim D, Kim W, Yim JY, Park MJ, Kim YJ. **Non-alcoholic fatty liver disease across the spectrum of hypothyroidism**. *J Hepatol* (2012) **57**. DOI: 10.1016/j.jhep.2012.02.027
13. Zhang L, Wang F, Wang L, Wang W, Liu B, Liu J. **Prevalence of chronic kidney disease in China: A cross-sectional survey**. *Lancet* (2012) **379**. DOI: 10.1016/s0140-6736(12)60033-6
14. **Chinese Elderly Type 2 Diabetes Prevention and Treatment of Clinical Guidelines Writing Group; Geriatric Endocrinology and Metabolism Branch of Chinese Geriatric Society; Geriatric Endocrinology and Metabolism Branch of Chinese Geriatric Health Care Society; Geriatric Professional Committee of Beijing Medical Award Foundation; National Clinical Medical Research Center for Geriatric Diseases (PLA General Hospital). Clinical guidelines for prevention and treatment of type 2 diabetes mellitus in the elderly in China (2022 Edition)**. *Zhonghua Nei Ke Za Zhi* (2022) **61** 12-50. DOI: 10.3760/cma.j.cn112138-20211027-00751
15. **National guideline for hypertension management in China (2019)**. *Zhonghua Xin Xue Guan Bing Za Zhi* (2020) **48** 10-46. DOI: 10.3760/cma.j.issn.0253-3758.2020.01.004
16. Song QB, Zhao Y, Liu YQ, Zhang J, Xin SJ, Dong GH. **Sex difference in the prevalence of metabolic syndrome and cardiovascular-related risk factors in urban adults from 33 communities of China: The chpsne study**. *Diabetes Vasc Dis Res* (2015) **12**. DOI: 10.1177/1479164114562410
17. Davis TME. **Diabetes and metabolic dysfunction-associated fatty liver disease**. *Metabolism* (2021) **123**. DOI: 10.1016/j.metabol.2021.154868
18. Wang TY, Wang RF, Bu ZY, Targher G, Byrne CD, Sun DQ. **Association of metabolic dysfunction-associated fatty liver disease with kidney disease**. *Nat Rev Nephrol* (2022) **18**. DOI: 10.1038/s41581-021-00519-y
19. Lim S, Kim JW, Targher G. **Links between metabolic syndrome and metabolic dysfunction-associated fatty liver disease**. *Trends Endocrinol Metab* (2021) **32**. DOI: 10.1016/j.tem.2021.04.008
20. Yang C, He Q, Chen Z, Qin JJ, Lei F, Liu YM. **A bidirectional relationship between hyperuricemia and metabolic dysfunction-associated fatty liver disease**. *Front Endocrinol (Lausanne)* (2022) **13**. DOI: 10.3389/fendo.2022.821689
21. Lee KW, Bang KB, Rhee EJ, Kwon HJ, Lee MY, Cho YK. **Impact of hypothyroidism on the development of non-alcoholic fatty liver disease: A 4-year retrospective cohort study**. *Clin Mol Hepatol* (2015) **21**. DOI: 10.3350/cmh.2015.21.4.372
22. Chen P, Hou X, Wei L, Feng L, Zhong L, Jiao L. **Free triiodothyronine is associated with the occurrence and remission of nonalcoholic fatty liver disease in euthyroid women**. *Eur J Clin Invest* (2019) **49**. DOI: 10.1111/eci.13070
23. Liu Y, Wang W, Yu X, Qi X. **Thyroid function and risk of non-alcoholic fatty liver disease in euthyroid subjects**. *Ann Hepatol* (2018) **17**. DOI: 10.5604/01.3001.0012.3136
24. Tao Y, Gu H, Wu J, Sui J. **Thyroid function is associated with non-alcoholic fatty liver disease in euthyroid subjects**. *Endocr Res* (2015) **40**. DOI: 10.3109/07435800.2014.952014
25. Zhang X, Zhang J, Dai Y, Qin J. **Serum thyroid hormones levels are significantly associated with nonalcoholic fatty liver disease in euthyroid Chinese population**. *Clin Lab* (2020) **66**. DOI: 10.7754/Clin.Lab.2020.200219
26. Wang B, Wang B, Yang Y, Xu J, Hong M, Xia M. **Thyroid function and non-alcoholic fatty liver disease in hyperthyroidism patients**. *BMC Endocr Disord* (2021) **21** 27. DOI: 10.1186/s12902-021-00694-w
27. Chaker L, Bianco AC, Jonklaas J, Peeters RP. **Hypothyroidism**. *Lancet* (2017) **390**. DOI: 10.1016/s0140-6736(17)30703-1
28. Cheng SY, Leonard JL, Davis PJ. **Molecular aspects of thyroid hormone actions**. *Endocr Rev* (2010) **31**. DOI: 10.1210/er.2009-0007
29. Grasselli E, Voci A, Canesi L, De Matteis R, Goglia F, Cioffi F. **Direct effects of iodothyronines on excess fat storage in rat hepatocytes**. *J Hepatol* (2011) **54**. DOI: 10.1016/j.jhep.2010.09.027
30. Mavromati M, Jornayvaz FR. **Hypothyroidism-associated dyslipidemia: Potential molecular mechanisms leading to nafld**. *Int J Mol Sci* (2021) **22**. DOI: 10.3390/ijms222312797
31. Marschner RA, Arenhardt F, Ribeiro RT, Wajner SM. **Influence of altered thyroid hormone mechanisms in the progression of metabolic dysfunction associated with fatty liver disease (Mafld): A systematic review**. *Metabolites* (2022) **12**. DOI: 10.3390/metabo12080675
32. Ferrandino G, Kaspari RR, Spadaro O, Reyna-Neyra A, Perry RJ, Cardone R. **Pathogenesis of hypothyroidism-induced nafld is driven by intra- and extrahepatic mechanisms**. *Proc Natl Acad Sci U.S.A.* (2017) **114**. DOI: 10.1073/pnas.1707797114
33. Tanase DM, Gosav EM, Neculae E, Costea CF, Ciocoiu M, Hurjui LL. **Hypothyroidism-induced nonalcoholic fatty liver disease (Hin): Mechanisms and emerging therapeutic options**. *Int J Mol Sci* (2020) **21**. DOI: 10.3390/ijms21165927
34. De Pergola G, Ciampolillo A, Paolotti S, Trerotoli P, Giorgino R. **Free triiodothyronine and thyroid stimulating hormone are directly associated with waist circumference, independently of insulin resistance, metabolic parameters and blood pressure in overweight and obese women**. *Clin Endocrinol (Oxf)* (2007) **67**. DOI: 10.1111/j.1365-2265.2007.02874.x
35. Reinehr T, Isa A, de Sousa G, Dieffenbach R, Andler W. **Thyroid hormones and their relation to weight status**. *Horm Res* (2008) **70**. DOI: 10.1159/000129678
36. Taylor PN, Richmond R, Davies N, Sayers A, Stevenson K, Woltersdorf W. **Paradoxical relationship between body mass index and thyroid hormone levels: A study using mendelian randomization**. *J Clin Endocrinol Metab* (2016) **101**. DOI: 10.1210/jc.2015-3505
37. Roef GL, Rietzschel ER, Van Daele CM, Taes YE, De Buyzere ML, Gillebert TC. **Triiodothyronine and free thyroxine levels are differentially associated with metabolic profile and adiposity-related cardiovascular risk markers in euthyroid middle-aged subjects**. *Thyroid* (2014) **24**. DOI: 10.1089/thy.2013.0314
38. Ortega FJ, Jílková ZM, Moreno-Navarrete JM, Pavelka S, Rodriguez-Hermosa JI, Kopeck Ygrave J. **Type I iodothyronine 5'-deiodinase mrna and activity is increased in adipose tissue of obese subjects**. *Int J Obes (Lond)* (2012) **36**. DOI: 10.1038/ijo.2011.101
39. Xia SF, Duan XM, Hao LY, Li LT, Cheng XR, Xie ZX. **Role of thyroid hormone homeostasis in obesity-prone and obesity-resistant mice fed a high-fat diet**. *Metabolism* (2015) **64**. DOI: 10.1016/j.metabol.2014.12.010
40. Araujo RL, Andrade BM, Padrón AS, Gaidhu MP, Perry RL, Carvalho DP. **High-fat diet increases thyrotropin and oxygen consumption without altering circulating 3,5,3'-triiodothyronine (T3) and thyroxine in rats: The role of iodothyronine deiodinases, reverse T3 production, and whole-body fat oxidation**. *Endocrinology* (2010) **151**. DOI: 10.1210/en.2010-0026
41. Araujo RL, Carvalho DP. **Bioenergetic impact of tissue-specific regulation of iodothyronine deiodinases during nutritional imbalance**. *J Bioenerg Biomembr* (2011) **43** 59-65. DOI: 10.1007/s10863-011-9327-x
42. Walczak K, Sieminska L. **Obesity and thyroid axis**. *Int J Environ Res Public Health* (2021) **18**. DOI: 10.3390/ijerph18189434
43. Santini F, Marzullo P, Rotondi M, Ceccarini G, Pagano L, Ippolito S. **Mechanisms in endocrinology: The crosstalk between thyroid gland and adipose tissue: Signal integration in health and disease**. *Eur J Endocrinol* (2014) **171**. DOI: 10.1530/eje-14-0067
44. Staníková D, Krajčovičová L, Demková L, Forišek-Paulová P, Slobodová L, Vitariušová E. **Food preferences and thyroid hormones in children and adolescents with obesity**. *Front Psychiatry* (2022) **13**. DOI: 10.3389/fpsyt.2022.962949
45. Gutiérrez-Cuevas J, Santos A, Armendariz-Borunda J. **Pathophysiological molecular mechanisms of obesity: A link between mafld and Nash with cardiovascular diseases**. *Int J Mol Sci* (2021) **22**. DOI: 10.3390/ijms222111629
46. Harrison SA, Bashir MR, Guy CD, Zhou R, Moylan CA, Frias JP. **Resmetirom (Mgl-3196) for the treatment of non-alcoholic steatohepatitis: A multicentre, randomised, double-blind, placebo-controlled, phase 2 trial**. *Lancet* (2019) **394**. DOI: 10.1016/s0140-6736(19)32517-6
47. Harrison SA, Bashir M, Moussa SE, McCarty K, Pablo Frias J, Taub R. **Effects of resmetirom on noninvasive endpoints in a 36-week phase 2 active treatment extension study in patients with Nash**. *Hepatol Commun* (2021) **5**. DOI: 10.1002/hep4.1657
48. Younossi ZM, Stepanova M, Taub RA, Barbone JM, Harrison SA. **Hepatic fat reduction due to resmetirom in patients with nonalcoholic steatohepatitis is associated with improvement of quality of life**. *Clin Gastroenterol Hepatol* (2022) **20** 1354-61.e7. DOI: 10.1016/j.cgh.2021.07.039
49. Perra A, Simbula G, Simbula M, Pibiri M, Kowalik MA, Sulas P. **Thyroid hormone (T3) and trbeta agonist gc-1 Inhibit/Reverse nonalcoholic fatty liver in rats**. *FASEB J* (2008) **22**. DOI: 10.1096/fj.08-108464
50. Ding WJ, Wang MM, Wang GS, Shen F, Qin JJ, Fan JG. **Thyroid function is associated with non-alcoholic fatty liver disease in chronic hepatitis b-infected subjects**. *J Gastroenterol Hepatol* (2015) **30**. DOI: 10.1111/jgh.12998
51. Zhang J, Sun H, Chen L, Zheng J, Hu X, Wang S. **Relationship between serum tsh level with obesity and nafld in euthyroid subjects**. *J Huazhong Univ Sci Technolog Med Sci* (2012) **32** 47-52. DOI: 10.1007/s11596-012-0008-8
52. Carulli L, Ballestri S, Lonardo A, Lami F, Violi E, Losi L. **Is nonalcoholic steatohepatitis associated with a high-Though-Normal thyroid stimulating hormone level and lower cholesterol levels**. *Intern Emerg Med* (2013) **8** 297-305. DOI: 10.1007/s11739-011-0609-4
53. Tian L, Song Y, Xing M, Zhang W, Ning G, Li X. **A novel role for thyroid-stimulating hormone: Up-regulation of hepatic 3-Hydroxy-3-Methyl-Glutaryl-Coenzyme a reductase expression through the cyclic adenosine Monophosphate/Protein kinase a/Cyclic adenosine monophosphate-responsive element binding protein pathway**. *Hepatology* (2010) **52**. DOI: 10.1002/hep.23800
54. Sorisky A, Bell A, Gagnon A. **Tsh receptor in adipose cells**. *Horm Metab Res* (2000) **32**. DOI: 10.1055/s-2007-978672
55. Valyasevi RW, Harteneck DA, Dutton CM, Bahn RS. **Stimulation of adipogenesis, peroxisome proliferator-activated receptor-gamma (Ppargamma), and thyrotropin receptor by ppargamma agonist in human orbital preadipocyte fibroblasts**. *J Clin Endocrinol Metab* (2002) **87**. DOI: 10.1210/jcem.87.5.8472
56. Lu S, Guan Q, Liu Y, Wang H, Xu W, Li X. **Role of extrathyroidal tshr expression in adipocyte differentiation and its association with obesity**. *Lipids Health Dis* (2012) **11**. DOI: 10.1186/1476-511x-11-17
57. Menendez C, Baldelli R, Camiña JP, Escudero B, Peino R, Dieguez C. **Tsh stimulates leptin secretion by a direct effect on adipocytes**. *J Endocrinol* (2003) **176** 7-12. DOI: 10.1677/joe.0.1760007
58. Santini F, Galli G, Maffei M, Fierabracci P, Pelosini C, Marsili A. **Acute exogenous tsh administration stimulates leptin secretion in vivo**. *Eur J Endocrinol* (2010) **163**. DOI: 10.1530/eje-10-0138
|
---
title: 'Global trends in research of high-throughput sequencing technology associated
with chronic wounds from 2002 to 2022: A bibliometric and visualized study'
authors:
- Hao Meng
- Yu Peng
- Pinxue Li
- Jianlong Su
- Yufeng Jiang
- Xiaobing Fu
journal: Frontiers in Surgery
year: 2023
pmcid: PMC9992981
doi: 10.3389/fsurg.2023.1089203
license: CC BY 4.0
---
# Global trends in research of high-throughput sequencing technology associated with chronic wounds from 2002 to 2022: A bibliometric and visualized study
## Abstract
### Background
Chronic wounds are a complex medical problem. With the difficulty of skin healing, the microbial ecology of chronic wounds is an essential factor affecting wound healing. High-throughput sequencing (HTS) technology is a vital method to reveal the microbiome diversity and population structure of chronic wounds.
### Objective
The aim of this paper was to delineate the scientific output characteristics, research trends, hotspots and frontiers of HTS technologies related to chronic wounds globally over the past 20 years.
### Methods
We searched the Web of Science Core Collection (WoSCC) database for articles published between 2002 and 2022 and their full record information. The Bibliometrix software package was used to analyze bibliometric indicators and VOSviewer visualization analysis results.
### Results
Ultimately, a total of 449 original articles were reviewed, and the results showed that the number of annual publications (Nps) about HTS associated with chronic wounds has steadily increased over the last 20 years. The United States and China produce the most articles and have the highest H-index, while the United States and England have the largest number of citations (Nc) in this field. The University of California, Wound Repair and Regeneration and National Institutes of Health Nih United States were the most published institutions, journals and fund resources, respectively. The global research could be divided into 3 clusters as follows: microbial infection of chronic wounds, the healing process of wounds and microscopic processes, skin repair mechanism stimulated by antimicrobial peptides and oxidative stress. In recent years, “wound healing”, “infections”, “expression”, “inflammation”, “chronic wounds”, “identification” and “bacteria” “angiogenesis”, “biofilms” and “diabetes” were the most frequently used keywords. In addition, research on “prevalence”, “gene expression”, “inflammation” and “infection” has recently become a hotspot.
### Conclusions
This paper compares the research hotspots and directions in this field globally from the perspectives of countries, institutions and authors, analyzes the trend of international cooperation, and reveals the future development direction of the field and research hotspots of great scientific research value. Through this paper, we can further explore the value of HTS technology in chronic wounds to better solve the problem of chronic wounds.
## Introduction
Chronic wounds on the body surface refer to wounds that have not healed and have no tendency to heal after three months of treatment [1, 2]. The International Association for Wound Healing defines chronic wounds as those that cannot achieve anatomical and functional integrity through a normal, orderly and timely repair process. Chronic wounds will seriously affect the quality of life of patients and increase the high medical costs due to the need for repeated treatment [3, 4]. The repair of chronic refractory wounds is an ancient and complex medical problem. With the improvement of global medical level, technology and cognition in recent decades, many achievements have been made in the repair of chronic wounds, and key points and potential methods for treating chronic wounds in the future have also been found [5, 6].
A complex wound microbial environment is an important reason for chronic wound persistence [7, 8]. Infection is also an important pathogenic factor for sepsis and multiple organ failure in patients with chronic wounds and a major threat to the life of patients with chronic wounds (9–12). It is currently believed that the bacterial-host interaction may also lead to wound healing damage [8, 13]. Clarifying the microbial environment of the wound is conducive to wound treatment [14]. Although the traditional culture method is mature and reliable, only approximately $1\%$ of the bacteria in the biological world can be cultured, which cannot fully reflect the real situation of the wound microenvironment [15]. In recent years, high-throughput sequencing (HTS) technology has developed rapidly and become a new hotspot in wound microbial detection (16–18). HTS technology can analyze the genome of all microorganisms in the sample and can find many bacteria that cannot be cultured and are not affected by amplification preference [19]. The identification results can be accurate to species, even strain level, effectively making up for the shortcomings of the culture method [20]. It is of great significance to study the application status, research hotspots and future prospects of HTS technology in the field of chronic wounds.
## Data source and search strategy
The database we selected is the Web of Science Core Collection. The data were downloaded from the Web of Science Core Collection on the same day as October 5, 2022. The search formula was TS = ((chronic wounds) OR (diabetes wounds) OR (chronic diabetic foot)) AND TS = ((16S rRNA) OR (16S rDNA) OR (metagenome) OR (metagenomics) OR (high-throughput sequencing) OR (sequencing)), and the search time range was from October 4, 2002 to October 4, 2022. A total of 491 articles were retrieved, excluding proceeding papers [13], early access [11] and meeting abstracts [4]. In addition, 14 non-English articles were excluded, so ultimately, 449 articles were included in our study (Figure 1).
**Figure 1:** *Flowchart of the screening process.*
## Data collection and cleaning
A total of 449 studies were exported in the form of all records and references, saved as the format of BibTex and Table delimited file. Coauthors (PY and MH) independently browsed and extracted all the data from these articles, manually deleted duplicate and wrong data, corrected spelling errors, and resolved all the divergences by discussing with experts to reach a final consensus. Finally, the corrected data were imported into the bibliometric package and VOSviewer for bibliometric analysis.
## Bibliometric analysis
The techniques of bibliometrics analysis are presented in two categories: performance analysis and science mapping. In essence, performance analysis accounts for the contributions of the components under study, while scientific mapping focuses on the relationships between the components under study. Bibliometric performance analysis indicators usually include the number of publications (Np) and the number of citations without self-citation (Nc) [21, 22]. *In* general, *Np is* used to measure the scientific research productivity of a country, institution or author, and *Nc is* used to measure the scientific research influence of a country, institution or author. Np and Nc are two main perspectives for evaluating the research level. The H-index is an evaluation index that links and balances Np and Nc [23, 24]. It is increasingly used to evaluate researchers' academic contributions and predict future scientific achievements [25]. H-index means that if a researcher has published H papers and each paper has been cited at least H times, then the researcher will have an H-index [26]. In addition, the impact of a journal can be evaluated by the impact factor (IF) in the latest edition of the Journal Citation Report (JCR), which is the average number of citations of articles published by the journal in a year and can reflect the academic quality and impact of the journal [27].
## Visualized analysis
For the visual analysis of the analyzed data, we used the Bibliometric package based on R language and the VOS viewer based on Java for analysis [11]. The types of bibliometric functions include the main processes of scientific knowledge graph rendering, such as data import, format transformation, data cleaning and sorting, descriptive statistics, co-occurrence matrix establishment, data standardization, and graph rendering [22]. The VOS viewer tool is useful at generating any type of text map. It can perform cooperative network analysis, co-occurrence analysis, citation analysis, document coupling analysis, and co-citation analysis [22].
## Trend of publication outputs
According to the retrieval criteria, a total of 491 articles were retrieved, except for proceedings papers (13 articles), early access (11 articles) and meeting abstracts (4 articles). In addition, 14 non-English articles were excluded, and a total of 449 articles were finally included in our study. As shown in Table 1, these 449 articles come from 292 sources, including journals and books. The annual growth rate was 22.51 percent. On average, each article was cited 36.64 times. Eleven authors participated in the single-author document, while 2,585 authors participated in the multiauthor document. There were 6.6 coauthors in each paper, and $20.49\%$ were international coauthors. From 2002 to 2022, the number of global studies showed a steady growth trend year by year. In 2002, there was only one, while in 2022, there were 58. The majority of studies were published in 2021 (63,$14.03\%$). The polynomial fitting curve of the annual trend of publication volume is given. The annual Np was significantly correlated with the year of publication, and the correlation coefficient R2 reached 0.9574. The number of published papers is expected to reach 250 by 2030 (Figure 2C). Taken together, these results indicate that HTS technology related to chronic wounds has attracted increasing attention from researchers and has reached a stage of rapid development.
**Figure 2:** *(A) The number of publications by year over the past 20 years. (B) Distribution of scientific production numbers between countries in the world map. (C) Curve fitting of the total annual growth trend of publications (R2 = 0.9574).* TABLE_PLACEHOLDER:Table 1
## Distribution of countries/regions and institutions
We rank the 10 countries/regions with high output for all authors according to their Np values (Figure 2A). The United States (Np = 156, $34.74\%$) and China (Np = 74, $16.48\%$) contributed $51.22\%$ of the papers and were the main drivers of research in this field, followed by Germany (Np = 27, $6.01\%$), the United Kingdom (Np = 24, $5.35\%$) and Australia (Np = 15, $3.34\%$). The distribution of scientific production numbers between countries in the world map is shown in Figure 2B. The United States papers were cited 9,520 times, accounting for $58.39\%$ of the total, followed by the UK (Nc = 1,672) and Germany (Nc = 1,152) (Figure 3A). For each type of citation, publications from Switzerland had the highest average number of citations (Ac), which was 157 times. Romania ranked second in average citations (Ac = 95), ahead of the United Kingdom (Ac = 66.88), the United States (Ac = 60.64) and the Netherlands (Ac = 61.76) (Figure 3B). In addition, the United States had the highest H-index (H-index = 33), followed by China (H-index = 17), Australia (H-index = 9), Germany (H-index = 8) and the UK (H-index = 8) (Figure 3C). Regarding publication ranking, the top 5 contributive institutions were listed here. The first was Michigan University (28 publications), followed by Stanford University (26 publications) and Miami University (26 publications). Kunming Med University and The Ohio State University were tied for third (20 publications). As seen from Figure 3D, the cooperative network map mainly existed in North America, Western Europe and East Asia.
**Figure 3:** *(A) The top 20 countries/regions of total citations. (B) The top 20 countries/regions of the average citations per paper. (C) The top 20 countries/regions of the H-index. (D) The geographical network map of cooperative relations between countries.*
## Analysis of global leading journals, authors and funding sources
For the analysis of global leading journals, the results are shown in Table 2. Wound Repair and Regeneration (Np = 18, IF: 3.401) published the most papers on HTS technology for chronic wounds, followed by Plos One (Np = 12, IF: 3.752). The International Journal of Wounds (Np = 7, IF: 3.099) and Frontiers in Cell and Infection Microbiology (Np = 7, IF: 6.073) tied for third place. In terms of Nc, Wound Repair and Regeneration were the highest (Nc = 2,567), followed by BMC microbiology (Nc = 729), Plos One (Nc = 334), Clinical Microbiology and Infection (Nc = 240) and International Journal of Wound (Nc = 223). The H-index of Wound Repair and Regeneration ranked first (H-index = 18), followed by Plos One (H-index = 12). The H-indexes of BMC Microbiology, International Journal of Wound and Compendial Wound Clinical Research and Practice were all 5, tied for third place.
**Table 2**
| RANK | Journal | Np | Nc | H-index | IF (2022) |
| --- | --- | --- | --- | --- | --- |
| 1 | Wound Repair and Regeneration | 18 | 2567 | 12 | 3.401 |
| 2 | Plos One | 12 | 334 | 8 | 3.752 |
| 3 | International Wound Journal | 7 | 223 | 5 | 3.099 |
| 4 | Frontiers in Celluar and Infection Microbiology | 7 | 71 | 4 | 6.073 |
| 5 | JCI Insight | 6 | 103 | 4 | 9.484 |
| 6 | BMC Microbiology | 5 | 729 | 5 | 4.465 |
| 7 | Wounds | 5 | 60 | 5 | 1.441 |
| 8 | Clinical Microbiology and Infection | 4 | 240 | 4 | 13.31 |
| 9 | Advance in Wound Care | 4 | 106 | 4 | 4.947 |
| 10 | Annals of Plastic Surgery | 3 | 37 | 3 | 1.763 |
Referring to the analysis of global leading authors, the top 10 productive authors are listed in Table 3. They contributed 63 publications, accounting for $14.03\%$ of the total number of papers. Both Li X and Wang Y contributed 9 productions, ranking first, followed by Malone M (Np = 7). Among the top 10 productive authors, four were Chinese, three were Australians, two were Americans and one was from Denmark. Li X (H-index = 7) had the highest H-index, and Grice Ea (H-index = 662) had the highest total citation frequencies, followed by Wolcott Rd (H-index = 628).
**Table 3**
| RANK | Author | Country | Np | Nc | H-index |
| --- | --- | --- | --- | --- | --- |
| 1 | LI X | China | 9 | 204 | 7 |
| 2 | GRICE EA | United States | 6 | 662 | 6 |
| 3 | MALONE M | Australia | 7 | 135 | 5 |
| 4 | TANG J | China | 6 | 110 | 5 |
| 5 | WANG Y | China | 9 | 101 | 5 |
| 6 | WOLCOTT RD | United States | 5 | 628 | 5 |
| 7 | YANG M | Australia | 6 | 99 | 5 |
| 8 | YANG X | China | 6 | 86 | 5 |
| 9 | BJARNSHOLT T | Denmark | 5 | 185 | 4 |
| 10 | HU H | Australia | 4 | 146 | 4 |
About analysis of global leading founding resources: National Institutes of Health (Np = 92) ranked in No.1, followed by United States Department of Health Human Services (Np = 92), National Natural Science Foundation of China (Np = 48), European Commission (Np = 20) and NIH National Institute of General Medical Sciences (Np = 18).
## Bibliographic coupling analysis of country, journal and institution
Bibliographic coupling analysis is used to measure the relevance of institutions, journals and countries by analyzing the number of references cited to the same reference between them [28]. Countries (defined as the minimum number of documents is 5) bibliographic coupling analysis in VOS viewer is presented in Figure 4A. A total of 28 countries meet the thresholds, among which the 5 with the strongest link strength were “United States (total link strength = 12,305)”, “England (total link strength = 5,493)”, “Germany (total link strength = 3,591)”, “Australia (total link strength = 3,154)”, and “France (total link strength = 2,548)”.
**Figure 4:** *Mapping of bibliographic coupling analysis of HTS technology associated with chronic wounds. (A) Mapping of the 28 identified countries. (B) Mapping of the 32 identified journals. (C) Mapping of the 27 identified institutions.*
Journals (defined as the minimum number of documents is 3) bibliographic coupling analysis in VOS viewer is presented in Figure 4B. A total of 32 journals meet the thresholds, among which the 5 journals with the strongest link strength were “Wound Repair and Regeneration (total link strength = 914)”, “Frontiers in Cellular and Infection Microbiology (total link strength = 623)”, “International Wound Journal (total link strength = 588)”, “Plos One (total link strength = 551)”, and “BMC Microbiology (total link strength = 348)”.
A total of 27 institutions (defined as the number of published articles at least 5) were analyzed, among which the 5 with the strongest link strength were “Univ Penn (total link strength = 863)”, “Western Sydney Univ (total link strength = 831)”, “Ingham Inst Appl Med Res (total link strength = 716)”, “Yunnan Minzu Univ (total link strength = 706)”, and “Univ Copenhagen (total link strength = 641)” (Figure 4C).
## Co-citation analysis of cited journals, authors and references
Co-citation analysis is a scientific mapping technique that assumes that publications that are frequently cited together are similar in topic. This analysis can be used to reveal the knowledge structure of a research area, such as its underlying topics. In a co-citation network, two publications are linked when they appear in another publication's reference list at the same time.
When performing the co-citation analysis of cited journals on VOS viewer, we screened out the 30 references and journals cited above, and 172 of them were selected out of 4,453 journals. Sorted according to total link strength, the top five were J Biol Chem (total link strength = 31,615), Plos One (total link strength = 23,399), P Natl Acad Sci United States (total link strength = 22,616), Wound Repair Regen (total link strength = 22,430), and J Invest Dermatol (total link strength = 19,622) (Figure 5A).
**Figure 5:** *Mapping of co-citation analysis of HTS technology associated with chronic wounds. (A) Mapping of the 172 identified journals. (B) Mapping of the 48 identified authors. (C) Mapping of the 25 identified references.*
For the co-citation analysis of cited authors, we screen the authors who are cited 15 or more and find 48 out of 17,979 authors, among which the five authors with the highest connection strength are cited “Wolcott, Rd (total link strength = 1,027)” “Dowd, Se (total link strength = 969)” “Gardner, Se (total link strength = 860)” “Grice, Ea (total link strength = 781)” “Lipsky, Ba (total link strength = 512)” (Figure 5B).
Co-citation analysis of cited references by analyzing the cited number reveals the relevance of different cited references. The minimum cited number of cited references is 15, and 25 cited articles are selected out of 23,807 cited articles, among which the five articles with the highest connection strength are “Dowd Se, 2008, Plos One, v3, doi 10.1371/journal.pone.0003326 (total link strength:165)” “Gardner se, 2013, Diabetes, v62, p923, doi 10.2337/db12-0771 (total link strength:145)” “Loesche M, 2017, J Invest Dermatol, v137, p237, doi 10.1016/j.jid.2016.08.009 (total link strength:139)” “Wolcott Rd, 2016, Wound Repair Regen, v24, p163, doi 10.1111/wrr.12370 (total link strength:138)” “James Ga, 2008, Wound Repair Regen, v16, p37, doi 10.1111/j.1524-475x.2007.00321.x (total link strength:133)” (Figure 5C).
## Co-authorship analysis of author, institution, and country
Co-authoring analyses examine the interactions between scholars in a field of study. Because co-authorship is a formal form of intellectual collaboration between scholars. Therefore, it is important to understand how scholars interact with each other, including relevant authorial attributes such as affiliates and countries. With the complexity of research methods and theories, cooperation among scholars has become a common phenomenon. Indeed, collaboration between scholars can lead to improvements in research, and contributions from different scholars can contribute to clearer and richer insights.
Regarding the co-authorship analysis of countries, we set the condition that there are more than 7 works in the country, and 20 of 61 countries meet the condition. The five countries with the strongest connections and influence are “United States (total link strength = 54)”, “England (total link strength = 32)”, “Germany (total link strength = 22)”, “Australia (total link strength = 18)”, and “Italy (total link strength = 15)”. It is worth mentioning that although *China is* second only to the United States in Np, it has weak cooperative relations with other countries, with a total link strength of only 4 (Figure 6A).
**Figure 6:** *Mapping of co-authorship analysis about HTS technology associated with chronic wounds. (A) Mapping of the 20 identified countries. (B) Mapping of the 55 identified authors. (C) Mapping of the 30 identified institutions.*
We used VOS viewer to conduct co-authorship analysis of the author and set the condition as the authors of at least 3 documents. A total of 55 authors were screened out. However, due to the small sample size, only 9 authors showed a strong co-authorship relationship. The first five authors are listed as follows: Yang Xinwang (total link strength = 23), Wang Ying (total link strength = 23), Li Xiaojie (total link strength = 20), Yang Meifeng (total link strength = 20), and Tang Jing (total link strength = 20) (Figure 6B).
When conducting co-authorship analysis of institutions, we set the condition that each institution has at least 5 works, and 30 out of 890 institutions meet the conditions after screening. Among them, the five most closely related institutions are “Western Sydney Univ (total link strength = 8)”, “Yunnan Minzu Univ (total link strength = 8)”, “Ingham Inst Appl Med Res (total link strength = 7)”, “Kunming Med Univ (total link strength = 7)”, and “Univ Penn (total link strength = 7)” (Figure 6C).
## Co-occurrence analysis of keywords
The co-occurrence analysis of keywords can show the hot keywords in the research field and predict future research hotspots and trends [29]. At the same time, the development trajectory and future trend of the field can be understood by analyzing the change in keywords over time [30]. Keywords are words used more than 10 times in the title/abstract of all papers, selected and analyzed by VOS viewer.
In addition to the searched words, a total of 67 keywords were frequently mentioned in the main text and abstract of 449 articles. These 67 keywords can be divided into 3 clusters. As seen from Figure 7A, Cluster 1 (red) is about microbial infection of chronic wounds such as diabetic foot. Cluster 2 (green) is about the healing process of wounds and microscopic processes such as cell expression and factor regulation in the inflammatory response, while Cluster 3 (blue) is about the skin repair mechanism stimulated by antimicrobial peptides and oxidative stress. The top 10 words with the highest frequency were wound healing (total link strength:256), expression (total link strength:241), inflammation (total link strength:190), identification (total link strength:179), chronic wounds (total link strength:177), bacteria (total link strength:162), infections (total link strength:150), and angiogenesis(total link strength:128), biofilms(total link strength:125) and diabetes(total link strength:119) indicate that the HTS of chronic wounds aims to study the identification of bacteria and microorganisms and the expression of inflammatory factors in the healing process of chronic wounds. The HTS of chronic wounds is a combination of basic and clinical research. In addition, the statistics of the average publication year (APY) of keywords are shown in Figure 7B. The color (from purple to yellow) represents the change in research hotspots of keywords. It can be seen that the latest five keywords are “chronic wound microbiota (APY = 2020.0)”, “staphylococcus aureus (APY = 2019.7)”, “fibroblasts (APY = 2019.0)”, “prevalence (APY = 2019.0)”, and “chronic wound (APY = 2018.6)”, which are recent research hotspots. The study of bacterial flora in chronic wounds is a new hotspot.
**Figure 7:** *Mapping of co-occurrence analysis of HTS technology associated with chronic wounds. (A) Mapping of the 149 identified keywords. The frequency is represented by point size, and the keywords of research fields are divided into three clusters: microbial infection of chronic wounds (red), healing process of wounds and microscopic processes (green), skin repair mechanism stimulated by antimicrobial peptides and oxidative stress (blue). (B) Distribution of 149 identified keywords according to frequency and average time of occurrence; keywords in yellow appeared later than those in blue.*
Then, we used bibliometriex to analyze the topic trend change from 2010 to 2022 and the duration of the popularity of related topics. As shown in Figure 8, topics with good performance in innovation and duration include “protein”, “expression”, “skin”, “biofilms” and “angiogenesis”. The basis of HTS research on chronic wounds is still related to several processes of wound healing. At the same time, the newly erupted topics are “prevalence”, “gene expression”, “inflammation” and “infection”. It can be seen that keywords related to HTS technology will be the popular trend of studying chronic wound healing in the future. The study of inflammation and bacteria in chronic wounds is a future trend [31].
**Figure 8:** *Global trend topic analysis of HTS technology associated with chronic wounds. Distribution of 31 identified topics according to average time of occurrence and frequency.*
## Three-field analysis of keyword, country and journal
Meanwhile, by drawing the three-field plot of the top 20 keywords, country and journal, we also found different research priorities in different countries (Figure 9). The research trends in the area of HTS technology associated with chronic wounds were observed as follows: wound healing, diabetes, microbiome, inflammation, angiogenesis, chronic wounds, fibrosis, biofilm, sequencing, Pseudomonas aeruginosa, Staphylococcus aureus, infection, 16S rRNA and fibroblasts. In Western countries (mainly the United States, the UK and Australia), scholars focus more on the “microbiome”, “inflammation” and “biofilm” than in other countries (mainly referring to China). Chinese scholars show more interest in “diabetes”, “angiogenesis”, “chronic wounds”, and “sequencing”. We speculate that possible reasons include the large number of diabetic foot patients in China and the fact that scholars pay more attention to clinically relevant issues. However, Western scholars have paid more attention to the microscopic process and mechanism of inflammatory reactions and have proposed the concept of biofilms. Moreover, scholars in the United States prefer to publish their articles in the following journals: Wound Repair and Regeneration, Plos One and Frontiers in Cellular and Infection Microbiology. Scholars in China prefer to publish their articles in the following journals: Cochrane database of systematic reviews, international journal of low-extremity wounds and wound repair and regeneration.
**Figure 9:** *Three-field plot of analysis countries, keywords and journals about HTS technology associated with chronic wounds (middle field: countries, left field: journals, right field: keywords).*
## Trend of global publications
Delayed healing of chronic wounds (especially diabetic foot ulcers) has always been a hot topic in clinical research, among which the cellular ecology of chronic wounds and identification of bacterial pathogens have attracted increasing attention [32]. Studies have shown that the microbiota of a wound can affect wound healing by either speeding up or slowing it down, depending on the type of microbiota on the wound [14, 15, 33, 35]; therefore, it is necessary to decrease the cellular and molecular perturbations driving this abnormal wound healing state [35]. HTS technology is a technology that emerged after 2000 and is increasingly applied in clinical and research [36]. With the rapid development of this technology, it is possible to detect pathogens and monitor characteristic cell subsets [30, 37]. HTS has several advantages: high sensitivity, the ability to detect more pathogens, the ability to identify rare bacteria, and the ability to identify dominant bacteria in mixed infections [38]. In recent years, many researchers have published insightful literature in the field of HTS technology [39, 40]. Furthermore, with the rapid development of second-generation sequencing technology, the scientific community has begun to increasingly use second-generation sequencing technology to solve biological problems (41–44). In terms of chronic wounds, the application of HTS technology to detect the distribution of wound microbiota and comprehensively understand the characteristics of wound microbiota to better guide clinical diagnosis and treatment has attracted the interest of an increasing number of scholars and has become a new research hotspot [14, 34]. In this paper, we combined HTS technology with chronic wounds, especially diabetic foot wounds, to investigate the developmental trends and hotspots of research between the two fields from the Web of Science core collection database by using VOS viewer and Bibliometrix.
## Quality and status of global publications
Among the top countries/regions, the United States ranked first and far ahead of second in both Np and Nc. This is closely related to America's global leading level of science and technology [45]. The United States was the first country to master and try to use high-throughput technology sequencing. Most of the technology and related research was initially proposed by American scholars, and the United States went deeper in this field compared with the rest of the world [46, 47]. In terms of Np, China was firmly in second place. It is worth mentioning that since 2010, China has only begun to publish articles in this field, and in the following years, the number of articles in this field has increased rapidly, indicating that an increasing number of Chinese scholars began to turn their attention to HTS technology to find solutions to the problem of chronic wound healing. However, the Nc of China only ranked fourth, far behind the United States, and the average citations per item ranked 14th in the world. This result may indicate that scholars and institutes in China should make more efforts on the quality of their papers in this field. In addition, we found that Switzerland and Romania did not have a high number of publications, but the average single citation volume of the articles was very high (ranked 1st and 3rd), indicating that the articles of these two countries were of high quality and were widely recognized in the medical field.
Among the top 10 authors with the most publications, 4 authors were affiliated with institutions in China, 3 authors were from Australia, 2 authors were from the United States and 1 was affiliated with institutions in Denmark.
Although the Np and the Nc of the United States ranked first, the author from the United States only ranked third in the top 10 published articles, while the author from China with the most published articles was LI X, whose H-index was also the highest (H-index = 9), indicating that Li X's output in this field was both quantitative and qualitative. The inclusion of Australian authors may be related to Australia's close external cooperation in this field (Figures 6, 7). Strengthening international cooperation is of great help to exploration.
## Research focus on high-throughput sequencing technology associated with chronic wounds
The hotspots and research trend predictions of HTS technology associated with chronic wounds are mainly based on cooccurrence analysis [48, 49]. The map of co-occurrence analysis of keywords showed three main directions of this field (Figure 7A). These three directions represent different focuses, from clinical to basic, from macro to micro, and from phenomenon to mechanism, comprehensively covering the key issues in this field. Three-field analysis of keywords, country and journal illustrates the differentiation of research focuses and journals among countries. Modern microbial ecology research of human skin is the basis of chronic wound microbial ecology research [50]. Since HTS technology is relatively relaxed to culture conditions and is convenient to use, HTS technology primarily iterates microarray and fingerprinting techniques and has become the method of choice for understanding microbiome diversity, evolutionary patterns, and population structure [20]. Recently, some studies using HTS technology to identify biofilms of chronic wounds have been published [51]. Bacteria in chronic wounds have been observed to form biofilms, resulting in delayed healing. In mature biofilms, bacterial growth is slowed by a lack of nutrients, leading to bacterial resistance to antibiotics [11].
## Limitations of this study
Although this paper has made a detailed bibliometric analysis of HTS technology associated with chronic wounds and put forward numerous innovative opinions for the future research trends of this field based on these data, there are still some limitations that should be mentioned. First, the selected database is only one Web of Science core collection, and the number of articles obtained is limited. However, this is a fresh subject, and the research in this paper can still be of great significance, which provides a direction for the following research direction and content. Second, all the articles included in this study are in English, and the book chapter, proceedings paper, correction, letter, meeting abstract, note, review and other types of articles are excluded. This may bias the results of our study, but we still have reason to believe that the impact of the excluded articles on the true results is relatively minor, and the articles we included are relatively sufficient to illustrate the problem. Third, for the traceability of the research, 2 coauthors (PY and MH) selected the same time point to retrieve the database articles, while the database articles were updated daily, which may leave out some valuable studies. Although coauthors selected the data separately, encountered problems were resolved by consulting with experts, ensuring the authenticity and reliability of the data.
## Conclusion
In conclusion, this study is the first bibliometric analysis to scientifically and comprehensively analyze the global trend of HTS technology application associated with chronic wounds over the past 30 years. This paper compares the research hotspots and directions in this field globally from the perspectives of countries, institutions and authors, analyzes the trend of international cooperation, and reveals the future development direction of the field and research hotspots of great scientific research value. Future research efforts in this field should include the following aspects: HTS technology to assist in identifying the microbial ecology of chronic wounds, analysis of the microbiota of diabetic foot wounds, and search for targets to promote the healing of chronic wounds. Through this paper, we can further explore the value of HTS technology in chronic wounds to better solve the problem of chronic wounds.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.
## Author contributions
YP and HM: performed this bibliometric analysis and wrote the manuscript. YP, HM, JS, YJ and XF: participated in the experimental design and manuscript writing. YP and HM: designed this study and organized the manuscript writing. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## References
1. Morton LM, Phillips TJ. **Wound healing and treating wounds: differential diagnosis and evaluation of chronic wounds**. *J Am Acad Dermatol* (2016) **74** 589-605. DOI: 10.1016/j.jaad.2015.08.068
2. Bowers S, Franco E. **Chronic wounds: evaluation and management**. *Am Fam Physician* (2020) **101** 159-66. PMID: 32003952
3. Groppa E, Colliva A, Vuerich R, Kocijan T, Zacchigna S. **Immune cell therapies to improve regeneration and revascularization of non-healing wounds**. *Int J Mol Sci* (2020) **21**. DOI: 10.3390/ijms21155235
4. Krishnaswamy VR, Mintz D, Sagi I. **Matrix metalloproteinases: the sculptors of chronic cutaneous wounds**. *Biochim Biophys Acta Mol Cell Res* (2017) **1864** 2220-7. DOI: 10.1016/j.bbamcr.2017.08.003
5. Kathawala MH, Ng WL, Liu D, Naing MW, Yeong WY, Spiller KL. **Healing of chronic wounds: an update of recent developments and future possibilities**. *Tissue Eng Part B Rev* (2019) **25** 429-44. DOI: 10.1089/ten.teb.2019.0019
6. Fritz BG, Kirkegaard JB, Nielsen CH, Kirketerp-Møller K, Malone M, Bjarnsholt T. **Transcriptomic fingerprint of bacterial infection in lower extremity ulcers**. *Apmis* (2022) **130** 524-34. DOI: 10.1111/apm.13234
7. Sun H, Pulakat L, Anderson DW. **Challenges and new therapeutic approaches in the management of chronic wounds**. *Curr Drug Targets* (2020) **21** 1264-75. DOI: 10.2174/1389450121666200623131200
8. Xu Z, Hsia HC. **The impact of microbial communities on wound healing: a review**. *Ann Plast Surg* (2018) **81** 113-23. DOI: 10.1097/SAP.0000000000001450
9. Mattera E, Iovene MR, Rispoli C, Falco G, Rocco N, Accurso A. **Assessment of bacterial infection in chronic wounds in the elderly: biopsy versus VERSAJET**. *Int J Surg* (2014) **12** S50-5. DOI: 10.1016/j.ijsu.2014.08.381
10. Powell LC, Cullen JK, Boyle GM, De Ridder T, Yap PY, Xue W. **Topical, immunomodulatory epoxy-tiglianes induce biofilm disruption and healing in acute and chronic skin wounds**. *Sci Transl Med* (2022) **14** eabn3758. DOI: 10.1126/scitranslmed.abn3758
11. Rahim K, Saleha S, Zhu X, Huo L, Basit A, Franco OL. **Bacterial contribution in chronicity of wounds**. *Microb Ecol* (2017) **73** 710-21. DOI: 10.1007/s00248-016-0867-9
12. Kim JH, Ruegger PR, Lebig EG, VanSchalkwyk S, Jeske DR, Hsiao A. **High levels of oxidative stress create a microenvironment that significantly decreases the diversity of the microbiota in diabetic chronic wounds and promotes biofilm formation**. *Front Cell Infect Microbiol* (2020) **10** 259. DOI: 10.3389/fcimb.2020.00259
13. Libertucci J, Bassis CM, Cassone M, Gibson K, Lansing B, Mody L. **Bacteria detected in both urine and open wounds in nursing home residents: a pilot study**. *mSphere* (2019) **4**. DOI: 10.1128/mSphere.00463-19
14. Wong VW, Martindale RG, Longaker MT, Gurtner GC. **From germ theory to germ therapy: skin microbiota, chronic wounds, and probiotics**. *Plast Reconstr Surg* (2013) **132** 854e-61e. DOI: 10.1097/PRS.0b013e3182a3c11e
15. Kalan LR, Brennan MB. **The role of the microbiome in nonhealing diabetic wounds**. *Ann N Y Acad Sci* (2019) **1435** 79-92. DOI: 10.1111/nyas.13926
16. Lightbody G, Haberland V, Browne F, Taggart L, Zheng H, Parkes E. **Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application**. *Brief Bioinformatics* (2019) **20** 1795-811. DOI: 10.1093/bib/bby051
17. Bálint M, Bahram M, Eren AM, Faust K, Fuhrman JA, Lindahl B. **Millions of reads, thousands of taxa: microbial community structure and associations analyzed via marker genes**. *FEMS Microbiol Rev* (2016) **40** 686-700. DOI: 10.1093/femsre/fuw017
18. Krehenwinkel H, Pomerantz A, Prost S. **Genetic biomonitoring and biodiversity assessment using portable sequencing technologies: current uses and future directions**. *Genes* (2019) **10**. DOI: 10.3390/genes10110858
19. Oniciuc EA, Likotrafiti E, Alvarez-Molina A, Prieto M, Santos JA, Alvarez-Ordóñez A. **The present and future of Whole Genome Sequencing (WGS) and Whole Metagenome Sequencing (WMS) for surveillance of antimicrobial resistant microorganisms and antimicrobial resistance genes across the food chain**. *Genes* (2018) **9**. DOI: 10.3390/genes9050268
20. Tedersoo L, Albertsen M, Anslan S, Callahan B. **Perspectives and benefits of high-throughput long-read sequencing in microbial ecology**. *Appl Environ Microbiol* (2021) **87** e0062621. DOI: 10.1128/AEM.00626-21
21. Oyewola DO, Dada EG. **Exploring machine learning: a scientometrics approach using bibliometrix and VOSviewer**. *SN Appl Sc* (2022) **4** 143. DOI: 10.1007/s42452-022-05027-7
22. Roldan-Valadez E, Salazar-Ruiz SY, Ibarra-Contreras R, Rios C. **Current concepts on bibliometrics: a brief review about impact factor, eigenfactor score, CiteScore, SCImago journal rank, source-normalised impact per paper, H-index, and alternative metrics**. *Ir J Med Sci* (2019) **188** 939-51. DOI: 10.1007/s11845-018-1936-5
23. Hirsch JE. **An index to quantify an individual’s scientific research output**. *Proc Natl Acad Sci U S A* (2005) **102** 16569-72. DOI: 10.1073/pnas.0507655102
24. Schubert A. **Equally or equitably? Author roles and co-author shares in scientific publication**. *Orv Hetil* (2016) **157** 512-6. DOI: 10.1556/650.2016.30418
25. Würtz M, Schmidt M. **The stratified H-index makes scientific impact transparent**. *Ugeskr Laeg* (2017) **179**
26. Dinis-Oliveira RJ. **The H-index in life and health sciences: advantages, drawbacks and challenging opportunities**. *Curr Drug Res Rev* (2019) **11** 82-4. DOI: 10.2174/258997751102191111141801
27. Zhang B, Jin Z, Zhai T, Ding Q, Yang H, Wang J. **Global research trends on the links between the gut microbiota and diabetes between 2001 and 2021: a bibliometrics and visualized study**. *Front Microbiol* (2022) **13** 1011050. DOI: 10.3389/fmicb.2022.1011050
28. Guo H, Shen Z, Zeng J, Hong N. **Hybrid methods of bibliographic coupling and text similarity measurement for biomedical paper recommendation**. *Stud Health Technol Inform* (2022) **290** 287-91. DOI: 10.3233/SHTI220080
29. Bornmann L, Haunschild R, Hug SE. **Visualizing the context of citations referencing papers published by Eugene Garfield: a new type of keyword co-occurrence analysis**. *Scientometrics* (2018) **114** 427-37. DOI: 10.1007/s11192-017-2591-8
30. Radhakrishnan S, Erbis S, Isaacs JA, Kamarthi S. **Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature**. *PLoS One* (2017) **12** e0172778. DOI: 10.1371/journal.pone.0172778
31. Sharifi S, Hajipour MJ, Gould L, Mahmoudi M. **Nanomedicine in healing chronic wounds: opportunities and challenges**. *Mol Pharm* (2021) **18** 550-75. DOI: 10.1021/acs.molpharmaceut.0c00346
32. Gardiner M, Vicaretti M, Sparks J, Bansal S, Bush S, Liu M. **A longitudinal study of the diabetic skin and wound microbiome**. *PeerJ* (2017) **5** e3543. DOI: 10.7717/peerj.3543
33. Johnson TR, Gómez BI, McIntyre MK, Dubick MA, Christy RJ, Nicholson SE. **The cutaneous microbiome and wounds: new molecular targets to promote wound healing**. *Int J Mol Sci* (2018) **19**. DOI: 10.3390/ijms19092699
34. Choi Y, Banerjee A, McNish S, Couch KS, Torralba MG, Lucas S. **Co-occurrence of anaerobes in human chronic wounds**. *Microb Ecol* (2019) **77** 808-20. DOI: 10.1007/s00248-018-1231-z
35. Januszyk M, Chen K, Henn D, Foster DS, Borrelli MR, Bonham CA. **Characterization of diabetic and non-diabetic foot ulcers using single-cell RNA-sequencing**. *Micromachines* (2020) **11** 815. DOI: 10.3390/mi11090815
36. Jnana A, Muthuraman V, Varghese VK, Chakrabarty S, Murali TS, Ramachandra L. **Microbial community distribution and core microbiome in successive wound grades of individuals with diabetic foot ulcers**. *Appl Environ Microbiol* (2020) **86**. DOI: 10.1128/AEM.02608-19
37. Weterings V, Bosch T, Witteveen S, Landman F, Schouls L, Kluytmans J. **Next-generation sequence analysis reveals transfer of methicillin resistance to a methicillin-susceptible staphylococcus aureus strain that subsequently caused a methicillin-resistant staphylococcus aureus outbreak: a descriptive study**. *J Clin Microbiol* (2017) **55** 2808-16. DOI: 10.1128/JCM.00459-17
38. Mudrik-Zohar H, Carasso S, Gefen T, Zalmanovich A, Katzir M, Cohen Y. **Microbiome characterization of infected diabetic foot ulcers in association with clinical outcomes: traditional cultures versus molecular sequencing methods**. *Front Cell Infect Microbiol* (2022) **12** 836699. DOI: 10.3389/fcimb.2022.836699
39. Wolcott RD, Hanson JD, Rees EJ, Koenig LD, Phillips CD, Wolcott RA. **Analysis of the chronic wound microbiota of 2,963 patients by 16S rDNA pyrosequencing**. *Wound Repair Regen* (2016) **24** 163-74. DOI: 10.1111/wrr.12370
40. Kalan LR, Meisel JS, Loesche MA, Horwinski J, Soaita I, Chen X. **Strain- and species-level variation in the microbiome of diabetic wounds is associated with clinical outcomes and therapeutic efficacy**. *Cell Host Microbe* (2019) **25** 641-55.e5. DOI: 10.1016/j.chom.2019.03.006
41. Chang B, Morita M, Lee KI, Ohnishi M. **Whole-genome sequence analysis of streptococcus pneumoniae strains that cause hospital-acquired pneumonia infections**. *J Clin Microbiol* (2018) **56**. DOI: 10.1128/JCM.01822-17
42. Özkurt E, Fritscher J, Soranzo N, Ng DYK, Davey RP, Bahram M. **Lotus2: an ultrafast and highly accurate tool for amplicon sequencing analysis**. *Microbiome* (2022) **10** 176. DOI: 10.1186/s40168-022-01365-1
43. Natarajan KN, Miao Z, Jiang M, Huang X, Zhou H, Xie J. **Comparative analysis of sequencing technologies for single-cell transcriptomics**. *Genome Biol* (2019) **20** 70. DOI: 10.1186/s13059-019-1676-5
44. Cali DS, Kim JS, Ghose S, Alkan C, Mutlu O. **Nanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directions**. *Brief Bioinformatics* (2019) **20** 1542-59. DOI: 10.1093/bib/bby017
45. Patrick K. **454 Life sciences: illuminating the future of genome sequencing and personalized medicine**. *Yale J Biol Med* (2007) **80** 191-4. PMID: 18449390
46. Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI, Knight R. **Bacterial community variation in human body habitats across space and time**. *Science* (2009) **326** 1694-7. DOI: 10.1126/science.1177486
47. Lanni F. **Analysis of sequence patterns in ribonuclease, i. Sequence vectors and vector maps**. *Proc Natl Acad Sci U S A* (1960) **46** 1563-76. DOI: 10.1073/pnas.46.12.1563
48. Zhao F, Du F, Zhang J, Xu J. **Trends in research related to keratoconus from 2009 to 2018: a bibliometric and knowledge mapping analysis**. *Cornea* (2019) **38** 847-54. DOI: 10.1097/ICO.0000000000001984
49. Shen S, Cheng C, Yang J, Yang S. **Visualized analysis of developing trends and hot topics in natural disaster research**. *PLoS One* (2018) **13** e0191250. DOI: 10.1371/journal.pone.0191250
50. Cundell AM. **Microbial ecology of the human skin**. *Microb Ecol* (2018) **76** 113-20. DOI: 10.1007/s00248-016-0789-6
51. Kalan L, Zhou M, Labbie M, Willing B. **Measuring the microbiome of chronic wounds with use of a topical antimicrobial dressing - A feasibility study**. *PLoS One* (2017) **12** e0187728. DOI: 10.1371/journal.pone.0187728
|
---
title: Analysis of factors that influence the occurrence of otitis media with effusion
in pediatric patients with adenoid hypertrophy
authors:
- Wenjing Chen
- Guoping Yin
- Yijing Chen
- Lijun Wang
- Yingying Wang
- Chunmei Zhao
- Wan Wang
- Jingying Ye
journal: Frontiers in Pediatrics
year: 2023
pmcid: PMC9992982
doi: 10.3389/fped.2023.1098067
license: CC BY 4.0
---
# Analysis of factors that influence the occurrence of otitis media with effusion in pediatric patients with adenoid hypertrophy
## Abstract
### Objective
Adenoid hypertrophy (AH) and otitis media with effusion (OME) are common pediatric otolaryngological diseases and often occur concurrently. The purpose of this study was to comprehensively analyze the factors that influence the occurrence of OME pediatric patients with AH.
### Methods
Patients younger than 12 years with AH, who were hospitalized for treatment at Beijing Tsinghua Changgung Hospital in Beijing, China, between March 2018 and February 2022 were enrolled. The patients were divided into an AH group and an AH + OME group based on the presence of OME. The authors collected the following clinical data for univariable analysis: sex; age; body mass index (BMI); comorbid nasal congestion/rhinorrhea, recurrent tonsillitis, or allergic rhinitis (AR); adenoid and tonsil grade; tonsillar hypertrophy; food/drug allergy; history of adenoidectomy and congenital diseases; breastfeeding status; preterm birth; exposure to environmental tobacco smoke (ETS); family history of adenotonsillectomy, otitis media, and AR; main data of polysomnography and oropharyngeal conditional pathogen culture data of some patients. Univariate analysis was performed as a basis for logistic regression analysis.
### Results
A total of 511 children (329 boys and 182 girls) were included, their mean age was 5.37 ± 2.10 years. Of them, 407 ($79.6\%$) were in the AH group and 104 ($20.4\%$) in the AH + OME group. Univariate analysis revealed statistically significant differences in age, BMI, adenoid grade, AR, breastfeeding status, and ETS exposure between the two groups. Multivariate stepwise logistic regression analysis showed that age, adenoid grade, AR, breastfeeding status, and ETS influenced the occurrence of OME in pediatric patients with AH. The risk of OME decreased with increasing age. High adenoid grade, ETS exposure, and comorbid AR were risk factors for OME in pediatric patients with AH, but breastfeeding was a protective factor. The final analytical results of the oropharyngeal conditional pathogen culture data showed that *Streptococcus pneumoniae* positivity was associated with OME in AH.
### Conclusion
The pathogenesis of AH with OME is complex. Young age, high adenoid grade, ETS exposure, non-breastfed status, comorbid AR, and the presence of S. pneumoniae in the oropharynx are risk factors for OME in pediatric patients with AH.
## Introduction
Adenoid hypertrophy (AH) and otitis media with effusion (OME) are common pediatric otolaryngological diseases. Repeated stimulation by bacteria, viruses, and allergens causes pathological AH, resulting in clinical symptoms [1], such as nasal congestion, rhinorrhea, open-mouthed breathing, obstructive sleep apnea (OSA), snoring, and “adenoid face” caused by chronic airway obstruction, which is an important factor that can induce or worsen OME, eustachian tube dysfunction (ETD), and acute/chronic rhinosinusitis in children [2].
OME is a middle ear effusion without an acute middle ear infection. It is most common in children aged six months to four years old. Approximately $90\%$ of preschoolers and $25\%$ of schoolchildren have had OME [3]. The prevalence of OME ranges from $1.3\%$ to $31.3\%$ [4] depending on diagnostic methods, race, and environmental factors. However, the pathogenesis and etiology of OME remain unclear. Viral infection, bacterial colonization, allergies, and immune factors can promote the occurrence and progression of OME; mechanical obstruction and ETD also play critical roles. Adenoids obstruct the posterior nostrils and affect ventilation and drainage in the nasal cavity and sinuses, leading to chronic sinusitis. Pathogenic microorganisms and secretions pass through the eustachian tube (ET) and oropharynx to cause ET mucosal inflammation, congestion, edema, and retrograde infection, thereby aggravating or inducing OME [5]. AH is the main predisposing factor for OME that often accompanies it in children [6]. However, not all pediatric patients with AH develop OME. It is vital to understand the influencing factors associated with OME incidence in pediatric patients with AH. Previous studies have found that atopic or allergic rhinitis (AR), frequent tonsillitis, daycare attendance, exposure to smoke, and multiple family members were major risk factors for OME in pediatric patients with AH [7]. Further and more comprehensive analysis of the risk factors for OME in children with AH is needed to provide a reference for the prevention and treatment of such cases and a basis for future in-depth mechanistic studies. Thus, the aim of this study was to comprehensively analyze the factors that influence the occurrence of OME in pediatric patients with AH.
## Study population
In this retrospective study, patients with AH aged ≤12 years old who were hospitalized for treatment at Beijing Tsinghua Changgung Hospital in Beijing, China, between March 2018 and February 2022 were enrolled. The reasons for hospitalization were related clinical symptoms and manifestations of AH, mainly nasal congestion, rhinorrhea, open-mouthed breathing, OSA, snoring, and “adenoid face” caused by chronic airway obstruction. All patients were admitted to the hospital for an adenoidectomy. Some surgeries were combined with tonsillectomy. Two major indications for tonsillectomy and/or adenoidectomy include obstruction and recurrent infection [8]. The age limit of 12 years was chosen because the upper age limit in OME guidelines is 12 years [9]. Children with OME underwent intraoperative tympanocentesis or tympanostomy tube insertion. During the study period, a total of 518 patients were hospitalized due to the above reasons in an ENT outpatient department at our hospital. Seven were excluded (one case of undetermined neurological disease, one case of nephroblastoma, one case of abnormal coagulation function, one case of hereditary deafness with intellectual disability, one case of middle ear malformation, one case of immune disease, and one case of severe heart disease combined with slow growth). Finally, a total of 511 patients were included as study participants.
The inclusion criteria were as follows: age ≤ 12 years; presence of adenoid tissue obstruction of the posterior nostril (>$50\%$); and presence of nasal congestion, snoring, mouth breathing, and other clinical symptoms. Patients who had acute upper respiratory tract infection in the last 2 weeks that was treated using antibiotics or immune modulators, and patients with cleft palate and other craniofacial deformities; intellectual disability; immunodeficiency; cardiovascular, lung, genetic, autoimmune, and neuromuscular diseases; or other severe underlying diseases were excluded from this study. The included patients were subdivided into an AH group and an AH + OME group based on the presence of OME, and into three groups by age: 0–4 years, 5–8 years, and 9–12 years.
Diagnosis of AH was based on: signs, symptoms, and the results of fiberoptic nasopharyngoscopy, and that of OME was based on: signs, symptoms, and the results of auxiliary examinations (pure tone audiometry/behavioral audiometry, tympanometry, and ear endoscopy), and the intraoperative confirmation of tympanic effusion.
The clinical data of the pediatric patients, including sex; age; body mass index (BMI); comorbid nasal congestion/rhinorrhea (mucoid or mucopurulent), recurrent tonsillitis, or AR; adenoid grade; tonsil grade; tonsillar hypertrophy; food/drug allergy; history of adenoidectomy; history of congenital diseases; breastfeeding status; preterm birth; exposure to environmental tobacco smoke (ETS); family history of adenotonsillectomy, otitis media and AR; and main polysomnography (PSG) data, were collected for univariate analysis. In addition, upper respiratory tract (oropharynx) conditional pathogen culture data were collected to analyze the relationship between the presence of conditional pathogens and the occurrence of OME in pediatric patients with OME.
The study design was approved by Beijing Tsinghua Changgung Hospital Ethics Committee (NO.: 22538-6-01, Nov. 8th, 2022). The minor(s)' legal guardian/next of kin consented to the collection of medical history, and examination and operation information.
## Grouping criteria
The key points of diagnosis of OME were as follows: [1] ear symptoms and signs without acute middle ear infection; [2] hearing loss, self-hearing enhancement, or hearing changes with posture changes occuring; [3] a tympanogram showed a “B” or “C” curve; [4] pure tone/behavioral audiometry indicating that the affected ear had mild to moderate conductive hearing loss; and [5] patients who showed tympanic effusion during the ear endoscopy before the operation which was confirmed intraoperatively. Patients diagnosed with OME according to the above criteria were included in the AH + OME group. Pediatric patients without OME were included in the AH group.
## Criteria for collection of clinical data
Adenoid grading was based on endoscopic findings of the percentage of the posterior nostril blocked by the adenoid. Grades II–IV indicate a $26\%$–$50\%$, $51\%$–$75\%$, and ≥$76\%$ obstruction, respectively [10]. Tonsil grading was performed according to Friedman's criteria. The tonsil grades include grade 0 (patients who have had their tonsils removed), grade 1 (the tonsils are inside the tonsillar fossa), grade 2 (the tonsils extend beyond the tonsillar pillars), grade 3 (the tonsils extend beyond the tonsillar pillars but do not reach the midline), and grade 4 (the tonsils extend as far as the midline [11]. Tonsil hypertrophy was defined as tonsil grade II or higher. Regarding breastfeeding status, included children were those who were breastfed for more than six months after birth. Children in close contact with at least one active smoker (one or more cigarettes per day by any family member living with them) were considered to have ETS exposure [12]. AR was diagnosed if a child showed excessive sneezing and at least one of the following symptoms: ocular pruritus, nasal pruritus, oropharyngeal pruritus, or clear nasal discharge [13]. Preterm birth was defined as a gestational age of fewer than 37 weeks at the time of delivery. Family history was defined as the medical history of first-degree relatives. PSG monitoring data were collected, including obstructive apnea index (OAI) and apnea hypopnea index (AHI). An OAI > 1 time/h or AHI > 5 times/h for every nighttime sleep was considered abnormal [14]. The severity of OSA was categorized as follows: mild, 5 times/h < AHI ≤10 times/h or 1 time/h < OAI ≤ 5 times/h; moderate, 10 times/h < AHI ≤ 20 times/h or 5 times/h < AHI ≤ 10 times/h; severe, AHI > 20 times/h or OAI > 10 times/h. Bacterial culture sampling was performed after general anesthesia and before surgery. For the collection of the samples, $0.9\%$ sodium chloride solution was used to flush the oropharynx, and sterile pharyngeal swabs were used to swab the oropharynx repeatedly. The samples were then sent for bacterial culture. These strains were identified using MALDI-TOF MS (Bruker Dalton GmbH, Leipzig, Germany). The samples were cultured on Columbia agar supplemented with $5\%$ sheep blood and Chocolate agar plates and incubated at 37°C for 48 h.
## Statistical analysis
SAS Analysis Software (version 9.4, SAS Institute Inc, Cary, NC, USA) was used for data processing. $P \leq 0.05$ was considered to be statistically significant. The study was mainly divided into two parts for statistical and data analysis. Firstly, the influencing factors of the clinical data of AH complicated by OME were analyzed. Then the relationship between different pathogenic bacteria and AH complicated by OME was further analyzed based on a limited number of cases. In the process, the relevant clinical data of the AH group and AH + OME group were first analyzed by univariate analysis. Measurement data meeting the normality test and homogeneity analysis of variance were compared between the two groups by T-test; otherwise, Wilcoxon non-parametric test was used. Pearson's Chi-square test was used for enumeration data meeting the condition of the test; otherwise, a correction test or Fisher's exact test was used. Then, variables with $P \leq 0.1$ from the univariate analysis results were included in the multivariate logistic stepwise regression to screen out related factors affecting the pediatric patients with AH complicated by OME. In the analysis of correlation with pathogenic bacteria, the relevant pathogenic bacteria were screened out by univariate analysis. Furthermore, variables with $P \leq 0.05$ were taken as covariables, and the relationship between pathogenic bacteria and OME in pediatric patients with AH was further analyzed by multivariate logistic regression.
## Analysis of clinical data
A total of 511 pediatric patients with AH were included in this study. Of the 511 patients, 329 were boys, and 182 were girls. The age distribution of the patients was 5.37 ± 2.10 years. Regarding the two patient groups, 407 ($79.6\%$) patients were included in the AH group, whereas 104 ($20.4\%$) were included in the AH + OME group. Thirteen of the patients were missing PSG data owing to a lack of parental consent or lack of cooperation by the pediatric patients. Of the 498 patients with PSG data, 397 ($79.7\%$) were in the AH group, and 101 ($20.3\%$) were in the AH + OME group. The upper respiratory tract conditional pathogen culture data of 220 pediatric patients were collected. Of these, 178 ($80.9\%$) were in the AH group, and 42 ($19.1\%$) were in the AH + OME group.
Univariate analysis of the clinical data of the patients (Table 1) showed that there were statistically significant differences in age, BMI, adenoid grade, AR, breastfeeding status, and ETS between the AH and AH + OME groups ($P \leq 0.05$). The age distribution of the patients in the AH group was 5 (4–7) years, whereas that of those in the AH + OME group was 4 (4–5) years, and the difference between the two groups was statistically significant ($P \leq 0.0001$). The incidence of OME was higher in younger children and significantly higher in the 0–4 years age group than in the older age groups ($$P \leq 0.0002$$). The BMI (kg/m2) of the pediatric patients in the AH group was higher than that of the patients in the AH + OME group ($$P \leq 0.0030$$). The incidence of OME among patients in the adenoid grade IV group was $26.86\%$, which was higher than that among patients in the grade III group ($14.5\%$) ($$P \leq 0.0005$$). The incidence of OME among pediatric patients exposed to ETS was $36.72\%$, which was higher than that among patients who were not exposed to ETS ($11.68\%$) ($P \leq 0.0001$). The incidence of OME among breastfed pediatric patients was $17.32\%$, which was lower than that among those not breastfed ($29.23\%$) ($$P \leq 0.0036$$). The incidence of OME among patients with AR was $25.31\%$, which was higher than that among those without AR ($15.93\%$) ($$P \leq 0.0085$$). There were no statistically significant differences in sex, tonsil grade, comorbid nasal congestion/rhinorrhea, history of adenoidectomy, history of congenital diseases, preterm birth, history of food/drug allergy, family history of otitis media, adenotonsillectomy and AR, PSG monitoring results: AHI, OAI, and diagnosis and severity of OSA between the AH and AH + OME groups ($P \leq 0.05$).
**Table 1**
| Variable | AH | AH + OME | χ 2 | P-value |
| --- | --- | --- | --- | --- |
| Sex | | | 1.3379 | 0.2474 |
| Male | 257 (78.12) | 72 (21.88) | | |
| Female | 150 (82.42) | 32 (17.58) | | |
| Age (years) | 5 (4–7) | 4 (4–5) | 18.1938 | <0.0001b |
| Age group (years) | | | 16.7516 | 0.0002 |
| 0–4 | 164 (72.25) | 63 (27.75) | | |
| 5–8 | 194 (83.62) | 38 (16.38) | | |
| 9–12 | 49 (94.23) | 3 (5.77) | | |
| BMI | 15.13 (13.96–16.9) | 14.2 (13.66–16.21) | 8.7825 | 0.0030b |
| Nasal congestion /rhinorrhea | | | 0.2556 | 0.6132 |
| No | 67 (81.71) | 15 (18.29) | | |
| Yes | 340 (79.25) | 89 (20.75) | | |
| Allergic rhinitis | | | 6.9194 | 0.0085 |
| No | 227 (84.07) | 43 (15.93) | | |
| Yes | 180 (74.69) | 61 (25.31) | | |
| Recurrent tonsillitis | | | 0.0036 | 0.9523 |
| No | 283 (79.72) | 72 (20.28) | | |
| Yes | 124 (79.49) | 32 (20.51) | | |
| Adenoid grade | | | 12.0086 | 0.0005 |
| III | 230 (85.5) | 39 (14.5) | | |
| IV | 177 (73.14) | 65 (26.86) | | |
| Tonsil grade | | | 4.1044 | 0.3921 |
| 0 | 3 (60) | 2 (40) | | |
| 1 | 31 (75.61) | 10 (24.39) | | |
| 2 | 156 (77.23) | 46 (22.77) | | |
| 3 | 164 (81.59) | 37 (18.41) | | |
| 4 | 53 (85.48) | 9 (14.52) | | |
| Tonsil hypertrophy | | | 2.7378 | 0.0980 |
| No | 190 (76.61) | 58 (23.39) | | |
| Yes | 217 (82.51) | 46 (17.49) | | |
| History of adenoidectomy | | | 0.0018 | 0.9666a |
| No | 398 (79.76) | 101 (20.24) | | |
| Yes | 9 (75) | 3 (25) | | |
| History of congenital diseases | | | 0.0 | 1.0000a |
| No | 399 (79.64) | 102 (20.36) | | |
| Yes | 8 (80) | 2 (20) | | |
| Preterm birth | | | 2.5011 | 0.1138 |
| No | 385 (80.38) | 94 (19.62) | | |
| Yes | 22 (68.75) | 10 (31.25) | | |
| Breastfeeding | | | 8.4788 | 0.0036 |
| No | 92 (70.77) | 38 (29.23) | | |
| Yes | 315 (82.68) | 66 (17.32) | | |
| History of food/drug allergy | | | 2.4518 | 0.1174 |
| No | 350 (80.83) | 83 (19.17) | | |
| Yes | 57 (73.08) | 21 (26.92) | | |
| Family history of otitis media | | | 0.0 | 1.0000a |
| No | 389 (79.55) | 100 (20.45) | | |
| Yes | 18 (81.82) | 4 (18.18) | | |
| Family history of adenotonsillectomy | | | 0.2268 | 0.6339 |
| No | 381 (79.87) | 96 (20.13) | | |
| Yes | 26 (76.47) | 8 (23.53) | | |
| Family history of allergic rhinitis | | | 0.01274 | 0.9101 |
| No | 248 (79.49) | 64 (20.51) | | |
| Yes | 159 (79.9) | 40 (20.1) | | |
| Environmental tobacco smoke | | | 44.7721 | <0.0001 |
| No | 295 (88.32) | 39 (11.68) | | |
| Yes | 112 (63.28) | 65 (36.72) | | |
| AHI | 3.8 (1.9–7.9) | 4.6 (2.2–10.5) | 1.1448 | 0.2846 |
| OAI | 0.1 (0–0.8) | 0.2 (0–1.2) | 1.5794 | 0.2088 |
| OSA | | | 2.2065 | 0.1374 |
| No | 233 (82.04) | 51 (17.96) | | |
| Yes | 164 (76.64) | 50 (23.36) | | |
| Severity of OSA | | | 4.0171 | 0.2596 |
| No | 233 (82.04) | 51 (17.96) | | |
| Mild | 78 (78.79) | 21 (21.21) | | |
| Middle | 53 (71.62) | 21 (28.38) | | |
| Severe | 33 (80.49) | 8 (19.51) | | |
The factors with a Pvalue <0.1 in the univariate analysis were included in the multivariate stepwise logistic regression analysis (Table 2). The results showed that age, adenoid grade, AR, breastfeeding status, and ETS exposure were important factors that influence the occurrence of OME in pediatric patients with AH. The results also showed that the risk of OME decreases with age. The patients in the 5–8 years [$$P \leq 0.0062$$, OR: 0.494 (0.299–0.819)] and 9–12 years [$$P \leq 0.0055$$, OR: 0.169 (0.048–0.592)] age groups had lower risks for OME than those in the 0–4 years group. The results also showed that a high adenoid grade was a risk factor for OME in pediatric patients with AH. The incidence of OME among patients in the adenoid grade IV group was 1.662 times than those in the grade III group [$$P \leq 0.0438$$, OR: 1.662 (1.014–2.723)]. ETS exposure was a risk factor for OME in pediatric patients with AH. Exposure to ETS increased the risk for OME by 4.839 times compared with non-exposure [$P \leq 0.0001$, OR: 4.839 (2.994–7.819)]. Children with AR were 1.906 times more likely to develop OME than those without AR [$$P \leq 0.008$$, OR: 1.906 (1.183–3.071)]. Breastfeeding was a protective factor against OME in pediatric patients with AH [$$P \leq 0.0126$$, OR: 0.523 (0.314–0.870)].
**Table 2**
| Variable | Estimate | Standard Error | Wald χ2 | P-value | OR | OR (95% CI) | OR (95% CI).1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Age group (years) | Age group (years) | Age group (years) | Age group (years) | Age group (years) | Age group (years) | Age group (years) | Age group (years) |
| 0–4 | Ref. | | | | | | |
| 5–8 | −0.7042 | 0.2573 | 7.4909 | 0.0062 | 0.494 | 0.299 | 0.819 |
| 9–12 | −1.7805 | 0.6407 | 7.7234 | 0.0055 | 0.169 | 0.048 | 0.592 |
| Adenoid grade | Adenoid grade | Adenoid grade | Adenoid grade | Adenoid grade | Adenoid grade | Adenoid grade | Adenoid grade |
| III | Ref. | | | | | | |
| IV | 0.5079 | 0.2519 | 4.065 | 0.0438 | 1.662 | 1.014 | 2.723 |
| Allergic rhinitis | Allergic rhinitis | Allergic rhinitis | Allergic rhinitis | Allergic rhinitis | Allergic rhinitis | Allergic rhinitis | Allergic rhinitis |
| No | Ref. | | | | | | |
| Yes | 0.645 | 0.2433 | 7.0289 | 0.008 | 1.906 | 1.183 | 3.071 |
| Breastfeeding | Breastfeeding | Breastfeeding | Breastfeeding | Breastfeeding | Breastfeeding | Breastfeeding | Breastfeeding |
| No | Ref. | | | | | | |
| Yes | −0.6482 | 0.2599 | 6.2215 | 0.0126 | 0.523 | 0.314 | 0.870 |
| Environmental tobacco smoke | Environmental tobacco smoke | Environmental tobacco smoke | Environmental tobacco smoke | Environmental tobacco smoke | Environmental tobacco smoke | Environmental tobacco smoke | Environmental tobacco smoke |
| No | Ref. | | | | | | |
| Yes | 1.5766 | 0.2449 | 41.4469 | <.0001 | 4.839 | 2.994 | 7.819 |
## Conditional pathogen culture analysis
The major pathogens identified in the upper respiratory tract (oropharynx) conditional pathogen culture analysis of the pediatric patients with AH included Staphylococcus aureus, Streptococcus pneumoniae, Moraxella catarrhalis, and *Haemophilus influenzae* (Table 3). Univariate analysis showed that the incidence of OME was $22.97\%$ in the conditional pathogen-positive group, which was higher than that in the conditional pathogen-negative group ($11.11\%$) ($$P \leq 0.0357$$). The incidence of OME was $34.88\%$ in the S. pneumoniae-positive group, which was higher than that in the S. pneumoniae-negative group ($15.25\%$) ($$P \leq 0.0033$$). The incidence of OME was $33.33\%$ in the H. influenzae-positive group, which was higher than that in the H. influenzae-negative group ($16.84\%$) ($$P \leq 0.0327$$). The difference between the two groups was statistically significant ($P \leq 0.05$).
**Table 3**
| Variable | AH | AH + OME | χ2 | P-value |
| --- | --- | --- | --- | --- |
| Conditional pathogen | | | 4.4122 | 0.0357 |
| No | 64 (88.89) | 8 (11.11) | | |
| Yes | 114 (77.03) | 34 (22.97) | | |
| Streptococcus pneumoniae | | | 8.63 | 0.0033 |
| No | 150 (84.75) | 27 (15.25) | | |
| Yes | 28 (65.12) | 15 (34.88) | | |
| Staphylococcus aureus | | | 3.6534 | 0.056 |
| No | 127 (77.91) | 36 (22.09) | | |
| Yes | 51 (89.47) | 6 (10.53) | | |
| Moraxella catarrhalis | | | 2.4219 | 0.1196 |
| No | 153 (82.7) | 32 (17.3) | | |
| Yes | 25 (71.43) | 10 (28.57) | | |
| Haemophilus influenzae | | | 4.5618 | 0.0327 |
| No | 158 (83.16) | 32 (16.84) | | |
| Yes | 20 (66.67) | 10 (33.33) | | |
The conditional pathogen culture analysis showed that age and adenoid grade were correlated with AH complicated by OME ($P \leq 0.05$) (Supplementary Table S1). Hence, after correcting for age and adenoid grade, the relationship between the presence of conditional pathogens and AH complicated by OME was analyzed (Table 4). Oropharyngeal S. pneumoniae positivity was found to be correlated with AH complicated by OME [$$P \leq 0.0161$$, OR: 2.647 (1.198–5.847)].
**Table 4**
| Variable | Estimate | Standard Error | Wald χ2 | P-value | OR | OR (95% CI) | OR (95% CI).1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Conditional pathogen | Conditional pathogen | Conditional pathogen | Conditional pathogen | Conditional pathogen | Conditional pathogen | Conditional pathogen | Conditional pathogen |
| No | Ref. | | | | | | |
| Yes | 0.5858 | 0.4428 | 1.7505 | 0.1858 | 1.796 | 0.754 | 4.279 |
| Streptococcus pneumoniae | Streptococcus pneumoniae | Streptococcus pneumoniae | Streptococcus pneumoniae | Streptococcus pneumoniae | Streptococcus pneumoniae | Streptococcus pneumoniae | Streptococcus pneumoniae |
| No | Ref. | | | | | | |
| Yes | 0.9735 | 0.4043 | 5.797 | 0.0161 | 2.647 | 1.198 | 5.847 |
| Hemophilus influenzae | Hemophilus influenzae | Hemophilus influenzae | Hemophilus influenzae | Hemophilus influenzae | Hemophilus influenzae | Hemophilus influenzae | Hemophilus influenzae |
| No | Ref. | | | | | | |
| Yes | 0.8834 | 0.4648 | 3.6123 | 0.0574 | 2.419 | 0.973 | 6.016 |
## Discussion
OME is a major cause of hearing loss in children and can affect language and behavioral development [15]. Epidemiological surveys have shown that more than $50\%$ of children aged < 1 year and $60\%$ of children aged <2 years have a history of OME [16]. The high prevalence of OME among young patients is due to their incomplete structural and functional development of ET, which are affected by age-related ET factors, including length, angle, and closure capacity [17]. In our study, age group analysis revealed that the proportion of pediatric patients with AH complicated by OME aged <4 years was higher than that of the other age groups. The results of the analysis also showed that the risk for OME decreased with increasing age. The difference between the two groups was statistically significant ($P \leq 0.05$). There is no consensus on the relationship between gender and OME. The disease is expected to be more common in boys as mastoid pneumatization is more rapid in girls and boys experience upper respiratory infection episodes more frequently. Some studies also showed that OME is more common in males [18]. On the contrary, other studies showed no relationship between sex and the prevalence of OME (19–21). In our study, there was no statistical difference in sex distribution between the AH and AH + OME groups ($$P \leq 0.2475$$).
AH is the main cause of ETD, and OME is associated with ETD [10, 22]. One study showed that $29.2\%$ of children with adenoid enlargement had a co-morbidity of asymptomatic OME [23]. The etiology of OME mainly includes anatomical, immune, microbiological, and environmental factors [6]; however, its etiology is not completely clear. Hence, there is a need to examine the mechanisms and factors that influence AH complicated by OME. It is necessary to fully understand the risk factors related to OME incidence in pediatric patients with AH to better screen for and manage this disease. Our study fully collected various data from the etiology and mechanism. A comprehensive and in-depth analysis of these factors early can facilitate appropriate, timely intervention, thereby preventing disease progression. It provides a reference for the prevention and treatment of diseases. It also provides a basis and ideas for researching the deep mechanism and correlation of each influencing factor.
## Mechanical obstruction
Hypertrophic adenoids, particularly tissues near the torus and pharyngeal opening of the ET, can directly compress and obstruct the ET, resulting in impaired middle ear drainage, negative middle ear pressure, mucosal exudation, and OME. Studies have shown that the degree of AH is significantly correlated with OME and that the greater the degree, the higher the incidence of OME. In addition, OME tends to persist, and conservative treatment tends to fail in cases of higher degrees of AH. Children with a higher grade of AH have a higher risk of OME persistence, leading to conservative treatment failure and requiring surgical intervention, but this study had a limited sample size of only 65 cases [24]. The present study showed that the incidence of OME in the adenoid grade IV group was $26.86\%$, which was significantly higher than that in the grade III group ($14.5\%$) ($$P \leq 0.0005$$). The incidence of OME in the adenoid grade IV group was 1.662 (1.014–2.723) times than that in the grade III group. A high adenoid grade is a risk factor for AH complicated by OME. The higher the adenoid grade, the greater the respiratory tract obstruction and the more severe the OSA in pediatric patients [25]. However, PSG monitoring data analysis in this study revealed no significant correlation between the AHI, OAI, diagnosis and severity of OSA, and the incidence of OME in pediatric patients with AH ($P \leq 0.05$). This might be due to the population distribution in the present study and multiple potential factors. Further in-depth studies are required to clarify the relationship between these variables and the incidence of OME. Regardless, clinicians should be vigilant in managing pediatric patients with AH and OME who present with OSA. Timely screening and intervention should be performed in such cases.
## Pathogenic microorganisms
OME may be a sequela of acute otitis media (AOM). OME tends to occur in cases of AH and ETD; however, the middle ear environment determines the occurrence of OME [26]. The upper respiratory tract is an important region for the occurrence of otitis media. According to the pathogen reservoir theory, long-term retention of pathogen-carrying secretions in hypertrophic adenoid crypts can become a microorganism “reservoir”. Pathogens can reach the middle ear through the ET, causing disease. Bacteria detected in adenoid tests include normal bacteria and conditional pathogens in the nasopharynx. Conditional pathogens mainly include S. pneumoniae, H. influenzae, S. aureus, and M. catarrhalis. These pathogens can cause otitis media, nasal sinusitis, upper respiratory tract infections, pneumonia, and systemic diseases (26–30). Typical ear pathogens isolated from middle ear effusions, nasopharyngeal samples, and adenoid samples include S. pneumoniae, H. influenzae, and M. catarrhalis. In addition, multi-pathogen infections are often present in OME [31, 32]. After viral infection of the upper respiratory tract occurs, the pathogens can ascend to the middle ear through the ET [33] and disrupt the respiratory tract flora. Viruses can cause bacteria to transform from commensal to pathogenic, disrupt the airway epithelial barrier, decrease mucus and cilia clearance, induce the host to provide nutrients to pathogens, and promote adhesion and virulence in ear pathogens [34]. Oropharyngeal and nasopharyngeal pathogens in children tend to translocate to the middle ear owing to the structural characteristics of the ET and the middle ear negative pressure caused by AH [35]. Some studies have compared the bacteriology of the adenoids and tonsils in children by culture, and found an overall similarity in the bacteria sampled from the surfaces of tonsils and adenoids of children [36, 37]. Surface samples from the nasopharynx and oropharynx may easily be contaminated by saliva, tears, and other secretions. Other studies used 16S rRNA gene pyrosequencing. One study found that the microbiome differs between crypts of the adenoids and crypts of the tonsils, including the relative abundances of potential pathogens such as H. influenzae, S. pneumoniae, and M. catarrhalis [38]. Another study has reported combined analyses of the adenoids and tonsils microbiome in pediatric. The microbiome was not significantly different at the phylum level between the adenoids and tonsils [39]. In future studies, 16S rRNA gene pyrosequencing technology can be used to identify the differences in bacterial distribution. In addition, whole-genome sequencing should be conducted to analyze the specificity of bacterial capsules and virulence factors. In our study, conditional pathogen culture analysis of the oropharynx which represented the upper respiratory tract revealed that the conditional pathogens detected in the oropharynx of the pediatric patients mainly consisted of S. aureus, S. pneumoniae, M. catarrhalis, and H. influenzae. The results of the univariate analysis (Table 3) showed that the incidence of OME was higher in the conditional pathogen-positive group, S. pneumoniae-positive group, and H. influenzae-positive group than in the corresponding negative groups; the inter-group differences were statistically significant ($P \leq 0.05$). In addition, there were statistically significant differences in age and adenoid grade between the groups ($P \leq 0.05$) (Supplementary Table S1). After correcting for the effects of age and adenoid grade on the incidence of OME, the results showed that presence of S. pneumoniae in the oropharynx is a risk factor for the occurrence of OME in pediatric patients with AH. The incidence of OME in the S. pneumoniae-positive group was 2.647 times that in the S. pneumoniae-negative group [$$P \leq 0.0161$$, OR: 2.647 (1.198–5.847)] (Table 4). Future studies with larger sample sizes are needed for further analysis of these results.
## Local immune dysregulation in adenoids and allergic reactions
The middle ear is an independent immune organ that is structurally connected to the ET and the upper respiratory tract. Based on the same airway principle, antigens that stimulate the nasal mucosa can also produce mucosal immune responses in the ET and the middle ear. Adenoids contain T and B lymphocytes in different developmental stages and are major secondary lymphoid organs that constitute Waldeyer's ring. They participate in innate and acquired immunity to resist upper respiratory tract infections in children. Local immune dysregulation decreases adenoid barrier function and increases the incidence of host OME. There are differences in adenoid lymphocyte subset distribution in pediatric patients with AH. Patients with AH + OME show higher T and B lymphocyte counts than those with AH only, which leads to increased local antigen-presenting dendritic cell count [40], increased capture of airway pathogens, decreased immune function, and increased host susceptibility. Immunoglobulins produced by B cells are an important defense mechanism against otitis media and other upper respiratory tract infections [41]. The present study showed that AR increases the risk for OME in pediatric patients with AH. AR is an IgE-mediated type I hypersensitivity reaction that is characterized by increased vascular permeability, increased mast cells and related inflammatory cell secretion of histamine, leukotriene, and other inflammatory mediators, occurrence of mucosal edema and exudation, obstruction of the ET, and decreased cilia motility, resulting in OME. Allergies contribute to the occurrence and progression of AH and OME. The incidence of OME is significantly higher in pediatric patients with AR than in pediatric patients without AR [42, 43]. However, the specific incidence of OME is determined by population characteristics and diagnostic criteria. A systematic review of the pathophysiology by which allergy increases the risk of otitis media showed that allergy is related to the occurrence and acute exacerbation of OME [44]. Allergen stimulation causes local immune responses, elevated Th2 cytokine secretion, weakened upper respiratory tract mucosal barrier function, increased nasopharyngeal bacteria [45], and increased incidence of OME. IgE, IL-4, histamine, and eosinophil levels in middle ear effusions are increased in patients with OME. In patients with AR, IL-4, IL-5, IFN-γ, and histamine secretions in the nasal mucosa are increased [46]. In a previous study of a mouse model of OME created by inducing middle ear infection, middle ear mucosal immune responses towards bacterial lipopolysaccharides were increased in the AR group [47].
Besides etiological factors, several immutable factors such as genetic factors and family history, and variable factors such as environment and lifestyle can affect the occurrence and progression of the disease [48]. ETS exposure is also known as “secondhand smoking” or “passive smoking”. Animal studies have demonstrated that nicotine can stimulate the hypothalamus α3β4 nicotinic acetylcholine receptor to decrease appetite, energy intake, and body weight [49]. Parental smoking is a common source of ETS exposure in children. Interventional measures should be employed to decrease ETS exposure in children and improve their health [50, 51]. In the United States, $29.2\%$ of adolescents are exposed to ETS [52].Thus, protocols for improving home-smoking behavior should be studied and implemented [53]. ETS exposure and AR in children are associated with increased prevalence of eczema [54]. Studies have shown that passive smoking and ETS exposure are environmental risk factors for OME in children [20, 55]. Maternal smoking habits and the number of family members who are smokers are significantly correlated with the risk of otitis media in children aged < 4 years old [56]. However, some studies did not show an association between ETS exposure and otitis media in children [19]. Our study showed that ETS exposure is a risk factor for the occurrence of OME in pediatric patients with AH. The main harmful components of cigarettes, including nicotine, tar, carbon monoxide, and acrolein, can cause respiratory diseases [57] and destroy ET surfactants. Cilia toxins decrease ciliary beat frequency and cause the ET mucosa and cilia in children to be susceptible to damage. Thus, adult smoking can disrupt upper respiratory tract flora [58] and affect children.
Our study showed that the incidence of OME in breastfed pediatric patients was $17.32\%$, which was lower than that in the patients who were not ($29.23\%$) ($$P \leq 0.0036$$). This indicates that breastfeeding is a protective factor against the occurrence of OME in pediatric patients with AH (Table 2). Breast milk not only contains antibacterial substances, but can also promote the development of healthy flora and is negatively correlated with respiratory tract infections [59, 60]. In addition, breast milk can optimize immune function and decrease the risk of otitis media and respiratory tract infections in infants and toddlers (61–63). A Lancet paper analyzed the potential mechanisms underlying the effects of breastfeeding in terms of immunology, epigenetics, microbiomics, and stem cell research. Increased breastfeeding can decrease rate of mortality in children and prevent infectious diseases. In addition, the protective effects of breastfeeding against otitis media in children can extend up to age 2 and older [64]. In addition to providing environmental pathogen-specific IgA, breast milk can develop non-specific defenses against bacterial pathogens. Besides preventing upper respiratory tract infections, maternal antibodies can also act on middle ear pathogens and interfere with bacterial adhesion to the nasopharyngeal epithelium to prevent acute otitis media [65]. Antigen stimulation can result in the production of IgG antibodies against non-typeable H. influenzae [66] and regulate an infant's humoral immune responses towards common OME pathogens. Milk bottle suction and the swallowing pressure gradient can cause ETD and increase susceptibility to otitis media [67].
In summary, the pathogenesis of AH with OME is complex and is influenced by many factors. Younger age and a high adenoid grade are major risk factors for the occurrence of OME in pediatric patients with AH. ETS exposure, a non-breastfed status, AR, and presence of conditional pathogens (mainly S. pneumoniae) in the upper respiratory tract also influence the pathogenesis of AH complicated by OME. This study provides a foundation and basis for future etiological mechanism studies and treatments. It is necessary to study deep mechanisms and correlations in the future, such as the establishment of animal models related to the incidence of OME influencing factors and whole-genome sequencing for the analysis of the specificity of bacterial capsules and virulence factors.
The presented study still had some main limitations. The sample sizes were limited, especially the cases of conditioned pathogen culture. In addition, the study time range included the global COVID-19 pandemic, which involved Beijing's strict implementation of the dynamic zero-COVID policy during this period. The prevalence of the novel coronavirus did not affect the incidence and progression of the disease in the study population. However, the overall number of patients hospitalized for AH and related diseases decreased compared to before the epidemic. In particular, there were very few patients from other cities, which seemed to have a potentially unavoidable effect on the distribution of the study population.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Beijing Tsinghua Changgung Hospital Ethics Committee. Written informed consent from the participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.
## Author contributions
CWJ collected data, statistical analysis, and wrote the article. YGP revised the manuscript. CYJ, WYY, ZCM, and WW participated in data collection. WLJ proposed ideas for the experiment. YJY helped with study design and project administration. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fped.2023.1098067/full#supplementary-material.
## References
1. Kheirandish-Gozal L, Gozal D. **Obesity, asthma, and sleep-disordered breathing**. *J Pediatr* (2012) **160** 713-4. DOI: 10.1016/j.jpeds.2011.11.036
2. Dogru M, Evcimik MF, Calim OF. **Does adenoid hypertrophy affect disease severity in children with allergic rhinitis**. *Eur Arch Otorhinolaryngol* (2017) **274** 209-13. DOI: 10.1007/s00405-016-4196-x
3. Rosenfeld RM, Culpepper L, Doyle KJ, Grundfast KM, Hoberman A, Kenna MA. **Clinical practice guideline: otitis media with effusion**. *Otolaryngol Head Neck Surg* (2004) **130** S95-118. DOI: 10.1016/j.otohns.2004.02.002
4. Abdel Tawab HM, Tabook SMS. **Correlation between adenoid hypertrophy, tympanometry findings, and viscosity of middle ear fluid in chronic otitis media with effusion, southern Oman**. *Ear Nose Throat J* (2021) **100** NP141-6. DOI: 10.1177/0145561319875438
5. Teschner M. **Evidence and evidence gaps in the treatment of eustachian tube dysfunction and otitis media**. *GMS Curr Top Otorhinolaryngol Head Neck Surg* (2016) **15** Doc05. DOI: 10.3205/cto000132
6. Buzatto GP, Tamashiro E, Proenca-Modena JL, Saturno TH, Prates MC, Gagliardi TB. **The pathogens profile in children with otitis media with effusion and adenoid hypertrophy**. *PLoS One* (2017) **12** e0171049. DOI: 10.1371/journal.pone.0171049
7. Songu M, Islek A, Imre A, Aslan H, Aladag I, Pinar E. **Risk factors for otitis media with effusion in children with adenoid hypertrophy**. *Acta Otorhinolaryngol Ital* (2020) **40** 133-7. DOI: 10.14639/0392-100X-2456
8. Ingram DG, Friedman NR. **Toward adenotonsillectomy in children: a review for the general pediatrician**. *JAMA Pediatr* (2015) **169** 1155-61. DOI: 10.1001/jamapediatrics.2015.2016
9. Rosenfeld RM, Shin JJ, Schwartz SR, Coggins R, Gagnon L, Hackell JM. **Clinical practice guideline: otitis media with effusion executive summary (update)**. *Otolaryngol Head Neck Surg* (2016) **154** 201-14. DOI: 10.1177/0194599815624407
10. Cassano P, Gelardi M, Cassano M, Fiorella ML, Fiorella R. **Adenoid tissue rhinopharyngeal obstruction grading based on fiberendoscopic findings: a novel approach to therapeutic management**. *Int J Pediatr Otorhinolaryngol* (2003) **67** 1303-9. DOI: 10.1016/j.ijporl.2003.07.018
11. Friedman M, Tanyeri H, La Rosa M, Landsberg R, Vaidyanathan K, Pieri S. **Clinical predictors of obstructive sleep apnea**. *Laryngoscope* (1999) **109** 1901-7. DOI: 10.1097/00005537-199912000-00002
12. Evcimik MF, Dogru M, Cirik AA, Nepesov MI. **Adenoid hypertrophy in children with allergic disease and influential factors**. *Int J Pediatr Otorhinolaryngol* (2015) **79** 694-7. DOI: 10.1016/j.ijporl.2015.02.017
13. Olusesi AD, Undie NB, Amodu JE. **Allergy history as a predictor of early onset adenoids/adenotonsillar hypertrophy among Nigerian children**. *Int J Pediatr Otorhinolaryngol* (2013) **77** 1032-5. DOI: 10.1016/j.ijporl.2013.04.004
14. Marcus CL, Omlin KJ, Basinki DJ, Bailey SL, Rachal AB, Von Pechmann WS. **Normal polysomnographic values for children and adolescents**. *Am Rev Respir Dis* (1992) **146** 1235-9. DOI: 10.1164/ajrccm/146.5_Pt_1.1235
15. Samuels TL, Yan JC, Khampang P, Dettmar PW, MacKinnon A, Hong W. **Association of gel-forming mucins and aquaporin gene expression with hearing loss, effusion viscosity, and inflammation in otitis media with effusion**. *JAMA Otolaryngol Head Neck Surg* (2017) **143** 810-7. DOI: 10.1001/jamaoto.2017.0386
16. Simon F, Haggard M, Rosenfeld RM, Jia H, Peer S, Calmels M-N. **International consensus (ICON) on management of otitis media with effusion in children**. *Eur Ann Otorhinolaryngol Head Neck Dis* (2018) **135** S33-9. DOI: 10.1016/j.anorl.2017.11.009
17. Vanneste P, Page C. **Otitis media with effusion in children: pathophysiology, diagnosis, and treatment. A review**. *J Otol* (2019) **14** 33-9. DOI: 10.1016/j.joto.2019.01.005
18. Ungkanont K, Charuluxananan S, Komoltri C. **Association of otoscopic findings and hearing level in pediatric patients with otitis media with effusion**. *Int J Pediatr Otorhinolaryngol* (2010) **74** 1063-6. DOI: 10.1016/j.ijporl.2010.06.006
19. Gultekin E, Develioğlu ON, Yener M, Ozdemir I, Külekçi M. **Prevalence and risk factors for persistent otitis media with effusion in primary school children in Istanbul, Turkey**. *Auris Nasus Larynx* (2010) **37** 145-9. DOI: 10.1016/j.anl.2009.05.002
20. Erdivanli OC, Coskun ZO, Kazikdas KC, Demirci M. **Prevalence of otitis media with effusion among primary school children in eastern Black Sea, in Turkey and the effect of smoking in the development of otitis media with effusion**. *Indian J Otolaryngol Head Neck Surg* (2012) **64** 17-21. DOI: 10.1007/s12070-011-0131-z
21. Kucur C, Şimşek E, Kuduban O, Özbay İ. **Prevalence of and risk factors for otitis media with effusion in primary school children: case control study in erzurum, Turkey**. *Turk J Pediatr* (2015) **57** 230-5. PMID: 26701940
22. Manno A, Iannella G, Savastano V, Vittori T, Bertin S, Pasquariello B. **Eustachian tube dysfunction in children with adenoid hypertrophy: the role of adenoidectomy for improving ear ventilation**. *Ear Nose Throat J* (2021) 145561321989455. DOI: 10.1177/0145561321989455
23. Sogebi OA, Oyewole EA, Ogunbanwo O. **Asymptomatic otitis media with effusion in children with adenoid enlargement**. *J Natl Med Assoc* (2021) **113** 158-64. DOI: 10.1016/j.jnma.2020.08.005
24. Galić MZ, Klančnik M. **Adenoid size in children with otitis media with effusion**. *Acta Clin Croat* (2022) **60** 532-9. DOI: 10.20471/acc.2021.60.03.25
25. Xiao L, Su S, Liang J, Jiang Y, Shu Y, Ding L. **Analysis of the risk factors associated with obstructive sleep apnea syndrome in Chinese children**. *Front Pediatr* (2022) **10** 900216. DOI: 10.3389/fped.2022.900216
26. Chan CL, Wabnitz D, Bardy JJ, Bassiouni A, Wormald P-J, Vreugde S. **The microbiome of otitis media with effusion**. *Laryngoscope* (2016) **126** 2844-51. DOI: 10.1002/lary.26128
27. Johnston JJ, Douglas R. **Adenotonsillar microbiome: an update**. *Postgrad Med J* (2018) **94** 398-403. DOI: 10.1136/postgradmedj-2018-135602
28. Johnston J, Hoggard M, Biswas K, Astudillo-García C, Radcliff FJ, Mahadevan M. **Pathogen reservoir hypothesis investigated by analyses of the adenotonsillar and middle ear microbiota**. *Int J Pediatr Otorhinolaryngol* (2019) **118** 103-9. DOI: 10.1016/j.ijporl.2018.12.030
29. Ren T, Glatt DU, Nguyen TN, Allen EK, Early SV, Sale M. **16S rRNA survey revealed complex bacterial communities and evidence of bacterial interference on human adenoids**. *Environ Microbiol* (2013) **15** 535-47. DOI: 10.1111/1462-2920.12000
30. Huang C-C, Chang T-H, Lee C-Y, Wu P-W, Chen C-L, Lee T-J. **Tissue microbiota in nasopharyngeal adenoid and its association with pneumococcal carriage**. *Microb Pathog* (2021) **157** 104999. DOI: 10.1016/j.micpath.2021.104999
31. Silva MD, Lima A, Marçal N, Dias L, Gama M, Sillankorva S. **Identification of the bacterial pathogens in children with otitis media: a study in the northwestern Portuguese district of Braga**. *Microorganisms* (2021) **10** 54. DOI: 10.3390/microorganisms10010054
32. Jörissen J, van den Broek M, De Boeck I, Beeck WV, Wittouck S, Boudewyns A. **Case-control microbiome study of chronic otitis media with effusion in children points at streptococcus salivarius as a pathobiont-inhibiting species**. *mSystems* (2021) **6** e00056-21. DOI: 10.1128/mSystems.00056-21
33. Lappan R, Jamieson SE, Peacock CS. **Reviewing the pathogenic potential of the otitis-associated bacteria**. *Front Cell Infect Microbiol* (2020) **10** 51. DOI: 10.3389/fcimb.2020.00051
34. Walker RE, Walker CG, Camargo CA, Bartley J, Flint D, Thompson JMD. **Nasal microbial composition and chronic otitis media with effusion: a case-control study**. *PLoS One* (2019) **14** e0212473. DOI: 10.1371/journal.pone.0212473
35. Minovi A, Dazert S. **Diseases of the middle ear in childhood**. *Laryngorhinootologie* (2014) **93** S1-23. DOI: 10.1055/s-0033-1363213
36. Brook I, Shah K. **Bacteriology of adenoids and tonsils in children with recurrent adenotonsillitis**. *Ann Otol Rhinol Laryngol* (2001) **110** 844-8. DOI: 10.1177/000348940111000908
37. Taylan I, Ozcan I, Mumcuoğlu I, Baran I, Murat Özcan K, Akdoğan O. **Comparison of the surface and core bacteria in tonsillar and adenoid tissue with Beta-lactamase production**. *Indian J Otolaryngol Head Neck Surg* (2011) **63** 223-8. DOI: 10.1007/s12070-011-0265-z
38. Fagö-Olsen H, Dines LM, Sørensen CH, Jensen A. **The adenoids but not the palatine tonsils serve as a reservoir for bacteria associated with secretory otitis media in small children**. *mSystems* (2019) **4** e00169-18. DOI: 10.1128/mSystems.00169-18
39. Kim KS, Min HJ. **Correlations between the adenotonsillar microbiome and clinical characteristics of pediatric patients with snoring**. *Clin Exp Otorhinolaryngol* (2021) **14** 295-302. DOI: 10.21053/ceo.2020.01634
40. Kotowski M, Niedzielski A, Niedzielska G, Lachowska-Kotowska P. **Dendritic cells and lymphocyte subpopulations of the adenoid in the pathogenesis of otitis media with effusion**. *Int J Pediatr Otorhinolaryngol* (2011) **75** 265-9. DOI: 10.1016/j.ijporl.2010.11.014
41. Jung SY, Kim D, Park DC, Lee EH, Choi Y-S, Ryu J. **Immunoglobulins and transcription factors in otitis media**. *Int J Mol Sci* (2021) **22** 3201. DOI: 10.3390/ijms22063201
42. Cheng X, Sheng H, Ma R, Gao Z, Han Z, Chi F. **Allergic rhinitis and allergy are risk factors for otitis media with effusion: a meta-analysis**. *Allergol Immunopathol (Madr)* (2017) **45** 25-32. DOI: 10.1016/j.aller.2016.03.004
43. Pau BC, Ng DK. **Prevalence of otitis media with effusion in children with allergic rhinitis, a cross sectional study**. *Int J Pediatr Otorhinolaryngol* (2016) **84** 156-60. DOI: 10.1016/j.ijporl.2016.03.008
44. De Corso E, Cantone E, Galli J, Seccia V, Lucidi D, Di Cesare T. **Otitis media in children: which phenotypes are most linked to allergy? A systematic review**. *Pediatr Allergy Immunol* (2021) **32** 524-34. DOI: 10.1111/pai.13431
45. Chawes BLK. **Upper and lower airway pathology in young children with allergic- and non-allergic rhinitis**. *Dan Med Bull* (2011) **58** B4278. PMID: 21535990
46. Steelant B, Seys SF, Van Gerven L, Van Woensel M, Farré R, Wawrzyniak P. **Histamine and T helper cytokine-driven epithelial barrier dysfunction in allergic rhinitis**. *J Allergy Clin Immunol* (2018) **141** 951-63. DOI: 10.1016/j.jaci.2017.08.039
47. Kim DK, Park HE, Back SA, Park HR, Kim SW, Park Y. **Otitis media with effusion in an allergic animal model: a functional and morphological study**. *Int J Pediatr Otorhinolaryngol* (2016) **84** 6-11. DOI: 10.1016/j.ijporl.2016.02.018
48. Szőke H, Maródi M, Vagedes J, Székely B, Magyarosi I, Bedő A. **The P.E.A.N.U.T. method: update on an integrative system approach for the treatment of chronic otitis media with effusion and adenoid hypertrophy in children**. *Antibiotics (Basel)* (2021) **10** 134. DOI: 10.3390/antibiotics10020134
49. Seeley RJ, Sandoval DA. **Neuroscience: weight loss through smoking**. *Nature* (2011) **475** 176-7. DOI: 10.1038/475176a
50. Chiswell C, Akram Y. **Impact of environmental tobacco smoke exposure on anaesthetic and surgical outcomes in children: a systematic review and meta-analysis**. *Arch Dis Child* (2017) **102** 123-30. DOI: 10.1136/archdischild-2016-310687
51. Behbod B, Sharma M, Baxi R, Roseby R, Webster P. **Family and carer smoking control programmes for reducing children's Exposure to environmental tobacco smoke**. *Cochrane Database Syst Rev* (2018) **1** CD001746. DOI: 10.1002/14651858.CD001746.pub4
52. Jain RB. **Rates of exposure to environmental tobacco smoke from various indoor environments among US children and nonsmoker adolescents and adults**. *Environ Sci Pollut Res Int* (2018) **25** 17002-11. DOI: 10.1007/s11356-018-1891-8
53. Ferris E, Cummins C, Chiswell C, Jones L. **Exploring stakeholder views on intervening in hospital around childhood secondhand smoke exposure (Precedent): a protocol for a qualitative study**. *BMJ Open* (2021) **11** e047817. DOI: 10.1136/bmjopen-2020-047817
54. Singh S, Sharma BB, Salvi S, Chhatwal J, Jain KC, Kumar L. **Allergic rhinitis, rhinoconjunctivitis, and eczema: prevalence and associated factors in children**. *Clin Respir J* (2018) **12** 547-56. DOI: 10.1111/crj.12561
55. Kırıs M, Muderris T, Kara T, Bercin S, Cankaya H, Sevil E. **Prevalence and risk factors of otitis media with effusion in school children in eastern anatolia**. *Int J Pediatr Otorhinolaryngol* (2012) **76** 1030-5. DOI: 10.1016/j.ijporl.2012.03.027
56. Jensen RG, Koch A, Homøe P, Bjerregaard P. **Tobacco smoke increases the risk of otitis media among Greenlandic inuit children while exposure to organochlorines remain insignificant**. *Environ Int* (2013) **54** 112-8. DOI: 10.1016/j.envint.2013.01.015
57. Kong S-K, Chon K-M, Goh E-K, Lee I-W, Lee J-W, Wang S-G. **Histologic changes in the auditory tube mucosa of rats after long-term exposure to cigarette smoke**. *Am J Otolaryngol* (2009) **30** 376-82. DOI: 10.1016/j.amjoto.2008.07.009
58. Charlson ES, Chen J, Custers-Allen R, Bittinger K, Li H, Sinha R. **Disordered microbial communities in the upper respiratory tract of cigarette smokers**. *PLoS One* (2010) **5** e15216. DOI: 10.1371/journal.pone.0015216
59. Biesbroek G, Bosch AATM, Wang X, Keijser BJF, Veenhoven RH, Sanders EAM. **The impact of breastfeeding on nasopharyngeal microbial communities in infants**. *Am J Respir Crit Care Med* (2014) **190** 298-308. DOI: 10.1164/rccm.201401-0073OC
60. de Steenhuijsen Piters WAA, Sanders EAM, Bogaert D. **The role of the local microbial ecosystem in respiratory health and disease**. *Philos Trans R Soc Lond B Biol Sci* (2015) **370** 20140294. DOI: 10.1098/rstb.2014.0294
61. SŞ E, Öztürk M, Sevinç R, Derin S, Dinç AE, Erdem D. **Risk factors for otitis media effusion in children who have adenoid hypertrophia**. *Int J Pediatr Otorhinolaryngol* (2015) **79** 374-7. DOI: 10.1016/j.ijporl.2014.12.030
62. Martines F, Salvago P, Ferrara S, Messina G, Mucia M, Plescia F. **Factors influencing the development of otitis media among Sicilian children affected by upper respiratory tract infections**. *Braz J Otorhinolaryngol* (2016) **82** 215-22. DOI: 10.1016/j.bjorl.2015.04.002
63. Lodge CJ, Bowatte G, Matheson MC, Dharmage SC. **The role of breastfeeding in childhood otitis media**. *Curr Allergy Asthma Rep* (2016) **16** 68. DOI: 10.1007/s11882-016-0647-0
64. Victora CG, Bahl R, Barros AJD, França GVA, Horton S, Krasevec J. **Breastfeeding in the 21st century: epidemiology, mechanisms, and lifelong effect**. *Lancet* (2016) **387** 475-90. DOI: 10.1016/S0140-6736(15)01024-7
65. Abrahams SW, Labbok MH. **Breastfeeding and otitis media: a review of recent evidence**. *Curr Allergy Asthma Rep* (2011) **11** 508-12. DOI: 10.1007/s11882-011-0218-3
66. Sabirov A, Casey JR, Murphy TF, Pichichero ME. **Breast-feeding is associated with a reduced frequency of acute otitis media and high serum antibody levels against NTHi and outer membrane protein vaccine antigen candidate P6**. *Pediatr Res* (2009) **66** 565-70. DOI: 10.1203/PDR.0b013e3181b4f8a6
67. Labbok MH, Clark D, Goldman AS. **Breastfeeding: maintaining an irreplaceable immunological resource**. *Nat Rev Immunol* (2004) **4** 565-72. DOI: 10.1038/nri1393
|
---
title: Evaluation of depression and obesity indices based on applications of ANOVA,
regression, structural equation modeling and Taguchi algorithm process
authors:
- Nur Anisah Mohamed
- Ayed R. A. Alanzi
- Noor Azlinna Azizan
- Suzana Ariff Azizan
- Nadia Samsudin
- Hashem Salarzadeh Jenatabadi
journal: Frontiers in Psychology
year: 2023
pmcid: PMC9993013
doi: 10.3389/fpsyg.2023.1060963
license: CC BY 4.0
---
# Evaluation of depression and obesity indices based on applications of ANOVA, regression, structural equation modeling and Taguchi algorithm process
## Abstract
### Introduction
Depression and obesity are the main threat among women which have been considered by many research scholars in psychology studies. In their analysis for measuring and estimating obesity and depression they were involving statistical functions.
### Methods
Regression, Analysis of Variance (ANOVA), and in the last two decades Structural Equation Modeling are the most familiar statistical methods among research scholars. Taguchi algorism process is one the statistical methods which mostly have been applying in engineering studies. In this study we are looking at two main objectives. The first one is to introduce Taguchi algorism process and apply it in a case study in psychology area. The second objective is challenging among four statistical techniques include ANOVA, regression, SEM, and Taguchi technique in a same data. To achieve those aims we involved depression and obesity indices with other familiar indicators contain socioeconomic, screen time, sleep time, and usage fitness and nutrition mobile applications.
### Results and discussion
Outputs proved that Taguchi technique is able to analyze some correlations which are not achieved by applying ANOVA, regression, and SEM. Moreover, SEM has a special capability to estimate some hidden correlations which are not possible to evaluate them by using ANOVA, regression, and even Taguchi method. In the last, we found that some correlations are significant by SEM, however, in the same data with regression those correlation were not significant. This paper could be a warning for psychology research scholars to be more careful with involving statistical methods for measuring and estimating of their research variables.
## Introduction
Over time, there has been a consistent rise in the number of people receiving a depression diagnosis. The patient’s ability to work, financial situation, and interpersonal relationships are all impacted by this mental disease (Muharam et al., 2018). Passive behaviors such as disinterest, guilt-ridden thoughts, low self-esteem, lack of sleep, poor appetite, perpetual sadness, or signs of weariness can all be indicators of depression (El-Gilany et al., 2018; Zeng et al., 2018). Being depressed on a day-to-day basis results in a major handicap that can cause mental and behavioral difficulties (Archana et al., 2017). It is very likely that this condition will have an effect on the patient’s physical well-being, which will ultimately lead to an elevated risk of morbidity and mortality (Elamoshy et al., 2018; Jia et al., 2018; Muharam et al., 2018). According to the World Health Organization (WHO), more than 300 million people worldwide experienced symptoms of depression in World Health Organization [2017]. However, earlier research found that women, rather than men, were more likely to suffer from depression (Jin et al., 2016). It was shown that hormonal changes, such as those that occur during puberty, pregnancy, and menopause, were the most significant contributors to depression in women. Particularly after giving birth, a woman needs to have extra care and obtain the right kind of health care priorities because any unpleasant act can cause depression at this stage, which will be devastating to the entire family (Correa et al., 2017). In addition, a woman needs to obtain the right kind of health care priorities because any unpleasant act can cause depression at this stage.
On the other hand, the epidemic of overweight and obesity is quickly spreading, especially to younger age groups; as a result, it has become a serious concern for healthcare systems due to the tremendous economic and psychosocial load it places on those systems (Rippe, 2021). In 2017, it was estimated that $13\%$ of the adult population globally was obese, and this number has consistently risen over the past few decades in both developed and developing countries (650 million people) (World Health Organization, 2017). Several types of cancer, heart disease, and diabetes type 2 are just a few of the many chronic illnesses for which obesity is a major risk factor.
On the other side, technology can help people reach their health goals. The Global Observatory for eHealth defines mobile health (mHealth) as the “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices” (World Health Organization, 2011) has become a major focus in the delivery of health care in recent years because of its potential impact on a wide range of health outcomes (Boulos et al., 2014). With the use of mobile technology, psychosocial and health behavior therapies are now accessible to a wider range of patients, in their natural environments, and in real time (Heron and Smyth, 2010). Mobile applications, sometimes known as “apps,” enable remote access to health services by linking patients with health experts all over the world in a safe, confidential, and secure manner, with quick results (Qudah and Luetsch, 2019). There are now a great number of smartphone apps that may be downloaded to help manage the symptoms of anxiety and depression.
Healthcare professionals are turning to mobile health (mHealth) technologies (Yuan et al., 2015), such as nutrition and fitness apps (Bt Wan Mohamed Radzi et al., 2020; Taylor et al., 2022) to support women in managing obesity and depression from home. The nutritional apps engage women because they are convenient for monitoring daily food intake for healthier eating. Meanwhile, fitness apps help women chart their weight loss progress and BMI levels while following various exercises (Cho et al., 2015). Women also benefit from quick and easy access to health information and use mHealth apps to communicate with health professionals and peers.
There is some encouraging evidence of their usefulness, but this still has to be validated because the majority of them have not been planned or studied with the level of rigor required (Rathbone and Prescott, 2017). Moreover, inadequate scientific coverage, inaccurate weight-related information, lack of important evidence-based features, lack of involvement of health-care experts in the development process, overlooking behavior change techniques, and not undergoing rigorous scientific testing are some of the issues that plague the majority of commercial mobile apps for weight loss and management (Bardus et al., 2016).
## Gaps in previous psychology studies
There are three gaps in previous psychology studies regarding measuring depression and obesity. Some previous studies just focused on estimating either obesity or depression. There are few studies also considered depression as an input for estimating obesity, or they involved obesity as an input for estimating depression. However, based on some former studies we found that there is a correlation between depression and obesity. The first gap of these types of studies is that lack of analysis for estimating both obesity and depression as two dependent variables in a single model. We full-fill this gap with introducing a single model with two dependent variables (depression and BMI) based on SEM technique. In terms statistical methods, ANOVA, simple correlation, regression, and SEM are the most familiar statistical methods among research scholar in psychology studies. These statistical modeling or even mathematical modeling techniques including neuro-fuzzy inference systems can determine the impact of significant independent (input) variables on dependent variable/s.
They are unable to demonstrate, however, which levels or groups of independent variables result in higher, lower, or nominal dependent variable (output) rate levels. Therefore, the question of what level or category of independent variables results in higher or lower dependent variables cannot be addressed by statistical or mathematical modeling techniques. Based on our research variables we could turn this question to, what level of usage nutrition and fitness apps, sleep time, screen time, and demographic leads women to have high level of depression or BMI? Therefore, Taguchi method can answer this question. The second gap of former in phycology studies that, based on best on knowledge there is lack of studies to answer this question. In other words, Taguchi technique, been applied in various engineering studies but there is scarce evidence for applying this pattern in psychology studies. The final gap is that, while they frequently used fitness and nutrition apps separately, it was uncommon to find apps that effectively treated both obesity and depression using a single model or pattern.
## Objectives of the study
Based on above matters, this study is trying to achieve the following objectives: First objective: to find out the impact of nutrition apps and physical apps usage on women’s obesity and depression with application of ANOVA. This is a common analysis which have been done in previous studies.
Second objective: to estimate the effectiveness of nutrition apps and physical apps (with other research variables) on women’s depression and obesity based on regression modeling. In this part of the study, we will have separates models for estimating BMI and depression.
Third objective: to estimate the effectiveness of nutrition apps and physical apps (with other research variables) on women’s depression and obesity in a single model based on SEM.
Fourth objective: to recognize the combination of levels of both types of nutrition and physical apps (with other research variables) leads to higher obesity and depression. For this objective of the study, we applied Taguchi algorithm process.
Fifth objective: To compare outputs of data analysis with different above statistical techniques.
## Taguchi method structure
In the Taguchi method, standard deviation and variation need to be measured for the expected value. The observed values were spread out from the expected value by a high standard deviation due to noise factors. The lower standard deviation indicates that the observed values are near the expected value due to noise factors. Observed and noise factor values can be controlled by the Signal-to-Noise (SN) ratio. The SN ratio effects noise factors on performance features and quantifies the variability (Salarzadeh Jenatabadi et al., 2016). The formula to calculate the signal to noise ratio (Manigandan et al., 2020) is given in Table 1.
**Table 1**
| Optimisation type | Calculation of SN |
| --- | --- |
| Lower is better | SN=−10log1n∑i=1nyi2 |
| Larger is better | SN=−10log1n∑i=1n1yi2 |
The Taguchi method process analysis is as follows, which refers to the previous literature (Salarzadeh Jenatabadi et al., 2016): Step 1: Identifying the Indicators.
Step 2: Calculate the weight of each indicator.
Step 3: Choosing an appropriate experimental design based on the Taguchi method.
Step 4: Identifying the Optimal Levels of the Indicators.
Step 5: Data Analysis.
Step 6: Assessing the Factors in the Columns of the Orthogonal Array.
Step 7: Introducing suitable patterns based on the optimal levels.
## Participants
The sample size estimation was calculated using Krejcie and Morgan’s method (Chuan and Penyelidikan, 2006). The formula below is commonly used to establish the total sample size requirement (Krejcie and Morgan, 1970): In the above equation, s is the required sample size; χ2 is the table value of chi-square for one degree of freedom at the desired confidence level; N is the population size; P is the population proportion; and d is the degree of accuracy expressed as a proportion (0.05). This research subject focused on Malaysian women who lived in urban and populated cities, i.e., Kuala Lumpur, Selangor, Penang, and Johor. The data was distributed after the MCO via online questionnaires by sending the questionnaire links through WhatsApp to 878 respondents.
Note: Approaching to the participant were based on two data sets (a) University Malaya grant [title: New Framework and Statistical Approaches for Health Index Studies: Case Study in Malaysia (GPF066B-2018)] and (b) our previous studies data set (Bt Wan Mohamed Radzi et al., 2020, 2021).
## Measurement
The research variables were classified into five sections: demographic information, lifestyle, BMI, depression level, and mHealth app frequency. The demographic details were measured in terms of four criteria as follows:• Age: (a) 21–25 years old (value 1), (b) 26–30 years old (value 2), (c) 31–35 years old (value 3), and (d) over 35 years old (value 4).• Education: (a) less than a high school diploma (value 1); (b) a high school diploma (value 2); (c) a diploma (value 3); (d) a bachelor’s degree (value 4); (e) a master’s or PhD degree (value 5).• Job Experience: (a) no job experience (value 1); (b) less than 3 years (value 2); (c) 3–6 years (value 3); (d) 6–10 years (value 4); and (e) more than 10 years (value 5).• Income: (a) less than RM 2,000 (value 1); (b) between RM 2,000 and RM 3,000 (value 2); (c) between RM 3,000 and RM 4,000 (value 3); (d) between RM 4,000 and RM 5,000 (value 4); (e) greater than RM 5,000 (value 5).
Lifestyle was measured based on Nakayama et al. [ 2001] and Khajeheian et al. [ 2018] research. The indicators included average working hours per day; physical activity per week; average screen time use per day (e.g., TV, smartphone, tablet); and average sleeping hours per night. These indicators were grouped as follows:• Working hours: (a) none; (b) less than 7 h; (c) 7–8 h; (d) 8–9 h; (e) more than 9 h.• Physical activity: (a) none; (b) once; (c) twice; (d) three times; (e) four times; (f) more than four times.• Screen time: (a) less than 1 h; (b) 1–2 h; (c) 2–3 h; (d) 3–4 h; (e) more than 4 h.• Sleeping hours: (a) less than 6 h; (b) 6–7 h; (c) 7–8 h; (d) 8–9 h; (e) more than 9 h.
Individuals’ BMI ranges were calculated using the standardized formula: (Weight in kilograms)/(Height in meters)/2 (Carlson et al., 2016). Respondents gave their weight and height, so we can group their BMI according to the following categories (Fu et al., 2018): BMI classification: (a) underweight (18.5 kg/m2); (b) normal (18.5–23.9 kg/m2); (c) overweight (24.0–27.9 kg/m2); (d) obese (28.0 kg/m2).
The Center for Epidemiologic Studies Depression Scale (CES-D), created by Radloff [1977] was used to measure depression. Twenty items make up the CES-D questionnaire, each of which is graded on a 4-point Likert scale. Higher scores showed that the depression was more severe.
The frequency of respondents using mHealth apps was measured based on previous studies (Rasche et al., 2018). In this study, fitness and nutrition apps usage were indicated as follows: The frequency with which mHealth apps are used: (a) Every day; (b) every couple of days; (c) weekly; (d) monthly; (e) never.
## ANOVA, regression, and SEM
Before Taguchi analysis, we applied ANOVA and regression as the main common statistical approaches. ANOVA is used to evaluate the total sum of squares, the sum of squares due to BMI, and the sum of squares due to depression level. Estimating significant factors on BMI and depression involves regression modeling. From the outputs of Table 2, it can be seen that there are some significant differences between the outputs of using nutrition and fitness apps.
**Table 2**
| N-App | Depression level | Unnamed: 2 | Sum of squares | df | Mean square | F | Significant |
| --- | --- | --- | --- | --- | --- | --- | --- |
| N-App | Depression level | Between groups | 137.037 | 4 | 34.259 | 1.939 | 0.102 |
| N-App | Depression level | Within groups | 15423.820 | 873 | 17.668 | | |
| N-App | Depression level | Total | 15560.858 | 877 | | | |
| N-App | BMI | Between groups | 169.461 | 4 | 42.365 | 1.737 | 0.140 |
| N-App | BMI | Within groups | 21289.792 | 873 | 24.387 | | |
| N-App | BMI | Total | 21459.254 | 877 | | | |
| F-App | Depression level | | Sum of squares | df | Mean square | F | Significant |
| F-App | Depression level | Between groups | 360.638 | 4 | 90.159 | 5.178 | 0.000 |
| F-App | Depression level | Within groups | 15200.220 | 873 | 17.411 | | |
| F-App | | Total | 15560.858 | 877 | | | |
| F-App | BMI | Between groups | 2612.257 | 4 | 653.064 | 40.250 | 0.000 |
| F-App | BMI | Within groups | 18846.997 | 873 | 21.589 | | |
| F-App | BMI | Total | 21459.254 | 877 | | | |
Tables 3, 4 show the regression outputs for BMI and depression. Both tables show that using nutrition apps among women does not significantly impact BMI and depression. Moreover, using fitness apps helped Malaysian women reduce their BMI and depression. However, if we use SEM and include both fitness and nutrition apps in a single model to estimate depression and BMI, we can see different results. Figure 1 shows there is a significant correlation between the usage of fitness apps and nutrition apps, and both fitness and nutrition apps have significant effects on both depression and BMI.
## Taguchi method analysis
Based on the Taguchi method, data analysis was performed on the primary dataset extracted for the Taguchi experiment. In this study, all the variables were taken into consideration for the Taguchi experimental analysis. However, three indicators, including physical activity and average working hours, were eliminated from the analysis for the lifestyle variable. According to the previous studies, the average screen time used and sleeping hours were retained in the analysis (Khajeheian et al., 2018). For the demographic variable, we calculated the average level of the respondents’ backgrounds and distributed them into five categories, as indicated in Table 2. Each variable has five levels. Hence, the L25 [55] Taguchi experimental design was utilized. Table 5 contains the Taguchi coding structure for data analysis using the MINITAB software.
**Table 5**
| Level | Coding | Level.1 | Coding.1 |
| --- | --- | --- | --- |
| Demographic (Average) | Demographic (Average) | N-App | |
| Very Low | Code “1” | Never | Code “1” |
| Low | Code “2” | Monthly | Code “2” |
| Moderate | Code “3” | Weekly | Code “3” |
| High | Code “4” | Every 2–3 days | Code “4” |
| Very High | Code “5” | Daily | Code “5” |
| F-App | F-App | Screen time | Screen time |
| Never | Code “1” | Less than 1 h | Code “1” |
| Monthly | Code “2” | 1–2 h | Code “2” |
| Weekly | Code “3” | 2–3 h | Code “3” |
| Every 2–3 days | Code “4” | 3–4 h | Code “4” |
| Daily | Code “5” | More than 4 h | Code “5” |
| Sleep Amount | Sleep Amount | | |
| Less than 6 h | Code “1” | | |
| 6–7 h | Code “2” | | |
| 7–8 h | Code “3” | | |
| 8–9 h | Code “4” | | |
| More than 9 h | Code “5” | | |
Figures 1, 2 and Tables 6, 7 express the Taguchi method outputs from MINITAB software for BMI and depression levels, respectively.
**Figure 2:** *Taguchi output for BMI pattern with MINITAB software.* TABLE_PLACEHOLDER:Table 6 TABLE_PLACEHOLDER:Table 7 Figures 1, 2 show the Taguchi output for women’s obesity and women’s depression, respectively. Figure 1 illustrates that the highest BMI occurred in women who never use the fitness apps or use them once a month, with a 4-h average screen time per day and more than 9 h of sleep. However, according to Figure 2, the highest depression level among women can be observed for respondents who never use fitness apps, spend more than 4 h per day on screen, and have an average sleeping time of fewer than 6 h or more than 9 h per day.
## Discussion based on Taguchi method
In this study, we designed the Taguchi method based on demographic details, screen time, sleep amount, F-App, and N-App. We chose “*Larger is* Better” (See Table 1) in Taguchi’s design. We want to know which combination of research variable levels causes higher BMI and depression levels among women. Figures 1, 2 illustrate the MINITAB software output according to the Taguchi method design for BMI and depression levels, respectively. Based on Figures 1, 2, the demographic and N-App are not significant for both outputs. The patterns shown in these two variables are near the dotted lines. Therefore, it can be interpreted that the demographic and N-App do not significantly affect BMI and depression levels. In other words, for women who have higher BMI and depression levels, their demographics, and N-App do not significantly impact their BMI and depression levels.
Figures 1, 2 show that the F-App has a negative slope, and the diagram has high variation. Therefore, F-*App is* effective in reducing BMI and depression levels. For better understanding, we grouped this variable (F-App) into groups: group 1 = never; group 2 = monthly; group 3 = weekly; group 4 = 2–3 times per week, and group 5 = more than three times per week. Figure 1 shows that the outputs for groups 1, 2, and 3 are the same in reducing depression. However, there is a significant decrease between groups 3 and 4, and a slight decrease from group 4 to group 5. As a result, if the F-App matches these groups; never, monthly, and weekly, the outputs regarding reducing depression levels are the same, and second, there is no significant effect on reducing depression levels. Females who were using the F-App 2–3 times per week showed better progress in reducing depression. Figure 2 shows similar outputs for groups 1 and 2. However, there is a significant difference between group 2 and group 3 and also between group 3 and group 4. Note that there are no significant differences between groups 4 and 5. As a result, females who use F-App weekly, particularly 2–3 times per week, have a higher likelihood of reducing their BMI over time.
The third diagram (from left) of Figures 1, 2 shows the impact of screen time on depression level and BMI, respectively. The diagram has a positive slope and high variation. We grouped this variable into groups: group 1 = less than 1 h; group 2 = 1–2 h; group 3 = 2–3 h; group 4 = 3–4 h; and group 5 = more than 4 h per day. The diagrams of screen time from Figures 1, 2 show that BMI and depression levels increase drastically with increasing screen time. However, respondents who spend less than 2 h (group 1 and group 2) on screen time have low depression symptoms. If they spend more time on-screen, the findings indicate that they have a higher depression level. This result is supported by Kim et al. [ 2017]’s previous studies in which smartphone addiction positively correlates with depression. The findings of this study suggest that more screen time increases respondents’ levels of BMI. Respondents who sleep more per night would have a higher BMI as well. This output is supported by previous studies (Maddahi et al., 2020; Salarzadeh Jenatabadi et al., 2020).
The last diagrams of Figures 1, 2 show how sleep amount matters in measuring women’s BMI and depression, respectively. When the women have an average of 7–8 h or 8–9 h of sleep, it will not change their BMI levels. To have a normal BMI range, sleeping between 7 and 9 h is not effective at all. The Taguchi output for depression levels is different compared to the BMI level output. Respondents who use fitness apps every 2–3 days or daily seem to have lower depression levels. The use of fitness apps regularly decreased the level of depression among women. The Taguchi output in Figure 2 for sleeping hours shows exciting patterns. Respondents who sleep less than 6 h or more than 9 h every day have the highest level of depression (i.e., likely depression). The impact of these two levels of sleep on depression levels is the same. Sleep quality might be one of the main possibilities for respondents to suffer from depression. Besides, previous studies also claim that depression is linked to women’s lifestyle choices, e.g., sleep quality (Yang et al., 2019). Berk et al. [ 2013] abridged that claim by stressing that poor lifestyle choices cause depression.
## Discussion based on SEM
We find a strong and consistent link between obesity and depression in this sample of Malaysian women. Additionally, we noticed a stepwise increase in both directions: increasing body mass index was strongly associated with higher risk of depressive disorder and increasing severity of depressive symptoms was strongly associated with higher risk of obesity. We discover that the link between depression and obesity was not just present in cases of more severe obesity. Accounting for potential confounders like demographic, screen time, sleep time, and use of mobile applications for nutrition and fitness had a negligible impact on this association. The correlation could not be explained by the specific effects of obesity on somatic depression symptoms. The demographic groups where the obesity-depression association is strongest may have been the focus of our survey. Despite the fact that we did not find differences in the relationship between obesity and depression across demographic groups, earlier studies have suggested that this connection may differ based on ethnicity, education, age, and monthly income (Gavin et al., 2010; Assari et al., 2014; Lincoln, 2019).
We investigate two likely mediators of the link between obesity and depression: screen time and sleep time. In our study, after controlling for the link between sleep duration and obesity, depression was found to be independently associated with less sleep. Of course, both hypotheses—that obesity causes depression or that depression causes obesity—are consistent with this observation. In the first scenario, depression might cause less sleep, which would then result in weight gain. In the latter case, fewer hours of sleep brought on by obesity may be a factor in low mood. In either case, though, our data imply that the amount of sleep that a person gets may play a role in mediating this relationship. After taking into account the connection between screen time and BMI, screen time was also linked to depression. This result supports the idea that obesity causes depression by stigmatizing the condition and reducing screen time.
## Comparison of Taguchi outputs with ANOVA, regression analysis, and SEM process
Can fitness and nutrition apps help women control their BMI and depression during the COVID-19 breakdown? From Tables 3–5 and Figures 1, 2, we can deduce that using nutrition apps does not help women to control BMI and depression levels during the COVID-19 outbreak. However, using fitness apps can increase the frequency of usage and may lower BMI and depression levels among women. From Tables 4, 5, the ANOVA and regression methods show us which variables effectively identify BMI and depression levels. It is worth noting that the Taguchi method gave information that ANOVA and regression could not present. Significantly, the Taguchi outputs of Figures 1, 2 show which combinations of research variables based on their level effectively identify women with higher BMI and depression. However, Figure 1 shows that, with considering both F-APP and N-App for estimating both depression and BMI in a same model, we will have different outputs compare Taguchi method, regression, and ANOVA.
## Contributions of the study
The novel contribution of this paper to theory are multifield. First, and the main contribution, the study adds its valuable contribution the effectiveness of different ways of estimating correlation among research variables with depression and BMI. With considering correlation or effectiveness of F-App and N-App on BMI and depression, ANOVA, regression, and Taguchi method were useful. However, these methods are not able to evaluate both F-App and N-App with other research variables on both BMI and depression in a single model. As you can see in data analysis part for ANOVA analysis we have to analyze the effectiveness of F-App and N-App separately and for every of dependent variables need to present different analysis (see Table 2). This weakness of the analysis can be seen in regression (see Tables 3, 4) and Taguchi (see Figures 2, 3). Therefore, for analyzing one dependent variable analysis, these methods might be applicable. However, estimating two or more than dependent variables in a single model or analysis is applicable by involving SEM.
**Figure 3:** *Taguchi output for depression pattern with MINITAB software.*
Second, this study adds valuables results to the mHealth literature. The study revealed demographic does not have significant impact on both depression and BMI based on regression, Taguchi method, and even SEM. However, with SEM technique, we realized demographic has an indirect significant effect on depression through screen time. The sudden and unexpected changes way of analysis, we are able to find hidden significant relationships whish have not been considered before.
Third, another novel contribution of the study is that we found out there is a strong correlation between the usage of F-App and N-App based on SEM technique which has nobody have done before.
## This research also has a few limitations
Self-reported: The questionnaires were self-reported by respondents, especially the weight and height measurements, to determine their BMI. The CES-D and frequency of using mHealth were also self-reported. The validity of this method has been verified in previous studies (Sanchez et al., 2010; Banting et al., 2014). So, we considered the research data acceptable to be analyzed.
The CES-D questionnaire: It should be noted that in this study, we only analyzed the depression symptoms of the respondents. As we calculated the score, we grouped the respondents according to their depression levels, as described in the method section. The questionnaire is not a substitute for clinical diagnosis.
Additional treatments: Some of the respondents might also receive other additional treatments such as personal trainers or psychologists, which could probably contribute to the BMI and depression levels. Future research should look into how these indicators affect female obesity and depression.
## Conclusion
We conclude that throughout the pandemic in Malaysia, using fitness apps consistently was more effective than using nutrition apps to establish a better quality of life among women. As we live in the era of ICT, the availability of thousands of mHealth apps would help people organize their daily lives. For example, fitness apps help promote suitable physical activity, diet control, weight management, stress relief, and sleep monitoring (Higgins, 2016). Apart from that, we believe that poor sleep is also correlated with women’s depression and obesity development, which has been proven in previous studies (Saucedo et al., 2021) and extensive screen time usage. The Taguchi method, which we used in this study, gave public health researchers a way to look at the levels of obesity and depression in women. The last and the main conclusion of this studies, with application of SEM, research scholar are able to find out significant different correlations among research variables compare to familiar mythologies include ANOVA, regression, and Taguchi methods.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by University of Malaya Research Ethics Committee (UMREC). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
NM, AA, and HJ contributed to conception and design of the study. NM, AA, and HJ organized the database and performed the statistical analysis. NS, NA, and SA wrote the first draft of the manuscript. NM, AA, NA, SA, NS, and HJ wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.
## Funding
This research is funded by Universiti Malaya Research Grant (Grant No. GPF083B-2020) and Prince Sultan University.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## References
1. Archana P. S., Das S., Philip S., Philip R. R., Joseph J., Punnoose V. P.. **Prevalence of depression among middle aged women in the rural area of Kerala**. *Asian J. Psychiatr.* (2017) **29** 154-159. DOI: 10.1016/j.ajp.2017.05.016
2. Assari S., Das S., Philip S., Philip R. R., Joseph J., Punnoose V. P.. **Association between obesity and depression among American blacks: role of ethnicity and gender**. *J. Racial Ethn. Health Disparities* (2014) **1** 36-44. DOI: 10.1007/s40615-014-0007-5
3. Banting L. K., Gibson-Helm M., Polman R., Teede H. J., Stepto N. K.. **Physical activity and mental health in women with polycystic ovary syndrome**. *BMC Womens Health* (2014) **14** 51. DOI: 10.1186/1472-6874-14-51
4. Bardus M., van Beurden S. B., Smith J. R., Abraham C.. **A review and content analysis of engagement, functionality, aesthetics, information quality, and change techniques in the most popular commercial apps for weight management**. *Int. J. Behav. Nutr. Physical Act.* (2016) **13** 1-9. DOI: 10.1186/s12966-016-0359-9
5. Berk M., Sarris J., Coulson C. E., Jacka F. N.. **Lifestyle management of unipolar depression**. *Acta Psychiatr. Scand.* (2013) **127** 38-54. DOI: 10.1111/acps.12124
6. Boulos M. N. K., Brewer A. C., Karimkhani C., Buller D. B., Dellavalle R. P.. **Mobile medical and health apps: state of the art, concerns, regulatory control and certification**. *Online J. Public Health Inform.* (2014) **5** 229. DOI: 10.5210/ojphi.v5i3.4814
7. Bt Wan Mohamed Radzi C. W. J., Salarzadeh Jenatabadi H., Samsudin N.. **mHealth apps assessment among postpartum women with obesity and depression**. *Healthcare* (2020) **8** 72. DOI: 10.3390/healthcare8020072
8. Bt Wan Mohamed Radzi C. W. J., Salarzadeh Jenatabadi H., Samsudin N.. **Postpartum depression symptoms in survey-based research: a structural equation analysis**. *BMC Public Health* (2021) **21** 1-12. DOI: 10.1186/s12889-020-09999-2
9. Carlson J. A., Remigio-Baker R. A., Anderson C. A. M., Adams M. A., Norman G. J., Kerr J.. **Walking mediates associations between neighborhood activity supportiveness and BMI in the Women's Health Initiative San Diego cohort**. *Health Place* (2016) **38** 48-53. DOI: 10.1016/j.healthplace.2016.01.001
10. Cho J., Lee H. E., Kim S. J., Park D.. **Effects of body image on college students' attitudes toward diet/fitness apps on smartphones**. *Cyberpsychol. Behav. Soc. Netw.* (2015) **18** 41-45. DOI: 10.1089/cyber.2014.0383
11. Chuan C. L., Penyelidikan J.. **Sample size estimation using Krejcie and Morgan and Cohen statistical power analysis: a comparison**. *Jurnal Penyelidikan IPBL* (2006) **7** 78-86
12. Correa M. S. M., de Oliveira Feliciano K. V., Pedrosa E. N., de Souza A. I.. **Postpartum follow-up of women's health**. *Cad. Saude Publica* (2017) **33** e00136215. DOI: 10.1590/0102-311X00136215
13. Elamoshy R., Bird Y., Thorpe L., Moraros J.. **Risk of depression and suicidality among diabetic patients: a systematic review and meta-analysis**. *J. Clin. Med.* (2018) **7** 445. DOI: 10.3390/jcm7110445
14. El-Gilany A. H., Elkhawaga G. O., Sarraf B. B.. **Depression and its associated factors among elderly: a community-based study in Egypt**. *Arch. Gerontol. Geriatr.* (2018) **77** 103-107. DOI: 10.1016/j.archger.2018.04.011
15. Fu L.-Y., Wang X. X., Wu X., Li B., Huang L. L., Li B. B.. **Association between obesity and sickness in the past two weeks among middle-aged and elderly women: a cross-sectional study in southern China**. *PLoS One* (2018) **13** e0203034. DOI: 10.1371/journal.pone.0203034
16. Gavin A. R., Simon G. E., Ludman E. J.. **The association between obesity, depression, and educational attainment in women: the mediating role of body image dissatisfaction**. *J. Psychosom. Res.* (2010) **69** 573-581. DOI: 10.1016/j.jpsychores.2010.05.001
17. Heron K. E., Smyth J. M.. **Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments**. *Br. J. Health Psychol.* (2010) **15** 1-39. DOI: 10.1348/135910709X466063
18. Higgins J. P.. **Smartphone applications for patients' health and fitness**. *Am. J. Med.* (2016) **129** 11-19. DOI: 10.1016/j.amjmed.2015.05.038
19. Jia H., Zack M. M., Gottesman I. I., Thompson W. W.. **Associations of smoking, physical inactivity, heavy drinking, and obesity with quality-adjusted life expectancy among US adults with depression**. *Value Health* (2018) **21** 364-371. DOI: 10.1016/j.jval.2017.08.002
20. Jin Q. G., Mori E., Sakajo A.. **Risk factors, cross-cultural stressors and postpartum depression among immigrant Chinese women in Japan**. *Int. J. Nurs. Pract.* (2016) **22** 38-47. DOI: 10.1111/ijn.12438
21. Khajeheian D., Colabi A., Ahmad Kharman Shah N., Bt Wan Mohamed Radzi C., Jenatabadi H.. **Effect of social media on child obesity: application of structural equation modeling with the Taguchi method**. *Int. J. Environ. Res. Public Health* (2018) **15** 1-22. DOI: 10.3390/ijerph15071343
22. Kim E., Cho I., Kim E. J.. **Structural equation model of smartphone addiction based on adult attachment theory: mediating effects of loneliness and depression**. *Asian Nurs. Res.* (2017) **11** 92-97. DOI: 10.1016/j.anr.2017.05.002
23. Krejcie R. V., Morgan D. W.. **Determining sample size for research activities**. *Educ. Psychol. Meas.* (1970) **30** 607-610. DOI: 10.1177/001316447003000308
24. Lincoln K. D.. **Social stress, obesity, and depression among women: clarifying the role of physical activity**. *Ethn. Health* (2019) **24** 662-678. DOI: 10.1080/13557858.2017.1346190
25. Maddahi N. S., Yarizadeh H., Setayesh L., Nasir Y., Alizadeh S., Mirzaei K.. **Association between dietary energy density with mental health and sleep quality in women with overweight/obesity**. *BMC. Res. Notes* (2020) **13** 1-6. DOI: 10.1186/s13104-020-05025-1
26. Manigandan S., Atabani A. E., Ponnusamy V. K., Pugazhendhi A., Gunasekar P., Prakash S.. **Effect of hydrogen and multiwall carbon nanotubes blends on combustion performance and emission of diesel engine using Taguchi approach**. *Fuel* (2020) **276** 118120. DOI: 10.1016/j.fuel.2020.118120
27. Muharam R., Setiawan M. W., Ikhsan M., Rizkinya H. E., Sumapraja K.. **Depression and its link to other symptoms in menopausal transition**. *Middle East Fertil. Soc. J.* (2018) **23** 27-30. DOI: 10.1016/j.mefs.2017.08.003
28. Nakayama K., Yamaguchi K., Maruyama S., Morimoto K.. **The relationship of lifestyle factors, personal character, and mental health status of employees of a major Japanese electrical manufacturer**. *Environ. Health Prev. Med.* (2001) **5** 144-149. DOI: 10.1007/BF02918290
29. Qudah B., Luetsch K.. **The influence of mobile health applications on patient-healthcare provider relationships: a systematic, narrative review**. *Patient Educ. Couns.* (2019) **102** 1080-1089. DOI: 10.1016/j.pec.2019.01.021
30. Radloff L. S.. **The CES-D scale: a self-report depression scale for research in the general population**. *Appl. Psychol. Meas.* (1977) **1** 385-401. DOI: 10.1177/014662167700100306
31. Rasche P., Wille M., Bröhl C., Theis S., Schäfer K., Knobe M.. **Prevalence of health app use among older adults in Germany: national survey**. *JMIR Mhealth Uhealth* (2018) **6** e26. DOI: 10.2196/mhealth.8619
32. Rathbone A. L., Prescott J.. **The use of mobile apps and SMS messaging as physical and mental health interventions: systematic review**. *J. Med. Internet Res.* (2017) **19** e7740. DOI: 10.2196/jmir.7740
33. Rippe J. M.. **The epidemiology of adult obesity**. *Obesity Prevention and Treatment* (2021) 21-27
34. Salarzadeh Jenatabadi H., Babashamsi P., Yusoff N. I. M.. **The combination of a fuzzy analytical hierarchy process and the Taguchi method to evaluate the Malaysian users’ willingness to pay for public transportation**. *Symmetry* (2016) **8** 90. DOI: 10.3390/sym8090090
35. Salarzadeh Jenatabadi H., Bt Wan Mohamed Radzi C. W. J., Samsudin N.. **Associations of body mass index with demographics, lifestyle, food intake, and mental health among postpartum women: a structural equation approach**. *Int. J. Environ. Res. Public Health* (2020) **17** 5201. DOI: 10.3390/ijerph17145201
36. Sanchez S. E., Williams M. A., Pacora P. N., Ananth C. V., Qiu C., Aurora S. K.. **Risk of placental abruption in relation to migraines and headaches**. *BMC Womens Health* (2010) **10** 30. DOI: 10.1186/1472-6874-10-30
37. Saucedo M., Esteves-Pereira A. P., Pencolé L., Rigouzzo A., Proust A., Bouvier-Colle M. H.. **Understanding maternal mortality in women with obesity and the role of care they receive: a national case-control study**. *Int. J. Obes.* (2021) **45** 258-265. DOI: 10.1038/s41366-020-00691-4
38. Taylor G., Bylund C. L., Kastrinos A., Alpert J. M., Puig A., Krajewski J. M. T.. **Practicing mindfulness through mHealth applications: emerging adults health-enhancing and inhibiting experiences**. *Int. J. Environ. Res. Public Health* (2022) **19** 2619. DOI: 10.3390/ijerph19052619
39. World Health Organization (2011). mHealth New Horizons for Health Through Mobile Technologies. Available at: https://www.who.int/about/. (2011)
40. World Health Organization (2017). Depression: Let’s Talk. Available at: https://www.who.int/mental_health/management/depression/en/ (Accessed December 20, 2018).. (2017)
41. Yang C.-L., Schnepp J., Tucker R. M.. **Increased hunger, food cravings, food reward, and portion size selection after sleep curtailment in women without obesity**. *Nutrients* (2019) **11** 663. DOI: 10.3390/nu11030663
42. Yuan S., Ma W., Kanthawala S., Peng W.. **Keep using my health apps: discover users' perception of health and fitness apps with the UTAUT2 model**. *Telemed. e-Health* (2015) **21** 735-741. DOI: 10.1089/tmj.2014.0148
43. Zeng N., Pope Z., Lee J., Gao Z.. **Virtual reality exercise for anxiety and depression: a preliminary review of current research in an emerging field**. *J. Clin. Med.* (2018) **7** 42. DOI: 10.3390/jcm7030042
|
---
title: 'Food insecurity among Finnish private service sector workers: validity, prevalence
and determinants'
authors:
- Hanna M Walsh
- Jaakko Nevalainen
- Tiina Saari
- Liisa Uusitalo
- Turkka Näppilä
- Ossi Rahkonen
- Maijaliisa Erkkola
journal: Public Health Nutrition
year: 2022
pmcid: PMC9993037
doi: 10.1017/S1368980022000209
license: CC BY 4.0
---
# Food insecurity among Finnish private service sector workers: validity, prevalence and determinants
## Body
Limited systematic data on food insecurity levels in Europe exist, whereas the USA and Canada are the few high-income countries that have regularly monitored national food insecurity levels within the population since the 1990s[1]. Yet, there are reports of rising numbers of people looking for emergency food support in Europe[2]. The sporadic studies measuring food insecurity in Europe have found it exists(3–6), even in countries often characterised as having social-democratic welfare regimes such as the Nordic countries[7,8].
There is some research on the entrenchment of food aid and charity in Finland[9,10], yet there are little data on national levels of food insecurity. In a 2017 FAO report[11,12], the prevalence of moderate and severe food insecurity in Finland (8·3 %) was higher than that of any other Nordic country (Sweden, Norway, Denmark, Iceland: moderate and severe food insecurity 5·1–6·7 %) and that of the United Kingdom (5·6 %). According to a 2001 Finnish study examining food insecurity, 11 % of a nationally representative sample reported experiences of running out of money to buy food, 9 % reported fears of running out of food due to economic problems and 3 % had had too little food due to lack of money[13].
In Finland, low household income, recent unemployment and economic problems in childhood were all associated with food insecurity[13]. There are similar findings worldwide, as food insecurity has been found to be a consequence of multiple economic and resource issues such as lower household income(5,7,8,14–16), lack of assets and savings[17] and income instability[18,19]. Other vulnerabilities such as receiving disability pensions/benefits[7,20], being a single-parent household[6,7], having lower education[3,5], being an immigrant or asylum seeker[8,21,22] and renting housing[14] have also been associated with food insecurity. Food insecurity has also been linked with unhealthy diets[4,6,7,23] and lower nutrients intake[24]. A range of health outcomes have also been linked to food insecurity, including higher mortality[25,26], higher prevalence of chronic conditions such as hypertension[27], diabetes[28], arthritis and back problems[29], mental health problems including depression and stress[30] and mood and anxiety disorders[29]. Thus unsurprisingly, household food insecurity has also been found to predict increased universal health care utilisation and costs of working-age adults[31].
A vulnerable group in Finland are the workers in the private service sector where many of the characteristics associated with higher risk of in-work poverty accumulate such as part-time employment, immigrant background, low education and being female[32,33]. Due to the high prevalence of trade union membership in Finland, recruiting participants via trade unions can offer a new way of reaching a hard-to-survey population group. Finland had the fourth highest trade union density of OECD member countries in 2018[34] and according to the Ministry of Economic Affairs and Employment of Finland, the trade union membership rate among private service sector workers was 48 % in Finland in 2017[35].
Thus, the aim of this study was to inspect the prevalence of food insecurity among private service sector union members in Finland during 2019 and identify the main socio-demographic, economic, health and work-related factors associated with food insecurity. To our knowledge, no food insecurity measure has been validated in Finland before and thus we will also inspect the validity of the food insecurity measurement tool among the private service sector union members in Finland.
## Abstract
### Objective:
To examine the prevalence and determinants of food insecurity among private sector service workers in Finland and assess validity of the Household Food Insecurity Access Scale (HFIAS) tool.
### Design:
In this cross-sectional study, food insecurity and background characteristics were collected from Finnish private service workers via electronic questionnaires [2019] and national register data (2018–2019). We conducted univariate and multivariate logistic regression analyses to determine the variables explaining food insecurity. Validity of HFIAS was assessed with rotated principal component analysis and Cronbach’s α.
### Setting:
Members of the trade union for private sector service workers, Service Union United (PAM), from all municipalities in Finland participated in the study in 2019.
### Participants:
The subjects were 6435 private sector workers that were members of the Service Union United (PAM) in Finland. Mean age of participants was 44 years (sd 12·7 years).
### Results:
Two-thirds of the participants (65 %) were food insecure with over a third (36 %) reporting severe food insecurity. Reporting great difficulties in covering household expenses and young age markedly increased the risk of severe food insecurity (OR 15·05; 95 % CI 10·60, 21·38 and OR 5·07; 95 % CI 3·94, 6·52, respectively). Not being married, low education, working in the hospitality industry, being male and living in rented housing also increased the probability of severe food insecurity. The HFIAS tool demonstrated acceptable construct and criterion validity.
### Conclusions:
Severe food insecurity was widespread and associated with low socio-economic status, young age and being male among Finnish private sector service workers, emphasising the need for regular monitoring of food insecurity in Finland.
## Study design
The data were collected in collaboration with the Finnish Service Union United (PAM). PAM has almost 210 000 members, 76 % of them women, working in retail trade, property services, security services as well as tourism, restaurant and leisure services[36].
Data were collected during April and May 2019 via an online Study Survey form. The invitation to the study was sent to 111 850 PAM members, that is, to all Finnish-speaking employed, unemployed and retired members who had provided their email address in the PAM member register (student members were excluded) (online supplementary material, Supplement 1). The Study Survey included questions on food insecurity and background characteristics. After this, PAM’s own annual Member Survey, including work-related questions, was similarly sent (May–June 2019) to PAM members [110 833] via email.
Participants were asked for permission to link their survey answers with national register data provided by Statistics Finland for the years 2018–2019. Data obtained from Statistics Finland from 2019 included sex, year of birth, municipality type, region of residence, as well as income and income transfers from 2018.
## Participants
The Study Survey was initially answered by 6573 participants (6·5 % of those invited) while the Member Survey was answered by 6528 (6·5 %) participants. Once those who had denied use of their data for study purposes were deleted, erroneous ID were fixed and participants that did not have a national identification number in the background data were deleted, data were available for 6435 for the Study Survey and 6375 for the Member Survey of which 3998 participants had answered both surveys. National register data from the years 2018 and 2019 were available for 6431 and 6421 participants, respectively, of the 6435 who had answered the Study Survey.
## Food insecurity
Food insecurity has previously been measured in Finland using the Food Insecurity Experience Scale[11], a collection of questions based on the Edmonton Food Policy Council’s survey[13], and a modified version of the Household Food Insecurity Access Scale (HFIAS) among food aid recipients[37], all of which measured food insecurity on an individual level. Due to availability of the translated version used and to enable comparison between studies, we selected the modified HFIAS questionnaire for our study.
The HFIAS tool has been developed by the USAID’s Food and Nutrition Technical Assistance project[38] based on a thorough review of the commonalities found across different cultures in qualitative food insecurity studies, thus allowing it to distinguish food insecure from food secure households across different cultural contexts. The HFIAS questionnaire (online supplementary material, Supplement 2) was first translated to Finnish and adapted in cooperation with the Finnish Blue-Ribbon association for a recent study of 129 participants receiving food aid[37]. As discussed by Coates et al.[38], the concept of ‘household’ is highly context specific and should be defined uniformly for participants. Due to the nature of the participants, many of whom were homeless, the questionnaire was modified to focus on the individual experience of food insecurity due to the fact that ‘household’ may not have been a relevant unit for the participants. The modified questionnaire was then piloted among nine residents of a supported housing service unit for homeless adults. All nine were able to answer the questionnaire well and no modifications were made.
We felt that individual-level measurements were relevant for this study not only to enable comparison with previous research in Finland[11,13,37], but also because the HFIAS was administered as an online survey, meaning we could not ensure that the respondent was the person in the household who was most involved with food preparation and meals, as advised by Coates et al. [ 38]. Furthermore, the focus on household-level food insecurity has been questioned as it assumes a standard model where there is one decision-maker who always acts for the benefit of the household, where resources are pooled and where worry about food is a collective experience[39]. Research has shown that, in reality, power imbalances and differences in domains of responsibility exist within households across the world and resources are not always distributed equitably, nor are experiences of food insecurity the same[39,40]. The experience may differ especially between men and women due to the gendered roles in food acquisition, preparation and providing income to buy food. For example, Coates et al. [ 39] found that nearly one-third of Bangladeshi households were classified into different food security categories using female v. male responses to the questions. Furthermore, O’Connell and Brannen[41] stated that it is often mothers who go without food to prioritise the needs of children and male partners. Thus, Coates et al. [ 39] and FAO[11] suggest using individual-level food insecurity analyses which will allow inspection of intra-household differences too.
PAM members answered the nine HFIAS questions as part of the online Study Survey, based on which, participants were then categorised as food secure or mildly, moderately or severely food insecure, as instructed by Coates et al. [ 38]. The HFIAS questions ask if participants have experienced issues related to worry about having enough food, limited food quality and limited food quantity and how often (rarely, sometimes, often) they have experienced these issues in the last 30 d. Thus, classification into the different food insecurity categories depends on affirmative answers to certain questions as well as frequency of the experience, as explained by Coates et al. [ 38].
## Background and work-related characteristics
Variables obtained from the Study Survey included self-reported height and weight (used to calculate BMI), highest obtained education level, marital status, household size, number of children under 18 in the household, type of housing, employment status, self-assessed adequacy of income compared with expenses and self-perceived health status. The industry of employment was obtained from the Member Survey. All data were self-reported in the Study and Member Surveys. Variables obtained from national register data provided by Statistics Finland included sex [2019], year of birth from which age was calculated [2019], type of municipality [2019], region of residence [2019], individual earned income in state taxation[42] during the year 2018 and received income transfers[43] during the year 2018.
## Analysis
The accuracy of the modified HFIAS tool was evaluated by examining content, construct and criterion validity. Content validity refers to the extent to which the items on a test are representative of the entire domain the test seeks to measure. Thus, we evaluated whether the nine questions of HFIAS covered all aspects of food security in a Finnish context, based on previous literature. Construct validity is the degree to which a tool measures what it is supposed to measure. Thus, we evaluated whether HFIAS has a multidimensional construct and what dimensions of food insecurity it measures in the Finnish context. This was evaluated by factor analysis (rotated principal component analysis) as done in previous studies(44–46). The factors were computed from the correlation matrix and oblique rotation (Oblimin with Kaiser normalisation) was selected because the different domains of food insecurity may be correlated. The number of factors was determined by the scree plot and those with an eigenvalue > 1. The internal consistency of the scale and domains revealed in the factor analysis of the tool were measured by Cronbach’s α.
Criterion validity measures how well one measure predicts an outcome of another measure. The criterion validity of the HFIAS tool was investigated by looking at whether it distinguished between different socio-demographic groups with established differences in food insecurity such as sex, age, family structure, housing, income, education, living area, marital status and occupation[7,14,44]. The associations between the food insecurity levels and socio-demographic variables were assessed with chi-squared tests.
Univariate binary logistic regression was used to explore which individual variables increased the risk of severe food insecurity. Food insecurity was made into a dichotomous variable where the food secure, mildly and moderately food insecure categories were combined and the severely food insecure category was compared with this group. Self-perceived health status and BMI were not investigated due to the uncertainty in causal direction. We also conducted univariate multinomial logistic regressions to see how determinants were associated with the different levels of food insecurity.
Multiple explanatory variables were included into a multivariate binary logistic regression model explaining severe food insecurity to control for confounding and to obtain an adjusted estimate of the magnitude of the associations. The variables included in the model were based on which socio-demographic, economic and work-related variables were found to be associated with an increased risk of severe food insecurity in univariate logistic regression analyses. Income in state taxation was removed due to its collinearity with self-assessed adequacy of household income to cover expenses and the latter was deemed to be a better representation of the participants’ situation in 2019 and a stronger determinant. Received income transfers were removed from the model due to the challenges in interpreting its meaning as it includes a whole range of transfers including earnings-related and national pensions, other social security benefits (e.g. parental allowance) and social allowances (e.g. study grant)[43]. Household size correlated with marital status and was thus removed. Employment status was not significant in the model and was removed. The level of statistical significance used was 0·05. The participants with missing data on sex, age and education were excluded from the model. However, the participants with missing data on employment industry were included, due to there being so many (56 %). All statistical analyses were performed using the Statistical Package for the Social Sciences statistical software package version 27 (SPSS Inc.).
## Validity of the Household Food Insecurity Access Scale measure
The questions (1–3) related to milder forms of food insecurity received the most affirmative answers and the questions (7–9) relating to more severe levels of food insecurity received the most negative answers though the trend was not entirely consistent (e.g. questions one and six) (Table 1). The factor analysis of the nine HFIAS questions revealed two factors which explained 56·3 and 12·4 % of the total variance, respectively (Table 1). The first factor had high loadings on questions one to five reflecting a less severe form of food insecurity, while questions six to nine loaded onto the second factor, reflecting a more severe form of food insecurity (lacking food in quantity).
Table 1Finnish Service Union United members’ response rates to each HFIAS question and each question’s factor loadings, 2019Response (%)Factor loadings* Modified HFIAS questionsNoRarelySometimesOftenFirst factor† Second factor‡ 1Have you been worried about the adequacy of food?67·316·112·44·3 0·786 0·0872Have you had to limit foods that you would have wanted to have eat?55·618·916·88·7 0·929 −0·0933Have you had to eat more limitedly than you would have wanted?51·616·819·012·5 0·898 −0·0304Have you had to eat foods that you did not want to eat?68·619·09·52·9 0·765 0·0295Have you had to eat smaller portions than you would have wanted?74·314·38·52·9 0·632 0·2626Have you had to skip meals?70·115·010·64·40·248 0·640 7Have you been in a situation where you had no food to eat?81·112·25·11·60·200 0·695 8Have you gone to sleep hungry?73·517·37·12·10·133 0·747 9Have you gone the whole day without eating?84·310·24·31·1−0·187 0·909 HFIAS, Household Food Insecurity Access Scale.**Factor analysis* (rotated principal component analysis), Kaiser–Meyer–Olkin measure of sampling adequacy = 0·912, factor loadings greater than 0·6 are indicated in bold font.†Eigenvalue = 5·071.‡Eigenvalue = 1·119.
Cronbach’s α of the scale was 0·899 which indicated good internal consistency. The subscales had satisfactory internal consistency too as Cronbach’s α for the first factor was 0·888 and 0·816 for the second factor.
## Participants’ characteristics
The food security levels were calculated for 6435 participants, but socio-demographic and work-related details were not available for all participants. The majority of the participants were women (80 %), and the mean age of all participants was 44 years (sd 12·7 years) ranging from 17 to 83 years (Table 2). Most participants had completed upper secondary school, vocational education, obligatory education or less as their highest level of education (83 %). Being married or cohabiting were the most common situations and correspondingly most people lived with at least one other person. The majority of participants did not have any children under 18 years in their households (68 %). Over half of the participants lived in housing they owned (57 %) and the majority lived in urban areas (74 %). The region of residence was obtained for 6421 participants and they were spread out across all nineteen Finnish regions, the majority residing in Uusimaa (capital city region) (23 %), Pirkanmaa (Tampere region) (11 %), Southwest Finland (10 %) and Northern Ostrobothnia (8 %).
Table 2The associations of socio-demographic and health-related variables with food insecurity among Finnish Service Union United members, 2019 n %Food secureFood insecure P * %Mild %Moderate %Severe %Overall prevalence64351003512173695 % CI34·3, 36·610·8, 12·316·3, 18·234·6, 36·9Sex6435100< 0·01 Female51208036121835 Male13012035101540 Missing data140·2297·11450Age (years)6435100< 0·01 17–299871522111452 30–4422143431111839 45–5923813739121931 60+8391354121520 Missing data140·2297·11450Highest education64351000·01 Obligatory education or less7081136111439 Upper secondary school or vocational46557235111836 Undergraduate9641538131831 Postgraduate1041·641132125 Missing data40·1500050Marital status6435100< 0·01 Married or registered partnership25233943121827 Cohabitation17852835121637 Divorced or separated5979·328112041 Widow971·540111533 Single143322269·91748Household size6435100< 0·01 117692730111742 225714041111532 39151432111938 48271334142131 5+3535·530132532Number of children under 18 years in the HH6435100< 0·01 043946837111635 19341531121939 27431233131935 32473·830142631 4+881·423112541 Missing data290·538101041Housing6435100< 0·01 Owner-occupied housing36615742121729 Right of occupancy housing1832·8378·71936 Rented municipal housing7191128102042 Other rented dwelling or company housing18632926111747 Supported housing20·03000100 Homeless70·10141471Municipality type64351000·42 Urban47557435111736 Semi-rural9291435121933 Rural7371138121535 Missing data140·2297·11450BMI6435100< 0·01 < 18·5791·2348851 18·5–24·9922633536121636 25–29·9921243338121734 ≥ 3019273032111737 Missing data420·138121931Self-perceived health status6435100< 0·01 Good18922942121531 Quite good25173935121735 Average15402431111940 Quite poor4146·4268·22243 Poor721·128111447HH, household.*Chi-squared test The majority of the participants were employed (70 %) and the mean earned income in state taxation was 2043 €/month (sd 746 €/month) ranging from 0 to 7450 €/month (Table 3). Employment industry was obtained for 44 % of the participants. Of those who replied, the majority worked in the retail industry (50 %), followed by those in hospitality (20 %) and then those in property maintenance (12 %). One-third of the participants received minimal (0–9 €/month) income transfers from the government, while the rest received 10–5701 €/month, the overall median being 200 €/month.
Table 3The associations of economic and work-related variables with food insecurity among Finnish Service Union United members, 2018–2019 n %Food secureFood insecure P * MildModerateSevere%%%%Overall prevalence64351003512173695 % CI34·3, 36·610·8, 12·316·3, 18·234·6, 36·9Employment status6435100< 0·01 Employed45297037111734 Partly working, partly retired1362·1409·61931 Laid-off120·225178·350 Unemployed5799·0309·51645 Parental leave/stay at home parent1742·733141836 Long-term sick leave (over 6 months)1071·7333·72142 Retired3014·750121424 Not working for other reasons290·517212438 Other5688·824151843Employment industry6435100< 0·01 Retail139322392·51930 Hospitality5708·9398·81637 Property maintenance3455·4369·32035 Security1211·9389·92032 Hairdressing280·436141436 Other3475·440121731 Missing data36315633121739Earned income in state taxation (monthly)† 6435100< 0·01 0–999 €5099·423121749 1000–1599 €12491930111742 1600–1999 €12101933121936 2000–2499 €18912936131833 2500 €+15702444111629Missing data60·15001733Current transfers received (monthly)† 6435100< 0·01 0–9 €20913242121531 10–199 €12592035131933 200–399 €6611033121739 400–799 €9671529121841 800–1200 €658103292038 >1200 €7931232101741 Missing data60·15001733How well can households cover expenses with income?6435100< 0·01 With great difficulty3876·07·23·91673 With difficulty784129·37·02459 With small difficulties18372922142341 Quite easily18202844131527 Easily11301851121221 Very easily4777708·66·115*Chi-squared test.†Data from 2018, all other data from 2019.
## Prevalence and determinants of food insecurity
Of the 6435 respondents, over a third (36 %) were severely food insecure, 29 % were mildly or moderately food insecure and a third (35 %) were food secure (Table 2). Tables 2 and 3 present the distribution of food insecurity status by socio-demographic, health, economic and work-related variables. All variables apart from municipality type were associated with food insecurity levels.
## Explanatory variables of severe food insecurity in binary logistic analysis
In the univariate binary logistic regression analyses, being able to cover household expenses ‘with great difficulty’ and being young increased the probability of severe food insecurity markedly (online supplementary material, Supplements 3–4). Of the socio-demographic variables being single, divorced/separated or cohabiting, renting housing (or supported housing or being homeless), low educational level, a household size of one and three and being male increased the risk of being severely food insecure (online supplementary material, Supplement 3). Neither the number of children under 18 years in the household nor municipality type was associated with an increased risk of severe food insecurity. Of the economic and work-related variables, low income, high levels of income transfers from the government, being unemployed or laid off and working in hospitality and property maintenance also increased the probability of being severely food insecure (online supplementary material, Supplement 4).
## Explanatory variables of food insecurity levels in multinomial logistic analysis
In the univariate multinomial logistic regression analyses, the same determinants increased the risk of mild and moderate food insecurity as for severe food insecurity, apart from the following instances. Being male, having low education, being unemployed or laid off and working in hospitality did not increase risk of mild or moderate food insecurity (data not shown). In the binary analysis, having children under the age of 18 years was not associated with severe food insecurity. However, in the multinomial analysis, having children was associated with increased risks of mild, moderate and severe food insecurity: having any number of children, especially having three or more (compared with having none), increased the risk of mild and moderate food insecurity, whereas only having one child (compared with having none) increased the risk of severe food insecurity. Similarly, almost all family sizes (compared with a family size of two) increased the risk of mild and moderate food insecurity with large family sizes appearing to increase the risk the most in the multinomial model. A family size of five or more increased the risk of severe food insecurity in the multinomial model, as well as the family sizes of one and three also identified in the original binary logistic regression analysis. All levels of income transfers (compared with receiving 0–9 €/month) were associated with an increased risk of moderate and severe food insecurity, but only lower levels (10–199 and 400–700 €/month) were associated with an increased risk of mild food insecurity.
## Explanatory variables of severe food insecurity in multivariate binary logistic model
In the multivariate binary logistic regression model, being able to cover household expenses ‘with great difficulty’ and young age (18–29 years) retained their effect, markedly increasing the probability of severe food insecurity by 15- and 5-fold, respectively (Fig. 1). This is evident from the fact that 73 % of those ‘with great difficulty’ in covering expenses were severely food insecure, whereas 15 % of those who could ‘very easily’ cover expenses were severely food insecure (OR 15·05; 95 % CI 10·60, 21·85) (online supplementary material, Supplement 5). Half (52 %) of those in the youngest age bracket (18–29 years) were severely food insecure, while only a fifth (20 %) of those in the oldest age bracket (60+ years) were severely food insecure (OR 5·07; 95 % CI 3·94, 6·52). Having low education (OR 2·85; 95 % CI 1·70, 4·76), being single (OR 1·43; 95 % CI 1·21, 1·75), working in hospitality (OR 1·40; 95 % CI 1·12, 1·75), being male (OR 1·34; 95 % CI 1·17, 1·54) and living in rented housing (including company or supported housing or being homeless) (OR 1·24; 95 % CI 1·07, 1·43) also increased the risk of severe food insecurity.
Fig. 1OR and CI for variables included in adjusted model explaining severe food insecurity among Finnish Service Union United members, 2019. $P \leq 0$·015 for all variables in the model, n 6417, Nagelkerke R 2 = 22 %. * Includes: other rented dwelling, company housing, supported housing and homeless. †Full question: How well can household cover expenses in comparison with income
## Discussion
The findings indicated that almost two-thirds of the private service sector workers were food insecure to some extent with over a third being severely food insecure. There are limited studies on the associations between individuals’ food insecurity levels and socio-demographic determinants, as most studies have looked at household-level food insecurity. However, the FAO worldwide study[47] of 147 countries measured individual-level food insecurity using the Food Insecurity Experience Scale and compared country clusters. Similar to our results, they found that for the second cluster of ‘Rich and developed countries’ (including Finland), living without a partner (being single, widowed or divorced) and having a lower education level were associated with food insecurity. However, they found that sex was not significant in the second cluster, but in the first cluster of ‘Very rich and developed countries mainly outside Europe’, men were at more risk of food insecurity. Based on these comparisons, our results reflect decent criterion validity.
The HFIAS is designed to measure the three main domains found to constitute the household food insecurity experience: anxiety and uncertainty about the household food supply (question 1), insufficient quality (questions 2–4) and insufficient food intake and its physical consequences (questions 5–9). Arguably these domains are specific for household food insecurity[48]; however, Radimer et al.[49] reported individual hunger to comprise four major components including intake insufficiency (a problem of intake quantity), diet inadequacy (a problem of intake quality) and disrupted eating patterns (not eating the three meals per day typical in high-income countries). The fourth, psychological, component was whether the person felt deprived and/or without eating choices. HFIAS covers the first two components mentioned. Furthermore, questions about having to skip meals or go a day without eating reflect the third component. The first question in HFIAS about worry reflects the fourth component to some extent. In addition, the Food Insecurity Experience Scale measure used on an individual level has been built on the research that informed HFIAS and contains very similar questions on worry and inadequate quality and quantity of food[50,51].
To our knowledge, no factor analysis has been conducted on individual-level food insecurity measurement tools; hence, we can only make comparisons with studies that use the original household-level HFIAS. The factor analysis revealed two factors of milder and severer food insecurity somewhat reflecting two of the food insecurity domains (insufficient food quality and insufficient food quantity). The two factors explained 68·8 % of the variance similarly to previous studies(44–46). As in previous studies[44,45], we found that the anxiety and uncertainty domain did not appear as a separate construct of food insecurity, but rather as part of the ‘milder food insecurity’ factor. We found the fifth question (‘have you had to eat smaller portions than you would have wanted?’) also loaded onto the first factor (milder food insecurity), though it is part of the ‘insufficient food quantity’ domain. Our results were similar to those of the studies using household-level food insecurity measures(44–46); however, more research is needed to understand the differences between the content and constructs of individual and household food insecurity.
The prevalence of food insecurity was higher in this study compared with the previous nationally representative studies conducted in Finland[11,13], but lower than that found among Finnish food aid recipients[37]. This is in line with what can be expected, as many of the food aid recipients were unemployed and homeless, whereas, compared with the general population, our study sample had lower education and income levels[52,53]. Furthermore, Lund et al.[7] found that interviews conducted via the internet more than doubled the prevalence of low/very low food security compared with computer-assisted telephone interviews. This may be due to people downplaying the severity of food insecurity to maintain social desirability. Face-to-face interviews and computer-assisted telephone interviews were used in the previous studies in Finland[12,13], whereas in this study, the participants answered the survey online.
A weakness of this study is the low response rate, which raises questions about how representative the sample is. Based on statistics provided via email by PAM (A Veirto, Research Manager, personal communication, 29 November 2021), at the end of 2019, PAM had a slightly higher percentage of male members compared with the percentage in our study (online supplementary material, Supplement 6). Furthermore, the youngest age groups (under 31 years) and oldest (over 60 years) were under-represented in our study, which is partly not unexpected as student members were not surveyed. Of the 2804 (44 %) participants for whom the employment industry category was available, the percentages of participants working in retail, hospitality and security were very similar. Property maintenance was slightly under-represented, and the ‘Others’ group was over-represented. During 2019, 7 % of PAM members had listed their first language as other than Finnish and thus the survey was not sent to them. Hence, our study sample may not be completely representative of all PAM members, but it seems that Finnish-speaking non-student members were reasonably well captured. Because severe food insecurity was more common among men and young age groups in our study, and among immigrants in other studies[21,22], it would seem more likely that the levels of food insecurity in this study were underestimates rather than overestimations of food insecurity levels in all PAM members. Furthermore, the fact that response rates are often lower among people of lower socio-economic status[54] and food insecurity is higher among groups of lower socio-economic status further supports this interpretation. Moreover, trade union membership is lower among young people, men, the unemployed and those in part-time or fixed term contracts[35] indicating that food insecurity may be even more prevalent among non-PAM members working in the private service sector.
Nevertheless, the multivariate model provides an adjusted estimate of the magnitude of the associations and can be used to indicate which groups within PAM members are at highest risk of being severely food insecure. Interestingly employment status was not significant in the model, possibly highlighting that work needs to be adequately compensated[9,32,55]. Alternatively, it could mean that the unemployed, laid-off and those on sick leave have adequate incomes to protect them from severe food insecurity considering most PAM union members would be on earning-related daily allowances rather than on basic social security. The latter has been found to be inadequate in covering reasonable minimum costs in Finland[56]. It must also be considered that the large majority of the participants were employed and that the unemployed, laid off and other groups may have been too small in size to be able to detect any statistically significant differences.
Respondents working in hospitality had a higher risk of severe food insecurity compared with retail workers. The number of young employees and employees with low educational level are especially emphasised in the restaurant and cleaning industries, respectively[32]. Jobs in the private service sector are characterised by part-time work, zero-hour and temporary contracts, and subcontracted work, especially in the hospitality industry[32]. Low work-intensity groups (those in part-time or temporary work) are experiencing increasingly higher poverty rates[55] which are only expected to increase further due to the breakdown of collective agreements[57,58] and the increasingly insufficient basic social security[56,59] that the United Nations Committee on Economic, Social and Cultural Rights and the European Committee of Social Rights has warned Finland of[60,61]. Historically, in-work poverty has been comparatively low in Finland compared with other European countries due to the once relatively generous income transfer system resulting in the government essentially subsidising the inadequate incomes paid by the private sector[32,55]. Thus, one policy measure should be to ensure employers provide employees with adequate salaries and better contracts, especially paying attention to industries employing young and low education status groups, considering their increased risk of severe food insecurity. Furthermore, the adequacy of social security must be improved[59]. Considering the proportion of household expenditure spent on housing in all EU countries was highest in Finland[62], providing more affordable housing, especially for those living alone, could also ensure improved livelihood as emphasised by the fact that rented housing was a risk factor for severe food insecurity. However, causality cannot be assumed from this study and renting may simply be associated with low wages and a lower socio-economic status.
Another alternative policy option to social security is universal basic income. A Finnish trial found that the basic income recipients’ perceptions of economic and health-related well-being and income were significantly better than that of the control group[63,64]. The recipients experienced significantly fewer problems related to health, stress and ability to concentrate[63]. They also had less issues with maintaining livelihood and reported themselves more happy[64]. This is relevant, as our results indicated that worse self-perceived health was associated with food insecurity.
Self-rated health has previously been found to be a strong predictor of mortality as well as associated with the number of physician contacts per year in Finnish populations[65,66]. Considering this and that food insecurity has been associated with multiple negative health outcomes in previous studies such as hypertension[27], diabetes[28], arthritis and back problems[29], it is extremely worrying that over a third of the participants were severely food insecure and only a third were food secure. The association of BMI with food insecurity also highlights the seriousness of the situation people are facing. This could have major consequences for public health, for example, a Canadian study found that annual health care costs were 76 % higher in households with severe food insecurity compared with food-secure households[31]. Additionally, mental health problems have been found to be associated with food insecurity[29,30] and they are also the main cause of premature retirement on a disability pension in Finland[67].
There is a lack of reliable data and understanding of the extent of food insecurity and related public health implications in Finland to tackle the problem[68]. Our study focused on severe food insecurity, but it is important to understand the risk factors for mild and moderate food insecurity too, especially considering families with many children were at increased risk. Nevertheless, our findings show that food insecurity is a widespread issue among private sector service workers. The precariousness of employment, which can be seen in temporary contracts and jobs without a living wage, has increased in-work poverty[9,32]. This has resulted in the need for food charity, highlighting that food insecurity is a problem faced by many working people[9,10]. The Finnish Ministry of Social Affairs and *Health is* supporting charitable food aid financially, which is distributed by an unorganised and unregulated sector with unclear conditions[9,69,70], to help citizens fulfil their basic needs. This is in direct contradiction to Finland identifying as a Nordic welfare state[9,10]. Finland along with other high-income countries should start monitoring food insecurity nationally on a regular basis, especially among vulnerable sections of the population.
## References
1. Borch A, Kjærnes U. **Food security and food insecurity in Europe: an analysis of the academic discourse (1975–2013)**. *Appetite* (2016.0) **103** 137-147. PMID: 27067740
2. Loopstra R, Reeves A, Stuckler D. **Rising food insecurity in Europe**. *Lancet* (2015.0) **385** 2041
3. Neter JE, Dijkstra SC, Visser M. **Food insecurity among Dutch food bank recipients: a cross-sectional study**. *BMJ Open* (2014.0) **4** e004657
4. Bocquier A, Vieux F, Lioret S. **Socio-economic characteristics, living conditions and diet quality are associated with food insecurity in France**. *Public Health Nutr* (2015.0) **18** 2952-2961. PMID: 25563304
5. Álvares L, Amaral TF. **Food insecurity and associated factors in the Portuguese population**. *Food Nutr Bull* (2014.0) **35** 395-402. PMID: 25639124
6. Nelson M, Erens B, Bates B. *Low Income Diet and Nutrition Survey* (2007.0)
7. Lund TB, Holm L, Tetens I. **Food insecurity in Denmark-socio-demographic determinants and associations with eating- and health-related variables**. *Eur J Public Health* (2018.0) **28** 283-288. PMID: 29020375
8. Henjum S, Morseth MS, Arnold CD. **‘I worry if I will have food tomorrow’: a study on food insecurity among asylum seekers living in Norway**. *BMC Public Health* (2019.0) **19** 592. PMID: 31101092
9. Silvasti T, Tikka V, Lambie-Mumford H, Silvasti T. *The Rise of Food Charity in Europe* (2020.0) 19-47
10. Salonen AS, Ohisalo M, Laihiala T. **Undeserving, disadvantaged, disregarded: three viewpoints of charity food aid recipients in Finland**. *Int J Environ Res Public Health* (2018.0) **15** E2896
11. 11.
FAO (2019) World Food and Agriculture – Statistical Pocketbook 2019. Rome: FAO.. *World Food and Agriculture – Statistical Pocketbook 2019* (2019.0)
12. 12.
FAO Statistics Division (2019) Food Insecurity Experience Scale (FIES), Finland 2017. Microdata FAO. https://microdata.fao.org/index.php/catalog/876/study-description (accessed July 2021).. (2019.0)
13. Sarlio-Lähteenkorva S, Lahelma E. **Food insecurity is associated with past and present economic disadvantage and body mass index**. *J Nutr* (2001.0) **131** 2880-2884. PMID: 11694612
14. Tarasuk V, Fafard St-Germain A-A, Mitchell A. **Geographic and socio-demographic predictors of household food insecurity in Canada, 2011–2012**. *BMC Public Health* (2019.0) **19** 12. PMID: 30606152
15. Loopstra R, Reeves A, Tarasuk V. **The rise of hunger among low-income households: an analysis of the risks of food insecurity between 2004 and 2016 in a population-based study of UK adults**. *J Epidemiol Community Health* (2019.0) **73** 668-673. PMID: 31036607
16. Loopstra R, Tarasuk V. **Severity of household food insecurity is sensitive to change in household income and employment status among low-income families**. *J Nutr* (2013.0) **143** 1316-1323. PMID: 23761648
17. Guo B. **Household assets and food security: evidence from the survey of program dynamics**. *J Fam Econ Issues* (2011.0) **32** 98-110
18. Leete L, Bania N. **The effect of income shocks on food insufficiency**. *Rev Econ Househ* (2010.0) **8** 505-526
19. Tarasuk VS. **Household food insecurity with hunger is associated with women’s food intakes, health and household circumstances**. *J Nutr* (2001.0) **131** 2670-2676. PMID: 11584089
20. Power M, Uphoff EP, Stewart-Knox B. **Food insecurity and socio-demographic characteristics in two UK ethnic groups: an analysis of women in the Born in Bradford cohort**. *J Public Health Oxf Engl* (2018.0) **40** 32-40
21. Maynard M, Dean J, Rodriguez PI. **The experience of food insecurity among immigrants: a scoping review**. *J Int Migr Integr* (2019.0) **20** 375-417
22. Tarraf D, Sanou D, Giroux I, Muenstermann I. *Peoples Movements in the 21st Century – Risks, Challenges and Benefits* (2017.0) 37-46
23. Leung CW, Epel ES, Ritchie LD. **Food insecurity is inversely associated with diet quality of lower-income adults**. *J Acad Nutr Diet* (2014.0) **114** 1943.e2-1953.e2. PMID: 25091796
24. Kirkpatrick SI, Tarasuk V. **Food insecurity is associated with nutrient inadequacies among Canadian adults and adolescents**. *J Nutr* (2008.0) **138** 604-612. PMID: 18287374
25. Men F, Gundersen C, Urquia ML. **Association between household food insecurity and mortality in Canada: a population-based retrospective cohort study**. *CMAJ* (2020.0) **192** E53-E60. PMID: 31959655
26. Banerjee S, Radak T, Khubchandani J. **Food insecurity and mortality in American adults: results from the NHANES-linked mortality study**. *Health Promot Pract* (2021.0) **22** 204-214. PMID: 32748673
27. Seligman HK, Laraia BA, Kushel MB. **Food insecurity is associated with chronic disease among low-income NHANES participants**. *J Nutr* (2010.0) **140** 304-310. PMID: 20032485
28. Seligman HK, Bindman AB, Vittinghoff E. **Food insecurity is associated with diabetes mellitus: results from the national health examination and nutrition examination survey (NHANES) 1999–2002**. *J Gen Intern Med* (2007.0) **22** 1018-1023. PMID: 17436030
29. Tarasuk V, Mitchell A, McLaren L. **Chronic physical and mental health conditions among adults may increase vulnerability to household food insecurity**. *J Nutr* (2013.0) **143** 1785-1793. PMID: 23986364
30. Pourmotabbed A, Moradi S, Babaei A. **Food insecurity and mental health: a systematic review and meta-analysis**. *Public Health Nutr* (2020.0) **23** 1778-1790. PMID: 32174292
31. Tarasuk V, Cheng J, de Oliveira C. **Association between household food insecurity and annual health care costs**. *CMAJ* (2015.0) **187** E429-E436. PMID: 26261199
32. Tanner N. (2019.0)
33. Ahrendt D, Sándor E, Revello A. *In-Work Poverty in the EU* (2017.0)
34. 34.
OECD Statistics (2018) Trade Union Membership and Trade Union Density. https://stats.oecd.org/Index.aspx?DataSetCode=TUD (accessed February 2021).. (2018.0)
35. Ahtiainen L. *Palkansaajien Järjestäytyminen Vuonna 2017 (Organization of Employees into Trade Unions in 2017)* (2019.0)
36. 36.
Service Union United PAM About PAM. https://www.pam.fi/en/about-pam.html (accessed August 2020).
37. Erkkola M, Uusitalo L, Puputti K. *Food Insecurity among Finnish Private Sector Service Workers and Food Pantry Clients* (2020.0)
38. Coates J, Swindale A, Bilinsky P. *Household Food Insecurity Access Scale (HFIAS) for Measurement of Food Access: Indicator Guide: Version 3* (2007.0)
39. Coates J, Webb P, Houser R. **‘He said, she said’: who should speak for households about experiences of food insecurity in Bangladesh?**. *Food Secur* (2010.0) **2** 81-95
40. Brunelli C, Viviani S. *Exploring Gender-Based Disparities with the FAO Food Insecurity Experience Scale* (2014.0)
41. O’Connell R, Brannen J. *Families and Food in Hard Times. European Comparative Research* (2021.0)
42. 42.
Statistics Finland Earned Income Concepts. https://www.stat.fi/meta/kas/tyotulot_en.html (accessed December 2021).
43. 43.
Statistics Finland Current Transfers Received Concepts. https://www.stat.fi/meta/kas/saatu_tulonsiir_en.html (accessed December 2021).
44. Mohammadi F, Omidvar N, Houshiar-Rad A. **Validity of an adapted household food insecurity access scale in urban households in Iran**. *Public Health Nutr* (2012.0) **15** 149-157. PMID: 21806860
45. Naja F, Hwalla N, Fossian T. **Validity and reliability of the Arabic version of the household food insecurity access scale in rural Lebanon**. *Public Health Nutr* (2015.0) **18** 251-258. PMID: 24702865
46. Knueppel D, Demment M, Kaiser L. **Validation of the household food insecurity access scale in rural Tanzania**. *Public Health Nutr* (2010.0) **13** 360-367. PMID: 19706211
47. Grimaccia E, Naccarato A. **Food insecurity individual experience: a comparison of economic and social characteristics of the most vulnerable groups in the world**. *Soc Indic Res* (2019.0) **143** 391-410
48. Coates J, Frongillo EA, Rogers BL. **Commonalities in the experience of household food insecurity across cultures: what are measures missing?**. *J Nutr* (2006.0) **136** 1438S-1448S. PMID: 16614441
49. Radimer KL, Olson CM, Campbell CC. **Development of indicators to assess hunger**. *J Nutr* (1990.0) **120** 1544-1548. PMID: 2243303
50. 50.
INDDEX Project (2018) Data4Diets: Building Blocks for Diet-Related Food Security Analysis. Boston, MA: Tufts University; available at https://inddex.nutrition.tufts.edu/data4diets/indicator/food-insecurity-experience-scale-fies (accessed December 2021).. (2018.0)
51. 51.
Food and Agriculture Organization of the United Nations (FAO) Food Insecurity Experience Scale. Voices Hungry. https://www.fao.org/in-action/voices-of-the-hungry/fies/en/ (accessed December 2021).
52. 52.
Statistics Finland (2020) Educational Structure of Population. Findicator. https://findikaattori.fi/en/9 (accessed May 2021).. (2020.0)
53. 53.
Statistics Finland (2021) Wages, Salaries and Labour Costs. Statistics Finland; available at https://www.tilastokeskus.fi/tup/suoluk/suoluk_palkat_en.html (accessed May 2021).. (2021.0)
54. Tolonen H, Helakorpi S, Talala K. **25-year trends and socio-demographic differences in response rates: Finnish adult health behaviour survey**. *Eur J Epidemiol* (2006.0) **21** 409-415. PMID: 16804763
55. Kangas O, Kalliomaa-Puha L. *ESPN Thematic Report on In-work Poverty – Finland, European Social Policy Network (ESPN)* (2019.0)
56. 56.
Kela Research Department (2015) Adequacy of Basic Social Security in Finland 2011–2015. Helsinki: Finnish Institute for Health and Welfare.. *Adequacy of Basic Social Security in Finland 2011–2015* (2015.0)
57. Savolainen A. (2017.0)
58. Teivainen A. (2021.0)
59. Tuulia R, Aho T, Mustonen N. *Kapeneeko Kuilu? Tilannekatsaus Terveyserojen Kaventamiseen Suomessa 2007–2010 (Is the Gap Narrowing? Review of the Reduction of Health Inequalities in Finland (2007–2010))* (2011.0)
60. 60.
Finnish Ministry for Foreign Affairs (2021) Recommendations to Finland on the Implementation of Economic, Social and Cultural Rights. https://valtioneuvosto.fi/en/-/recommendations-to-finland-on-the-implementation-of-economic-social-and-cultural-rights (accessed December 2021).. (2021.0)
61. 61.
Council of Europe (2019) Decisions Adopted by the European Committee of Social Rights at its 308th Session. https://www.coe.int/en/web/european-social-charter/coming-events/-/asset_publisher/aRI58gE6clPz/content/decisions-adopted-by-the-european-committee-of-social-rights-at-its-308th-session (accessed December 2021).. (2019.0)
62. 62.
Eurostat (2020) How Much Do We Spend on Housing? https://ec.europa.eu/eurostat/web/products-eurostat-news/-/ddn-20201210-1 (accessed December 2021).. (2020.0)
63. Kangas O, Jauhiainen S, Simanainen M. *The Basic Income Experiment 2017–2018 in Finland. Preliminary Results* (2019.0)
64. Kangas O, Jauhiainen S, Simanainen M. *Suomen Perustulokokeilun Arviointi (The Evaluation of the Basic Income Experiment in Finland)* (2020.0)
65. Heistaro S, Jousilahti P, Lahelma E. **Self rated health and mortality: a long term prospective study in eastern Finland**. *J Epidemiol Community Health* (2001.0) **55** 227-232. PMID: 11238576
66. Miilunpalo S, Vuori I, Oja P. **Self-rated health status as a health measure: the predictive value of self-reported health status on the use of physician services and on mortality in the working-age population**. *J Clin Epidemiol* (1997.0) **50** 517-528. PMID: 9180644
67. 67.
Finnish Centre for Pensions (2020) Mental Disorders the Most Common Reason for Retirement on a Disability Pension. https://www.etk.fi/en/topical-issues/mental-disorders-the-most-common-reason-for-retirement-on-a-disability-pension/ (accessed December 2021).. (2020.0)
68. Placzek O. *Socio-Economic and Demographic Aspects of Food Security and Nutrition* (2021.0)
69. Skön K. (2021.0)
70. Skön K. (2021.0)
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---
title: Sex-biased gene expression in nutrient-sensing pathways
authors:
- Suzanne Bennett-Keki
- Emily K. Fowler
- Leighton Folkes
- Simon Moxon
- Tracey Chapman
journal: 'Proceedings of the Royal Society B: Biological Sciences'
year: 2023
pmcid: PMC9993052
doi: 10.1098/rspb.2022.2086
license: CC BY 4.0
---
# Sex-biased gene expression in nutrient-sensing pathways
## Abstract
Differences in lifespan between males and females are found across many taxa and may be determined, at least in part, by differential responses to diet. Here we tested the hypothesis that the higher dietary sensitivity of female lifespan is mediated by higher and more dynamic expression in nutrient-sensing pathways in females. We first reanalysed existing RNA-seq data, focusing on 17 nutrient-sensing genes with reported lifespan effects. This revealed, consistent with the hypothesis, a dominant pattern of female-biased gene expression, and among sex-biased genes there tended to be a loss of female-bias after mating. We then tested directly the expression of these 17 nutrient-sensing genes in wild-type third instar larvae, once-mated 5- and 16-day-old adults. This confirmed sex-biased gene expression and showed that it was generally absent in larvae, but frequent and stable in adults. Overall, the findings suggest a proximate explanation for the sensitivity of female lifespan to dietary manipulations. We suggest that the contrasting selective pressures to which males and females are subject create differing nutritional demands and requirements, resulting in sex differences in lifespan. This underscores the potential importance of the health impacts of sex-specific dietary responses.
## Introduction
Sex differences in lifespan and in disease incidence are pervasive, but whether there are unifying reasons for them remains unclear. Such differences are influenced by the fundamental selective forces that result in sexual dimorphism across all aspects of organismal physiology [1]. Three main hypotheses have been proposed to explain ultimate causes for sex differences in lifespan. The first arises from the ‘unguarded’ nature of the maternally inherited X chromosome in males, meaning that X-linked genes with deleterious impacts on male lifespan and fitness may be expressed [2–5]. A related explanation concerns ‘toxic Y’ effects on lifespan [6]. A second hypothesis stems from the asymmetric, maternal, inheritance of mitochondria (mt). Hence mutations carried by mtDNA that are detrimental to males are less strongly selected against, than those with adverse effects on females (i.e. the ‘mother's curse’ [7–11]). A third hypothesis stems from sex-specific selection over many aspects of male and female life history [12–17]. A key role for sex-specific selection comes from observations of associations of lifespan with mating systems [15]. Hence sexual dimorphism in lifespan and ageing may result from sex-specific trade-offs between longevity and reproduction [12,18–21].
In terms of the directionality of lifespan differences, females are generally assumed to live longer in natural contexts. However, there is considerable variation across and within species in terms of which sex generally lives longest [1,17,20]. For example, in humans, cats, rats, pilot whales and many species of monkeys, females generally outlive males, whereas in dogs and some bats lifespans are very similar or males live longer. Counter-examples to the major hypotheses described above are also known. Thus, whether there are general unifying principles determining the directionality of sex differences in lifespan is unclear [1].
Whatever the ultimate explanation, it is clear that sex differences in lifespan persist and are associated with distinct profiles of health and disease. For example, women have been reported to live longer than men since UK records began in 1841 and deaths from heart disease, cancer and diabetes mellitus are more common in men, while women have a higher incidence of death due to cerebrovascular disease, osteoporosis, autoimmune disorders and Alzheimer's disease [22,23]. Length of life has also long been linked to dietary intake. For example, experiments in rats first showed that dietary restriction led to an increase of approximately $30\%$ in length of life [24]. Since then, the effect of dietary restriction on longevity has been described in many taxa [25–31]. The quality and quantity of specific nutrients have pervasive and robust effects on lifespan and reproductive success [32,33], with the balance between protein and carbohydrate being critical [34–37]. Furthermore, the nutritional requirements of each sex can differ. For example, in D. melanogaster, protein is required for production of eggs and higher protein intake occurs in mated over virgin females [38,39]. A male's reproductive success can be increased via the intake of carbohydrates, to produce energy for finding and attracting mates [35,40]. Nutritional inputs of macronutrient ratio, such as proteins and carbohydrates, can alter sex-specific phenotypes such as egg production and laying, male calling behaviour and digestive efficiency [41,42]. The ingestion of the monosaccharide d-galactose, present in milk, fruit and vegetables, is reported to induce distinct behavioural outcomes in male and female mice, in a dose-dependent manner, and with opposing effects on key senescence traits [43]. Overall, the emerging picture is that each sex has nutritional requirements that have sex-specific effects on lifespan and health.
Evidence showing that manipulations of diet have robust, and potentially sex-specific effects, on longevity has prompted much interest in whether these effects are mediated by nutrient-sensing pathways. A huge body of research now shows that the activity of the insulin/insulin like growth factor (IIS) and target of rapamycin (TOR) pathway is associated with lifespan determination across a huge range of animal species [44]. IIS and TOR pathways are highly conserved [45,46] and many components of them have been shown to affect lifespan directly (figure 1, electronic supplementary material, table S1; [47–49]). For example, in Drosophila melanogaster, the level of expression of insulin-like peptide genes (dilp1 and dilp2) [50] secreted from insulin producing cells in the brain can interact to regulate ageing [51]. Dietary restriction-mediated lifespan extension is also associated with dilp5, and over-expression of dilp6 in the adult fat body leads to extended lifespan [52]. Loss of the intracellular substrate encoded by chico also extends lifespan in Drosophila [53] as does loss of function of the IIS regulator Lnk [54] and activation of 4E-BP [55]. Related findings are also found in other model systems, for example, suppression of insulin-induced Akt signalling in C. elegans increases longevity [56], elevated Tsc1 expression increases longevity in female mice [57], S6k1 affects both health and lifespan in mice [58] and FOXO expression has direct effects on longevity in several species [59,60]. Figure 1. IIS/TOR signalling network in Drosophila illustrating IIS/TOR genes with (a) previously reported effects on lifespan determination and (b) significant sex-biased gene expression as determined in this study. The IIS/TOR pathway is shown, with extracellular, intracellular and nuclear components. Arrows indicate activation steps and bar-ended lines indicate inhibitory interactions. Broken lines indicate indirect or potential interactions. ( a) IIS/TOR genes previously reported to play a role in lifespan determination in D. melanogaster (electronic supplementary material, table S1) are bordered in red. ( b) highlighted in purple is a summary of the IIS/TOR genes from this study that have the potential to show significantly sex-biased gene expression from RNA-seq data (in either body part in virgin or mated adult flies), or from qRT-PCR (in 5 or 16-day-old once mated adult males or females). The nutrient-sensing pathway shown is adapted from Partridge et al. [ 47] and Teleman [48]. Figures created under publication licence using Biorender.
Whether there is differential activity in nutrient-sensing pathways associated with sex differences in lifespan is the key hypothesis we test here. Our rationale is the emerging finding that direct manipulations of diet or IIS/TOR genes as described above often result in very different outcomes in each sex. For example, in D. melanogaster, nematodes and mice the benefits of dietary restriction result in larger effects on lifespan in females [17,61]. Similarly, perturbations to the IIS and TOR pathways can also result in sex-specific effects on lifespan [53,62].
In this study, we examined whether there was any evidence that the divergent responses of health and lifespan of each sex to differing diets, and specifically the high nutritional sensitivity of females in particular, is underpinned by sex differences in the expression of IIS/TOR network genes with reported effects of lifespan. The specific hypothesis was that nutrient-sensing genes with lifespan effects would show more activation, and more dynamic expression over time, in females than males. We tested for sex-biased expression in 17 nutrient-sensing genes in Drosophila melanogaster that have reported effects on lifespan [47] (electronic supplementary material, table S1; figure 1). We first used a published RNA-sequencing dataset [63] to undertake a new analysis to test for sex-biased expression of nutrient-sensing gene expression in both virgin and mated males and females (in two different body parts, the Head + Thorax and Abdomen). We then undertook direct tests for sex-biased expression of the same 17 nutrient-sensing genes in larvae and once mated, young and older males and females (whole individuals). Our predictions were that the differential sensitivity of female lifespan to dietary manipulations would be associated with significant female-biased gene expression in IIS / TOR genes, and secondly, that these nutrient-sensing genes would be more sensitive to reproductive state in females than males.
## Analysis of nutrient-sensing genes extracted from whole transcriptome RNA-sequencing of virgin and mated males and females (in head + thorax and abdomen body part samples)
We first reanalysed the mRNA-seq dataset of Fowler et al. [ 63]. In that original study, differences in the expression of mRNAs in the head/thorax (HT) and abdomen (AB) body parts of virgin versus mated males or females were reported. In this study, we tested specifically for sex-biased gene expression in virgin versus mated samples (by comparing the expression level of genes in males versus females directly). Raw sequencing reads (accession PRJNA521155) were downloaded from the Sequence Read Archive (SRA) [64] in FASTQ format. Reads were trimmed to remove both poor-quality calls and adapters using Trim Galore! ( v. 0.3.4) [65] with default settings. Quality control checks were carried out using FastQC (v. 0.11.8) [66] with default settings, both before and after adapter and read quality trimming. *Differential* gene expression analysis was performed using the Berkeley Drosophila Genome Project (BDGP6.28) genome and gene annotation in GTF format downloaded from Ensembl (release 90) [67]. Trimmed reads were aligned to the genome using HISAT2 (version 2.1.0) [68] with single-end and unstranded settings (all other parameters set as default). Quality control of mapping data was performed on the resulting BAM files using QualiMap RNA-Seq QC (v. 2.2.2) [69] with default settings. Gene counts were extracted from the BAM file for each sample using the GTF annotations and htseq-count (v. 0.9.1) [70] run with unstranded settings (all other parameters default). Differential expression analysis was performed using default settings in DESeq2 (v. 1.22.1) [71] via the Galaxy platform (https://usegalaxy.eu/) [72]. We normalized and analysed the mRNA-seq data to focus specifically on comparisons between the sexes - to test for sex differences in mRNA expression and detect the extent to which genes show higher or lower gene expression in males versus females. We then extracted from that dataset the IIS/TOR network genes (figure 1a) and compared their patterns of sex-biased gene expression.
## Direct quantification of nutrient-sensing gene expression in male and female larvae, and 5- and 16-day-old adults (whole larvae and adult samples)
To complement the above analysis, we undertook independent, direct tests for sex-biased patterns of expression in nutrient-sensing genes, by using quantitative RT-PCR. Wild-type D. melanogaster flies from a large laboratory population originally collected in the 1970s in Dahomey (Benin) were used. Experimental flies were obtained following 2 generations of standard rearing, to minimize parental carry over effects. Eggs were collected from purple grape juice media Petri dishes (1.342 l water, 61 g agar, 0.73 l red grape juice, 51 ml Nipagin $10\%$ w/v) that had been placed for 2–3 h in three stock cages. Plates were then removed, divided into 4 and each quarter placed in a $\frac{1}{3}$ pint glass bottle containing 70 ml SYA medium (100 g brewer's yeast powder, 50 g sugar, 15 g agar, 30 ml Nipagin ($10\%$ w/v solution) and 3 ml propionic acid, per litre of medium). This gave 4 bottles for each of 3 replicate stock cages which were then incubated (25°C, $50\%$ humidity, 12: 12 hour light:dark cycle). The emerging flies were placed in small egg-laying cages over purple grape juice media Petri dishes for 3–4 h. Eggs laid were transferred into glass vials (75 mm × 24 mm) each containing 8 ml SYA medium, at a density of 50 per vial (3 vials for each biological replicate). Ten larvae were sampled at the wandering instar 3 stage (day 5 from vial set up) at random from each vial for all replicates and placed directly in 1.5 ml tubes in a −80°C freezer. The day on which there was peak adult emergence was designated ‘day 0’. The experimental flies emerging on this day were allowed to mate for 24 h and then sex separated and stored in single sex groups of 5 in vials and transferred to fresh food every 2–3 days. On Day 5 and Day 16 after eclosion the adults were frozen at the same time of day (120 min after lights on). Flies were briefly anaesthetized using CO2, transferred into 1.5 ml tubes and then immediately placed into an −80°C freezer to await RNA extraction. We reasoned that this method of transfer would more easily standardize handling across samples than the alternatives of blowing or shaking flies into the tubes. All methods of transfer to the freezer have potential effects on gene expression. However, the gene patterns we describe are over and above any transfer effects and not expected to be confounded by them.
## RNA extraction
We extracted RNA from whole individuals of single larvae, and from groups of 5 adult flies from each sex for each sample day (day 5 and day 16). There were three biological replicates of each sample and RNA was extracted from whole larvae and adults. Tissues were disrupted by grinding using an electric micro pestle, and total RNA extracted (miRvana kit, Ambion, AM1561) according to the kit protocol (with adjustment of the initial lysis solution into 50 µl followed by 150 µl, to ensure proper grinding of the material). RNA was eluted in RNA storage solution (1 mM sodium citrate, pH 6.4 ± 0.2, Ambion). Samples were DNase treated (Ambion Turbo DNA-free kit, AM1907). RNA was assessed for quantity and quality using a NanoDrop 8000 spectrophotometer. cDNA was synthesized using the Revertaid RT kit for reverse transcriptase (Thermo Scientific K1621) by following the kit protocol, and stored at −20°C.
## Sex identification of larvae
We used a molecular method. βtubulin85D (FBgn0003889) is reported to be expressed specifically in testes [73] predicting a dimorphic pattern of βtub85D expression, high in males and low in female larvae. We first verified this by sexing a subset of larvae by eye on the basis of the larger testes versus ovary imaginal discs. We then quantified the level of βtub85D expression in the same larvae, normalized to reference genes elF1A (FBgn0026250) and αTub84B (FBgn0003884) (electronic supplementary material, table S2). As expected, larvae sexed as males had high βtub85D expression and larvae sexed as females had no βtub85D expression above background noise. Thus we used βtub85D expression assays to identify the sex of experimental larvae.
## RT-PCR
Quantitative RT-PCR was performed using a Bio-Rad CFX Connect Thermal Cycler (software CFX maestro) and iTaq universal SYBR green supermix (Bio-Rad no. 1725121). Primers were manufactured salt-free Eurofins Genomics provider; electronic supplementary material, table S2). Primer efficiencies were checked using a 5-fold standard curve of cDNA with a maximum input of 50 ng total cDNA, and primer concentrations that yielded efficiencies of between 90 and $110\%$ were determined (electronic supplementary material, table S2). Relative quantities of target transcripts were normalized using 2 reference genes, elF1A and αTub84B whose expression was stable across each sex (see raw data file electronic supplementary material, table S10). To avoid intra-plate variation, all samples for stage and sex were loaded onto a single qPCR plate for each set of primers. Sufficient stock cDNA at 2 ng/µl was prepared for all target primers and for each RT-PCR run a mastermix of primers and SYBR iTaq (62 µl forward primer, 62 µl reverse primer, 620 µl SYBR iTaq and 186 µl molecular grade water) prepared before aliquoting into each well (15 µl mastermix and 5 µl cDNA) of a 96-well plate (Bio-Rad MLL-9601). Plates were sealed with an adhesive film (Bio-Rad MSB-1001). The mean Ct value for both reference gene expression was used to normalize cDNA for each sample by subtracting it from the mean target gene expression Ct value. *Relative* gene expression was then calculated using the 2−ΔCt method [74]. One male sample (male, day 16, replicate 3) was removed from the dataset as it was identified as a statistically significant outlier using the Grubbs Test. A two-way ANOVA was then used to test for differences in relative expression of nutrient-sensing genes (R v. 4.1.1 [75]). Sex and life stage were designated as factors in the model and we also tested for interactions between them. Post hoc Tukey tests were subsequently used to detect between which life stages any differences in gene expression had occurred.
## General patterns of sex-biased gene expression—RNA-sequencing data
Expression data for the genes in figure 1a were obtained from the analysis of the RNA sequencing dataset previously provided by Fowler et al. [ 63] (electronic supplementary material, tables S3 and S4). Genes were called as significantly differentially expressed if they passed the stringent threshold of showing a greater than log 2 fold change (±2log2FC) and an adjusted p-value of < 0.05 from the DEseq2 analysis (with the adjusted p-value accounting for the effects of multiple comparisons). Significant sex-biased gene expression (a significantly greater level of gene expression in one sex than the other, in either direction) was detected in the majority of genes involved in the IIS/TOR network (figure 1b). Out of the 44 IIS/TOR genes examined, 35 showed evidence for significant sex bias in gene expression in at least one body part in virgin or mated flies. Across both body parts and in virgins and mated flies, consistently more sex biased nutrient-sensing genes showed female biased (FB) in comparison to male biased (MB) expression (electronic supplementary material, tables S3 and S4). Among the sex biased genes in the whole transcriptome data, more genes showed FB expression in the head + thorax, and more of them MB in the abdomen (electronic supplementary material, table S3). Thus, more nutrient-sensing genes showed a pattern of FB expression in the abdomens than expected in comparison to whole transcriptome data (electronic supplementary material, table S3), and nutrient-sensing genes showed a pattern of FB expression overall.
## General patterns of sex-biased gene expression—qRT-PCR data
Consistent with the above, direct tests for differences in expression of nutrient-sensing genes by using a 2 way ANOVA followed by post-hoc testing in wild-type males and females also revealed that significant sex bias was common (10 out of 17 genes tested showed significant effects of sex × life stage or of sex alone, with an additional locus (Pten) showing non-significant sex bias ($p \leq 0.1$) (figure 2; table 1; electronic supplementary material, tables S5 and S6). For the nutrient-sensing loci in which a significant difference in stage was identified, post hoc testing confirmed that the difference occurred between the larval and adult stages, and almost never between day 5 and day 16 adults. *In* general, larval gene expression was significantly lower than in adults, except for 4EBP and dilp3. For many genes there appeared to be a trend for differences in expression of day 5 to day 16 adults, but this was significant only for FOXO in which there was significantly higher expression at day 5 in males. Nutrient-sensing loci showed significant sex-biased expression only in adults. Loci showing a significant main effect of sex (dilp3, Lnk, Tsc2 and dTOR) had higher expression in females (figure 2; table 1; electronic supplementary material, tables S5 and S6). Similar expression patterns were observed for dilp2 and dilp5, with male expression being significantly higher in adult males compared to females. A similar but sex-reversed pattern was observed for Dm, MEK and Tsc1, with expression being significantly higher in adult females. FOXO showed significantly higher expression in young male adults. dTOR appeared to show higher expression in young females but this effect was not significant. Overall, there was consistency in the findings from the high and low throughput methods of quantifying sex-biased gene expression. The qPCR data confirmed that significant sex-biased expression of nutrient-sensing genes in adults is typical, and both datasets confirmed a pattern of female-biased expression in nutrient-sensing genes (electronic supplementary material, table S6). Figure 2. Relative expression levels in IIS/TOR nutrient-sensing pathway genes at three different life stages, as determined by qRT-PCR. Shown on the x-axis are the data for larvae (L), young adults day 5 [5] and old adults day 16 [16], for males and females. Normalized, relative gene expression is shown on the y-axis calculated using the 2−ΔCt method. Red = females; green = males. Table 1. Summary of IIS/TOR nutrient-sensing genes identified as showing significant differences in gene expression across sexes or life stages, from the qRT-PCR data. Shown are the summary results for the expression of 17 nutrient-sensing loci analysed by qRT-PCR in electronic supplementary material, table S4. Ticks indicate if there was a significant main effect of sex or stage, or their interaction (sex × stage) at $p \leq 0.05.$geneFbgn numbersex × stagesexstagedilp2FBgn0036046✓dilp3FBgn0044050✓✓dilp5FBgn0044048✓InRFBgn0283499chicoFBgn0024248✓LnkFBgn0028717✓✓PtenFBgn0026379FOXOFBgn0038197✓MEKFBgn0010269✓Tsc1FBgn0026317✓Tsc2FBgn0005198✓✓dTORFBgn0021796✓S6kFBgn0283472✓MycFBgn0262656✓4E-BPFBgn0261560✓sggFBgn0003371✓14-3-3FBgn0004907
## Sex-biased gene expression in different parts of the nutrient signalling pathway
To check for patterns of sex-biased gene expression across the nutrient signalling pathway in more detail, we divided it into upstream to downstream sections (electronic supplementary material, figure S1a–f; table S8) and examined the direction of sex-bias (as indicated by the analysis derived from the RNA-seq data). This analysis again highlighted the general dominance of FB gene expression, but with no particular focus of either FB or MB genes in any specific part of the nutrient-sensing pathway. The qRT-PCR data were consistent with these findings (electronic supplementary material, table S7) and showed that throughout the nutrient-sensing pathway there were nutrient-sensing loci showing expression differences due to sex, life stage or their interaction (figure 2; table 1; electronic supplementary material, tables S5 and S6).
## Changes in sex-biased expression in nutrient-sensing genes upon mating
Analysis of the RNA-seq data allowed us to probe whether sex-biased gene expression patterns changed upon mating. *In* general, mating did not result in a change in the dominant pattern of FB expression of nutrient-sensing genes in the abdomen (table 2). 23 nutrient-sensing genes showed significant FB expression before and 22 after mating. 5 genes showed significant MB in virgin abdomens and 6 in mated flies. Of those genes that did change pattern upon mating in the abdomen, Pdk1 was significantly FB before, but lost this pattern after mating, while SREBP expression showed no sex bias before and became MB after mating. By contrast, sex-biased patterns of expression in nutrient-sensing genes in the head + thorax varied significantly before and after mating. In virgins there were 9 FB and 5 MB genes, but only 4 FB genes in mated flies. Both MB and FB genes in the head + thorax of virgins lost their sex bias in mated flies. 4 genes showing no sex bias in virgin head + thorax gained a FB pattern after mating (table 2; electronic supplementary material, table S9). The dominant pattern in the head + thorax was the loss of sex bias upon initiation of reproduction: $\frac{10}{4}$ genes lost/gained FB expression and $\frac{5}{1}$ genes lost/gained MB expression. In the head + thorax of males, expression was reduced in 16 of the 18 nutrient-sensing genes after mating, while none showed an increase of expression following mating. In females, 9 genes showed lower expression after mating, with 6 having increased expression. When a non sex-biased gene became FB following mating, this was typically driven by a reduction in male expression, in some cases accompanied by an increase in female expression. Changes from FB to NS arose from either a reduction in gene expression in females or a reduction in both males and females. *The* genes showing a change in pattern from MB to NS resulted from either a reduction in gene expression in males, or both a reduction in male and increase in females. In the two genes showing change in sex bias in the abdomen after mating (PDK1 lost FB to become NS, while SREBP changed from NS to MB), an increase in expression was seen in both sexes, however, the direction in both instances was with greater expression in males (table 2; electronic supplementary material, table S9). Table 2. Summary of IIS/TOR nutrient-sensing genes showing a significant switch to or from sex-biased gene expression upon mating, from the RNA-seq data (±2log2FC and an adjusted p-value of less than 0.05). FB = female-biased, MB = male-biased, NS = no sex bias. Shown are nutrient-sensing genes for which there was evidence of a significant change in the pattern of sex-biased expression upon mating, from the RNA-sequencing data.geneFBgn numberbody partsex-biased expressionvirginmateddilp3FBgn0044050HTNSFBdilp 5Fgbn0044048HTFBNSdilp 6FBgn0044047HTNSFBchicoFgbn0024248HTMBNSLnkFgbn0028717HTFBNSsteppkeFgbn0086779HTMBNSRasFgbn0003204HTFBNSPDK1Fgbn0020386ABFBNSPtenFgbn0026379HTMBNSAMPKFgbn0023169HTFBNSSik2Fbgn0025625HTFBNS4E-BPFgbn0261560HTFBNS14-3-3Fgbn0004907HTMBNSS6kFbgn0283472HTNSFBTIF1AFgbn0032988HTMBNSSREBPFgbn0261283HTFBNSSREBPFgbn0261283ABNSMBMycFgbn0262656HTNSFBcalderonFgbn0086365HTFBNSMEKFBgn0010269HTFBNS
## Changes in sex-biased expression in nutrient-sensing genes across different life stages
The qRT-PCR analysis of expression changes in nutrient-sensing genes across life stages showed sex differences in expression were not established until adulthood. Patterns of sex bias in either direction were also consistent across young and older adults (i.e. did not cross over; figure 2). 9 genes showed significant FB and 4 MB in adults, with none showing sex bias in larvae. Most nutrient-sensing genes also had higher expression in adults than in larvae.
## Discussion
Overall, the findings were generally consistent with the hypothesis that nutrient-sensing genes with lifespan effects would show more activation, and more dynamic expression over time, in females than males. In line with our first prediction, there was an overall pattern of female-biased gene expression in lifespan-influencing IIS/TOR genes. Following the second, these nutrient-sensing genes also appeared to be more sensitive to reproductive state in females than males, with a general loss of female-biased expression upon mating.
That many of the IIS/TOR pathway genes tested showed significant sex bias is not surprising, given that there is sex biased expression in the majority of protein-coding genes. However, there was no consistent directionality in the sex bias of gene expression across the whole comparator transcriptome in the RNA-seq data analysed, whereas among nutrient-sensing genes, there were more FB than MB genes found across all body parts and in virgin and mated flies. This pattern was confirmed directly by using qRT-PCR. The pattern of sex-biased expression of nutrient-sensing genes was also observed to change significantly upon the transition to the mated state and this pattern differed across body parts, with the patterns of sex bias remaining stable in abdomens but being much more dynamic in the head + thorax. When changes in the patterns of sex-biased expression occurred, it was generally due to the loss of FB expression. Results from RNA-sequencing and qRT-PCR showed that no particular up- or downstream part of the nutrient-sensing pathway was more or less dominated by FB or MB expression. Nutrient-sensing genes were never sex-biased in expression during the larval stage, but developed patterns of sex bias in adulthood, which generally remained consistent. The changing dynamics of sex-biased gene expression across life stages and with mating status show that elevated FB expression in nutrient-sensing genes is not simply a consequence of females generally having larger body sizes than males. Overall, these results suggest that the greater sensitivity of female lifespan to diet could be associated with greater activation in their nutrient-sensing genes. We suggest that the consistent loss of FB patterns of gene expression could underlie the generally greater survival costs of reproduction in females than males.
*The* genes chosen for study were the nutrient-sensing genes with reported effects on lifespan (electronic supplementary material, table S1). These were initially identified as affecting lifespan mostly via tests with mutant strains, direct assays or by genetic interactions (electronic supplementary material, table S1). Those findings highlight the central importance of these genes in determining length of life. However, studies of the relationships of segregating genetic variation in nutrient genes and longevity in natural populations remain scarce. Studies of the functional differentiation between different dilp nutrient-sensing genes across Drosophila suggests that this pattern may have been selected because it confers fitness benefits [50]. However, the significance of genetic variation in nutrient-sensing genes in natural populations remains unclear, as is how any sex-specific regulatory architecture is encoded.
The results are of general relevance because nutrient-sensing genes show deep conservation and effects of these genes on lifespan are found across may different taxa [47]. This could suggest that the activation of nutrient-sensing genes is a general contributor to variation in male and female lifespan. The results also beg the question of whether other physiological processes might exhibit similarly dynamic patterns of sex-biased gene expression. Differences between the sexes and some sex-biased gene expression have been reported, for example, in energy metabolism, immunity, excretion and neurosensory pathways. We describe a few such examples in electronic supplementary material, box S1 and note that comprehensive investigations into the patterns and consequences of sex-biased gene expression in these pathways could be useful. Our results also add fundamental information to the emerging picture of how the distinct effects of diets on the life history and health of males and females might be determined. This could be of relevance to understanding the potential effectiveness of dietary interventions used to treat diseases such as type 2 diabetes [76,77]. An understanding of sex-specific changes in gene expression following the initiation of potentially costly reproductive activity might also suggest potential routes for health interventions. For example, in model systems, the drug mifepristone can block the negative effect of shortened lifespan that results in females after mating [78,79] and in humans mifepristone is used treat patients with high blood sugar [80]. Understanding the links between these effects of reproduction-induced changes in lifespan and nutrient metabolism could be useful.
## Directionality in sex-biased pattern of expression in nutrient-sensing genes
The findings were generally consistent with the prediction that the greater sensitivity of female lifespan and life history to variation in diet might be manifested as an increase in the activation of nutrient-sensing gene expression. For virgin and mated flies and across both body parts (RNA-seq data) and for young and adult once mated flies (qRT-PCR data) approx. 2–4 times as many nutrient-sensing genes showed FB than MB expression. This contrasted with the comparator whole transcriptome data, which showed more FB expression in the head + thorax, but more MB in the abdomen. Thus, it seems that the nutrient-sensing pathway is characterized by a pattern of FB gene expression among sex-biased genes. This has the potential to underlie the increased sensitivity of female lifespan and life history to nutrients, assuming that higher levels of expression in such genes in females translates into phenotypic sensitivity. Many studies have examined patterns of sex-biased gene expression in terms of documenting its occurrence [81] and relevance to the field of sexual selection [82], sexual conflict [83,84] and sex chromosome linkage [85]. These studies have sought to understand how sex-specific selection can alter genome-wide patterns of sex-biased gene expression. However, to our knowledge, there are few studies so far seeking to associate effects of nutrient-sensing genes to patterns of sex-biased gene expression.
## Mating alters sex-biased patterns of expression in nutrient-sensing genes
The pattern of sex bias in nutrient-sensing genes was stable before and after mating in the abdomen across all nutrient-sensing genes. By contrast, in the head + thorax the situation was more dynamic, with mating often leading to a change in sex-biased gene expression, usually the loss of FB expression in mated females. The induction of significant alterations to the pattern of sex bias in nutrient-sensing genes upon mating could potentially be associated with survival costs of mating in females. Mating and receipt of seminal fluid proteins have been shown to increase female nutrient acquisition [38], alter nutrient-sensing [39] and reduce lifespan [86–90]. It is not yet known whether/how these effects are directly linked—changes to the pattern of expression of nutrient-sensing genes in females upon mating that we describe here provide a potential bridging mechanism [79,91]. Future tests should investigate the direct links between these different facets [92].
## Sex-biased expression in nutrient-sensing genes differs across different body parts
Perhaps unsurprisingly, the results suggests that sex-biased expression varied significantly across different body parts. Most work in Drosophila has focused on sex bias gene expression in either gonads, the brain or whole-body samples [93]. Observations of gene expression in Drosophila have shown that MB expression tends to be restricted to sex-specific tissues, whereas FB genes are often more broadly expressed [94]. Our analyses showed that the pattern of sex-biased gene expression in the abdomen was different to that seen in the head + thorax, with sex-biased expression in the abdomen being consistently FB before and after mating. The head + thorax was more labile. The observation of changes in the IIS/TOR gene network, particularly in females, in response to mating supports the link between longevity of females and their sensitivity to mating and nutrient-sensing.
## Sex-biased expression in nutrient-sensing genes differs across the lifecourse
The direct tests of nutrient-sensing gene expression were undertaken by using qRT-PCR to determine gene expression in 17 nutrient-sensing loci reported to have effects on lifespan [47,95]. This analysis validated the dominant female-biased patterns of expression in nutrient-sensing genes observed in the RNA-sequencing and identified different patterns of gene expression across the lifecourse. The expression of nutrient-sensing genes did not differ between the sexes during development (consistent with previous evidence of lack of sexually dimorphic expression of in dilps [96]) and was generally, but not always, lower than in adults. Sex bias in the expression of nutrient-sensing genes during adulthood tended not to interact with adult age and was generally consistent in direction in young and older males and females. Whether the patterns described here differ across body parts or show interactions with mating status across age will be interesting to test in the future.
Overall, our results contribute to an increase in understanding sex-specific variation in nutritional effects and in the expression of genes in the IIS and TOR network. This offers potential in developing interventions to improve health during ageing [49] as well as giving greater understanding of mechanisms that maintain sex differences.
## Data accessibility
Raw sequencing data for this study are stored at the Sequence Read Archive (SRA) using the BioProject accession: PRJNA521155. Raw qRT-PCR data (Ct values and normalized gene expression) are appended in electronic supplementary material table S10 [97].
## Authors' contributions
S.B.-K.: data curation, formal analysis, investigation, methodology, visualization, writing—original draft, writing—review and editing; E.K.F.: conceptualization, formal analysis, investigation, methodology, supervision, visualization, writing—review and editing; L.F.: formal analysis, validation, writing—review and editing; S.M.: data curation, formal analysis, supervision, validation, writing—review and editing; T.C.: conceptualization, data curation, funding acquisition, investigation, methodology, project administration, supervision, writing—original draft, writing—review and editing.
All authors gave final approval for publication and agreed to be held accountable for the work performed therein.
## Conflict of interest declaration
We declare we have no competing interests.
## Funding
We thank the NERC (NE/R$\frac{000891}{1}$; NE/T$\frac{007133}{1}$), the Society for the Study of Evolution and the Norwich Research Park Seed Corn for funding.
## References
1. Austad SN, Fischer KE. **Sex differences in lifespan**. *Cell Metabolism* (2016) 1022-1033. PMID: 27304504
2. Charlesworth B. *Evolution in Age-structured populations* (1980)
3. Trivers R. *Social evolution* (1985)
4. Connallon T, Beasley IJ, McDonough Y, Ruzicka F. **How much does the unguarded X contribute to sex differences in life span?**. *Evol. Lett.* (2022) 319-329. DOI: 10.1002/evl3.292
5. Carazo P, Green J, Sepil I, Pizzari T, Wigby S. **Inbreeding removes sex differences in lifespan in a population of**. *Biol. Lett.* (2016) 20160337. DOI: 10.1098/rsbl.2016.0337
6. Nguyen AH, Bachtrog D. **Toxic Y chromosome: increased repeat expression and age-associated heterochromatin loss in male**. *PLoS Genetics* (2020) e1009438. DOI: 10.1371/journal.pgen.1009438
7. Frank SA, Hurst LD. **Mitochondria and male disease**. *Nature* (1996) 224. DOI: 10.1038/383224a0
8. Tower J. **Sex-specific regulation of aging and apoptosis**. *Mech. Ageing Dev.* (2006) 705-718. DOI: 10.1016/j.mad.2006.05.001
9. Innocenti P, Morrow EH, Dowling DK. **Experimental evidence supports a sex-specific selective sieve in mitochondrial genome evolution**. *Science* (2011) 845. DOI: 10.1126/science.1201157
10. Camus MF, Clancy DJ, Dowling DK. **Mitochondria, maternal inheritance, and male aging**. *Curr. Biol.* (2012) 1717-1721. DOI: 10.1016/j.cub.2012.07.018
11. Carnegie L, Reuter M, Fowler K, Lane N, Camus MF. **Mother's curse is pervasive across a large mitonuclear**. *Evol. Lett.* (2020) 230-239. DOI: 10.1002/evl3.221
12. Trivers RL. **Parent-offspring conflict**. *Am. Zool.* (1974) 249-264. DOI: 10.1093/icb/14.1.249
13. Promislow DEL. **Costs of sexual selection in natural populations of mammals**. *Proc. R. Soc. B* (1992) 203-210. DOI: 10.1098/rspb.1992.0030
14. Liker A, Székely T. **Mortality costs of sexualselection andparental care in natural populations of birds**. *Evolution* (2005) 890-897. PMID: 15926698
15. Clutton-Brock TH, Isvaran K. **Sex differences in ageing in natural populations of vertebrates**. *Proc. R. Soc. B* (2007) 3097-3104
16. Maklakov AA, Bonduriansky R, Brooks RC. **Sex differences, sexual selection and ageing: an experimental evolution approach**. *Evolution* (2009) 2491-2503. DOI: 10.1111/j.1558-5646.2009.00750.x
17. Regan JC, Partridge L. **Gender and longevity: why do men die earlier than women? Comparative and experimental evidence**. *Best Pract. Res. Clin. Endocrinol. Metab.* (2013) 467-479. DOI: 10.1016/j.beem.2013.05.016
18. Allman J, Rosin A, Kumar R, Hasenstaub A. **Parenting and survival in anthropoid primates: caretakers live longer**. *Proc. Natl Acad. Sci. USA* (1998) 6866-6869. DOI: 10.1073/pnas.95.12.6866
19. Dowling DK, Simmons LW. **Ejaculate economics: testing the effects of male sexual history on the trade-off between sperm and immune function in Australian crickets**. *PLoS ONE* (2012) e30172. DOI: 10.1371/journal.pone.0030172
20. Maklakov A, Lummaa V. **Evolution of sex differences in lifespan and aging: causes and constraints**. *Bioessays* (2013) 717-724. DOI: 10.1002/bies.201300021
21. Wigby S, Suarez SS, Lazzaro BP, Pizzari T, Wolfner MF. **Sperm success and immunity**. *Curr. Top. Dev. Biol.* (2019) 287-313. DOI: 10.1016/bs.ctdb.2019.04.002
22. 22. Office for National Statistics. 2016
Deaths registered in England and Wales 2015. London, UK: Office for National Statistics.. *Deaths registered in England and Wales 2015* (2016)
23. 23. Office for National Statistics. 2020
Leading causes of death, UK: 2001–2018. London, UK: Office for National Statistics.. *Leading causes of death, UK: 2001–2018* (2020)
24. Mccay CM, Crowell MF, Maynard LA. **The effect of retarded growth upon the length of life Span and upon the ultimate body size**. *J. Nutr.* (1935) 63-79. DOI: 10.1093/jn/10.1.63
25. Jiang JC, Jaruga E, Repnevskaya MV, Jazwinski SM. **An intervention resembling caloric restriction prolongs life span and retards aging in yeast**. *FASEB J.* (2000) 2135-2137. DOI: 10.1096/fj.00-0242fje
26. Masoro EJ. **Subfield History: caloric restriction, slowing aging, and extending life**. *Sci. Aging Knowl. Environ.* (2003) re2
27. Greer EL, Brunet A. **Different dietary restriction regimens extend lifespan by both independent and overlapping genetic pathways in**. *Aging Cell* (2009) 113-127. DOI: 10.1111/j.1474-9726.2009.00459.x
28. Maklakov AA. **Sex differences in nutrient-dependent reproductive ageing**. *Aging Cell* (2009) 324-330. DOI: 10.1111/j.1474-9726.2009.00479.x
29. Swindell W. **Dietary restriction in rats and mice: a meta-analysis and review of the evidence for genotype-dependent effects on lifespan**. *Ageing Res. Rev.* (2012) 254-270. DOI: 10.1016/j.arr.2011.12.006
30. Reddiex A, Gosden T, Bonduriansky R, Chenoweth S. **Sex-specific fitness consequences of nutrient intake and the evolvability of diet preferences**. *Am. Nat.* (2013) 91-102. DOI: 10.1086/670649
31. Jensen K, McClure C, Priest N, Hunt J. **Sex-specific effects of protein and carbohydrate intake on reproduction but not lifespan in**. *Aging Cell* (2015) 605-615. DOI: 10.1111/acel.12333
32. Lee KP. **Dietary protein:carbohydrate balance is a critical modulator of lifespan and reproduction in**. *J. Insect. Physiol.* (2015) 12-19. DOI: 10.1016/j.jinsphys.2015.02.007
33. Fontana L, Partridge L. **Promoting health and longevity through diet: from model organisms to humans**. *Cell* (2015) 106-118. DOI: 10.1016/j.cell.2015.02.020
34. Mair W, Piper MDW, Partridge L. **Calories do not explain extension of life span by dietary restriction in**. *PLoS Biol.* (2005) e223. DOI: 10.1371/journal.pbio.0030223
35. Lee KP, Simpson SJ, Clissold FJ, Brooks R, Ballard JWO, Taylor PW, Soran N, Raubenheimer D. **Lifespan and reproduction in**. *Proc. Natl Acad. Sci. USA* (2008) 2498-2503. DOI: 10.1073/pnas.0710787105
36. Bruce KD. **High carbohydrate–low protein consumption maximizes**. *Exp. Gerontol.* (2013) 1129-1135. DOI: 10.1016/j.exger.2013.02.003
37. Bonduriansky R, Maklakov A, Zajitschek F, Brooks R. **Sexual selection, sexual conflict and the evolution of ageing and life span**. *Funct. Ecol.* (2008) 443-453. DOI: 10.1111/j.1365-2435.2008.01417.x
38. Carvalho GB, Kapahi P, Anderson DJ, Benzer S. **Allocrine modulation of feeding behavior by the sex peptide of**. *Curr. Biol.* (2006) 692-696. DOI: 10.1016/j.cub.2006.02.064
39. Ribeiro C, Dickson BJ. **Sex peptide receptor and neuronal TOR/S6K signaling modulate nutrient balancing in**. *Curr. Biol.* (2010) 1000-1005. DOI: 10.1016/j.cub.2010.03.061
40. Corrales-Carvajal VM, Faisal AA, Ribeiro C. **Internal states drive nutrient homeostasis by modulating exploration-exploitation trade-off**. *Elife* (2016) e19920. DOI: 10.7554/eLife.19920
41. Maklakov AA, Simpson SJ, Zajitschek F, Hall MD, Dessmann J, Clissold F, Raubenheimer D, Bonduriansky R, Brooks RC. **Sex-specific fitness efects of nutrient intake on reproduction and lifespan**. *Curr. Biol.* (2008) 1062-1066. DOI: 10.1016/j.cub.2008.06.059
42. Le Gall M, Behmer ST. **Effects of protein and carbohydrate on an insect herbivore: the vista from a fitness landscape**. *Integr. Comp. Biol.* (2014) 942-954. DOI: 10.1093/icb/icu102
43. Baeta-Corral R, Castro-Fuentes R, Giménez-Llort L. **Sexual dimorphism in the behavioral responses and the immunoendocrine status in d-galactose-induced aging**. *J. Gerontol. A* (2018) 1147-1157. DOI: 10.1093/gerona/gly031
44. Templeman N, Murphy C. **Regulation of reproduction and longevity by nutrient-sensing pathways**. *J. Cell Biol.* (2018) 93-106. DOI: 10.1083/jcb.201707168
45. Barbieri M, Bonafe M, Franceschi C, Paolisso G. **Insulin/IGF-I-signaling pathway: an evolutionarily conserved mechanism of longevity from yeast to humans**. *Am. J. Physiol.—Endocrinol. Metab.* (2003) E1064-E1071. DOI: 10.1152/ajpendo.00296.2003
46. De Lucia C, Murphy T, Steves CJ, Dobson RJB, Proitsi P, Thuret S. **Lifestyle mediates the role of nutrient-sensing pathways in cognitive aging: cellular and epidemiological evidence**. *Commun. Biol.* (2020) 157. DOI: 10.1038/s42003-020-0844-1
47. Partridge L, Alic N, Bjedov I, Piper MDW. **Ageing in Drosophila: the role of the insulin/Igf and TOR signalling network**. *Exp. Gerontol.* (2011) 376-381. DOI: 10.1016/j.exger.2010.09.003
48. Teleman AA. **Molecular mechanisms of metabolic regulation by insulin in**. *Biochem. J.* (2010) 13-26. DOI: 10.1042/BJ20091181
49. Pan H, Finkel T. **Key proteins and pathways that regulate lifespan**. *J. Biol. Chem.* (2017) 6452-6460. DOI: 10.1074/jbc.R116.771915
50. Grönke S, Clarke D-F, Broughton S, Andrews TD, Partridge L. **Molecular evolution and functional characterization of**. *PLoS Genet.* (2010) e1000857. DOI: 10.1371/journal.pgen.1000857
51. Post S, Liao S, Yamamoto R, Veenstra JA, Nässel DR, Tatar M. *Aging Cell* (2019) e12863. DOI: 10.1111/acel.12863
52. Bai H, Kang P, Tatar M. *Aging Cell* (2012) 978-985. DOI: 10.1111/acel.12000
53. Clancy DJ, Gems D, Harshman LG, Oldham S, Stocker H, Hafen E, Leevers SJ, Partridge L. **Extension of life-span by loss of CHICO, a**. *Science* (2001) 104-106. DOI: 10.1126/science.1057991
54. Slack C. **Regulation of lifespan, metabolism, and stress responses by the**. *PLoS Genet.* (2010) e1000881. DOI: 10.1371/journal.pgen.1000881
55. Zid BM, Rogers AN, Katewa SD, Vargas MA, Kolipinski MC, Lu TA, Benzer S, Kapahi P. **4E-BP extends lifespan upon dietary restriction by enhancing mtochondrial activity in**. *Cell* (2009) 149-160. DOI: 10.1016/j.cell.2009.07.034
56. Dillin A, Crawford D, Kenyon C. **Timing requirements for insulin/IGF-1 signaling in**. *Science* (2002) 830-834. DOI: 10.1126/science.1074240
57. Zhang H-M, Diaz V, Walsh ME, Zhang Y. **Moderate lifelong overexpression of tuberous sclerosis complex 1 (TSC1) improves health and survival in mice**. *Sci. Rep.* (2017) 1-4. PMID: 28127051
58. Selman C. **Ribosomal protein S6 kinase 1 signaling regulates mammalian life span**. *Science* (2009) 140-144. DOI: 10.1126/science.1177221
59. Saxton RA, Sabatini DM. **mTOR sgnaling in growth, metabolism, and disease**. *Cell* (2017) 960-976. DOI: 10.1016/j.cell.2017.02.004
60. Johnson SC, Rabinovitch PS, Kaeberlein M. **mTOR is a key modulator of ageing and age-related disease**. *Nature* (2013) 338-345. DOI: 10.1038/nature11861
61. Magwere T, Chapman T, Partridge L. **Sex differences in the effect of dietary restriction on life span and mortality rates in female and male**. *J. Gerontol. A: Biol. Sci. Med. Sci.* (2004) B3-B9. DOI: 10.1093/gerona/59.1.B3
62. Graze RM, Tzeng R-Y, Howard TS, Arbeitman MN. **Perturbation of IIS/TOR signaling alters the landscape of sex-differential gene expression in**. *BMC Genom.* (2018) 1-16. DOI: 10.1186/s12864-018-5308-3
63. Fowler EK, Bradley T, Moxon S, Chapman T. **Divergence in transcriptional and regulatory responses to mating in male and female Fruitflies**. *Sci. Rep.* (2019) 16100. DOI: 10.1038/s41598-019-51141-9
64. Leinonen R, Sugawara H, Shumway M. **The sequence read archive**. *Nucleic Acids Res.* (2011) D19-D21. DOI: 10.1093/nar/gkq1019
65. Krueger F.. (2012)
66. Andrews S. (2010)
67. Cunningham F. **Ensembl 2019**. *Nucleic Acids Res.* (2019) D745-D751. DOI: 10.1093/nar/gky1113
68. Kim D, Langmead B, Salzberg SL. **HISAT: a fast spliced aligner with low memory requirements**. *Nat. Methods* (2015) 357-360. DOI: 10.1038/nmeth.3317
69. García-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, Götz S, Tarazona S, Dopazo J, Meyer TF, Conesa A. **Qualimap: evaluating next-generation sequencing alignment data**. *Bioinformatics* (2012) 2678-2679. DOI: 10.1093/bioinformatics/bts503
70. Anders S, Pyl PT, Huber W. **HTSeq–a Python framework to work with high-throughput sequencing data**. *Bioinformatics* (2015) 166-169. DOI: 10.1093/bioinformatics/btu638
71. Love MI, Huber W, Anders S. **Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2**. *Genome Biol.* (2014) 1-21
72. Afgan E. **The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update**. *Nucleic Acids Res.* (2018) W537-W544. DOI: 10.1093/nar/gky379
73. Chintapalli VR, Wang J, Dow JA. **Using FlyAtlas to identify better**. *Nat. Genet.* (2007) 15-720. DOI: 10.1038/ng2049
74. Livak KJ, Schmittgen TD. **Analysis of relative gene expression data using real-time quantitative PCR and the 2−**. *Methods* (2001) 402-408. DOI: 10.1006/meth.2001.1262
75. 75. R-Core. 2021
R: a language and environment for statistical computing, version 4.1.1. Vienna, Austria: R Foundation for Statistical Computing.. *R: a language and environment for statistical computing* (2021)
76. Sami W, Ansari T, Butt NS, Hamid M. **Effect of diet on type 2 diabetes mellitus: a review**. *Int. J. Health Sci.* (2017) 65-71
77. Hallberg SJ, Dockter NE, Kushner JA, Athinarayanan SJ. **Improving the scientific rigour of nutritional recommendations for adults with type 2 diabetes: a comprehensive review of the American Diabetes Association guideline-recommended eating patterns.**. *Diabetes Obesity Metab.* (2019) 1769-1779. DOI: 10.1111/dom.13736
78. Landis GN, Salomon MP, Keroles D, Brookes N, Sekimura T, Tower J. **The progesterone antagonist mifepristone/RU486 blocks the negative effect on life span caused by mating in female**. *Aging* (2015) 53-69. DOI: 10.18632/aging.100721
79. Landis GN. **Metabolic signatures of life span regulated by mating, Sex Peptide, and Mifepristone/RU486 in female**. *J. Gerontol. A* (2021) 195-204. DOI: 10.1093/gerona/glaa164
80. Ciato D, Mumbach AG, Paez-Pereda M, Stalla GK. **Currently used and investigational drugs for Cushing's disease**. *Expert Opin Investig. Drugs* (2017) 75-84. DOI: 10.1080/13543784.2017.1266338
81. Grath S, Parsch J. **Sex-biased gene expression**. *Annu. Rev. Genet.* (2016) 29-44. DOI: 10.1146/annurev-genet-120215-035429
82. Ellegren H, Parsch J. **The evolution of sex-biased genes and sex-biased gene expression**. *Nat. Rev. Genet.* (2007) 689-698. DOI: 10.1038/nrg2167
83. Connallon T, Knowles LL. **Intergenomic conflict revealed by patterns of sex-biased gene expression**. *Trends Genet.* (2005) 495-499. DOI: 10.1016/j.tig.2005.07.006
84. Wright AE, Fumagalli M, Cooney CR, Bloch NI, Vieira FG, Buechel SD, Kolm N, Mank JE. **Male-biased gene expression resolves sexual conflict through the evolution of sex-specific genetic architecture**. *Evol. Lett.* (2018) 52-61. DOI: 10.1002/evl3.39
85. Meisel RP, Malone JH, Clark AG. **Disentangling the relationship between sex-biased gene expression and X-linkage**. *Genome Res.* (2012) 1255-1265. DOI: 10.1101/gr.132100.111
86. Partridge L, Fowler K. **Non-mating costs of exposure to males in female**. *J. Insect. Physiol.* (1990) 419-425. DOI: 10.1016/0022-1910(90)90059-O
87. Chapman T. **A cost of mating with males that do not transfer sperm in female**. *J. Insect. Physiol.* (1992) 223-227. DOI: 10.1016/0022-1910(92)90070-T
88. Chapman T, Liddle LF, Kalb JM, Wolfner MF, Partridge L. **Cost of mating in**. *Nature* (1995) 241-244. DOI: 10.1038/373241a0
89. Wigby S, Chapman T. **Sex Peptide causes mating costs in female**. *Curr. Biol.* (2005) 316-321. DOI: 10.1016/j.cub.2005.01.051
90. Travers LM, Garcia-Gonzalez F, Simmons LW. **Live fast die young life history in females: evolutionary trade-off between early life mating and lifespan in female**. *Sci. Rep.* (2015) 15469. DOI: 10.1038/srep15469
91. Tower J, Landis GN, Shen J, Choi R, Fan Y, Lee D, Song J. **Mifepristone/RU486 acts in**. *Biogerontology* (2017) 413-427. DOI: 10.1007/s10522-017-9703-y
92. Nanfack-Minkeu F, Sirot LK. **Effects of mating on gene expression in female insects: unifying the field**. *Insects* (2022) 69. DOI: 10.3390/insects13010069
93. Assis R, Zhou Q, Bachtrog D. **Sex-biased transcriptome evolution in**. *Genome Biol. Evol.* (2012) 1189-1200. DOI: 10.1093/gbe/evs093
94. Parisi M. **A survey of ovary-, testis-, and soma-biased gene expression in**. *Genome Biol.* (2004) R40. DOI: 10.1186/gb-2004-5-6-r40
95. Kapahi P, Zid BM, Harper T, Koslover D, Sapin V, Benzer S. **Regulation of lifespan in**. *Curr. Biol.* (2004) 885-890. DOI: 10.1016/j.cub.2004.03.059
96. Sawala A, Gould AP. **The sex of specific neurons controls female body growth in**. *PLoS Biol.* (2017) e2002252. DOI: 10.1371/journal.pbio.2002252
97. Bennett-Keki S, Fowler EK, Folkes L, Moxon S, Chapman T. *Figshare* (2023). DOI: 10.6084/m9.figshare.c.6430360
|
---
title: Reference values for intake of six types of soluble and insoluble fibre in
healthy UK inhabitants based on the UK Biobank data
authors:
- Artem Shevlyakov
- Dimitri Nikogosov
- Leigh-Ann Stewart
- Miguel Toribio-Mateas
journal: Public Health Nutrition
year: 2022
pmcid: PMC9993053
doi: 10.1017/S1368980021002524
license: CC BY 4.0
---
# Reference values for intake of six types of soluble and insoluble fibre in healthy UK inhabitants based on the UK Biobank data
## Body
The term ‘dietary fibre’ refers to a diverse group of organic compounds found in edible plants. The exact definition has changed since its first appearance in scientific press[1]. Codex Alimentarius, a WHO-approved guideline to nutrition labelling, defines dietary fibre as ‘carbohydrate polymers with ten or more monomeric units, which are not hydrolysed by the endogenous enzymes in the small intestine of humans’[2]. Indigestibility by endogenous human enzymes is a core property of dietary fibres.
Foods high in fibre are less energy-dense, and indigestible fibre might inhibit the absorption of high-calorie nutrients(3–5), directly affecting food properties and human health. In the early 1970s, Heaton proposed that dietary fibre may decrease the energy/satiety ratio of food and that adding dietary fibre to diet may help combat obesity[6]. Research confirmed that dietary fibre promotes weight control and maintains healthy levels of metabolic markers in humans(7–12). This is intriguing, considering the steady increase in the prevalence of overweight and obesity(13–16) – risk factors of metabolic syndrome[17], type 2 diabetes[18,19], CVD[20,21] and cancer(22–25).
Gut flora plays a complex role in health and disease. Tens of trillions of commensal, symbiotic and pathogenic microorganisms inhabit the human gastrointestinal system[26,27], their diversity and quantities increasing from stomach to small intestine to colon[28,29]. Although often used interchangeably, microbiota is the term that refers to community of microorganisms themselves, while the collective microbial genomes are known as the microbiome[30].
Dysbiosis (a combination of unfavourable changes in the composition of gut microbiota) may contribute to the development of metabolic and immune disorders such as ulcerative colitis[31], Crohn’s disease[32], type 2 diabetes and obesity[33,34], and liver cirrhosis[35]. It may also be associated with cognitive function and mental health conditions[36,37]. Maintaining a sufficient population and diversity of microbiota seems crucial for human health.
As a main source of microbial nutrition, fibre contributes to the overall health and well-being. Dietary interventions may lead to changes in microbiota composition: the gut microbiota responds by altering fermentation, composition and colony sizes[38]. Diets rich in certain types of fibre seem to be better at promoting the growth of beneficial bacterial populations; different bacterial genera seem to thrive on different types of nutrients.
While some fibres, such as inulin, are widely acknowledged for their ability to induce beneficial modifications in gut microbiota composition and function(39–41), other types of fibre may also exert positive influence through less understood pathways. It is plausible that low fibre diversity (i.e. excess intake or deficiency of a certain type of fibre) may shift the composition of the gut microbiota, promoting excessive growth of specific bacteria while not supporting other ones[41,42].
According to a study published in 2010, up to 80 % of clinical decisions are based on an interpretation of laboratory results[43]. Reference ranges for the overwhelming majority of biochemically important nutrients have been developed and are widely applied. Yet there are no guidelines which take into account the diversity of fibre types and no reference values for intake of different types of fibre. Clearly, obtained test values are unusable unless it is known what reference they should be compared to.
Little to no research has been done to quantify and standardise the recommended daily intake of specific types of fibre. This research aims to make a step towards filling this gap. By calculating the reference values for the intake of specific types of fibre, we hope to expand the small pool of information, promoting further research and supporting more solid dietary and clinical decisions.
## Abstract
### Objective:
To obtain a set of reference values for the intake of different types of dietary fibre in a healthy UK population.
### Design:
This descriptive cross-sectional study used the UK *Biobank data* to estimate the dietary patterns of healthy individuals. Data on fibre content in different foods were used to calculate the reference values which were then calibrated using real-world data on total fibre intake.
### Setting:
UK *Biobank is* a prospective cohort study of over 500 000 individuals from across the United Kingdom with the participants aged between 40 and 69 years.
### Participants:
UK Biobank contains information on over 500 000 participants. This study was performed using the data on 19 990 individuals (6941 men, 13 049 women) who passed stringent quality control and filtering procedures and had reported above-zero intake of the analysed foods.
### Results:
A set of reference values for the intake of six different types of soluble and insoluble fibres (cellulose, hemicelluloses, pectin and lignin), including the corresponding totals, was developed and calibrated using real-world data.
### Conclusions:
To our knowledge, this is the first study to establish specific reference values for the intake of different types of dietary fibre. It is well known that effects exerted by different types of fibre both directly and through modulation of microbiota are numerous. Conceivably, a deficit or excess intake of specific types of dietary fibre may detrimentally affect human health. Filling this knowledge gap opens new avenues for research in discussion in studies of nutrition and microbiota and offers valuable tools for practitioners worldwide.
## Materials and Methods
Establishing the reference values for dietary intake requires a large sample representative of the whole population, and a method of collecting detailed information. For the UK population, such a database is provided by the UK Biobank, a prospective cohort study of over 500 000 individuals from across the United Kingdom. Participants aged between 40 and 69 years were invited to one of twenty-two centres across the UK between 2006 and 2010. Blood, urine and saliva samples were collected, physical measurements were taken, and each individual answered an extensive questionnaire about health and lifestyle. Full UK Biobank protocol and rationale are available online[44]. For this study, data access to UK Biobank was granted under application #36183.
Dietary intake of the UK Biobank participants was evaluated with Oxford WebQ tool, a validated web-based questionnaire which assesses the 24-h intake of 206 foods and 32 beverages[45]. This evaluation was performed several times, none of which were mandatory. These data, grouped into the ‘Diet by 24-hour recall’ (Category 100090) section of the UK Biobank, were used to estimate the amount and type of food consumed by the subjects of the study.
Unfortunately, these data do not contain a detailed breakdown of fibre types. Data on content of fibre by type were taken from the article by Marlett and Cheung[46], reporting the content of two different soluble and four different insoluble types of fibre, as well as the relevant totals, for 228 various foods. We used the provided serving weights to calculate the content of fibre per 100 g of food.
The fibres used in the analysis are summarised in Table 1 (the classification of fibre as ‘soluble’ or ‘insoluble’ corresponds to the original article) [46]. For total soluble, total insoluble and overall total fibre content, we used the data from the article[46]; total pectin and total hemicelluloses fibre content were calculated manually.
Table 1Types of fibre used in the current analysis[46] SolubleInsolubleHemicellulosesCellulosePectinHemicellulosesPectinLignin The Oxford WebQ questionnaire reports food consumption in servings. To convert servings to grams and calculate the net content of specific nutrients, we used the Food Portion Sizes book compiled by the Food Standards Agency (widely known as the Maff Handbook) [47], which had been used during the preparation of the Oxford WebQ[45]. This study used the 3rd edition for reference, as the 2nd edition (used for the Oxford WebQ) is out of print and inaccessible.
The foods reported in the Maff Handbook[47] do not precisely match the foods from the Oxford WebQ. We performed the mapping by hand, discarding several food items from the Oxford WebQ that were not present in the Maff Handbook[47]. In multiple cases, the matches were ambiguous due to the nature of reporting in the Maff Handbook[47] and low specificity of some of the UK Biobank foods. For example, a UK Biobank question ‘How many slices of sliced bread did you eat yesterday?’, matched to several items from the Maff Handbook[47] depending on the type of bread and the thickness of the slice. In such cases, the weight from the ‘average’ portion was selected. Full mappings are presented in Supplementary Table 1.
Oxford WebQ uses an open-ended system for the number of consumed servings. Prior to calculating individual food intake, the answers were converted to remove ambiguity, turning 3+, 4+, 5+ and 6+ servings into 3, 4, 5 and 6 servings, respectively. ‘ Less than 1’ serving was considered as 0·5 of the serving.
The foods analysed in the Marlett and Cheung article[46] also did not directly correspond to the foods reported either in the UK Biobank or the Maff Handbook[47]. We developed a tagging system and assigned from one to five tags to each food in the Marlett and Cheung article[46]. We then calculated the fibre content for each tag as the average fibre content per 100 g of each food with this tag. Full mapping of tags is presented in Supplementary Table 2.
All the foods present in the UK Biobank were also tagged as represented in Supplementary Table 3. The calculated per-tag fibre values were mapped to the Oxford WebQ items. We used the average of the values if the food had been assigned two or more tags and discarded the foods with no tags. After the labelling, 113 foods from the UK Biobank questionnaire remained, with fibre content per 100 g and weight of serving available for each. The breakdown of the fibre content per 100 g of each analysed food is presented in Supplementary Table 4.
We performed a quality check of the UK *Biobank data* to exclude unreliable and incomplete entries. The included participants had to meet all of the following criteria:Data on genetic sex (Data-Field 22001), self-reported ethnic background (Data-Field 21000), BMI (Data-Field 21001) and self-reported medical history (Data-Fields 20001 and 20002) were available;Self-reported sex (Data-Field 31) matched genetic sex (Data-Field 22001) and was consistent between visits;Self-reported ethnic background (Data-Field 21000) was either British, Irish or Other White and was consistent between visits;BMI (Data-Field 21001) and weight (Data-Field 21002) had been measured at least once each;Participant had reported consuming at least one of the foods that had successfully been mapped to detailed fibre content.
After filtering, information on the consumption of 113 foods by 196 608 participants (88 626 males and 107 982 females) remained. Of these, we kept only the individuals who deemed themselves healthy and reported no diseases at any of the visits (Data-Fields 22001 and 22002 contained no entries) and had BMI within normal range of (18·5, 25).
For each of the selected participants, the intake of specific types of fibre, as well as total intake of fibre, were calculated as follows: where n – total number of reportedly consumed food items containing the analysed type of fibre; i – the number of reportedly consumed Oxford WebQ food; FC100i – calculated fibre content in 100 g of food i; nservi – number of consumed servings of food i; and mservi – mass of a single serving of food i (according to[47]). Total daily intake of fibre was calculated by adding up the total fibre content of each consumed food provided in the Marlett and Cheung article[46]. The BMI and weight of each participant were calculated as the means of all BMI and weight measures, respectively, reported across visits, with the missing values omitted.
Biological data often follow a log-normal distribution, as values of measured parameters cannot go below 0[48]. We adjusted the intake for body weight and applied a log-transformation to the data, discarding samples with no reported fibre intake and bringing the distribution close to normal. That led to another shrinkage of the dataset (final number of individuals between 19 987 and 19 990 depending on a particular type of fibre, see Table 2).
Table 2Numbers of subjects in final reference groups used for calculation of reference values for different types of fibreFibre type n MenWomenSoluble Soluble hemicelluloses19 988694013 048 Soluble pectin19 897689812 999Insoluble Insoluble cellulose19 990694113 049 Insoluble hemicelluloses19 990694113 049 Insoluble pectin19 980693513 045 Insoluble lignin19 987694113 046Total Total hemicelluloses19 990694113 049 Total pectin19 980693513 045 Total soluble fibre19 988694013 048 Total insoluble fibre19 990694113 049 Total fibre19 990694113 049 The reference ranges were calculated for 2·5th and 97·5th percentiles, which is the interval used most commonly in practice[49]. The CI for the reference range limits and the medians were calculated using the sampled data according to the method described by Hahn and Meeker[50] and are guaranteed to be equal to or greater than 95 %. All the obtained values were then exponentiated to transfer them back into linear space.
Statistical analysis was performed using the Microsoft R Open programming language, version 3.5.2, and RStudio IDE, version 1.2·1335. P-value < 0·05 was considered statistically significant.
## Results
Shrinking the dataset was associated with an inevitable loss of fidelity, as illustrated in Fig. 1. As both the number of participants and the number of foods decrease, each food is reported less often, and the number of reported foods for each participant declines.
Fig. 1Counts of people reporting consumption of different numbers of foods at different stages of the sample preparation., Before filtering;, after filtering;, after filtering: only healthy The association between body weight and the amount of ingested nutrients is typically accounted for in dietary studies. We performed the adjustment by dividing the fibre intake values by the corresponding body weight (prior to applying log-transformation).
The resulting reference values and the median values for the healthy population are presented in Table 3 as grams of fibre per kilogram of body weight. The graphical representations of population distributions of consumption for different types of fibre, as well as the reference and median values, are presented in Figs 2 and 3.
Table 3A summary of the obtained reference values and medians for daily intake of different types of fibre, stratified by sex and fibre typeFibre typeDaily reference intake, g/kgMenWomenRangeMedianRangeMedianSoluble hemicelluloses0·013–0·1720·0590·013–0·1790·062Soluble pectin0·002–0·0830·0220·003–0·0950·027Insoluble cellulose0·03–0·4220·1390·034–0·4760·163Insoluble hemicelluloses0·029–0·4580·1460·033–0·4950·164Insoluble pectin0·004–0·1550·0420·006–0·1840·053Insoluble lignin0·012–0·190·0590·014–0·2090·067Total hemicelluloses0·044–0·6290·2060·048–0·6690·227Total pectin0·006–0·2370·0640·009–0·2770·08Total soluble fibre0·02–0·280·0920·022–0·2990·103Total insoluble fibre0·082–1·2030·3930·096–1·3540·454Total fibre0·103–1·4840·4850·12–1·6540·561 Fig. 2Consumption of different types of dietary fibre in a healthy male population. The calculated 2·5th and 97·5th percentiles are indicated by the orange vertical lines, and the calculated median is indicated by the green vertical line Fig. 3Consumption of different types of dietary fibre in a healthy female population. The calculated 2·5th and 97·5th percentiles are indicated by the orange vertical lines, and the calculated median is indicated by the green vertical line
## Analysis of the obtained results
The obtained results provide an estimated description of fibre consumption patterns in the healthy UK population. The final reported values have been adjusted for body weight.
According to the obtained reference values, insoluble fibre dominates over its soluble counterpart in typical diets. It is challenging to argue about the reason, as the insight into the specifics of the participants’ diets is limited. An intriguing explanation would be that it is caused by a specific dietary pattern which healthy individuals tend to adhere to. It is also plausible that food in general tends to contain more insoluble than soluble fibre, a fact which would inevitably affect the population statistic. Either way, this presents a new area of research which may be explored when a more robust dataset becomes available.
In the analysed cohort, fibre consumption per kilogram of body weight was slightly higher in women than in men for every type of analysed fibre. We propose two possible explanations for this fact. First of all, it has been shown that women tend to be more conscientious in their food choices, attaching greater importance to a ‘healthy’ diet[51]. Limiting the intake of high-fat and high-calorie foods is commonly associated with increased fibre intake, as fibre-rich foods tend to be less energy-dense(3–5). Secondly, on average women tend to weigh less than men[52], which would result in a higher intake per kilogram of body weight if the diets were identical.
## Analysis of methods in context of existing studies
Comparing the methods used in this study to the methods used in the similar epidemiological studies in the field of nutrition presents a challenging task, as there are, to our knowledge, no studies regarding the intake of fibre subtypes. It is, however, possible to compare the methodology of this study to the methods used in epidemiological studies of overall fibre intake. In this regard, it is necessary to look into the methods used for the estimation of dietary intake and for the quantification of fibre in the consumed food.
Twenty-four hour recall questionnaires seem to be a widely used tool in epidemiological research. This type of analysis has been used to estimate fibre intake in the US[53] and Australian[54] populations by nutrition researchers. In her review, Block[55] states that, although insufficient to accurately analyse the diet of a single individual, 24-h recalls provide considerable insight into the dietary intake of a group of persons. The precision of this method is discussed by Bingham et al., who conclude that a ‘24 recall method… compared surprisingly well with weighed records’[56]. This favourable account is further reinforced by Johansson[57] who, in his study of different methods of food intake assessment, concluded that the foods reported by recording and recalling methods follow the same misreporting patterns and, therefore, the error is individual-specific and not method-specific.
The data on fibre content provided by Marlett and Cheung[46] were obtained using the method A developed by Theander, more widely known as the Uppsala method[58]. In their analysis, Knudsen et al. [ 59] argue that this the precision of this method is primarily dependent on laboratory equipment and personnel experience, and that it has reached an acceptable level. Furthermore, they state that that the variation between the results obtained in different laboratories which use this method is comparable to the variation in the results obtained by other methods, such as the AOAC enzymatic-gravimetric methods and the Englyst methods.
## Analysis of results in context of existing studies
Comparing the obtained values to the existing reference values is also challenging, because the existing reference values do not account for specific types of fibre. Total fibre values from our study can be compared with the existing dietary guidelines; however, studies show that the dietary advice provided by the guidelines is rarely adhered to(60–62), thus, such a comparison would introduce a certain degree of error.
A possible solution would be to use the ‘real-world’ values obtained in population studies for comparison. We used the work by Rippin et al. [ 60], which provides a weighted mean of fibre intake in twenty-one European countries of 19 and 21 g of fibre per d for women and men, respectively.
To calculate the ‘recommended’ intake for men and women using the values obtained in our study, we used the reference body weights provided by the Institute of Medicine[52] (men: 70 kg, women: 57 kg). We multiplied these weights by the median of the total fibre consumption calculated in our study to obtain the ‘recommended’ fibre intake for these hypothetic persons. The resulting totals were compared to the values provided by the Institute of Medicine[52] and by Rippin et al. [ 60] (Table 4). For convenience, we provide the reference ranges and medians for persons of this weight for all the analysed fibre types in Table 5.
Table 4A comparison of recommended daily total intake of fibre between this study, Institute of Medicine,[52] and Rippin et al. [ 60] SexDaily total fibre intake, gThis study, medianInstitute of Medicine[52] Rippin et al. [ 60] Male33·9530–3821Female31·97721–2519 Table 5The reference range and median daily intake of different types of fibre for a person of reference weight (70 kg for males and 57 kg for females)[52], stratified by sex and fibre typeFibre typeDaily reference intake, gMale, 70 kgFemale, 57 kgRangeMedianRangeMedianSoluble hemicelluloses0·91–12·044·130·741–10·2033·534Soluble pectin0·14–5·811·540·171–5·4151·539Insoluble cellulose2·1–29·549·731·938–27·1329·291Insoluble hemicelluloses2·03–32·0610·221·881–28·2159·348Insoluble pectin0·28–10·852·940·342–10·4883·021Insoluble lignin0·84–13·34·130·798–11·9133·819Total hemicelluloses3·08–44·0314·422·736–38·13312·939Total pectin0·42–16·594·480·513–15·7894·56Total soluble fibre1·4–19·66·441·254–17·0435·871Total insoluble fibre5·74–84·2127·515·472–77·17825·878Total fibre7·21–103·8833·956·84–94·27831·977 The result of our study corresponds to the reference values provided by the Institute of Medicine for individuals between 30 and 70 years old, although the reported values for women tend to be higher than expected. This difference is easily explainable: our calculation uses real-world consumption data, and it is logical that total consumption of fibre in men and women seems to be more uniform. The values provided by the Institute of Medicine[52], on the contrary, represent a threshold to aim for, but not a precise snapshot of real-world consumption patterns.
The difference between our values and the real-world data reported by Rippin et al. [ 60] is more challenging to explain. One possible reason lies in the analytical techniques used for dataset preparation: some bias could have been introduced with removal of foods or participants. Another plausible explanation is the aggregate nature of the data produced by Rippin et al. [ 60]: it encompasses many studies exploring different populations. Between-population variation of fibre intake would affect the weighted mean.
Fry et al. [ 63] found that the cohort of the UK *Biobank is* ‘healthier’ than the general population in terms of having less detrimental habits and chronic diseases. This fact is accounted for in the selection process of this study, as we implicitly removed the participants with any reported diseases to ensure that the analysed cohort consisted only of ‘completely healthy’ individuals. This ‘healthy bias’, however, could also stem from the certain dietary patterns in the analysed individuals; it is tempting to suggest that the better health of the UK Biobank cohort and the observed higher-than-average fibre intake are, in fact, interrelated. Further analysis would be required to confirm or disprove this hypothesis.
## A healthy diet: what is it, exactly?
Dietary fibre is a necessary component of a healthy diet[64]. Evidence shows that level of education may impact diet adherence; insufficient knowledge about nutrient content is among the most significant factors to influence a person’s decision to abandon a recommended regimen(65–67). Conducting research into fibre seems to go hand in hand with promoting awareness about its importance, which can support public health initiatives as well as practitioners working with patients.
Different types of fibre exhibit different properties, so a balanced intake of various types of fibre is needed to satisfy the daily fibre requirement. Upsetting this balance may not only provide no benefit, but even become detrimental[68].
However, this topic suffers from an alarming lack of clarity: the absence of reference values for specific types of fibre has been mentioned in literature, and such values were not available as of several years ago[69]. To the best of our knowledge, this is the first published study to estimate a reference intake range not for dietary fibre as a whole, but for its different soluble and insoluble subtypes in a population of healthy individuals. Hopefully, this article will lay the groundwork for developing this topic further.
## Dietary fibre and gut microbiota
Fibre serves as a substrate for beneficial bacteria to feed on[39,70,71], and modifying its intake can shift microbial abundance and diversity. Non-digestible carbohydrates provide the primary source of energy for most gut microbes, and changes here impact bacterial communities that depend on particular fibre substrates ‘rapidly and reproducibly’[72]. Seemingly small increases in daily fibre content (as low as 6–8 g of wheat fibre per d) mediate changes to microbiota composition, species diversity, species abundance and metabolic indicators of microbiota fermentation such as SCFA or faecal nitrogen[73]. Changes in gut bacterial diversity and abundance correlate with improvements in cardiometabolic(74–76), immune and inflammatory(77–80) markers. In a series of systematic reviews and meta-analyses[81], Reynolds et al. found that consumption of 25 to 29 g of fibre daily is associated with significant reductions in both mortality and incidence of a variety of pathological conditions. Similar intake is recommended by multiple other guidelines[82,84].
There have been attempts to estimate the dietary fibre ‘preferences’ of different bacterial taxa. McKeown, Sawicki and colleagues used evidence mapping methodology, contributing to the creation of the Diet-Related Fibers & Human Health Outcomes Database[84,85]. Currently, quality evidence from randomised controlled trials is limited to the ability of the *Bifidobacterium genus* to ferment oligosaccharides, fructooligosaccharides in particular(86–88).
McKeown and Sawicki also identified several methodological limitations in research on the effect of fibre subtypes on different types of gut microbes, including the use of diverse microbe identification and quantification methods. This lack of uniformity complicates the comparison of study results[84,85].
Attempting to be more specific in matching bacterial genera with their preferred types of fibre thus remains an elusive task – not only because most foods provide a mix of soluble and insoluble fibres, but also due to other, more intricate, factors which impact microbial composition and abundance indirectly. One such factor is cross-feeding, a symbiotic relationship which enables certain microbes to survive by feeding on the metabolic byproducts of each other. This is seen in complex biological systems[89] and particularly in the gut, where lactate produced by Bifidobacteria has been reported to stimulate the formation of butyrate by bacteria of other genera[90,91]. Another example is provided by butyrate-producing Clostridiales, a microbial order belonging to the *Firmicutes phylum* that are able to metabolise oligosaccharides in human milk and cross-feed on mucin via conserved pathways[92].
Effects of cross-feeding are not always beneficial to the host. Hydrogenotrophic microbes (sulphate-reducing, acetogenic and methanogenic bacteria) are able to convert hydrogen into hydrogen sulphide, acetate and methane, respectively. Higher levels of these metabolites correlate with worse symptoms in irritable bowel syndrome, and other diseases of the gut(93–97).
Some microbes in the Lachnospiraceae family, particularly the Roseburia genus, stand out in microbiome studies of Mediterranean diets, as does the Faecalobacterium genus. Specifically, the *Faecalobacterium prausnitzii* species[98,99] can utilise pectin as a substrate for growth[100]. Both the Roseburia and the *Faecalobacterium* genera are known for their ability to ferment fibre, producing SCFA and other metabolites with bifidogenic properties[101].
Eubacterium and *Coprococcus* genera share a similar behaviour and are often characteristic for people who consume diverse types of plant fibres[102,105], alongside some members of the Prevotella genus[105,105]. Also observed in Mediterranean-style diets is a lesser abundance of microbes in the Proteobacteria phylum, particularly of Enterobacteria [106,107].
The availability of a specific feeding substrate is not the only factor influencing microbiota composition: stomach and small intestine pH, pancreatic and biliary function, transit time(108–111), and even non-dietary psychosocial factors relating to mental health(112–114) and levels of physical activity[115] all play their role. These factors may affect the ratios and abundance of SCFA[116], known to influence the composition of the microbiota via a decrease in colonic pH[117,118].
It seems prudent to focus on overall changes in microbial diversity and composition associated with dietary patterns. ‘ Mediterranean-type’ diets rich in varied types of fibre from brightly coloured fresh produce, legumes/pulses, wholegrains and oily fish are well known for their ability to influence microbiota. This dietary pattern is associated with positive health outcomes in a range of conditions(119–122). Microbiome of individuals following a Mediterranean-style diets is highly abundant in Bifidobacteria and Lactobacilli (123–125).
## The role of gut microbiota in human health
As a community of microorganisms, the microbiota interacts with their human host through immune, neuroendocrine and neural pathways[126], casting local and systemic effects on the host’s health and affecting their disease risks. These risks are modulated, in part, by fermenting non-digestible substrates such as dietary fibres[127] and polyphenols[128,129]. This supports the growth of specialist microbes that produce SCFA[130], as well as gases like methane and hydrogen(131–133), further supporting the symbiotic relationship between microbial communities and the host. For instance, *Akkermansia muciniphila* [134], certain Bacteroides [135] and some Bifidobacteria [136] degrade the polysaccharides and highly glycosylated proteins present within the intestinal mucus(137–139), supporting tissue barrier function[140] and alleviating inflammation[141,142].
Microbiota influences blood glucose homoeostasis and intestinal permeability and is associated with the modulation of gene expression in lipid and glucose uptake and transport pathways. Many of the effects are mediated by the production of butyrate by beneficial bacteria, which use prebiotic fibre present in food as an energy substrate. Such bacteria are depleted in fibre-poor dietary patterns such as the Western diet[143,144].
Butyrate is the main source of energy for colonic epithelial cells; it contributes to healthy intestinal permeability[145] and modulation of metabolic endotoxemia[36,146,147]. It has been shown that decreasing carbohydrate intake can lead to lower butyrate production in the colon of obese patients. Duncan et al. found that obese volunteers put on a 4-week diet of medium-carbohydrate intake, followed by 4 weeks on a low-carbohydrate diet showed a ‘disproportionate’ decrease in faecal butyrate and reduction in butyrate-producing bacteria[148]. Acetate and propionate, SCFA also produced by the colonic microbiota from prebiotic fibres, have been shown to participate in fat storage and appetite control. In addition, associations have been found between lean body mass and the presence of *Akkermansia muciniphila* [149], an acetate producer and mucin-degrading bacterial species whose activities stimulate the production of mucins in the mucosa, thus contributing to improved intestinal barrier function.
## Limitations and concerns
As a pilot study, this research has several shortcomings. Due to voluntary 24-h self-recall style of collection, the data could have been self-censored by the participants or could differ from their typical dietary pattern. The existence of the issue of self-censoring is indirectly confirmed by Bradbury et al. [ 150], who found that dietary findings were less reproducible in participants who had BMI > 25 than in those who had BMI < 25. Although this research only included individuals with BMI within a specific range, it is hard to estimate the degree of self-censorship their data had gone through.
The limited amount of data may have affected the distribution of fibre intake values. Limiting the number of analysed foods may have both decreased the reported fibre intake and skewed the distribution to the right. These effects would artificially lower the obtained reference values, which we have discussed earlier. However, the majority of foods excluded from the analysis were unlikely to contain fibre, as they were meat-, fish-, poultry- and dairy-based.
Applying a log-transformation meant that the individuals with no reported fibre intake had to be excluded from the analysis (logarithm of 0 is undefined). However, these individuals might constitute a significant portion of the population, which means that our approach resulted in an increase in the obtained values. This may offset the decrease described earlier.
The difference between the 2·5th and 97·5th percentiles can be increasingly large, sometimes even reaching the order of several magnitudes. This may be explained by the right skew of the data, which results in the increased value of the 97·5th percentile. This is especially apparent when reference ranges are calculated using specific body mass, as in Table 5. A possible solution would be to obtain higher-quality data on fibre content, preventing the exclusion of certain foods and certain individuals, thus decreasing the skew. An alternative solution would be to resort to using other, less canonical, values to limit the reference range, such as the 90th percentile, the 3rd quartile or even the median.
The nature of UK Biobank limited the age range of the subjects analysed in this study. Apart from the that, a typical area of concern is the low response rate and the existence of selection bias due to the volunteer-based nature of the cohort. Naturally, this raises concerns regarding the generalisability of the obtained results[151]. Despite our best efforts, we could not find any evidence regarding the validity of generalisations in the context of the 24-h recall questionnaire used to assess food intake. However, there are several studies that explore the representativeness of the analysed cohort and generalisability of the results regarding risk factor profiles. Perhaps, the most notable is the study by Batty et al. [ 152], who compared the risk factor profiles obtained using the UK Biobank to the risk profiles obtained using 18 cohort studies of English and Scottish populations. The authors found that, despite the low response rate of the UK Biobank participants, the data were comparable and concluded that the data obtained from the UK Biobank are generalisable to England and Scotland.
The most substantial issue that may have influenced the accuracy of the calculated values is associated with food mapping. Insufficient data on portion size of UK Biobank foods and their fibre content were available, and to merge UK *Biobank data* with the selected reference literature[46,47] we had to use multiple generalisations. It has been shown that in certain cases substitutions may negatively affect the results even in closely related foods[153]. It would be intriguing to perform further similar analyses with datasets of improved quality, as it may improve the precision of the obtained result.
Insufficient data on portion size of UK Biobank foods and their fibre content were available. Therefore, in order to merge UK *Biobank data* with the selected reference literature[46,47], we had to use multiple generalisations. Our primary concerns were about the validity of Marlett and Cheung’s food composition database, chiefly about the accuracy of a resource that is representative of the food supply in the USA. However, upon a thorough review of the literature, we were not able to identify major differences in fibre content among diverse varieties of fruit, vegetables or wholegrains. As a recent example, Koutsos et al. [ 154] performed nutrient composition analysis of three commercial apple varieties available worldwide: Renetta Canada, Golden Delicious and Pink Lady and found negligible differences in total dietary fibre amongst them, 2·6, 2·4 and 2·6 g/100 g (AOAC), respectively. Additionally, a comparative analysis of the food composition table (*Tabela da* Composição de Alimentos) published by the Portuguese National Health Institute and the US Department of Agriculture FoodData database carried out by Delgado et al. [ 155] found no discernible differences in the total fibre content of a range of foods typically available in the UK, including garlic, onions, cabbage, turnips, lettuce, tomato, pumpkin, wild greens such as watercress, and herbs like parsley, oregano or coriander. The authors did not find any major differences in the total fibre content of different varieties of wheat, rye, rice, potatoes or pulses such as beans, lentils or chickpeas. On the basis of the arguments laid out above, we are reassured that it is unlikely that any significant differences in fibre content would be detectable in samples of different varieties of the same food obtained from different geographic regions.
We must highlight some additional caveats to the quantification of total fibre content. One relies on whether a fruit or vegetable is peeled or not. For example, potato peels are known sources of dietary fibre, so much so that it doubles if the peel is consumed[156]. Another important consideration is grouping of foods. As an example, green peas have very similar amount of dietary fibre to pulses, and a significant portion of their starch is digested in the large intestine, providing substrate for colonic bacteria[157]. Furthermore, an increasing number of consumer goods containing added fibre are launched every year, making it difficult to develop a wholly comprehensive database of fibre values that is always up to date.
Despite these concerns, we believe that this study may not only serve as a primer for research into consumption of types of fibre, but also be used as a helpful guide when planning dietary interventions. To further increase its usability, we provide several easy-to-use diagrams for quick reference (Fig. 4). Further areas of research on this topic may include refining the obtained data and increasing their precision or exploring the association between the consumption of certain types of fibre and subjective and objective outcomes, such as development of certain diseases and quality of life. Research should also be aimed at compiling more comprehensive datasets on fibre content of foods, which in turn would provide the basis for a more detailed and precise analysis.
Fig. 4Quick-reference visualisation of abundance of different types of fibre in various common foods: (a) foods rich in soluble fibre; (b) foods rich in insoluble fibre and (c) food by number of fibre types. Different food items are coded as follows: a – rice bran; b – lentils; c – oranges; d – wheat bran; e – carrots; f – cabbages; g – guava; h – apples; i – white bread; j – pears; k – green beans; l – kiwi; m – lettuce; n – kohlrabi; o – cauliflower; p – asparagus; q – cereal grains; r – sugar beets; s – figs; t – bananas; u – potatoes; v – black gram; w – legumes; x – rhubarb Based on our discussion, we propose a sample menu (Table 6) as a realistic and sustainable example of how to incorporate the amounts of soluble and insoluble fibres recommended in our research. The foods featured in this meal is presented visually in Figs 4(a), (b) and (c). This menu can be used as a sample to build other dietary options upon, or as a ready solution to be incorporated into the individual’s meal plan.
Table 6A sample 1-d menu designed to introduce a recommended amount of fibre subtypes discussed in the articleMealFoodQuantity, gFibre content, gSoluble hemicellulosesSoluble pectinCelluloseInsoluble hemicellulosesInsoluble pectinLigninTotalBreakfastAll bran cereal60 g0·7Trace000010Banana, sliced120 g0·30·30·40·30·30·72SnackApple130 g0·20·31·10·90·60·43·1LunchBaked beans150 g0·80·39·65·40·85·86·8Wholemeal toast (2 slices)70 g0·701·22·4Trace0·84·7DinnerBaked potato with skin, tuna mayonnaise180 g0·40·40·90·70·30·36·5Salad (lettuce, tomato and cucumber)138 g0·30·40·30·30·30·91·7Yogurt150 g0000000with strawberries100 g0·20·40·50·40·701·5and chopped almonds13 gTraceTrace0·40·80·40·51·3Total4·22·114·410·23·49·437·6
## Conclusion
We calculated reference intake ranges for six different types of soluble (hemicelluloses and pectin) and insoluble (cellulose, hemicelluloses, pectin and lignin) fibre, as well as the corresponding totals, for a healthy UK cohort of approximately 20 000 participants of the UK Biobank. As per standard protocols, we used the 2·5th and the 97·5th percentiles of daily intake as the lower and the upper bounds for the reference range (Table 3). The absolute values of reference ranges were then calculated using the median body mass provided by the Institute of Medicine[52] (Table 5). Comparable results were obtained for men and women, with the tendency for values in men to be slightly larger. A graphical summary of fibre content in different foods has been developed for practical convenience (Figs 4(a)–(c)), and a sample menu has been composed to introduce a balanced fibre intake.
## Supplementary material
The following are available online at https://docs.google.com/document/d/1T_RYMDb6IL-ojZv6uUdY1Jbr78P7l_6HAw0a4IO8jic/edit?usp=sharing
## References
1. 1.Institute of Medicine (2001) Dietary Reference Intakes: Proposed Definition of Dietary. Fiber. Washington, DC: The National Academies Press.. *Dietary Reference Intakes: Proposed Definition of Dietary. Fiber* (2001)
2. 2.
Joint FAO/WHO Food Standards Programme (2001) Food and Agriculture Organization of the United Nations. Codex Alimentarius: Food Labelling – Complete Texts. Rome: Food and Agriculture Organization of the United Nations, World Health Organization.. *Food and Agriculture Organization of the United Nations. Codex Alimentarius: Food Labelling – Complete Texts* (2001)
3. Grube B, Chong PW, Lau KZ. **A natural fiber complex reduces body weight in the overweight and obese: a double-blind, randomized, placebo-controlled study**. *Obesity* (2013) **21** 58-64. PMID: 23505169
4. Anderson JW. **Physiological and metabolic effects of dietary fiber**. *Fed Proc* (1985) **44** 2902-2906. PMID: 2996944
5. Topping DL. **Soluble fiber polysaccharides: effects on plasma cholesterol and colonic fermentation**. *Nutr Rev* (1991) **49** 195-203. PMID: 1658695
6. Heaton KW. **Food fibre as an obstacle to energy intake**. *Lancet* (1973) **302** 1418-1421
7. Bozzetto L, Costabile G, Della Pepa G. **Dietary fibre as a unifying remedy for the whole spectrum of obesity-associated cardiovascular risk**. *Nutrients* (2018) **10** 943. PMID: 30037123
8. Requena MC, Aguilar-González CN, Barragán LAP. **Dietary fiber: an ingredient against obesity**. *Emirates J Food Agriculture* (2016) **28** 522-530
9. Martel J, Ojcius DM, Chang CJ. **Anti-obesogenic, antidiabetic effects of plants, mushrooms**. *Nat Rev Endocrinol* (2017) **13** 149-160. PMID: 27636731
10. Lambert JE, Parnell JA, Tunnicliffe JM. **Consuming yellow pea fiber reduces voluntary energy intake and body fat in overweight/obese adults in a 12-week randomized controlled trial**. *Clin Nutr* (2017) **36** 126-133. PMID: 26811130
11. Brahe LK, Le Chatelier E, Prifti E. **Dietary modulation of the gut microbiota: a randomised controlled trial in obese postmenopausal women**. *Br J Nutr* (2015) **114** 406-417. PMID: 26134388
12. McKeown NM, Meigs JB, Liu S. **Whole-grain intake is favorably associated with metabolic risk factors for type 2 diabetes, cardiovascular disease in the Framingham Offspring Study**. *Am J Clin Nutr* (2002) **76** 390-398. PMID: 12145012
13. **Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants**. *Lancet* (2016) **387** 1377-1396. PMID: 27115820
14. **Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults**. *Lancet* (2017) **390** 2627-2642. PMID: 29029897
15. Jacobsen BK, Aars NA. **Changes in body mass index and the prevalence of obesity during 1994–2008: repeated cross-sectional surveys and longitudinal analyses. The Tromso Study**. *BMJ Open* (2015) **5** e007859
16. Razak F, Corsi DJ, Subramanian SV. **Change in the body mass index distribution for women: analysis of surveys from 37 low- and middle-income countries**. *PLoS Med* (2013) **10** e1001367. PMID: 23335861
17. Roomi MA, Mohammadnezhad M. **Prevalence of metabolic syndrome among apparently healthy workforce**. *J Ayub Med Coll Abbottabad* (2019) **31** 252-254. PMID: 31094127
18. Bellou V, Belbasis L, Tzoulaki I. **Risk factors for type 2 diabetes mellitus: an exposure-wide umbrella review of meta-analyses**. *PLoS One* (2018) **13** e0194127. PMID: 29558518
19. Yeboah P, Hsu FC, Bertoni AG. **Body mass index, change in weight, body weight variability and outcomes in type 2 diabetes mellitus (from the ACCORD Trial)**. *Am J Cardiol* (2019) **123** 576-581. PMID: 30553512
20. Riaz H, Khan MS, Siddiqi TJ. **Association between obesity and cardiovascular outcomes: a systematic review and meta-analysis of mendelian randomization studies**. *JAMA Netw Open* (2018) **1** e183788. PMID: 30646365
21. Yeh TL, Chen HH, Tsai SY. **The relationship between metabolically healthy obesity and the risk of cardiovascular disease: a systematic review and meta-analysis**. *J Clin Med* (2019) **8** 1228. PMID: 31443279
22. Hidayat K, Yang CM, Shi BM. **Body fatness at a young age, body fatness gain and risk of breast cancer: systematic review and meta-analysis of cohort studies**. *Obes Rev* (2018) **19** 254-268. PMID: 29130623
23. Dong Y, Zhou J, Zhu Y. **Abdominal obesity, colorectal cancer risk: systematic review, meta-analysis of prospective studies**. *Biosci Rep* (2017) **37** BSR20170945. PMID: 29026008
24. Gupta A, Das A, Majumder K. **Obesity is independently associated with increased risk of hepatocellular cancer-related mortality: a systematic review and meta-analysis**. *Am J Clin Oncol* (2018) **41** 874-881. PMID: 28537989
25. Freisling H, Arnold M, Soerjomataram I. **Comparison of general obesity and measures of body fat distribution in older adults in relation to cancer risk: meta-analysis of individual participant data of seven prospective cohorts in Europe**. *Br J Cancer* (2017) **116** 1486-1497. PMID: 28441380
26. Backhed F, Ley RE, Sonnenburg JL. **Host-bacterial mutualism in the human intestine**. *Science* (2005) **307** 1915-1920. PMID: 15790844
27. Backhed F, Roswall J, Peng Y. **Dynamics and stabilization of the human gut microbiome during the first year of life**. *Cell Host Microbe* (2015) **17** 690-703. PMID: 25974306
28. Brown EM, Sadarangani M, Finlay BB. **The role of the immune system in governing host-microbe interactions in the intestine**. *Nat Immunol* (2013) **14** 660-667. PMID: 23778793
29. Sekirov I, Russell SL, Antunes LC. **Gut microbiota in health and disease**. *Physiol Rev* (2010) **90** 859-904. PMID: 20664075
30. Valdes AM, Walter J, Segal E. **Role of the gut microbiota in nutrition, health**. *BMJ* (2018) **361** k2179. PMID: 29899036
31. Cummings JH, Macfarlane GT, Macfarlane S. **Intestinal bacteria and ulcerative colitis**. *Curr Issues Intest Microbiol* (2003) **4** 9-20. PMID: 12691258
32. Darfeuille-Michaud A, Boudeau J, Bulois P. **High prevalence of adherent-invasive**. *Gastroenterology* (2004) **127** 412-421. PMID: 15300573
33. Snedeker SM, Hay AG. **Do interactions between gut ecology and environmental chemicals contribute to obesity and diabetes?**. *Environ Health Perspect* (2012) **120** 332-339. PMID: 22042266
34. Menni C, Jackson MA, Pallister T. **Gut microbiome diversity and high-fibre intake are related to lower long-term weight gain**. *Int J Obes* (2017) **41** 1099-1105
35. Bajaj JS, Heuman DM, Hylemon PB. **Altered profile of human gut microbiome is associated with cirrhosis and its complications**. *J Hepatol* (2014) **60** 940-947. PMID: 24374295
36. Andre P, Laugerette F, Feart C. **Metabolic endotoxemia: a potential underlying mechanism of the relationship between dietary fat intake and risk for cognitive impairments in humans?**. *Nutrients* (2019) **11** 1887. PMID: 31412673
37. Kundu P, Lee HU, Garcia-Perez I. **Neurogenesis and prolongevity signaling in young germ-free mice transplanted with the gut microbiota of old mice**. *Sci Transl Med* (2019) **11** eaau4760. PMID: 31723038
38. Reichardt N, Vollmer M, Holtrop G. **Specific substrate-driven changes in human faecal microbiota composition contrast with functional redundancy in short-chain fatty acid production**. *ISME J* (2018) **12** 610-622. PMID: 29192904
39. Hiel S, Bindels LB, Pachikian BD. **Effects of a diet based on inulin-rich vegetables on gut health and nutritional behavior in healthy humans**. *Am J Clin Nutr* (2019) **109** 1683-1695. PMID: 31108510
40. Healey G, Murphy R, Butts C. **Habitual dietary fibre intake influences gut microbiota response to an inulin-type fructan prebiotic: a randomised, double-blind, placebo-controlled, cross-over, human intervention study**. *Br J Nutr* (2018) **119** 176-189. PMID: 29307330
41. So D, Whelan K, Rossi M. **Dietary fiber intervention on gut microbiota composition in healthy adults: a systematic review and meta-analysis**. *Am J Clin Nutr* (2018) **107** 965-983. PMID: 29757343
42. Cantu-Jungles TM, Hamaker BR. **New view on dietary fiber selection for predictable shifts in gut microbiota**. *mBio* (2020) **11** e02179-19. PMID: 32071263
43. Katayev A, Balciza C, Seccombe DW. **Establishing reference intervals for clinical laboratory test results: is there a better way?**. *Am J Clin Pathol* (2010) **133** 180-186. PMID: 20093226
44. Sudlow C, Gallacher J, Allen N. **UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age**. *PLoS Med* (2015) **12** e1001779. PMID: 25826379
45. Liu B, Young H, Crowe FL. **Development and evaluation of the Oxford WebQ, a low-cost, web-based method for assessment of previous 24 h dietary intakes in large-scale prospective studies**. *Public Health Nutr* (2011) **14** 1998-2005. PMID: 21729481
46. Marlett JA, Cheung TF. **Database and quick methods of assessing typical dietary fiber intakes using data for 228 commonly consumed foods**. *J Am Diet Assoc* (1997) **97** 1139-1148. PMID: 9336561
47. Mills A, Patel S. *Food Portion Sizes* (1994)
48. McDonald JH. *Handbook of Biological Statistics* (2014)
49. Marshall WJ, Lapsley M, Day AP. *Clinical Biochemistry: Metabolic, Clinical Aspects* (2014)
50. Hahn GJ, Meeker WQ. *Statistical Intervals: A Guide for Practitioners* (1991)
51. Wardle J, Haase AM, Steptoe A. **Gender differences in food choice: the contribution of health beliefs and dieting**. *Ann Behav Med* (2004) **27** 107-116. PMID: 15053018
52. 52.
Institute of Medicine (2005) Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids. Washington, DC: The National Academies Press.. *Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids* (2005)
53. Lanza E, Jones DY, Block G. **Dietary fiber intake in the US population**. *Am J Clin Nutr* (1987) **46** 790-797. PMID: 2823592
54. Fayet-Moore F, Cassettari T, Tuck K. **Dietary fibre intake in Australia. Paper I: Associations with demographic, socio-economic, and anthropometric factors**. *Nutrients* (2018) **10** 599. PMID: 29751656
55. Block G. **A review of validations of dietary assessment methods**. *Am J Epidemiol* (1982) **115** 492-505. PMID: 7041631
56. Bingham SA, Gill C, Welch A. **Comparison of dietary assessment methods in nutritional epidemiology: weighed records**. *Br J Nutr* (1994) **72** 619-643. PMID: 7986792
57. Johansson G. **Comparison of nutrient intake between different dietary assessment methods in elderly male volunteers**. *Nutr Diet* (2008) **65** 266-271
58. Theander O, Aman P, Westerlund E. **The Uppsala method for rapid analysis of total dietary fiber**. *Adv Exp Med Biol* (1990) **270** 273-281. PMID: 1964013
59. Bach Knudsen KE, Johansen HN, Glitsø V. **Methods for analysis of dietary fibre – advantage and limitations**. *J Animal Feed Sci* (1997) **6** 185-206
60. Rippin HL, Hutchinson J, Jewell J. **Adult nutrient intakes from current National Dietary Surveys of European Populations**. *Nutrients* (2017) **9** 1288. PMID: 29186935
61. Huysentruyt K, Laire D, Van Avondt T. **Energy and macronutrient intakes and adherence to dietary guidelines of infants and toddlers in Belgium**. *Eur J Nutr* (2016) **55** 1595-1604. PMID: 26246202
62. Kristjansdottir AG, Thorsdottir I. **Adherence to food-based dietary guidelines and evaluation of nutrient intake in 7-year-old children**. *Public Health Nutr* (2009) **12** 1999-2008. PMID: 19656438
63. Fry A, Littlejohns TJ, Sudlow C. **Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population**. *Am J Epidemiol* (2017) **186** 1026-1034. PMID: 28641372
64. Buyken AE, Mela DJ, Dussort P. **Dietary carbohydrates: a review of international recommendations and the methods used to derive them**. *Eur J Clin Nutr* (2018) **72** 1625-1643. PMID: 29572552
65. Paganizza S, Zanotti R, D’Odorico A. **Is adherence to a gluten-free diet by adult patients with celiac disease influenced by their knowledge of the gluten content of foods?**. *Gastroenterol Nurs* (2019) **42** 55-64. PMID: 30688709
66. Link LB, Jacobson JS. **Factors affecting adherence to a raw vegan diet**. *Complement Ther Clin Pract* (2008) **14** 53-59. PMID: 18243943
67. Ganiyu AB, Mabuza LH, Malete NH. **Non-adherence to diet, exercise recommendations amongst patients with type 2 diabetes mellitus attending Extension II Clinic in Botswana**. *Afr J Prim Health Care Fam Med* (2013) **5** 457
68. Chutkan R, Fahey G, Wright W. **Viscous versus nonviscous soluble fiber supplements: mechanisms and evidence for fiber-specific health benefits**. *J Am Acad Nurse Pract* (2012) **24** 476-487. PMID: 22845031
69. Stephen AM, Champ MMJ, Cloran SJ. **Dietary fibre in Europe: current state of knowledge on definitions, sources, recommendations, intakes and relationships to health**. *Nutr Res Rev* (2017) **30** 149-190. PMID: 28676135
70. Lu QY, Rasmussen AM, Yang J. **Mixed spices at culinary doses have prebiotic effects in healthy adults: a pilot study**. *Nutrients* (2019) **11** 1425. PMID: 31242596
71. Moreira-Rosario A, Pinheiro H, Calhau C. **Can wheat germ have a beneficial effect on human health? A study protocol for a randomised crossover controlled trial to evaluate its health effects**. *BMJ Open* (2016) **6** e013098
72. David LA, Maurice CF, Carmody RN. **Diet rapidly and reproducibly alters the human gut microbiome**. *Nature* (2014) **505** 559-563. PMID: 24336217
73. Jefferson A, Adolphus K. **The effects of intact cereal grain fibers, including wheat bran on the gut microbiota composition of healthy adults: a systematic review**. *Front Nutr* (2019) **6** 33. PMID: 30984765
74. Marungruang N, Tovar J, Bjorck I. **Improvement in cardiometabolic risk markers following a multifunctional diet is associated with gut microbial taxa in healthy overweight and obese subjects**. *Eur J Nutr* (2018) **57** 2927-2936. PMID: 29098426
75. Medina-Vera I, Sanchez-Tapia M, Noriega-Lopez L. **A dietary intervention with functional foods reduces metabolic endotoxaemia and attenuates biochemical abnormalities by modifying faecal microbiota in people with type 2 diabetes**. *Diabetes Metab* (2019) **45** 122-131. PMID: 30266575
76. Tuomainen M, Lindstrom J, Lehtonen M. **Associations of serum indolepropionic acid, a gut microbiota metabolite, with type 2 diabetes and low-grade inflammation in high-risk individuals**. *Nutr Diabetes* (2018) **8** 35. PMID: 29795366
77. Chambers ES, Byrne CS, Morrison DJ. **Dietary supplementation with inulin-propionate ester or inulin improves insulin sensitivity in adults with overweight and obesity with distinct effects on the gut microbiota, plasma metabolome and systemic inflammatory responses: a randomised cross-over trial**. *Gut* (2019) **68** 1430-1438. PMID: 30971437
78. Vanegas SM, Meydani M, Barnett JB. **Substituting whole grains for refined grains in a 6-wk randomized trial has a modest effect on gut microbiota and immune and inflammatory markers of healthy adults**. *Am J Clin Nutr* (2017) **105** 635-650. PMID: 28179226
79. Serrano-Villar S, Vazquez-Castellanos JF, Vallejo A. **The effects of prebiotics on microbial dysbiosis, butyrate production and immunity in HIV-infected subjects**. *Mucosal Immunol* (2017) **10** 1279-1293. PMID: 28000678
80. Carlson JL, Erickson JM, Llyd BB. **Health effects and sources of prebiotic dietary fiber**. *Curr Dev Nutr* (2018) **2** nzy005. PMID: 30019028
81. Reynolds A, Mann J, Cummings J. **Carbohydrate quality and human health: a series of systematic reviews and meta-analyses**. *Lancet* (2019) **393** 434-445. PMID: 30638909
82. Jones JM. **CODEX-aligned dietary fiber definitions help to bridge the ‘fiber gap’**. *Nutr J* (2014) **13** 34. PMID: 24725724
83. Hooper B, Spiro A, Stanner S. **30 g of fibre a day: an achievable recommendation?**. *Nutr Bull* (2015) **40** 118-129
84. McKeown NM, Livingston KA, Sawicki CM. **Evidence mapping to assess the available research on fiber, whole grains, and health**. *Nutr Rev* (2020) **78** 37-42. PMID: 32728740
85. Sawicki CM, Livingston KA, Obin M. **Dietary fiber and the human gut microbiota: application of evidence mapping methodology**. *Nutrients* (2017) **9** 125. PMID: 28208609
86. Cummings JH, Antoine JM, Azpiroz F. **PASSCLAIM gut health and immunity**. *Eur J Nutr* (2004) **43** ii118-ii173. PMID: 15221356
87. Tojo R, Suárez A, Clemente MG. **Intestinal microbiota in health and disease: role of bifidobacteria in gut homeostasis**. *World J Gastroenterol* (2014) **20** 15163-15176. PMID: 25386066
88. Azpiroz F, Molne L, Mendez S. **Effect of chicory-derived inulin on abdominal sensations and bowel motor function**. *J Clin Gastroenterol* (2017) **51** 619-625. PMID: 27680592
89. Antoniewicz MR. **A guide to deciphering microbial interactions and metabolic fluxes in microbiome communities**. *Curr Opin Biotechnol* (2020) **64** 230-237. PMID: 32711357
90. Duncan SH, Louis P, Flint HJ. **Lactate-utilizing bacteria, isolated from human feces, that produce butyrate as a major fermentation product**. *Appl Environ Microbiol* (2004) **70** 5810-5817. PMID: 15466518
91. Kanauchi O, Fujiyama Y, Mitsuyama K. **Increased growth of Bifidobacterium and Eubacterium by germinated barley foodstuff, accompanied by enhanced butyrate production in healthy volunteers**. *Int J Mol Med* (1999) **3** 175-179. PMID: 9917526
92. Pichler MJ, Yamada C, Shuoker B. **Butyrate producing colonic Clostridiales metabolise human milk oligosaccharides and cross feed on mucin via conserved pathways**. *Nat Commun* (2020) **11** 3285. PMID: 32620774
93. Smith NW, Shorten PR, Altermann EH. **Hydrogen cross-feeders of the human gastrointestinal tract**. *Gut Microbes* (2019) **10** 270-288. PMID: 30563420
94. Smith NW, Shorten PR, Altermann E. **A mathematical model for the hydrogenotrophic metabolism of sulphate-reducing bacteria**. *Front Microbiol* (2019) **10** 1652. PMID: 31379794
95. Lee KN, Lee OY, Koh DH. **Association between symptoms of irritable bowel syndrome and methane and hydrogen on lactulose breath test**. *J Korean Med Sci* (2013) **28** 901-907. PMID: 23772156
96. Rajilic-Stojanovic M, Jonkers DM, Salonen A. **Intestinal microbiota and diet in IBS: causes, consequences, or epiphenomena?**. *Am J Gastroenterol* (2015) **110** 278-287. PMID: 25623659
97. Singh SB, Lin HC. **Hydrogen sulfide in physiology and diseases of the digestive tract**. *Microorganisms* (2015) **3** 866-889. PMID: 27682122
98. Yang Q, Liang Q, Balakrishnan B. **Role of dietary nutrients in the modulation of gut microbiota: a narrative review**. *Nutrients* (2020) **12** 381. PMID: 32023943
99. Haro C, Montes-Borrego M, Rangel-Zuniga OA. **Two healthy diets modulate gut microbial community improving insulin sensitivity in a human obese population**. *J Clin Endocrinol Metab* (2016) **101** 233-242. PMID: 26505825
100. Lopez-Siles M, Khan TM, Duncan SH. **Cultured representatives of two major phylogroups of human colonic**. *Appl Environ Microbiol* (2012) **78** 420-428. PMID: 22101049
101. Rivière A, Selak M, Lantin D. **Bifidobacteria and butyrate-producing colon bacteria: importance and strategies for their stimulation in the human gut**. *Front Microbiol* (2016) **7** 979. PMID: 27446020
102. Harris S, Monteagudo-Mera A, Kosik O. **Comparative prebiotic activity of mixtures of cereal grain polysaccharides**. *AMB Express* (2019) **9** 203. PMID: 31865461
103. Pryde SE, Duncan SH, Hold GL. **The microbiology of butyrate formation in the human colon**. *FEMS Microbiol Lett* (2002) **217** 133-139. PMID: 12480096
104. Rinninella E, Cintoni M, Raoul P. **Food components and dietary habits: keys for a healthy gut microbiota composition**. *Nutrients* (2019) **11** 2393. PMID: 31591348
105. Dahl WJ, Rivero Mendoza D, Lambert JM. **Diet, nutrients and the microbiome**. *Prog Mol Biol Transl Sci* (2020) **171** 237-263. PMID: 32475524
106. D’Aquila P, Lynn Carelli L, De Rango F. **Gut microbiota as important mediator between diet and DNA methylation and histone modifications in the host**. *Nutrients* (2020) **12** 597. PMID: 32106534
107. Mitsou EK, Kakali A, Antonopoulou S. **Adherence to the Mediterranean diet is associated with the gut microbiota pattern and gastrointestinal characteristics in an adult population**. *Br J Nutr* (2017) **117** 1645-1655. PMID: 28789729
108. Mehta RS, Abu-Ali GS, Drew DA. **Stability of the human faecal microbiome in a cohort of adult men**. *Nat Microbiol* (2018) **3** 347-355. PMID: 29335554
109. El Oufir L, Flourié B, Bruley des Varannes S. **Relations between transit time, fermentation products, and hydrogen consuming flora in healthy humans**. *Gut* (1996) **38** 870-877. PMID: 8984026
110. Gupta A, Saha S, Khanna S. **Therapies to modulate gut microbiota: past, present and future**. *World J Gastroenterol* (2020) **26** 777-788. PMID: 32148376
111. Adamberg K, Adamberg S. **Selection of fast and slow growing bacteria from fecal microbiota using continuous culture with changing dilution rate**. *Microb Ecol Health Dis* (2018) **29** 1549922. PMID: 30532686
112. Halverson T, Alagiakrishnan K. **Gut microbes in neurocognitive and mental health disorders**. *Ann Med* (2020) **52** 1-43. PMID: 31903788
113. Kiecolt-Glaser JK, Wilson SJ, Bailey ML. **Marital distress, depression, and a leaky gut: translocation of bacterial endotoxin as a pathway to inflammation**. *Psychoneuroendocrinology* (2018) **98** 52-60. PMID: 30098513
114. Sylvia KE, Demas GE. **A gut feeling: microbiome–brain–immune interactions modulate social and affective behaviors**. *Hormones Behav* (2018) **99** 41-49
115. Ortiz-Alvarez L, Xu H, Martinez-Tellez B. **Influence of exercise on the human gut microbiota of healthy adults: a systematic review**. *Clin Transl Gastroenterol* (2020) **11** e00126. PMID: 32463624
116. Shortt C, Hasselwander O, Meynier A. **Systematic review of the effects of the intestinal microbiota on selected nutrients and non-nutrients**. *Eur J Nutr* (2018) **57** 25-49. PMID: 29086061
117. den Besten G, van Eunen K, Groen AK. **The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism**. *J Lipid Res* (2013) **54** 2325-2340. PMID: 23821742
118. Kasubuchi M, Hasegawa S, Hiramatsu T. **Dietary gut microbial metabolites, short-chain fatty acids, and host metabolic regulation**. *Nutrients* (2015) **7** 2839-2849. PMID: 25875123
119. Krznarić Ž, Vranešić Bender D, Meštrović T. **The Mediterranean diet and its association with selected gut bacteria**. *Curr Opin Clin Nutr Metab Care* (2019) **22** 401-406. PMID: 31232713
120. Nagpal R, Shively CA, Register TC. **Gut microbiome-Mediterranean diet interactions in improving host health**. *F1000Research* (2019) **8** 699. PMID: 32704349
121. Garcia-Mantrana I, Selma-Royo M, Alcantara C. **Shifts on gut microbiota associated to mediterranean diet adherence and specific dietary intakes on general adult population**. *Front Microbiol* (2018) **9** 890. PMID: 29867803
122. Maskarinec G, Hullar MAJ, Monroe KR. **Fecal microbial diversity and structure are associated with diet quality in the multiethnic cohort adiposity phenotype study**. *J Nutr* (2019) **149** 1575-1584. PMID: 31187868
123. De Filippis F, Pellegrini N, Vannini L. **High-level adherence to a Mediterranean diet beneficially impacts the gut microbiota, associated metabolome**. *Gut* (2016) **65** 1812-1821. PMID: 26416813
124. Gutiérrez-Díaz I, Fernández-Navarro T, Sánchez B. **Mediterranean diet and faecal microbiota: a transversal study**. *Food Funct* (2016) **7** 2347-2356. PMID: 27137178
125. Meslier V, Laiola M, Roager HM. **Mediterranean diet intervention in overweight and obese subjects lowers plasma cholesterol and causes changes in the gut microbiome and metabolome independently of energy intake**. *Gut* (2020) **69** 1258-1268. PMID: 32075887
126. Toribio-Mateas M. **Harnessing the power of microbiome assessment tools as part of neuroprotective nutrition and lifestyle medicine interventions**. *Microorganisms* (2018) **6** 35. PMID: 29693607
127. Makki K, Deehan EC, Walter J. **The impact of dietary fiber on gut microbiota in host health and disease**. *Cell Host Microbe* (2018) **23** 705-715. PMID: 29902436
128. Castro-Barquero S, Lamuela-Raventos RM, Domenech M. **Relationship between Mediterranean Dietary polyphenol intake and obesity**. *Nutrients* (2018) **10** 1523. PMID: 30336572
129. Springer M, Moco S. **Resveratrol and its human metabolites-effects on metabolic health and obesity**. *Nutrients* (2019) **11** 143. PMID: 30641865
130. Telle-Hansen VH, Holven KB, Ulven SM. **Impact of a healthy dietary pattern on gut microbiota and systemic inflammation in humans**. *Nutrients* (2018) **10** 1783. PMID: 30453534
131. Chaudhary PP, Conway PL, Schlundt J. **Methanogens in humans: potentially beneficial or harmful for health**. *Appl Microbiol Biotechnol* (2018) **102** 3095-3104. PMID: 29497795
132. Suri J, Kataria R, Malik Z. **Elevated methane levels in small intestinal bacterial overgrowth suggests delayed small bowel and colonic transit**. *Medicine* (2018) **97** e10554. PMID: 29794732
133. Pozuelo M, Panda S, Santiago A. **Reduction of butyrate- and methane-producing microorganisms in patients with Irritable Bowel Syndrome**. *Sci Rep* (2015) **5** 12693. PMID: 26239401
134. de Vos WM. **Microbe Profile: akkermansia muciniphila: a conserved intestinal symbiont that acts as the gatekeeper of our mucosa**. *Microbiology* (2017) **163** 646-648. PMID: 28530168
135. Xu J, Bjursell MK, Himrod J. **A genomic view of the human-Bacteroides thetaiotaomicron symbiosis**. *Science* (2003) **299** 2074-2076. PMID: 12663928
136. Garrido D, Kim JH, German JB. **Oligosaccharide binding proteins from Bifidobacterium longum subsp. infantis reveal a preference for host glycans**. *PLoS One* (2011) **6** e17315. PMID: 21423604
137. Johansson MEV, Sjövall H, Hansson GC. **The gastrointestinal mucus system in health and disease**. *Nat Rev Gastroenterol Hepatol* (2013) **10** 352-361. PMID: 23478383
138. Schroeder BO. **Fight them or feed them: how the intestinal mucus layer manages the gut microbiota**. *Gastroenterol Rep* (2019) **7** 3-12
139. Sicard JF, Le Bihan G, Vogeleer P. **Interactions of intestinal bacteria with components of the intestinal mucus**. *Front Cell Infect Microbiol* (2017) **7** 387. PMID: 28929087
140. Hiippala K, Jouhten H, Ronkainen A. **The potential of gut commensals in reinforcing intestinal barrier function and alleviating inflammation**. *Nutrients* (2018) **10** 988. PMID: 30060606
141. Wu HM, Wei J, Wang K. **Mucus protectors: promising therapeutic strategies for inflammatory bowel disease**. *Med Hypotheses* (2018) **120** 55-59. PMID: 30220341
142. Ottman N, Geerlings SY, Aalvink S. **Action and function of Akkermansia muciniphila in microbiome ecology, health and disease**. *Best Pract Res Clin Gastroenterol* (2017) **31** 637-642. PMID: 29566906
143. Zinocker MK, Lindseth IA. **The Western diet-microbiome-host interaction and its role in metabolic disease**. *Nutrients* (2018) **10** 365. PMID: 29562591
144. Holscher HD. **Dietary fiber and prebiotics and the gastrointestinal microbiota**. *Gut Microbes* (2017) **8** 172-184. PMID: 28165863
145. Peron G, Hidalgo-Liberona N, Gonzalez-Dominguez R. **Exploring the molecular pathways behind the effects of nutrients and dietary polyphenols on gut microbiota and intestinal permeability: a perspective on the potential of metabolomics and future clinical applications**. *J Agric Food Chem* (2020) **68** 1780-1789. PMID: 31083905
146. Moreira AP, Texeira TF, Ferreira AB. **Influence of a high-fat diet on gut microbiota, intestinal permeability and metabolic endotoxaemia**. *Br J Nutr* (2012) **108** 801-809. PMID: 22717075
147. Koh A, De Vadder F, Kovatcheva-Datchary P. **From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites**. *Cell* (2016) **165** 1332-1345. PMID: 27259147
148. Duncan SH, Belenguer A, Holtrop G. **Reduced dietary intake of carbohydrates by obese subjects results in decreased concentrations of butyrate and butyrate-producing bacteria in feces**. *Appl Environ Microbiol* (2007) **73** 1073-1078. PMID: 17189447
149. Dao MC, Everard A, Aron-Wisnewsky J. **Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology**. *Gut* (2016) **65** 426-436. PMID: 26100928
150. Bradbury KE, Young HJ, Guo W. **Dietary assessment in UK Biobank: an evaluation of the performance of the touchscreen dietary questionnaire**. *J Nutr Sci* (2018) **7** e6. PMID: 29430297
151. Swanson JM. **The UK Biobank and selection bias**. *Lancet* (2012) **380** 110
152. Batty GD, Gale CR, Kivimäki M. **Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis**. *BMJ* (2020) **368** m131. PMID: 32051121
153. Marlett JA, Vollendorf NW. **Dietary fiber content and composition of different forms of fruits**. *Food Chem* (1994) **51** 39-44
154. Koutsos A, Lima M, Conterno L. **Effects of commercial apple varieties on human gut microbiota composition and metabolic output using an in vitro colonic model**. *Nutrients* (2017) **9** 533. PMID: 28538678
155. Delgado AM, Parisi S, Vaz Almeida MD. *Greens and Other Vegetable Foods. Chemistry of the Mediterranean Diet* (2017)
156. Camire ME, Violette D, Dougherty MP. (1997)
157. Marinangeli CPF, Harding SV, Zafron M. **A systematic review of the effect of dietary pulses on microbial populations inhabiting the human gut**. *Benef Microbes* (2020) **11** 457-468. PMID: 32865026
|
---
title: 'Implementation of a Biopsychosocial History and Physical Exam Template in
the Electronic Health Record: Mixed Methods Study'
journal: JMIR Medical Education
year: 2023
pmcid: PMC9993233
doi: 10.2196/42364
license: CC BY 4.0
---
# Implementation of a Biopsychosocial History and Physical Exam Template in the Electronic Health Record: Mixed Methods Study
## Abstract
### Background
Patients’ perspectives and social contexts are critical for prevention of hospital readmissions; however, neither is routinely assessed using the traditional history and physical (H&P) examination nor commonly documented in the electronic health record (EHR). The H&P 360 is a revised H&P template that integrates routine assessment of patient perspectives and goals, mental health, and an expanded social history (behavioral health, social support, living environment and resources, function). Although the H&P 360 has shown promise in increasing psychosocial documentation in focused teaching contexts, its uptake and impact in routine clinical settings are unknown.
### Objective
The aim of this study was to assess the feasibility, acceptability, and impact on care planning of implementing an inpatient H&P 360 template in the EHR for use by fourth-year medical students.
### Methods
A mixed methods study design was used. Fourth-year medical students on internal medicine subinternship (subI) services were given a brief training on the H&P 360 and access to EHR-based H&P 360 templates. Students not working in the intensive care unit (ICU) were asked to use the templates at least once per call cycle, whereas use by ICU students was elective. An EHR query was used to identify all H&P 360 and traditional H&P admission notes authored by non-ICU students at University of Chicago (UC) Medicine. Of these notes, all H&P 360 notes and a sample of traditional H&P notes were reviewed by two researchers for the presence of H&P 360 domains and impact on patient care. A postcourse survey was administered to query all students for their perspectives on the H&P 360.
### Results
Of the 13 non-ICU subIs at UC Medicine, 6 ($46\%$) used the H&P 360 templates at least once, which accounted for $14\%$-$92\%$ of their authored admission notes (median $56\%$). Content analysis was performed with 45 H&P 360 notes and 54 traditional H&P notes. Psychosocial documentation across all H&P 360 domains (patient perspectives and goals, mental health, expanded social history elements) was more common in H&P 360 compared with traditional notes. Related to impact on patient care, H&P 360 notes more commonly identified needs ($20\%$ H&P 360; $9\%$ H&P) and described interdisciplinary coordination ($78\%$ H&P 360; $41\%$ H&P). Of the 11 subIs completing surveys, the vast majority ($$n = 10$$, $91\%$) felt the H&P 360 helped them understand patient goals and improved the patient-provider relationship. Most students ($$n = 8$$, $73\%$) felt the H&P 360 took an appropriate amount of time.
### Conclusions
Students who applied the H&P 360 using templated notes in the EHR found it feasible and helpful. These students wrote notes reflecting enhanced assessment of goals and perspectives for patient-engaged care and contextual factors important to preventing rehospitalization. Reasons some students did not use the templated H&P 360 should be examined in future studies. Uptake may be enhanced through earlier and repeated exposure and greater engagement by residents and attendings. Larger-scale implementation studies can help further elucidate the complexities of implementing nonbiomedical information within EHRs.
## Introduction
The posthospitalization period is a particularly vulnerable time for patients as they may need to adjust to new or evolving diagnoses, modify medication regimens, navigate new mobility limitations, and implement new lifestyle changes. To facilitate care transitions, reinforce chronic disease management, and prevent rehospitalization, it is essential to understand patient perspectives as well as patients’ unique social context. The presence of unmet social needs has been strongly linked with inferior health outcomes [1] as well as a higher risk of rehospitalization [2-4]. Despite this connection, patient perspectives and social context are not systematically assessed with the traditional history and physical (H&P) examination. While the World Health Organization supports inclusion of social, economic, and political aspects of health in the training of medical students globally [5], and the Institute of Medicine has recommended collection of social determinants of health in the electronic health record (EHR) [6], psychosocial documentation remains limited. The fundamental history-taking framework through which physicians approach diagnosis and management has changed very little over the past 50 years. Physician documentation still focuses more on biomedical domains rather than on the psychosocial context [7,8]. Consequently, graduating medical students and residents are not prepared to ask critical questions related to the psychosocial context [9].
The H&P 360 is a revised template for conducting an H&P that applies a 7-domain biopsychosocial framework, integrating assessment of patient perspectives and goals, mental health, and an expanded social history (behavioral health, social support, living environment and resources, and functional status) with collection of biomedical information (see Multimedia Appendix 1) [10]. This template has shown promise in increasing psychosocial documentation in standardized evaluation settings, including a one-time-point use in an inpatient subinternship (subI) [10] and an Objective Structured Clinical Examination exercise during which third- and fourth-year medical students were randomized to use of the H&P 360 or the standard H&P [11]. However, potential uptake and impact with more routine use of the H&P 360 in usual clinical teaching settings remain unknown.
Given the ubiquity and ease of developing templated notes to facilitate documentation within the EHR among practicing clinicians, residents, and students in many countries, creation of a templated note guiding students through the H&P 360 domains could be one approach to promote uptake. EHR-based template studies to date have focused on inpatient resident and faculty subjects with primary endpoints including note quality, quality of care, and time to note completion [12-14]. However, few studies have evaluated the use of templates to intentionally improve documentation related to patient perspectives and social and behavioral determinants of health [15,16]; most such initiatives have centered on interprofessional team members rather than on promoting psychosocial documentation within a physician’s scope of work [17,18].
The objective of this study was to assess the feasibility, acceptability, and impact on care planning of implementing an inpatient H&P 360 template in the EHR for use by fourth-year medical students during their internal medicine subI.
## Study Design
This implementation study included fourth-year medical students (MS4) completing their internal medicine inpatient subI at the University of Chicago (UC) during the 2020-2021 academic year. The evaluation plan was based on the Kirkpatrick model (Reaction, Learning, Behavior, Organizational Performance), building on prior work with standardized patients (“can do”) to use in usual clinical settings (“does”) [19]. Student reaction and learning were assessed through a postintervention survey. Behavioral change and organizational performance were assessed by measuring utilization of the EHR template and through content analysis of student notes.
## Ethics Approval
The UC Institutional Review Board granted the student survey an exemption under quality improvement status. The UC Institutional Review Board (IRB19-1800; IRB21-0571) granted exemption approval for the review and qualitative analysis of student clinical notes. A waiver of informed consent was granted due to the retrospective design and patients and students being unavailable for consent.
## EHR Template Development
A team composed of 2 general internists (one of whom was the course director for the internal medicine subI) and 2 hospital medicine physicians (one of whom was also a bioinformatics fellow) adapted the H&P 360 for use with inpatients and created the EHR templates [11]. The full H&P 360 template included components of a traditional H&P with expanded sections specific to the H&P 360 domains (see Multimedia Appendix 2). Under history of present illness, the template included prompts for: [1] patient understanding of health, [2] self-assessed control, [3] patient-identified strengths, [4] patient-identified barriers, [5] patient priorities and goals, and [6] psychosocial problems and concerns. Under social history, the template included prompts for documentation under the following domains: [1] behavioral health, [2] social support, [3] living environment and resources, and [4] function. Finally, under the assessment and plan, in addition to the typical headings prompting documentation of evaluation and management of acute and chronic biomedical problems, there was an added heading for interdisciplinary resource needs.
The team engaged a group of 4 fourth-year medical students participating in internal medicine subIs to pilot various iterations of the template to improve usability. Based on feedback from the students, who desired maximum flexibility with documentation, the decision was made to allow free-text responses under each domain rather than using drop-down response options. In addition, while some students preferred to use a full de novo H&P 360 template, others preferred to insert unique H&P 360 elements into other existing templates. As a result, two templates were created to accommodate flexibility in documentation: one that could be used as a complete H&P and another that allowed integration of only the unique H&P 360 domains into any H&P template or progress note. Students also suggested that we create a visual reminder for the H&P 360 domains that could be referenced during history-taking; based on this feedback, we created and offered cards for student ID badges listing the H&P 360 domains and relevant content areas.
## Participants
UC Pritzker School of Medicine fourth-year medical students participate in a 4-week inpatient subI of their choosing. SubIs in internal medicine choose to rotate in general internal medicine, clinical cardiology, or the medical intensive care unit (ICU) (all at UC) or at an offsite community hospital teaching affiliate. SubIs are on call every 3-4 days and may admit up to three patients per call day.
Between August 2020 and April 2021, 24 internal medicine subIs were enrolled in the H&P 360 educational program. Prior to their subI month, students received an orientation email from the course director (author IJA) describing the H&P 360 model and providing the note templates, smart phrases for pulling up the templates, and use expectations. The two H&P 360 templates were shared with the students via the EHR. One could be used as a full H&P note template (Multimedia Appendix 2). The second template contained only the elements unique to the H&P 360 and excerpts could be merged into any traditional H&P template (Multimedia Appendix 3). SubIs were asked to use one of these H&P 360 templates in at least one admitting note per call cycle. Students also received cards for their ID badge listing the H&P 360 domains to reference during the patient encounter. The on-service attending physicians were informed of the expectations via email and provided with informational materials about the H&P 360 and rationale for use. During a monthly subI noon conference with author IJA, students were invited to informally discuss their experience using the H&P 360 template.
## Utilization of the H&P 360 Template
H&P 360 template usage was measured to understand its feasibility and acceptability. Research coordinators conducted an EHR query to retrospectively identify all admission notes written by students during their subI in general internal medicine or cardiology at UC during the 2020-2021 academic year ($$n = 13$$ students). Notes written by subIs in the ICU were excluded because of expected admission note differences in this setting and competing priorities for ICU patients at admission. SubIs at the affiliate health care system conducted documentation in a separate, inaccessible EHR, thereby precluding collection of their notes. The research coordinators identified all subI admission notes utilizing an H&P 360 template; all other admission notes were labeled as utilizing traditional (ie, any non-H&P 360) templates. The proportion of all notes written using the H&P 360 template was calculated per student and in total.
Utilization of the H&P 360 could be directly measured among the 13 subIs rotating on the UC general medicine or cardiology services during the 2020-2021 academic year. This group authored a total of 164 admission notes in the EHR. Of all admission notes, 45 ($27.4\%$) were written with an H&P 360 template (Multimedia Appendix 5). As mentioned above, subIs rotating in the ICU or at the community hospital teaching affiliate were excluded from this analysis.
Of the 13 subIs, 6 ($46\%$) students authored at least one admission note using an H&P 360 template. These H&P 360 templated notes accounted for $14\%$-$92\%$ of their authored admission notes (median $56\%$). Seven students ($54\%$) never authored a note using the H&P 360 templates.
## Content Analysis
Content analysis was performed to assess the impact of the H&P 360 template. For purposes of qualitative comparison of note content, research coordinators collected and deidentified all of the H&P 360 notes and a sample of the traditional notes. The sample of traditional notes was drawn by attempting relatively balanced representation across students. Specifically, each student could contribute no more than 5 traditional notes to the total sample; for those with more than 5 traditional notes, a random subsample was selected for inclusion.
The content analysis team was composed of three internists involved in medical education (JWT, VGP, IJA) and one medical student (EYR). Throughout the process of analysis, team members discussed their preconceived notions and biases from their roles in education and patient care. The team began with a set of a priori content domains based on the H&P 360 template (eg, mental health, behavioral health, social support). The team members independently reviewed a set of notes—four from the H&P 360 group and four from the traditional group—to clarify the definition of the content domains, add additional de novo content domains as needed, and improve consistency between coders. Subsequently, for each of the notes, two team members extracted relevant text and entered it into a Research Electronic Data Capture (REDCap) template under the appropriate content domain. Discrepancies in coding were reviewed and resolved through email correspondence. The text from each content domain was then aggregated into a document and reviewed by two members of the team to identify themes within each content domain and to assess whether there were qualitative differences in the content between the H&P 360 and traditional templated notes. Each content domain was discussed at the weekly group video meeting. The number of notes categorized under each content domain was counted for the H&P 360 and traditional templated groups.
Content analysis was performed with 45 H&P 360 notes and 54 traditional H&P notes (Table 1).
**Table 1**
| Content domain | Content domain.1 | H&P 360 notes (n=45), n (%) | Traditional H&P notes (n=54), n (%) |
| --- | --- | --- | --- |
| Patient perspectives and mental health | Patient perspectives and mental health | Patient perspectives and mental health | Patient perspectives and mental health |
| | Patient understanding of health | 23 (51) | 16 (30) |
| | Patient priorities and goals | 18 (40) | 4 (7) |
| | Mental health | 15 (33) | 8 (15) |
| Expanded social history | Expanded social history | Expanded social history | Expanded social history |
| | Behavioral health (nonsubstance use) | 32 (71) | 23 (43) |
| | Social support | 44 (98) | 28 (52) |
| | Living environment and resources | 16 (36) | 10 (19) |
| | Function | 42 (93) | 31 (57) |
| Impact on patient care | Impact on patient care | Impact on patient care | Impact on patient care |
| | Needs identified | 9 (20) | 5 (9) |
| | Education and counseling | 12 (27) | 13 (24) |
| | Interdisciplinary resource coordination | 35 (78) | 22 (41) |
## Student Survey
A student survey was used to assess student perceptions of feasibility, acceptability, and impact of the H&P 360. Survey items assessing student perception of the H&P 360 were developed in collaboration with the American Medical Association H&P 360 Implementation Grantee team. The survey consisted of 14 5-point Likert-scale questions assessing feasibility, perceived impact on patient care, and perceived impact on educational experience. Short-response items elicited useful and challenging aspects of the H&P 360 and student recommendations (Multimedia Appendix 4).
At the conclusion of the educational program, all subIs ($$n = 24$$ students) were asked to complete the survey anonymously. Percentages of students who selected 5 (strongly agree) or 4 (somewhat agree) on the Likert scale were tabulated. Open-ended responses were read by two members of the research team and common statements (defined as reported by three or more students) were identified and summarized.
Of all subIs in internal medicine ($$n = 24$$), 11 ($45\%$) completed the survey regarding their experience with the H&P 360 (Table 2). Regarding feasibility of the H&P 360, the majority of respondents strongly or somewhat agreed that the H&P 360 took an appropriate amount of time to complete and strongly or somewhat agreed that it was easy to use. However, few respondents strongly or somewhat agreed that presentations using the H&P 360 were well-received by the clinical team. Regarding perceived impact on patient care, respondents strongly or somewhat agreed that the H&P 360 helped them better understand patient goals, facilitated a stronger provider-patient relationship, changed some of the questions they asked during the encounter, and added valuable information that they would not have known about the patient. Few students strongly or somewhat agreed that the H&P 360 helped them to create a more comprehensive problem list (Table 2).
**Table 2**
| Statement regarding H&Pa 360 | Statement regarding H&Pa 360.1 | Agree with statementb, n (%) |
| --- | --- | --- |
| Feasibility | Feasibility | Feasibility |
| | Took an appropriate amount of time to complete | 8 (73) |
| | Was easy to use | 7 (64) |
| | Could be incorporated into every patient interaction | 6 (55) |
| | Presentations were well-received by my clinical team | 3 (27) |
| Perceived impact on patient care | Perceived impact on patient care | Perceived impact on patient care |
| | Helped me better understand patients’ goals | 10 (91) |
| | Facilitated a stronger provider-patient relationship | 9 (82) |
| | Changed some of the questions I ask patients during the encounter | 10 (91) |
| | Added valuable information that I would not otherwise know about the patient | 9 (82) |
| | Facilitated care planning that included other health professionals | 7 (64) |
| | Helped improve the care I provided to my patients | 6 (55) |
| | Was able to incorporate information into management plans | 5 (45) |
| | Helped create a more comprehensive problem list | 4 (36) |
| Perceived impact on education | Perceived impact on education | Perceived impact on education |
| | Helped me learn to be a better clinician | 7 (64) |
| | Plan to use elements during other rotations | 7 (64) |
In open-ended prompts on the survey, five students shared that the H&P 360 served as a prompt to further explore or document social history. One student wrote: Three students stated that the template helped them clarify patient goals.
Regarding areas for improvement, four students noted the time that it took to complete the H&P 360. One of these students recommended having the option for shorter, drop-down answers available in the template.
Three students shared that they thought patients were surprised to be asked about some of the topics covered in the H&P 360. One student wrote: Finally, three students reported concerns about deviating from the note template typically used on their clinical service. Two students specifically reported receiving negative feedback from their clinical team. One wrote:
## Patient Perspectives
Text was coded for patient understanding of health and patient priorities or goals. Some H&P 360 notes retained and responded directly to the EHR template prompts for these elements within the patient subjective history, while others spontaneously integrated this content into other areas of the note.
While $7\%$ ($\frac{4}{54}$) of traditional notes documented patient priorities or goals, this was documented in $40\%$ ($\frac{18}{45}$) of H&P 360 templated notes. Qualitative differences between groups were also identified in the content coded for this domain. H&P 360 notes discussed priorities related to decreasing pain, increasing function, determining the cause of one’s symptoms, wanting to improve chronic disease management, and wanting to go home.
In contrast, for traditional notes, the only documented priorities or goals related to the patient wanting to leave the hospital: “She wants to go home.” [ Traditional note, Student N] Patient understanding of health was documented in $51\%$ ($\frac{23}{45}$) of H&P 360 notes and in $30\%$ ($\frac{16}{54}$) of traditional notes. Documentation across both groups related to patient perceptions as to the cause of their symptoms or patient familiarity with their medications: “He states he has recurrent episodes of Afib [atrial fibrillation] since 2013 w/ similar symptoms (he has a watch that alerts him).” [ Traditional note, Student K] Among H&P 360 notes, some also included information from the perspective of the patient or clinician of the patient’s level of understanding of their diagnoses, medications, or disease etiology.
## Mental Health
Overall, $33\%$ ($\frac{15}{45}$) of H&P 360 notes and $15\%$ ($\frac{8}{54}$) of traditional notes included the mental health domain. Across both groups, there was documentation regarding psychiatric diagnoses and related treatment, anxiety, stress, substance use, or documentation that there were no relevant concerns in this domain. Qualitative differences between groups were not identified. One such example was: “...increased stress related to her brother’s condition and the need to pay for his medical expenses.” [ Traditional note, Student G]
## Behavioral Health (Nonsubstance Use)
A majority of notes in both groups contained autopopulated text related to tobacco, alcohol, and drug use. Since it was unclear whether this information was input by the author of the note or had been documented in the EHR from a prior encounter, this information was not included for the purposes of this analysis. The behavioral health domain (excluding information about tobacco, alcohol, and drug use) was present in $71\%$ ($\frac{32}{45}$) of H&P 360 notes versus $43\%$ ($$n = 23$$/54) of traditional notes. Across both groups, text coded for behavioral health frequently documented patient adherence to medications. Physical activity and nutrition behaviors were also described across both groups. Qualitative differences in the coded text were not identified: “States takes meds regularly and doesn’t miss... States his wife cooks-does not use salt. Does little physical activity like stairs.” [ H&P 360 note, Student D]
## Social Support
Information about the patient’s social network was documented in $98\%$ ($\frac{44}{45}$) of H&P 360 notes and in $52\%$ ($\frac{28}{54}$) of traditional notes. The social support domain included information about the patient’s cohabitants, other important relationships, and presence of home health workers. Across both groups, there was also information about how the patient’s social network assisted in their care. No qualitative differences were observed in the coded text: “The patient currently lives with her daughter. her medications are managed at home by her son, who is a nurse.” [ Traditional note, Student A]
## Living Environment and Resources
Overall, $36\%$ ($\frac{16}{45}$) of H&P 360 notes and $19\%$ ($\frac{10}{54}$) of traditional notes documented information about patient’s access to housing, transportation, food, insurance, or financial resources. The coded content was qualitatively similar across both groups.
## Function
Patient functioning prior to hospitalization was documented in $93\%$ ($\frac{42}{45}$) of H&P 360 templated notes and in $57\%$ ($\frac{31}{54}$) of traditional notes. Across both groups, this domain was qualitatively similar. Both groups documented activities of daily living, instrumental activities of daily living, mobility, assistive devices, cognitive functioning, and occupation.
## Needs Identified
Resource needs were identified in $20\%$ ($\frac{9}{45}$) of H&P 360 templated notes and in $9\%$ ($\frac{5}{54}$) of traditional notes. Needs were commonly related to placement due to concerns about safety, insufficient caregiving in the home setting, or housing instability. Identified needs also commonly included insurance issues, medication refills, or outpatient follow up. In qualitative comparison, text from the H&P 360 notes contained more detail about resource needs. Plans for addressing needs were usually but not always explicitly described. The plans often involved acquiring equipment or involving social work. In situations where a plan was not stated, it was unclear if it was assumed that it would be addressed or if it ultimately was not addressed.
## Education and Counseling
Patient education or counseling was described in $27\%$ ($\frac{12}{45}$) of H&P 360 notes and in $24\%$ ($\frac{13}{54}$) of traditional notes. Across both groups, documented counseling most often involved nutrition, physical activity, and substance use, while some notes documented patient education regarding management options. There was little detail in excerpts from either group. No qualitative differences were identified: “Encourage elevation of legs during sitting and during bedtime. Compression stockings as outpatient.” [ Traditional note, Student O]
## Interdisciplinary Resource Coordination
Interdisciplinary resource coordination was documented in $78\%$ ($\frac{35}{45}$) of H&P 360 notes and in $41\%$ ($\frac{22}{54}$) of traditional notes. This code included inpatient and outpatient referrals to social work, physical or occupational therapy, nutrition, podiatry, and medical specialties. Across both groups, a majority of the documentation was simply noting that physical or occupational therapy services were ordered for the patient. There was not much detail in either group. Qualitative differences were not identified: Social work consulted on prior admission. Consider referral for inpatient vs outpatient rehab services. [ Traditional note, Student C].
## Principal Findings
In this inpatient implementation study of the H&P 360 EHR template, psychosocial documentation was more common across virtually all H&P 360 content domains among admission notes using the H&P 360 template compared to the traditional H&P note template. Importantly, documentation was also more common with respect to social needs identification and interdisciplinary collaboration. However, the overall impact of the tool was diminished by limited and variable uptake of the H&P 360 note template by the subI students.
While students generally provided positive feedback about the potential of the H&P 360 to improve understanding of patient goals and to enhance the patient-provider relationship, students less often reported that this added information changed treatment plans or improved care. There are several potential reasons for this apparent paradox. First, students are already including health-related social needs in care planning closer to the time of discharge (not captured in admission notes). Alternatively, they gather information but do not apply it (potentially due to barriers related to time, resources, or interdisciplinary support).
Many students did not use the H&P 360 template. Open-ended survey feedback suggested that a barrier to use may be the time required to complete the expanded H&P. Drop-down menu responses could increase ease of template use; however, these may also limit detailed communication of the patient’s context or preferences. Pacing collection of psychosocial information throughout the hospital stay beyond the admission day, perhaps through triggered alerts or reminders, could decrease and spread out the time required; this pacing may in some cases improve perceived relevance and acceptability to students and patients as acute biomedical issues have abated.
In addition to time constraints, several students also noted negative feedback from some team members who felt that the psychosocial information presented within the context of the H&P 360 appeared to deviate from expected convention. Students have strong incentives to assimilate with their team and thus likely felt pressure not to use the H&P 360 template even if they found it useful. The lack of interest among other team members in patients’ contextual information likely relates in part to the historical focus physicians have had on biomedical information. Further, the timing of presenting this information may have been a factor as students’ perceptions of the relative value of this contextual information may be lower in informing initial treatment and stabilization plans at admission as compared with the longer-term planning that occurs nearer to discharge.
This pragmatic implementation provided only a low-intensity orientation to the H&P 360 for faculty in the form of emailed materials. Future efforts will need to increase and improve orientation of faculty to the H&P 360 as well as include training for resident physicians. Student uptake of the H&P 360 EHR template may be further enhanced through exposure in the preclinical years in settings such as free clinics and clinical preceptor groups.
## Comparison With Prior Work
To date, EHR tools and templates have predominantly been leveraged to enhance biomedical documentation, targeting quality metrics, and optimizing reimbursement [13,20-22]. Our study represents an important contribution to this literature as there is limited research on use of EHR templates to improve psychosocial documentation or to intentionally elicit patients’ perspectives and goals. Several initiatives in the United States call for improved integration of screening of social determinants of health into health care delivery and the need for standardized methods for capturing this information in EHRs [6,23,24]. Systematic documentation of patients’ needs and goals during hospitalization has the potential to not only improve the care of individual patients (personalizing care, supporting shared decision-making, aiding discharge planning), but can provide critical context for health systems in designing programs and determining staffing needs to meet the needs of the patient population they serve [23,25].
While most interventions to promote psychosocial documentation in the EHR have focused on the completion of expanded checklists and screening tools primarily by nonphysician team members, we intentionally chose to include psychosocial documentation within the physician note template [17,18,26]. This choice was made to match the usual workflow for students and residents at our institution and to promote this documentation as a part of the physician’s sphere of work (rather than an area delegated to social workers, nurses, or others).
While EHR templates have been found to improve documentation of key measures, some studies suggest that this may occur at the expense of patient-centered care, prioritizing the clinician’s agenda above that of the patient [27]. However, in contrast to many EHR templates, the H&P 360 promotes a domain-based approach to discussing psychosocial concerns with patients (rather than a checklist-based approach) and further intentionally solicits patients’ goals and perspectives. Integration of patient-centered questions within templates used by general practitioner practices in England was actually found to increase the perception of patient-centeredness [16].
## Limitations
There are several important limitations to note. First, while we found that psychosocial documentation was more common in the H&P 360 notes as compared with traditional notes, our study design did not allow for rigorous statistical testing. Second, the low and variable uptake of the EHR template meant that our sample of representative H&P 360 notes was drawn from a small number of students, thus limiting the generalizability of our findings. Third, students self-selected when to use the H&P 360 as compared with traditional note templates. Consequently, it is possible that there may have been systematic differences among patients represented in each group (eg, ability to engage, presence and number of needs), which may have biased the results. Fourth, we focused solely on initial admission H&P notes and did not include review of progress notes or discharge summaries. As a result, we may have missed instances in which psychosocial information was documented later during a patient’s hospital course. Fifth, we did not survey patients or interdisciplinary team members about their experiences with the H&P 360 and did not collect any other objective systems-level data on the impact of the H&P 360 on discharge planning or resource provision. As a result, our findings are limited by the accuracy and completeness of subI documentation. Lastly, the survey response rate was low, in part due to inclusion of students on their ICU rotation who were unlikely to utilize the H&P 360 owing to competing acute priorities. While the response rate was overall lower than ideal, the students who did complete the survey likely represented a large majority of those who utilized the EHR template.
## Conclusions
Integrating the H&P 360 framework into templated notes in the EHR is feasible, and may increase assessment of goals and perspectives for patient-engaged care and contextual factors important to prevention of rehospitalization. Uptake of the note template may be enhanced through earlier and repeated exposure, encouraging paced usage over the course of a hospitalization, and greater engagement by residents and attendings. Larger-scale implementation studies with learners and practicing clinicians, paired with robust evaluation efforts involving patients, clinicians, and interprofessional staff, are needed to better understand the complexities of implementing nonbiomedical information within EHRs and the usual flow of care.
## Data Availability
Anonymized survey data are available from the corresponding author on reasonable request. The patient notes analyzed during the current study are not publicly available to protect patient anonymity.
## References
1. Kreuter MW, Thompson T, McQueen A, Garg R. **Addressing social needs in health care settings: evidence, challenges, and opportunities for public health**. *Annu Rev Public Health* (2021) **42** 329-344. DOI: 10.1146/annurev-publhealth-090419-102204
2. Bensken WP, Alberti PM, Koroukian SM. **Health-related social needs and increased readmission rates: findings from the Nationwide Readmissions Database**. *J Gen Intern Med* (2021) **36** 1173-1180. DOI: 10.1007/s11606-021-06646-3
3. Carter J, Ward C, Thorndike A, Donelan K, Wexler DJ. **Social factors and patient perceptions associated with preventable hospital readmissions**. *J Patient Exp* (2020) **7** 19-26. DOI: 10.1177/2374373518825143
4. Sentell TL, Seto TB, Young MM, Vawer M, Quensell ML, Braun KL, Taira DA. **Pathways to potentially preventable hospitalizations for diabetes and heart failure: a qualitative analysis of patient perspectives**. *BMC Health Serv Res* (2016) **16** 300. DOI: 10.1186/s12913-016-1511-6
5. Weisz G, Nannestad B. **The World Health Organization and the global standardization of medical training, a history**. *Global Health* (2021) **17** 96. DOI: 10.1186/s12992-021-00733-0
6. 6Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health RecordsBoard on Population Health and Public Health Practice;Institute of MedicineCapturing social and behavioral domains in the electronic health records: Phase 12014Washington, DCNational Academies Press. *Capturing social and behavioral domains in the electronic health records: Phase 1* (2014)
7. Binns-Calvey AE, Malhiot A, Kostovich CT, LaVela SL, Stroupe K, Gerber BS, Burkhart L, Weiner SJ, Weaver FM. **Validating domains of patient contextual factors essential to preventing contextual errors: a qualitative study conducted at Chicago Area Veterans Health Administration Sites**. *Acad Med* (2017) **92** 1287-1293. DOI: 10.1097/ACM.0000000000001659
8. Weiner SJ, Schwartz A, Weaver F, Goldberg J, Yudkowsky R, Sharma G, Binns-Calvey A, Preyss B, Schapira MM, Persell SD, Jacobs E, Abrams RI. **Contextual errors and failures in individualizing patient care: a multicenter study**. *Ann Intern Med* (2010) **153** 69-75. DOI: 10.7326/0003-4819-153-2-201007200-00002
9. Astin JA, Sierpina VS, Forys K, Clarridge B. **Integration of the biopsychosocial model: perspectives of medical students and residents**. *Acad Med* (2008) **83** 20-7. DOI: 10.1097/ACM.0b013e31815c61b0
10. Williams BC, Ward DA, Chick DA, Johnson EL, Ross PT. **Using a six-domain framework to include biopsychosocial information in the standard medical history**. *Teach Learn Med* (2019) **31** 87-98. DOI: 10.1080/10401334.2018.1480958
11. Kirley K, Hayer R, Khan T, Johnson E, Sanchez E, Kosowicz L, Terry V, Henderson D, Krebsbach C, Park Y, Dekhtyar M, Williams B. **Expanding the traditional history and physical examination to address chronic diseases and social needs: a multisite randomized control trial of 4 medical schools**. *Acad Med* (2020) **95** S44-S50. DOI: 10.1097/ACM.0000000000003640
12. Aylor M, Campbell EM, Winter C, Phillipi CA. **Resident notes in an electronic health record**. *Clin Pediatr* (2017) **56** 257-262. DOI: 10.1177/0009922816658651
13. Mehta R, Radhakrishnan N, Warring C, Jain A, Fuentes J, Dolganiuc A, Lourdes L, Busigin J, Leverence R. **The use of evidence-based, problem-oriented templates as a clinical decision support in an inpatient electronic health record system**. *Appl Clin Inform* (2017) **07** 790-802. DOI: 10.4338/aci-2015-11-ra-0164
14. Santoro JD, Sandoval Karamian AG, Ruzhnikov M, Brimble E, Chadwick W, Wusthoff CJ. **Use of electronic medical record templates improves quality of care for patients with infantile spasms**. *Health Inf Manag* (2021) **50** 47-54. DOI: 10.1177/1833358318794501
15. Savoy A, Frankel R, Weiner M. **Clinical thinking via electronic note templates: who benefits?**. *J Gen Intern Med* (2021) **36** 577-579. DOI: 10.1007/s11606-020-06376-y
16. Mann C, Shaw A, Wye L, Salisbury C, Guthrie B. **A computer template to enhance patient-centredness in multimorbidity reviews: a qualitative evaluation in primary care**. *Br J Gen Pract* (2018) **68** e495-e504. DOI: 10.3399/bjgp18X696353
17. LaForge K, Gold R, Cottrell E, Bunce A, Proser M, Hollombe C, Dambrun K, Cohen D, Clark K. **How 6 organizations developed tools and processes for social determinants of health screening in primary care: an overview**. *J Ambul Care Manage* (2018) **41** 2-14. DOI: 10.1097/JAC.0000000000000221
18. Gold R, Cottrell E, Bunce A, Middendorf M, Hollombe C, Cowburn S, Mahr P, Melgar G. **Developing electronic health record (EHR) strategies related to health center patients' social determinants of health**. *J Am Board Fam Med* (2017) **30** 428-447. DOI: 10.3122/jabfm.2017.04.170046
19. Kirkpatrick DL, Kirkpatrick JD. *Evaluating training programs: the four levels. 3rd edition* (2006)
20. Beck A, Sauers H, Kahn R, Yau C, Weiser J, Simmons J. **Improved documentation and care planning with an asthma-specific history and physical**. *Hosp Pediatr* (2012) **2** 194-201. DOI: 10.1542/hpeds.2012-0016
21. Fielstein EM, Brown SH, McBrine CS, Clark TK, Hardenbrook SP, Speroff T. **The effect of standardized, computer-guided templates on quality of VA disability exams**. *AMIA Annu Symp Proc* (2006) **2006** 249-253. PMID: 17238341
22. Riggio JM, Sorokin R, Moxey ED, Mather P, Gould S, Kane GC. **Effectiveness of a clinical-decision-support system in improving compliance with cardiac-care quality measures and supporting resident training**. *Acad Med* (2009) **84** 1719-1726. DOI: 10.1097/ACM.0b013e3181bf51d6
23. Daniel H, Bornstein SS, Kane GC, Carney JK, Gantzer HE, Henry TL, Lenchus JD, Li JM, McCandless BM, Nalitt BR, Viswanathan L, Murphy CJ, Azah AM, Marks L. **Addressing social determinants to improve patient care and promote health equity: an American College of Physicians Position Paper**. *Ann Intern Med* (2018) **168** 577-578. DOI: 10.7326/M17-2441
24. Machledt D. **Addressing the social determinants of health through Medicaid managed care**. *The Commonwealth Fund* (2017)
25. Adler NE, Stead WW. **Patients in context--EHR capture of social and behavioral determinants of health**. *N Engl J Med* (2015) **372** 698-701. DOI: 10.1056/NEJMp1413945
26. Freij M, Dullabh P, Lewis S, Smith SR, Hovey L, Dhopeshwarkar R. **Incorporating social determinants of health in electronic health records: qualitative study of current practices among top vendors**. *JMIR Med Inform* (2019) **7** e13849. DOI: 10.2196/13849
27. Morrissey M, Shepherd E, Kinley E, McClatchey K, Pinnock H. **Effectiveness and perceptions of using templates in long-term condition reviews: a systematic synthesis of quantitative and qualitative studies**. *Br J Gen Pract* (2021) **71** e652-e659. DOI: 10.3399/BJGP.2020.0963
|
---
title: Prevalence of physical activity and dietary patterns as risk factors for cardiovascular
diseases among semi-urban dwellers in Ibadan, Nigeria
authors:
- Posi Emmanuel Aduroja
- Yetunde Olufisayo John-Akinola
- Mojisola Morenike Oluwasanu
- Oladimeji Oladepo
journal: African Health Sciences
year: 2022
pmcid: PMC9993249
doi: 10.4314/ahs.v22i3.36
license: CC BY 4.0
---
# Prevalence of physical activity and dietary patterns as risk factors for cardiovascular diseases among semi-urban dwellers in Ibadan, Nigeria
## Abstract
### Background
Physical inactivity and unhealthy diet are leading risk factors for cardiovascular diseases globally. Limited studies have assessed the prevalence of these risk factors in community-based settings in Nigeria.
### Objectives
This study assessed the prevalence of physical activity and the dietary pattern of residents in selected semi-urban communities in Ibadan, Nigeria.
### Methods
This was a cross-sectional study carried out among 500 randomly selected residents from two semi-urban communities. Multi-stage random sampling technique was used to select households and participants. Data were collected using a pretested modified version of the WHO STEPS instrument. Descriptive and inferential statistical analyses were determined at $5\%$ level of significance.
### Results
The mean age was 35.36 ± 12.24 and a mean household size of 4.07 ± 1.85. Majority ($87.2\%$) of the respondents engaged in low physical activity (< 150–300 min/wk). Consumption of fruits and vegetables was low among respondents at $33\%$ and $36.4\%$ respectively. The employment status of respondents was significantly related to expected workplace physical activity level (χ2=11.27; $$P \leq 0.024$$).
### Conclusions
This study highlights the need for the development and implementation of community-driven, multi-layered public health promotion initiatives across different settings.
## Background
The well-being of individuals is a function of their level of physical activity1–4. Physical activity has a positive influence, not only on the physical fitness of individuals and reduction in the risk of cardiovascular diseases (CVDs)5,6 but also on improving the cognitive and psychological well-being of individuals7,8. Reports from World Health Organization9 showed that low physical activity-related Non-communicable Diseases (NCDs) contributed up to 3 million deaths in sub-Saharan Africa (SSA). From projections in literature10, this is expected to have increased to about $80\%$ in the year 2020. Globally, low physical activity has been found to contribute to $6\%$ of coronary heart disease burden, $7\%$ of type-2 diabetes disease burden, $10\%$ of breast cancer disease burden and $10\%$ of colon cancer disease burden11.
Low physical activity is responsible for $9\%$ of the global mortality rate and it is highlighted as the second leading cause of death in the US and fourth globally11. Data collected across one hundred and twenty-two [122] countries by Hallal and colleagues20 showed that $31\%$ of adults worldwide are inactive, with a high likelihood of an increase in subsequent years.
Low physical activity is fast becoming an epidemic in West African countries, and it is higher in this region compared to other middle-income countries12. In Nigeria, several reports have shown low physical activity levels between $25\%$ to $32\%$12,21–23, with NCDs accounting for around $25\%$ and $35\%$ of all deaths in Nigerian men and women, respectively24.
Studies carried out in Nigeria16 and Cameroon17 reported a sedentary lifestyle in workplaces, especially in urban areas; this has been attributed to low physical activity as a result of increase in white-collar jobs and long hours of sitting12,18. Assah and colleagues19 and Ejechi18, however reported that physical activity is higher in traditional rural settings of Africa as a result of farming, hunting, and fishing.
It has been documented that obesity and sedentary lifestyle coexist and are both associated with CVDs25 and this calls for public health urgent attention. Furthermore, putting into perspective that *Nigeria is* the sixth-largest and Africa's most populated country, between 48.9 and 62.6 million Nigerians are at risk of NCDs. This number is more than the entire population of South Africa, Kenya and Uganda. With an estimated increase in life expectancy by 0.68 years11, the need for the elimination of low physical activity within the Nigerian population is most vital. Studies across the world have shown that while individuals may contemplate engaging in physical activity, the environment they live in has a strong influence on their readiness to do so26–30.
Poor dietary pattern in combination with low physical activity increases the risk for the development of CVDs. In recent years, there has been a rapid increase in the number of fast foods restaurants, commercial food canteens, food hawkers, and street vendors, which are patronised by people of different ages13. According to Abubakari12, urbanisation has led to the increased consumption of food items high in fat and sugar with high levels of calories. In combination with low physical activity and sedentary lifestyle reported, this further puts a substantial number of the Nigerian population at risk of CVDs. Bamiro13 stated that urban residents purchase $37.9\%$ of the food they consume; these foods are referred to as food-away-from-home (FAFH). The consumption of FAFH has increased across the globe, both in developed and developing countries. Food-away-from-home has been established to have lesser nutritious content and high caloric content compared to food prepared at home14. Also, eating away from home has been linked to overweight and obesity in adults and children, especially abdominal obesity13. This constitutes a major risk factor to the increasing prevalence of CVDs15.
In perspective, published studies by Sun et al.31 and Almeida et al.32, and a systematic review conducted by Daskalopoulou et al.33 reported a positive association between physical activity and healthy aging. Weight gain is correlated with both physical activity and dietary pattern34,35. Diet and physical activity are considered to be major components of a healthy lifestyle and cannot be separated. Limited studies have assessed the prevalence of low physical activity and dietary pattern in community-based settings in Nigeria. Thus, this study's objectives are as follows: i. To identify the consumption pattern of fruits and vegetables among respondents.
ii. To identify the consumption pattern of sugar-sweetened drinks, pastries and food-away-from-home among respondents.
iii. To determine the level of physical activity among respondents
## Study design and setting
This was a cross-sectional study nested into a larger study which documented bio-behavioural data on NCD risk factors using the ODK tool. A community-based study was carried out among inhabitants of two semi-urban communities in Ibadan North Local Government Area, Oyo State, Nigeria from fourteenth November to third December 2018. The LGA has an estimated population of 856,988 and sanitary conditions are poor as the majority of houses do not have access to potable water and toilets.
## Participant size and sampling technique
The sample size for the study was determined using the Leslie Kish formula, a proportion of physical activity of $32\%$ from a previous study report23, $95\%$ confidence level, $5\%$ margin of error, and a design effect of 1.5. The final sample size was determined to be 334 and increased to 500 respondents (approximately $50\%$) to cater for incomplete responses and to increase the number of respondents to cover a larger portion of the communities. The participants consisted of 500 community members aged 18–65 years who were selected for the study from the total community population using a two-step multistage sampling of households and residents. For the first stage, a systematic random sampling technique was used to select every third households in the selected community, however, household who did not consent were not enrolled and the subsequent household was enrolled upon giving consent. The second stage, from each of the selected households, one participant was selected by the ballot method.
## Variables and definitions
Variables measured in this study are respondents' physical activity measured on a scale of World Health Organisation (WHO) recommendation of 150 minutes per week as moderate-intensity physical activity and 300 minutes per week as vigorous-intensity physical activity, consumption of fruits, vegetable, pastries, sugar-sweetened drinks, and FAFH was measured as regular (4 or more days of consumption in a week) and non-regular (less than 4 days of consumption in a week). These variables were self-reported by respondents.
## Data collection instrument and measurement
Data was collected using the modified version of the WHO STEPS instrument; the tool has two major parts: socio-demographic characteristics and behavioural profile. The modified instrument was translated into the local language Yoruba language, which was further back-translated into English language to ensure items retained their original meaning. Data was gathered through an electronic data capture tool (ODK Collect) which was interviewer-administered. Research assistants and supervisors were trained for two days, the training included research ethics, data collection procedures, and contents of the instrument to increase the quality of the data. The data collection procedure and tools were pretested in urban communities that shared similar characteristics to the study locations. Supportive supervision was carried out by supervisors daily during the data collection period in the two selected communities. The blood pressure reading was taken after the interviewer-administration of questionnaires using an electronic sphygmomanometer; weight and height were measured using a weighing scale and a stadiometer, respectively.
The collected data was exported into the IBM Statistical Package for Social Sciences version 21.0 for analysis. Descriptive and inferential statistical analyses were carried out. Chi-square test was used in testing for a significant relationship between respondents' demographic characteristics and dietary patterns variables and also physical activity, Analysis of Variance (ANOVA) was used to test for significant difference and Duncan Multiple Range Test (DMRT) was used in ranking variation in mean scores of fruits and vegetable consumption.
## Socio-demographic characteristics
There were 500 respondents in the study. The mean age of respondents was 35.36 ± 12.24, and more than half ($54.2\%$) were between the age of 26 and 45 years. The majority ($70.6\%$) of the respondents were females, over half ($52.6\%$) had completed secondary education, and the majority ($88.8\%$) were of Yoruba ethnicity. Many ($62.4\%$) were married and slightly more than half ($51\%$) were Christians. More than two-thirds of the respondents ($65.8\%$) had lived in the study location for 10 years or less, $72.6\%$ are self-employed, $67.2\%$ earn a regular monthly income of 20,000 naira ($51) or less which is below the minimum wage allocation in Nigeria as at the time this study was conducted. Household size ranged from 1 to 10 individuals with a median of 4 (Table 1).
**Table 1**
| Variables | Frequency | Percentage |
| --- | --- | --- |
| Sex | | |
| Male | 147.0 | 29.4 |
| Female | 353.0 | 70.6 |
| Age Group* | | |
| Youth (18 – 25 years) | 119.0 | 23.8 |
| Adult Middle age (26 – 45 years) | 271.0 | 54.2 |
| Adult Mature age (46 – 65 years) | 110.0 | 22.0 |
| Education | | |
| No formal schooling | 49.0 | 9.8 |
| Primary education | 78.0 | 15.6 |
| Secondary education | 263.0 | 52.6 |
| College/University education | 110.0 | 22.0 |
| Ethnic group | | |
| Yoruba | 444.0 | 88.8 |
| Igbo | 26.0 | 5.2 |
| Hausa | 4.0 | 0.8 |
| Others (Akwa Ibom, Benue, Cross River, Delta, Edo, Ibibio, Kogi, Tapa & Non-Nigerian) | 26.0 | 5.2 |
| Marital status | | |
| Never Married | 142.0 | 28.4 |
| Currently married | 320.0 | 64.0 |
| Not married | 38.0 | 7.6 |
| Religion | | |
| Christianity | 255.0 | 51.0 |
| Islam | 245.0 | 49.0 |
| Years of residence** | | |
| 10 years or less | 329.0 | 65.8 |
| 11 years to 20 years | 75.0 | 15.0 |
| 21 years to 30 years | 60.0 | 12.0 |
| More than 30 years | 36.0 | 7.2 |
| Employment status | | |
| Employed | 64.0 | 12.8 |
| Self-employed | 363.0 | 72.6 |
| Unemployed | 73.0 | 14.6 |
| Monthly income*** | | |
| No income | 34.0 | 6.8 |
| 20,000 naira or less | 336.0 | 67.2 |
| More than 20,000 naira | 130.0 | 26.0 |
| Household size**** | | |
| 1 member | 41.0 | 8.2 |
| 2 to 4 members | 274.0 | 54.8 |
| 5 or more members | 185.0 | 37.0 |
## Consumption of fruits and vegetables
Regular consumption of fruits was low among respondents ($33\%$), $73.9\%$ of respondents who consumed fruits regularly were female. Married respondents ($64.2\%$), respondents who had lived for 10 years or less within the study site ($61.2\%$), self-employed ($72.7\%$), respondents who earn 20,000 naira or less ($67.9\%$) and respondents from a household with 2 to 4 members ($59.4\%$) consumed fruits more regularly, years of residency was found to be significantly related to regular consumption of fruits X2 (3, $$n = 500$$) = 12.80 (Table 2).
**Table 2**
| Variables | Regular consumption of fruits n = 165 (33.0%) % | Regular consumption of vegetables n = 182 (36.4%) % | Regular consumption of sugar-sweetened drinks n = 121 (24.2%) % | Regular consumption of pastries n = 88 (17.6%) % | Regular consumption of FAFH n = 38 (7.6%) % |
| --- | --- | --- | --- | --- | --- |
| Sex | | | | | |
| Male | 26.1 | 27.5 | 28.9 | 28.4 | 78.9* |
| Female | 73.9 | 72.5 | 71.1 | 71.6 | 21.1* |
| Age Group | | | | | |
| Youth (18 – 25 | 23.0 | 17.0* | 33.9* | 37.5* | 36.8 |
| years) | 49.1 | 52.2* | 53.7* | 48.9* | 47.4 |
| Adult (26 – 45 years) | 27.9 | 30.8* | 12.4* | 13.6* | 15.8 |
| Middle age (46 – 65 years) | | | | | |
| Marital status | | | | | |
| Never Married | 30.9 | 27.5 | 24.0 | 21.6 | 18.4 |
| Currently married | 64.2 | 65.4 | 66.1 | 68.2 | 71.1 |
| Not married | 4.8 | 7.1 | 9.9 | 10.2 | 10.5 |
| Years of residence: | | | | | |
| 10 years or less | 61.2* | 55.5* | 66.9 | 65.9 | 68.4 |
| 11 years to 20 years | 11.5* | 14.8* | 14.9 | 15.9 | 15.8 |
| 21 years to 30 years | 18.8* | 19.2* | 13.2 | 15.9 | 10.5 |
| More than 30 years | 8.5* | 10.4* | 5.0 | 2.3 | 5.3 |
| Employment status: | | | | | |
| Employed | 15.2 | 12.1 | 12.4 | 10.2 | 10.5 |
| Self-employed | 72.7 | 73.6 | 74.4 | 75.0 | 86.8 |
| Unemployed | 12.1 | 14.3 | 13.2 | 14.8 | 2.6 |
| Monthly income: | | | | | |
| No income | 8.5 | 8.8 | 7.4 | 9.1 | 5.3 |
| 20,000 naira or less | 67.9 | 61.0 | 62.0 | 69.3 | 55.3 |
| More than 20,000 | 23.6 | 30.2 | 30.6 | 21.6 | 39.5 |
| Household size: | | | | | |
| 1 member | 8.5 | 8.2 | 5.0 | 9.1 | 13.2 |
| 2 to 4 members | 59.4 | 54.4 | 57.9 | 46.6 | 60.5 |
| 5 or more members | 32.1 | 37.4 | 37.2 | 44.3 | 26.3 |
Regular consumption of vegetables was low among respondents ($36.4\%$). Statistically, there was a significant relationship was found between regular consumption of vegetable and respondents age X2 (2, $$n = 500$$) = 15.72 with the adult respondents significantly consuming more than any other age groups, and also with respondents' years of residence X2 (3, $$n = 500$$) = 21.26 with respondents with 10 or fewer years of residency consuming more vegetables (Table 2).
## Consumption of sugar-sweetened drinks, pastries and food-away-from-home
Respondents' consumption of Sugar-Sweetened Drinks (SSD) was low at $24.2\%$, while female respondents were found to be higher consumers of SSD ($71.1\%$), significantly, Respondents' age X2 (2, $$n = 500$$) = 13.52 was related to SSD consumption, more among adult respondents (Table 2) Pastries consumption was low among respondents ($17.6\%$). While this was significantly related to respondents' age X2 (2, $$n = 500$$) = 12.38, the adults were found to consume more, likewise significantly related to pastries consumption was employment status X2 (4, $$n = 500$$) = 20.11 with self-employed respondents consuming more (Table 2).
Regular eating of food not cooked at home was very low among respondents ($7.6\%$), this is however more pronounced among the male respondents ($78.9\%$), adult ($47.4\%$), married respondents ($71.1\%$), residents of 10 years or less ($68.4\%$), self-employed ($86.6\%$), earners of 20,000 naira or less ($55.3\%$) and household of 2 to 4 members ($60.5\%$). Respondents' age X2 (1, $$n = 500$$) = 48.64 was significantly related to FAFH consumption (Table 2).
## Prevalence of physical activities
The prevalence of physical activities across respondents' demographic characteristics showed that vigorous-intensity physical activity was very low ($15.6\%$) among respondents. Higher vigorous-intensity activities were reported by females ($59\%$), adults ($64.1\%$), and self-employed ($74.4\%$) respondents. A statistically significant relationship was found between vigorous-intensity physical activity and respondents' sex X2 (1, $$n = 500$$) = 6.02 (Table 3). Moderate-intensity physical activity was equally very low ($20.8\%$) among respondents, this was however more pronounced among female respondents ($61.5\%$), adult ($54.8\%$), and self-employed ($72.1\%$) respondents. A statistically significant relationship was found between moderate-intensity physical activity and respondents' sex X2 (1, $$n = 500$$) = 5.20.
**Table 3**
| Variables | vigorous-intensity physical activity | vigorous-intensity physical activity.1 | moderate-intensity physical activity | moderate-intensity physical activity.1 |
| --- | --- | --- | --- | --- |
| | Yes n = 78 (15.6%) % | No n = 422 (84.4%) % | Yes n = 104 (20.8%) % | No n = 396 (79.2%) % |
| Sex | | | | |
| Male | 41.0* | 27.3* | 38.5* | 27.0* |
| Female | 59.0* | 72.7* | 61.5* | 73.0* |
| Age Group | | | | |
| Youth (18 - 25 years) | 17.9 | 24.9 | 25.0 | 23.5 |
| Adult (26 - 45 years) | 64.1 | 52.4 | 54.8 | 54.0 |
| Middle age (46 - 65 years) | 17.9 | 22.7 | 20.2 | 22.5 |
| Employment status: | | | | |
| Employed | 15.4 | 12.3 | 9.6 | 13.6 |
| Self-employed | 74.4 | 72.3 | 72.1 | 72.7 |
| Unemployed | 10.3 | 15.4 | 18.3 | 13.6 |
WHO [2011] recommends 300 minutes per week of moderate-intensity physical activity and 150 minutes per week of vigorous-intensity physical activity for an individual between the ages of 18 years to 64 years old. The mean moderate-intensity physical activity time by respondents was 122 minutes/week (min/wk), which is lower than the recommended 300 min/wk and 236 min/wk of vigorous-intensity physical activity which met the recommended 150 min/wk. There were $87\%$ of physically low active respondents (< 150–300 min/wk).
Male respondents engaged more in moderate and vigorous-intensity physical activity at an average time of 281 min/wk and 309 min/wk respectively; this was significantly different from the female. Youth engaged more in both moderate and vigorous-intensity physical activity at 227 min/wk and 208 min/wk respectively, significantly more than the combined average time recorded by an adult (97 min/wk & 82 min/wk) and middle age (69 min/wk & 54 min/wk) respondents. There was no significant difference in the moderate- and vigorous-intensity physical activities across respondents' education. Respondents earning more than 20,000 naira monthly had a significantly higher moderate-intensity and vigorous-intensity physical activity duration at 144 min/wk and 132 min/wk, although they did not meet either of the WHO recommendations. Physical activity duration was not significantly different for moderate-intensity and vigorous-intensity physical activities across respondents' years of residence, employment status, and household size (Table 4).
**Table 4**
| Variables | Moderate-intensity physical activity (122 ± 325.35) min/week | Vigorous-intensity physical activity (236 ± 742.34) min/week |
| --- | --- | --- |
| Sex | | |
| Male | 281a | 309a |
| Female | 56b | 21b |
| Age Group | | |
| Youth (18 - 25 years) | 227a | 208a |
| Adult (26 - 45 years) | 97b | 82b |
| Middle age (46 - 65 years) | 69b | 54b |
| Education | | |
| No formal education | 109 | 121 |
| Primary school education | 105 | 117 |
| Secondary school education | 118 | 117 |
| College/University education | 77 | 138 |
| Years of residence | | |
| 10 years or less | 125 | 111 |
| 11 years to 20 years | 93 | 111 |
| 21 years to 30 years | 160 | 105 |
| More than 30 years | 93 | 46 |
| Employment status | | |
| Employed | 118 | 160 |
| Self-employed | 118 | 124 |
| Unemployed | 35 | 81 |
| Monthly income | | |
| No income | 25b | 19b |
| 20,000 naira or less | 124ab | 105ab |
| More than 20,000 naira | 144a | 132a |
| Household size | | |
| 1 member | 149 | 159 |
| 2 to 4 members | 120 | 105 |
| 5 or more members | 119 | 96 |
The categorisation of respondents according to their physical activity level with blood pressure and BMI-categorised obesity is shown in Figure 1. Physically low active respondents accounted for the most proportion of obese individuals ($96.1\%$), stage one blood pressure individuals ($90.5\%$), and stage two blood pressure individuals ($93.3\%$).
**Fig 1:** *respondents' physical activity level with blood pressure level and obesity*
## Discussion
The purpose of this study was to estimate the level of physical activity and dietary patterns of residents in urban settlements in Ibadan. The prevalence of low physical activity at $87.2\%$ reported in this study is higher than that reported by Hallal20 at $27.5\%$. Equally, low physical activity over time has been associated with increased risk of chronic diseases36 and individuals at risk are the employed populace, especially white-collar jobs workers. Findings from this study highlighted a low prevalence of workplace moderate-intensity and vigorous-intensity physical activities, and lower among employed respondents. This group of individuals are characterised by prolonged sitting time at work, a behaviour linked to premature death even if regular physical activity is engaged in37.
In a Nigerian study, workplace physical activity was found to be lower among bankers16 and Akindutire et al.38 suggested that these group of individuals are more at risk of chronic diseases associated with low physical activity. In their study, the authors further proposed the need for gaining a better and well-grounded understanding of the dynamics of physical activity in workplaces38. Several studies have however acknowledged that physical activity in the workplace is influenced by the availability of sports facilities and equipment, time constraints, job satisfaction, and their awareness of physical activity benefits39,40. Findings show that physical activity level was found to be below the WHO recommendation of 300 minutes per week. However, youths spent significantly more time in moderate-intensity physical activity (227 min/wk) compared to the older adults. This is rightfully so as the age of 19 to 24 years has been suggested as the best period to inspire and develop regular physical activity behaviour41. Consequences of the low physical activity among the younger generation are mostly shown in the later stages of life42, during which non-communicable diseases sets in. This is creating a shift from communicable disease to more non-communicable disease burden in Nigeria43. Low physical activity behaviour is formed during adolescence into early adult age and deteriorate with age, which corroborates studies conducted by Bauman et al.44 and Sallis et al.45, and as shown that older respondents spent the lowest time engaging in physical activity.
Fruits and vegetables have been estimated to potentially save up to 2.7 million lives if they are sufficiently consumed46; this is attributable to the low glycaemic index which has been associated with low risk for CVDs47. However, regular consumption of fruits and vegetables was low among respondents. According to Ogundari et al.48, income plays a significantly influential role in the consumption of fruits as wealthier households tend to consume more. This was however not in conformity with findings from this study; households earning less than the national minimum wage tend to consume fruits more than the wealthier households. While Powel et al.49 argued that fruits and vegetables are often not simply available to low-income earners due to its high price, studies by Bondoin50 and Hodder51 indicated that high cost does not influence low consumption as with reduced cost, consumption was not significantly increased. A study by Bokeshemi et al.52, stated that consumption of fruits and vegetables was rather also dependent on their local and seasonal availability, social preference, cultural value, and their importance to people.
Healthy diet and exercise are important in the prevention of NCDs in populations. A combination of changes in diet and physical exercise will have resultant effects on Africans who are overweight or obese. An attempt at reducing an unhealthy diet in the absence of high physical activity may prove futile as these phenomena are not independent of each other in the control of cardiovascular diseases.
## Conclusions
This study has brought to light that the prevalence of low physical activity is high among respondents, males were more physically active than females. Findings also show low intake of vegetables and fruits among respondents. Study findings implies that the risk of developing CVDs is potentially higher among females compared to males. The younger respondents were expectedly more physically active than the older adults, a trend that indicates a reduced likelihood of healthy aging. Coupled with low physical activity, respondents' consumption of fruits and vegetables was low; this was significantly lower among employed respondents and with those with lower level of physical activity being at higher risk of the consequences. In conclusion, study findings highlight low physical activity levels in semi-urban areas and also poor dietary patterns as exhibited in the low consumption of fruits and vegetables. This highlights the need for public health interventions to promote healthy diet and active life style among semi-urban areas.
## Study Limitations
This study was cross-sectional and limited to individuals between the age of 18 years and 65 years old in semi-urban settings in a city in Nigeria. Thus, findings may not be generalisable to other settings, including rural communities. This study findings may not be generalisable to other geographical areas in Nigeria due to cultural and socioeconomic diversity. Also, study tools were based primarily on self-report which may give room for bias.
## Ethical approval and consent to participate
This study was approved by the University of Ibadan/University College Hospital Ethical Review Committee, Nigeria and the assigned reference number is UI/EC/$\frac{17}{0410.}$ Informed (by signing an informed consent form) and voluntary consents were sought and obtained from community leaders, stakeholders, and all study respondents, before the commencement of data collection.
## Consent for publication
Not applicable.
## Availability of data and materials
The dataset used and/or analysed during the current study are available from the corresponding author on reasonable request.
## Competing interests
Authors declare no competing interest.
## Funding
This research was supported by a planning grant awarded by the U.S. National Institutes of Health, Fogarty International Center, “Addressing NCDs In Nigeria Through Enhanced International Partnership and Interdisciplinary Research Training,” award number 1D71TW010876-01. The funding body had no role in the design of the study, collection of data, analysis, interpretation of data, and manuscript writing.
## Authors' contribution
The study was conceptualised by OO, YJA, and MO. Data collection and analysis were performed by PA and supervised by OO and YJA. The first draft of the manuscript was written by PA and all authors contributed to previous versions of the manuscript. All authors read and approved the final manuscript.
## References
1. Ortega FB, Ruiz JR, Castillo MJ, Moreno LA, González-Gross M, Wärnberg J, Gutiérrez Á. **Low level of physical fitness in Spanish adolescents. Relevance for future cardiovascular health (AVENA study)**. *Revista Española de Cardiología (English Edition)* (2005.0) **58** 898-909
2. Palomo IF, Torres GI, Alarcón MA, Maragaño PJ, Leiva E, Mujica V. **High prevalence of classic cardiovascular risk factors in a population of university students from south central Chile**. *Revista Española de Cardiología (English Edition)* (2006.0) **59** 1099-105
3. Tomkinson GR, Olds TS. **Secular changes in pediatric aerobic fitness test performance: the global picture**. *InPediatric Fitness* (2007.0) **50** 46-66
4. Ejechi EO, Ogege SO. **Socio-demographic, health-related and physical environmental correlates of physical activity among urban-dwelling Nigerians in early old age**. *International Journal of Development and Sustainability* (2015.0) **4**
5. Reiner M, Niermann C, Jekauc D, Woll A. **Long-term health benefits of physical activity-a systematic review of longitudinal studies**. *BMC Public Health* (2013.0) **13** 813. PMID: 24010994
6. Biswas A, Oh PI, Faulkner GE, Bajaj RR, Silver MA, Mitchell MS, Alter DA. **Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: a systematic review and meta-analysis**. *Annals of Internal Medicine* (2015.0) **162** 123-132. PMID: 25599350
7. Firth J, Stubbs B, Rosenbaum S, Vancampfort D, Malchow B, Schuch F, Elliott R, Nuechterlein KH, Yung AR. **Aerobic exercise improves cognitive functioning in people with schizophrenia: a systematic review and meta-analysis**. *Schizophrenia Bulletin* (2017.0) **43** 546-556. PMID: 27521348
8. Schuch FB, Vancampfort D, Richards J, Rosenbaum S, Ward PB, Stubbs B. **Exercise as a treatment for depression: a meta-analysis adjusting for publication bias**. *Journal of Psychiatric Research* (2016.0) **77** 42-51. PMID: 26978184
9. 9World Health OrganizationGlobal status report on noncommunicable diseases 20142014World Health Organization. *Global status report on noncommunicable diseases 2014* (2014.0)
10. Abegunde DO, Mathers CD, Adam T, Ortegon M, Strong K. **The burden and costs of chronic diseases in low-income and middle-income countries**. *The Lancet* (2007.0) **370** 1929-1938
11. Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. **Effect of low physical activity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy**. *The Lancet* (2012.0) **380** 219-229
12. Abubakari AR, Bhopal RS. **Systematic review on the prevalence of diabetes, overweight/obesity and low physical activity in Ghanaians and Nigerians**. *Public health* (2008.0) **122** 173-182. PMID: 18035383
13. Bamiro OM. **Determinants of consumption of food away from home in Lagos Metropolis, Nigeria**. *Research Journal of Social Sciences and Management* (2012.0) **2** 15-23
14. Variyam JN. **Nutrition labeling in the food-away-from-home sector: an economic assessment**. *USDA-ERS Economic Research Report* (2005.0)
15. Fan H, Li X, Zheng L, Chen X, Wu H, Ding X, Qian D, Shen Y, Yu Z, Fan L, Chen M. **Abdominal obesity is strongly associated with Cardiovascular Disease and its Risk Factors in Elderly and very Elderly Community-dwelling Chinese**. *Scientific Reports* (2016.0) **6** 1-9. PMID: 28442746
16. Ogungbenle ER, Omeonu PE, Aduroja PE, Kukoyi OB. **Workplace Health Promotion in Selected Banks of Mushin Local Government Area, Nigeria**. *Journal of Scientific Research & Reports* (2017.0) **16**
17. Sobngwi E, Mbanya JN, Unwin NC, Kengne AP, Fezeu L, Minkoulou EM, Aspray TJ, Alberti KG. **Physical activity and its relationship with obesity, hypertension and diabetes in urban and rural Cameroon**. *International Journal of Obesity* (2002.0) **26** 1009. PMID: 12080456
18. Ejechi EO. **A Comparative study of physical activity and cognitive function of a sample of elderly Nigerians living in a rural and an urban area**. *International Journal of Humanities and Social Sciences* (2013.0) **3** 140-150
19. Assah FK, Ekelund U, Brage S, Mbanya JC, Wareham NJ. **Urbanization, physical activity, and metabolic health in sub-Saharan Africa**. *Diabetes Care* (2011.0) **34** 491-496. PMID: 21270205
20. Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U. **Global physical activity levels: surveillance progress, pitfalls, and prospects**. *The Lancet* (2012.0) **380** 247-257
21. Oyeyemi AL, Oyeyemi AY, Jidda ZA, Babagana F. **Prevalence of physical activity among adults in a metropolitan Nigerian city: a cross-sectional study**. *Journal of Epidemiology* (2013.0) JE20120116
22. Akarolo-Anthony SN, Adebamowo CA. **Prevalence and correlates of leisure-time physical activity among Nigerians**. *BMC Public Health* (2014.0) **14** 529. PMID: 24885080
23. Oyeyemi AL, Oyeyemi AY, Omotara BA, Lawan A, Akinroye KK, Adedoyin RA, Ramírez A. **Physical activity profile of Nigeria: implications for research, surveillance and policy**. *The Pan African Medical Journal* (2018.0) 30
24. 24World Health OrganizationNon-communicable diseases (NCD) country profiles 20142015World Health Organization. *Non-communicable diseases (NCD) country profiles 2014* (2015.0)
25. Barnes AS. **Obesity and sedentary lifestyles: risk for cardiovascular disease in women**. *Texas Heart Institute Journal* (2012.0) **39** 224. PMID: 22740737
26. Bolívar J, Daponte A, Rodríguez M, Sánchez JJ. **The influence of individual, social and physical environment factors on physical activity in the adult population in Andalusia, Spain**. *International Journal of Environmental Research and Public Health* (2010.0) **7** 60-77. PMID: 20195433
27. Ding D, Sallis JF, Kerr J, Lee S, Rosenberg DE. **Neighborhood environment and physical activity among youth: a review**. *American Journal of Preventive Medicine* (2011.0) **41** 442-455. PMID: 21961474
28. Moran M, Van Cauwenberg J, Hercky-Linnewiel R, Cerin E, Deforche B, Plaut P. **Understanding the relationships between the physical environment and physical activity in older adults: a systematic review of qualitative studies**. *International Journal of Behavioral Nutrition and Physical Activity* (2014.0) **11** 79. PMID: 25034246
29. Solomon E, Rees T, Ukoumunne OC, Metcalf B, Hillsdon M. **Personal, social, and environmental correlates of physical activity in adults living in rural south-west England: a cross-sectional analysis**. *International Journal of Behavioral Nutrition and Physical Activity* (2013.0) **10** 129. PMID: 24261335
30. Oyeyemi AL, Adegoke BO, Sallis JF, Oyeyemi AY, De Bourdeaudhuij I. **Perceived crime and traffic safety is related to physical activity among adults in Nigeria**. *BMC Public Health* (2012.0) **12** 294. PMID: 22520066
31. Sun Q, Townsend MK, Okereke OI, Franco OH, Hu FB, Grodstein F. **Physical activity at midlife in relation to successful survival in women at age 70 years or older**. *Archives of Internal Medicine* (2010.0) **170** 194-201. PMID: 20101015
32. Almeida OP, Khan KM, Hankey GJ, Yeap BB, Golledge J, Flicker L. **150 minutes of vigorous physical activity per week predicts survival and successful ageing: a population-based 11-year longitudinal study of 12 201 older Australian men**. *Br J Sports Med* (2014.0) **48** 220-225. PMID: 24002240
33. Daskalopoulou C, Stubbs B, Kralj C, Koukounari A, Prince M, Prina AM. **Physical activity and healthy ageing: A systematic review and meta-analysis of longitudinal cohort studies**. *Ageing Research Reviews* (2017.0) **38** 6-17. PMID: 28648951
34. Jezewska-Zychowicz M, Gębski J, Plichta M, Guzek D, Kosicka-Gębska M. **Diet-related factors, physical activity, and weight status in Polish adults**. *Nutrients* (2019.0) **11** 2532. PMID: 31640114
35. Liebman M, Pelican S, Moore SA, Holmes B, Wardlaw MK, Melcher LM, Liddil AC, Paul LC, Dunnagan T, Haynes GW. **Dietary intake, eating behavior, and physical activity-related determinants of high body mass index in rural communities in Wyoming, Montana, and Idaho**. *International Journal of Obesity* (2003.0) **27** 684-692. PMID: 12833112
36. 36World Health OrganizationWorld health statistics 20092009World Health Organization. *World health statistics 2009* (2009.0)
37. Patel AV, Bernstein L, Deka A, Feigelson HS, Campbell PT, Gapstur SM, Colditz GA, Thun MJ. **Leisure time spent sitting in relation to total mortality in a prospective cohort of US adults**. *American Journal of Epidemiology* (2010.0) **172** 419-429. PMID: 20650954
38. Akindutire OI, Adegboyega AJ. **Participation in leisure-time physical activity by public servants in ekiti state, nigeria**. *European Journal of Social Sciences* (2012.0) **31** 251-260
39. Omolawon KO, Sanusi M. **Perceived determinants associated with non-participation of University of Ibadan academic staff in sports and PA**. *Journal of International council for Health, Physical Education, Recreation, Sport and Dance* (2006.0) **1** 97-100
40. Olorunsola EO. **Job satisfaction and personal characteristics of administrative staff in South West Nigeria Universities**. *Journal of Emerging Trends in Educational Research and Policy Studies* (2012.0) **3** 46-50
41. Oyeyemi AL, Muhammed S, Oyeyemi AY, Adegoke BO. **Patterns of objectively assessed physical activity and sedentary time: Are Nigerian health professional students complying with public health guidelines?**. *PloS One* (2017.0) **12** e0190124. PMID: 29281683
42. Spring B, Moller AC, Colangelo LA, Siddique J, Roehrig M, Daviglus ML, Polak JF, Reis JP, Sidney S, Liu K. **Healthy lifestyle change and subclinical atherosclerosis in young adults: Coronary Artery Risk Development in Young Adults (CARDIA) study**. *Circulation* (2014.0) **130** 10-17. PMID: 24982115
43. Aliyu SU, Chiroma AS, Jajere AM, Gujba FK. **Prevalence of low physical activity, hypertension, obesity and tobacco smoking: a case of NCDS prevention among adults in Maiduguri, Nigeria**. *Am J Med Sci* (2015.0) **3** 39-47
44. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJ, Martin BW. **Correlates of physical activity: why are some people physically active and others not?**. *The Lancet* (2012.0) **380** 258-271
45. Sallis JF, Bull F, Guthold R, Heath GW, Inoue S, Kelly P, Oyeyemi AL, Perez LG, Richards J, Hallal PC. **Lancet Physical Activity Series 2 Executive Committee. Progress in physical activity over the Olympic quadrennium**. *The Lancet* (2016.0) **388** 1325-1336
46. Chubike NE, Okaka JC, Okoli EC. **Evaluation of vegetable consumption in South Eastern Nigeria**. *International Journal of Nutrition and Metabolism* (2013.0) **5** 57-60
47. 47World Health OrganizationGlobal strategy on diet, physical activity and health. *Global strategy on diet, physical activity and health*
48. Ogundari K, Arifalo SF. **Determinants of household demand for fresh fruit and vegetable in Nigeria: A double hurdle approach**. *Quarterly Journal of International Agriculture* (2013.0) **52** 199-216
49. Powell LM, Zhao Z, Wang Y. **Food prices and fruit and vegetable consumption among young American adults**. *Health & Place* (2009.0) **15** 1064-1070. PMID: 19523869
50. Bondoin W. *production interventions to improve the availability of and access to fruit and vegetables for all* (2004.0)
51. Hodder A. *A global initiative and fruit and vegetable; FAO's interdisciplinary approach to the promotion of fruit and vegetable and the element of framework for action WHO report* (2004.0)
52. Bokeshemi SOC, Njoku JE. *The Nigerian Agricultural Environment on the Supply, Availability, Prices and Consumption of Fruits and Vegetables: A Case Study of Imo State A paper presented at 32nd Annual Conference of the Agricultural society of Nigeria held at the Federal College of Agriculture Isiagu, Ebonyi State* (1997.0)
|
---
title: Berberis lyceum root bark extract attenuates anticancer drugs induced neurotoxicityand
cardiotoxicity in rats
authors:
- Sidra Qurat-ul-Ain
- Anwar Rukhsana
- Sahar Isma Tariq
- Ashiq Kanwal
journal: African Health Sciences
year: 2022
pmcid: PMC9993256
doi: 10.4314/ahs.v22i3.22
license: CC BY 4.0
---
# Berberis lyceum root bark extract attenuates anticancer drugs induced neurotoxicityand cardiotoxicity in rats
## Abstract
### Background
Traditionally, *Berberis lyceum* was extensively used for the treatment of several human diseases.
### Objective
This study was undertaken to determine in vivo effects of *Berberis lyceum* root bark against doxorubicin-induced cardiotoxicity and cisplatin-induced neurotoxicity in Sprague Dawley rats.
### Methods
A single dose of doxorubicin (20 mg/ kg i. p) and cisplatin (4mg/kg i.p) was used to induce cardiotoxicity and neurotoxicity, respectively. Berberis lyceum methanolic extract was given orally (200 and 400 mg/ kg) to toxicity-induced rats. The cardiac biomarkers i.e. serum aspartate aminotransferase, alanine transaminase, lactate dehydrogenase, creatine kinase and creatine kinase MB were analyzed in blood collected from cardiotoxic rats. The tissue oxidative stress markers included protein, glutathione s-transferase specific activity, catalase activity, total glutathione, and malondialdehyde levels were measured in cardiac and brain homogenate of the respective groups.
### Results
Berberis lyceum methanolic extract has the potential to reduce the doxorubicin-induced cardiotoxicity and cisplatin-induced neurotoxicity significantly (*$p \leq 0.05$) by reducing the serum markers and oxidative stress parameters. Histopathological analysis exhibited a marked improvement in the morphology of cardiac and brain tissues.
### Conclusion
It is concluded that methanolic extract of *Berberis lyceum* root bark has the potential to protect and reverse anticancer drugs induced cardiotoxicity and neurotoxicity.
## Introduction
Cancer is a neoplastic disorder that is a chief cause of death in many developed and emerging countries. The global economic burden of cancer is substantially raised and due to this disease, 7.6 million ($13\%$) deaths are reported annually. For many years, medicinal plants have been used to cure or prevent a diverse range of disorders. There is a sufficient number of scientific literature is available that verified the anticancer potential of medicinal plants due to the presence of various bioactive chemicals.1 *Chemoprevention is* defined as either avoid or undo the toxic effect (neoplasia genesis) of a chemical by using another chemical agent. It is documented that the plant bioactive compounds provide protection against tumor genesis by several mechanisms such as instigation of apoptosis, reducing free radical production and oxidative stress, inhibition of cell proliferation, and interrupting the cancer cell cycle process. In previous studies, it is acknowledged that the concomitant use of antioxidant phytochemicals during chemotherapy can protect against anticancer drugs-related side effects.2 Doxorubicin (DOX) is a highly effective drug used to treat cancers like leukemia3, connective tissue cancers4, breast cancer 5, and lung cancer 6. However, its application is limited due to its cardiac targeted toxicity. Doxorubicin can induce cardiotoxicity by several mechanisms, including free radical generation, calcium excess, dysfunctional mitochondria, a mutation in gene expression, apoptosis, and causing defects in the natural immune system.7 Despite multifactorial doxorubicin-induced cardiotoxicity, oxidative stress plays a vital role to instigate cardiac injury. Doxorubicin produces oxidative stress by the generation of free radicals in the body. These free radicals are generated by the addition of electrons to doxorubicin quinone moiety, which rapidly regenerates to its parent structure by reducing oxygen to the superoxide anion and hydrogen peroxide (H2O2).8,9 The dismutation of the superoxide is catalyzed by acidic pH or superoxide dismutase (SOD) enzyme. As H2O2 is a less toxic molecule, it is eliminated by an enzymatic antioxidative defense system consisting of catalase and glutathione peroxidase.10 Further, a secondary metabolite called doxorubicinol which is formed by the reduction of the carbonyl group of doxorubicin also promotes heart toxicity.11 *Cisplatin is* a platinum-based chemical and widely used to cure various malignancies. It exerts its antineoplastic effects by the formation of DNA adducts, by induction of oxidative stress, and by triggering apoptosis in tumor cells. Cisplatin being an inert molecule requires activation before interacting with nucleophilic target sites. A non-enzymatic water-based reaction activates cisplatin into a charged molecule.12 Mitochondria are primary targets for cisplatin-induced toxicity. Mitochondrial DNA adducts are formed upon the interaction of activated cisplatin with mitochondrial DNA, resulting in direct damage, i.e. reduced protein synthesis, impaired electron transport chain function, oxidative stress, and activation of intracellular apoptosis.13 *Neurotoxicity is* the onerous side effect associated with cisplatin use. Cisplatin possibly causes neurotoxicity due to many reasons such as oxidative stress, deposition of cisplatin adduct in neurons, and inhibition of DNA repairing mechanisms mainly in peripheral neurons and dorsal root ganglia neurons. This menacing side effect leads to either cisplatin dose reduction or early discontinuation of therapy that can badly affect the patient's life.14 *Berberis lyceum* Royle belongs to the family Berberidaceae and it is a common medicinal plant found in Pakistan. Berberis lyceum fruit is known as “Kashmal” while the root is called “Darhald”.15 Traditionally, *Berberis lyceum* was extensively used for the treatment of several human diseases. The literature review reveals that it possesses antihyperlipidemic 16, anti-diabetic 17, antimicrobial, antifungal18, antioxidant and nephron-protective activity.19 *Berberis lyceum* contains valuable phytochemicals, i.e. alkaloids (berberine, berbamine, palmitine), hydrolysable tannins, cardioactive glycosides, and saponins which are responsible for its various pharmacological effects.20
The study aimed to evaluate the protective effects of *Berberis lyceum* root bark against doxorubicin-induced cardiotoxicity and cisplatin-induced neurotoxicity in Sprague Dawley rats because previous no such study was found through an extensive literature survey.
## Chemicals
Doxorubicin (Brand name: Adrim) intravenous infusion was purchased from Atco Pharma Laboratories (Pvt). Ltd. Anthrone reagent was obtained from Sigma life sciences, Germany. Folin and Ciocalteu's phenol reagent and Triton X acquired from Unichem chemicals, Ireland. Vitamin E was procured from Merck Pvt. Ltd. For aspartate transaminase (AST) and alanine transaminase (ALT) analysis, kits by Singapore Biosciences (SBio) PTE Ltd. were used. The kits by Randox Laboratories Ltd. were used to estimate lactate dehydrogenase (LDH), creatine kinase (CK), and creatine kinase myocardial band (CKMB). All chemicals were analytical grade.
## Extraction of Berberis lyceum Royle root bark
Berberis lyceum dried root bark was powdered. 250 g of powder was soaked in 800 mL of methanol and macerated for 6 days with intermittent stirring. This mixture was filtered and residues were again soaked in methanol for 3 days. The filtrate was pooled and dried by a rotary evaporator at 45°C. A total of 25 g of *Berberis lyceum* methanol extract (MEBL) was obtained. The extract was stored at 4 – 8°C.
## Chemical composition of Berberis lyceum methanolic extract
Qualitative phytochemical screening of *Berberis lyceum* methanolic extract was performed to detect the presence of various chemical compounds such as glycosides, saponins, tannins, alkaloids, flavonoids, triterpenoids, carbohydrates, proteins, fats and, fixed oils.
The quantitative analysis of the phytochemicals (primary and secondary metabolites) was also performed according to the standard procedures. Primary metabolites estimation was included total protein 21, total lipids 22, and total carbohydrates 23. Secondary metabolites were also determined such as total polyphenols 24, total flavonids 25, total glycosaponins and, total polysaccharides.26 *Berberis lyceum* qualitative and quantitative phytochemical screening (Table 1 and Table 2) revealed that plant root contains many important primary and secondary metabolites, i.e. proteins, lipids, carbohydrates, alkaloids, tannins, saponins, flavonoids, and polyphenols.
## Animals
Sprague Dawley rats were procured from the University of Health Sciences (UHS) Lahore. The animals were handled as per guidelines of an Animal Ethics Committee of University College of Pharmacy (PUCP), University of the Punjab Lahore, Pakistan. The committee reviewed and approved the experiment protocol (issued voucher no: AEC/PUCP/1051). The rats were acclimatized and maintained under controlled conditions; temperature 25°C ± 2 and $60\%$ relative humidity with a $\frac{12}{12}$ hour dark and light cycle. Animals were provided a standard diet and water ad libitum.
## Experimental protocol
For the in vivo study, animals were randomly selected from the animal house. The extract was dissolved in distilled water (DW) and vortexed to get a homogenous mixture. Animals were grouped as the following scheme having 6 rats in each group: GROUP 1 (Control): Rats were given water as a vehicle for 21 days.
GROUP 2 (DOX treated): Animals were given vehicle for 21 days and on the 19th day, a single dose of doxorubicin 20 mg/ kg body weight (BW) was administered intraperitoneally.
GROUP 3 (MEBL-200): Animals were administered *Berberis lyceum* methanol extract 200 mg/ kg BW through oral gavage for 21 days.
GROUP 4 (MEBL-400): Animals were administered 400 mg/ kg BW of *Berberis lyceum* methanol extract through oral gavage for 21 days.
GROUP 5 (DOX+MEBL-200): Rats were administered 200 mg/kg BW of *Berberis lyceum* methanol extract through oral gavage for 21 days. On the 19th day, a single injection of doxorubicin 20 mg/ kg was co-administered intraperitoneally.
GROUP 6 (DOX+MEBL-400): Animals were administered 400 mg/ kg BW of *Berberis lyceum* methanol extract through oral gavage for 21 days. On the 19th day, a single injection of doxorubicin 20 mg/ kg BW was co-administered intraperitoneally.
GROUP 7 (DOX+Vitamin E): Group was administered vitamin E 100 mg/ kg BW through oral gavage for 21 days. On the 19th day, a single injection of doxorubicin 20 mg/ kg BW was co-administered intraperitoneally GROUP 8 (Cisplatin treated): Animals were given vehicle for 21 days and on the 19th day, a single dose of cisplatin 4 mg/ kg BW was administered intraperitoneally.
GROUP 9 (Cisplatin+MEBL-200): Rats were administered 200 mg/kg BW of *Berberis lyceum* methanol extract through oral gavage for 21 days. On the 19th day, a single injection of cisplatin 4mg/ kg BW was co-administered intraperitoneally.
GROUP 10 (Cisplatin+MEBL-400): Animals were administered 400 mg/ kg BW of *Berberis lyceum* methanol extract through oral gavage for 21 days. On the 19th day, a single injection of cisplatin 4 mg/ kg BW was co-administered intraperitoneally.
Blood samples from each rat were taken after 24 hours of the last dose of doxorubicin and cisplatin. All the rats were sacrificed and the heart from cardiotoxic and brain from neurotoxic group rats was removed. A small section of the heart and brain from each group were preserved in $10\%$ formalin for histopathological examination. All the tissue samples were stored at -80°C for further analysis.
## Preparation of blood serum
The blood samples from rats were taken by cardiac puncture and collected in the serum gel separating tube. Then these samples were allowed to stand at 25°C for 30 minutes and subsequently centrifuged at 3000 rpm for about 15 minutes. The clear serum was collected from the gel top while cell debris settled down in the gel. Serum was transferred to labeled tubes and refrigerated at -80°C for future experimentation.
Preparation of heart homogenate and brain homogenate Pooled hearts were finely chopped in phosphate buffer through scissors. Heart tissues were then homogenized with the help of a homogenizer then centrifuged at 12,500 × g at 4°C for 20 minutes. The final post mitochondrial supernatant (PMS) was carefully transferred to Eppendorf 's tubes and stored at -80°C for further analysis.
## Estimation of biochemical parameters
The serum biochemical markers in cardiotoxic groups included aspartate transaminase (AST), creatine kinase (CK), alanine transaminase (ALT), lactate dehydrogenase (LDH), creatine kinase MB (CK-MB) were measured according to the procedure specified within the kits. The Lowry method was applied for the estimation of total protein in PMS.21
## Glutathione s-transferase (GST) assay
The GST-specific activity was estimated by Habig's method with slight modification. For GST-specific activity, the reaction mixture contained post mitochondrial supernatant of brain and heart, 100 mM phosphate buffer (pH 6.5), 30 mM GSH, and 30 mM CDNB. Change in the absorbance was measured at 340 nm on a spectrophotometer for 5 minutes. GST activity expressed in µmol/minute/mg protein.27
## Glutathione (tGSH) assay
Total GSH content was determined by Sedlak and Lindsay method with slight modification. Heart and brain tissues were homogenized in 67 mM phosphate buffer (pH 7.4) separately. $25\%$ trichloroacetic acid added to each homogenate and centrifuged at 4,200 rpm for 40 minutes. Afterward, supernatants of each sample were separated. 200 mM Tris HCl buffer containing 0.2 M EDTA (pH 7.5), 10 mM DTNB, and methanol added to each supernatant. Then, the reaction mixture was incubated for 30 minutes at 37°C. The absorbance of the final yellow solution was recorded on a spectrophotometer at wavelength 412 nm.28
## Catalase analysis for heart and brain tissues
Catalase activity measured according to Sinha prescribed method with minor modification. The tissue homogenate (heart/ brain) was vortexed with 0.01 M phosphate buffer (pH 7) and freshly prepared 0.2 M H2O2 Dichromate/acetic acid added to the reaction mixture. Then, this reaction mixture heated for 10 minutes till permanent green color appeared. The absorbance was taken at 570 nm at room temperature, and catalase activity was measured directly from the standard curve.29
## Malondialdehyde (MDA) assay
Malondialdehyde (MDA) test was carried out by using thiobarbituric (TBA) assay followed by Ohkawa method with a slight alteration in the experimental steps. Heart and brain tissue samples (1mg) were homogenized in $1.2\%$ KCl separately. To homogenate, $20\%$ acetic acid, $8\%$ sodium lauryl sulfate, $20.8\%$ TBA, and distilled water were added. The reaction mixture was incubated for 1 hour at 98°C. After incubation, butanol: pyridine (15:1) was added to the mixture and centrifuged for 30 min (4,000 rpm). The absorbance of the resultant supernatant was recorded at 532nm. The concentration of MDA (mM/g tissue) was directly calculated from the MDA standard curve.30
## Histopathological studies
$10\%$ formalin solution was used to fix each heart and brain tissue. The specimens were handled as per standard procedure and embedded in paraffin wax. The blocks were sectioned and stained using the hematoxylin-eosin (H & E) method and examined by light microscopy.31
## Statistical analysis
Graph Pad Prism version 7.01 software was used for the statistical computation of results. Analysis was done by using unpaired t-test, one-way ANOVA, and by Dunnett's test. All data expressed in mean standard deviation (Mean ± SD) and *$P \leq 0.05$ was considered statistically significant.
## Results
The effect of *Berberis lyceum* on doxorubicin-induced cardiotoxicity and cisplatin-induced neurotoxicity in rats was estimated by measuring serum markers, tissue markers, and by histopathology of the heart and brain tissues. *The* general appearance of all groups of animals was observed and noted throughout the study. In the doxorubicin treated group, there was drastic weight loss and fur had a pink tinge. Rats had soft watery feces and red exudates also appeared on the sides of the eyes and nose. These observations were significantly less in concurrent administration therapy (doxorubicin and *Berberis lyceum* treated group).
## Effect of Berberis lyceum methanolic extract on serum AST (U/L)
The study outcomes showed that serum AST level in the doxorubicin treated group was significantly (*$p \leq 0.05$) increased (240.66 ± 8.32 U/L) than the control group (148.37 ± 11.5 U/L). The methanolic extract groups (MEBL-200 and MEBL-400) demonstrated an insignificant change in the AST level against the control. The concurrent administration of the doxorubicin and *Berberis lyceum* methanolic extract significantly decreased the AST level as compared to the doxorubicin treated group. For group 5 and group 6, a decrease in the AST level: 166.03 ± 8.58 U/L and 150.21 ± 4.49 U/L was reckoned, respectively. The co-administration of the doxorubicin and vitamin E also showed a significant drop (152.47 ± 8.01 U/L) in the AST level (Figure 1A).
**Figure 1:** *Effect of Berberis lyceum methanolic extract: serum AST (A), serum ALT (B), Creatine kinase (C), serum CK-MB (D), LDH (E), PMS protein content (F) in doxorubicin-induced cardiotoxicity. The result analysis was conducted by using one-way ANOVA followed by Dunnett's test. Values expressed in Mean ± SD, (n=6) and p < 0.05 was considered significant.*
## Effect of Berberis lyceum methanolic extract on serum ALT (U/L)
This study has exhibited that the ALT level in the doxorubicin treated group was significantly (*$p \leq 0.05$) increased (77.07 ± 2.8 U/L) than the control group (40.6 ± 4.83 U/L). The methanolic extract groups (MEBL-200 and MEBL-400) showed an insignificant change in ALT level as compared to the control. The co-administration of doxorubicin and *Berberis lyceum* methanolic extract significantly reduced the ALT level in comparison with the doxorubicin-treated group. For group 5 and group 6, a decrease in the ALT level: 50.77 ± 1.93 U/L and 43.7 ± 2.20 U/L was observed, respectively. The concurrent administration of the doxorubicin and vitamin E displayed a significant reduction (42.97 ± 2.54 U/L) in the ALT level (Figure 1B).
## Effect of Berberis lyceum methanolic extract on serum CK (U/L)
The present study results revealed that serum CK level in the doxorubicin treated group was significantly (#$p \leq 0.05$) increased (497.7 ± 8.91 U/L) than the control group (156.23 ± 5.26 U/L). The methanolic extract groups (MEBL-200 and MEBL-400) showed an insignificant change in serum CK level in comparison to the control. The concurrent administration of the doxorubicin and *Berberis lyceum* methanolic extract significantly decreased CK level against the doxorubicin-treated group. For group 5 and group 6, a decrease in the CK level: 223 ± 20.25 U/L and 206.2 ± 13.86 U/L was noticed, respectively. The co-administration of the doxorubicin and vitamin E also showed a significant decrease (213.47 ±16.3 U/L) in serum CK level (Figure 1C).
## Effect of Berberis lyceum methanolic extract on serum CK- MB (U/L)
The current investigation demonstrated that serum CK-MB level in the doxorubicin treated group was significantly (*$p \leq 0.05$) increased (206.16 ± 1.48 U/L) than the control group (65.93 ± 2.86 U/L). The methanolic extract groups (MEBL-200 and MEBL-400) showed an insignificant change in serum CK-MB level as compared to the control. The concomitant administration of the doxorubicin and *Berberis lyceum* methanolic extract significantly decreased the CK- MB level against the doxorubicin-treated group. For group 5 and group 6, a decrease in the CK-MB level: 140.73 ± 1.92 U/L and 116.1 ± 1.56 U/L was seen, respectively. The co-administration of the doxorubicin and vitamin E manifested a significant reduction (115.93 ±2.89 U/L) in serum CK-MB level as compared to the doxorubicin-treated group (Figure 1D).
## Effect of Berberis lyceum methanolic extract on serum LDH (U/L)
This research outcome showed that serum LDH level in the doxorubicin-treated group was significantly (*$p \leq 0.05$) increased (837.43 ±8.44 U/L) than the control group (281.97 ± 23.2 U/L). The methanolic extract groups (MEBL-200 and MEBL-400) illustrated an insignificant change in serum LDH level contrasted with the control. The co-administration of doxorubicin and *Berberis lyceum* methanolic extract significantly decreased the LDH level as compared to the doxorubicin-treated group. For group 5 and group 6, a decrease in the LDH level: 588.5 ± 14.16 U/L and 395.27±14.70 U/L was observed, respectively. The concomitant use of the doxorubicin and vitamin E also exhibited a significant drop-off (381.07±14.69 U/L) in serum LDH level (Figure 1E).
## Effect of Berberis lyceum methanolic extract on serum protein content (mg/mL)
The current study results showed that the protein content in the doxorubicin-treated group was significantly (# $p \leq 0.05$) decreased (39.13 ± 0.24 mg/mL) than the control group (68.07 ± 0.74 mg/mL). Berberis lyceum methanolic extract groups (MEBL-200 and MEBL-400) showed an insignificant change in protein content against the control. The parallel administration of the doxorubicin and *Berberis lyceum* methanolic extract significantly increased protein content over the doxorubicin-treated group. For group 5 and group 6, the respective increase in protein content was calculated as 68.43 ± 0.69 and 60.13 ± 0.25. The co-administration of the doxorubicin and vitamin E also showed a significant increase (60.80 ± 0.33 mg/mL) in protein content (Figure 1F).
The protein content in the cisplatin-treated group was significantly (*$p \leq 0.05$) decreased (28.9 ± 1 mg/mL) than the control group (68.07 ± 0.74 mg/mL). Berberis lyceum methanolic extract groups (MEB-200 and MEBL-400) have indicated an insignificant change in protein content as compared to the control.he simultaneous administration of the cisplatin and *Berberis lyceum* methanolic extract significantly increased protein content in contrast with the cisplatin-treated group. For group 5 and group 6, the respective increase in protein content 43.7 ± 1.4 mg/mL and 44.9 ± 1.2 mg/mL was noticed. The concurrent administration of the cisplatin and vitamin E also demonstrated a significant increase (47.2 ± 0.3 mg/mL) in protein content (Figure 2).
**Figure 2:** *Effect of Berberis lyceum methanolic extract on PMS protein content in cisplatin-induced neurotoxicity. The result analysis was conducted by using one-way ANOVA followed by Dunnett's test. Values expressed in Mean ± SD, (n=6) and p < 0.05 was considered significant*
## Effect of Berberis lyceum methanolic extract on serum GST specific activity (µmol/min/mg)
This study showed that the GST specific activity in the doxorubicin-treated group was significantly (*$p \leq 0.05$) decreased (0.027 ± 0.006 µmol/min/mg) than the control group (0.068 ± 0.003 µmol/min/mg). The methanolic extract groups (MEBL-200 and MEBL-400) revealed an insignificant change in GST activity as compared to the control. The synchronous administration of the doxorubicin and *Berberis lyceum* methanolic extract significantly (*$p \leq 0.05$) enhanced the GST activity as compared to the doxorubicin-treated group. For group 5 and group 6, the increase in the GST activity was found to be 0.052 ± 0.001 µmol/min/mg and 0.056 ± 0.002 µmol/min/mg, respectively. The co-administration of the doxorubicin and vitamin E also exhibited a significant increase (0.057 ± 0.002 µmol/min/mg) in the GST activity as shown in Table 3.
**Table 3**
| GROUPS | GST (µM/min/mg protein) | MDA (µM/g tissue) | tGSH (mM/g tissue) | CAT (µM/g tissue) |
| --- | --- | --- | --- | --- |
| CONTROL | 0.068±0.003 | 14.2 ± 0.3 | 2.25±0.053 | 0.47± 0.12 |
| DOX | 0.027±0.006# | 38.01 ± 0.2# | 0.54±0.024# | 0.29 ±0.13# |
| MEBL200 | 0.075±0.075 | 14.13 ± 0.30 | 2.28±0.04 | 0.52 ±0.145 |
| MEBL400 | 0.08±0.005 | 13.93 ± 0.25 | 2.33±0.073 | 0.53 ± 0.096 |
| DOX+MEBL200 | 0.052±0.001* | 22.3 ± 0.46* | 1.79±0.03* | 0.37 ± 0.085* |
| DOX+MEBL400 | 0.056±0.002* | 18.76 ± 0.3* | 1.98±0.03* | 0.48 ± 0.160* |
| DOX+VIT-E | 0.057±0.002* | 18.4 ± 0.34* | 2.04 ±0.06* | 0.45±0.068* |
The GST specific activity in the cisplatin-treated group was significantly (*$p \leq 0.05$) decreased (0.011 ± 0.01 µmol/min/mg) than the control group (0.058 ± 0.01 µmol/min/mg). The methanolic extract treated groups (MEBL-200 and MEBL-400) showed an insignificant change in the GST activity as compared to the control. The concurrent administration of the cisplatin and *Berberis lyceum* methanolic extract significantly enhanced GST activity as compared to the cisplatin-treated group. For group 5 and group 6, an increase in the GST activity 0.043 ± 0.01 µmol/min/mg and 0.049 ± 0.01 µmol/min/mg was estimated, respectively. The parallel administration of the cisplatin and vitamin E exhibited a marked increase (0.055 ± 0.01 µmol/min/mg) in the GST activity (Table 4).
**Table 4**
| Groups | GST(µM/min/mg protein) | MDA (µM/g tissue) | tGSH (mM/g tissue) | CAT (µM/g tissue) |
| --- | --- | --- | --- | --- |
| Control | 0.058±0.01 | 88.4 ± 2.9 | 1.25±0.04 | 0.66 ± 0.02 |
| Cisplatin | 0.011±0.01# | 183.9 ± 3.3# | 0.48±0.06# | 0.32±0.02# |
| MEBL200 | 0.06±0.01 | 84.4 ± 1.0 | 1.26±0.06 | 0.67 ±0.01 |
| MEBL400 | 0.061±0.01 | 83.7 ± 1.0 | 1.29±0.03 | 0.68 ±0.02 |
| Cisplatin +MEBL200 | 0.043±0.01* | 93.4±0.6* | 0.98±0.1* | 0.59± 0.01* |
| Cisplatin +MEBL400 | 0.049±0.01* | 91.4±0.4* | 1.06±0.05* | 0.61± 0.02* |
| Cisplatin +VIT-E | 0.055±0.01* | 89.0±2.6* | 1.21±0.05* | 0.64±0.03* |
## Effect of Berberis lyceum methanolic extract on serum tGSH (mmol/mg tissue) content
The current study outcomes have demonstrated that the tGSH level in the doxorubicin-treated group was significantly (*$p \leq 0.05$) decreased (0.54 ± 0.03 mmol/mg) than thcontrol group (2.25 ± 0.053 mmol/mg). Berberis lyceum methanolic extract groups (MEBL-200 and MEBL-400) showed an insignificant change in the tGSH level against the control. The concurrent administration of the doxorubicin with different doses of *Berberis lyceum* methanolic extract treated groups has significantly increased the tGSH level over the doxorubicin-treated group. For group 5 and group 6, the respective increase of 1.79 ± 0.003 mmol/mg and 1.98 ± 0.03 mmol/mg was seen and the results are shown in Table 3.
The tGSH level in the cisplatin-treated group was significantly (*$p \leq 0.05$) decreased (0.48 ± 0.06 mmol/mg) than control group (1.25 ± 0.04 mmol/mg). Berberis lyceum methanolic extract groups (MEBL-200 and MEBL-400) have indicated an insignificant change in the tGSH level as compared to control. The co-administration of the cisplatin with different doses of *Berberis lyceum* methanolic extract treated groups significantly increased the tGSH level against the cisplatin-treated group. For group 5 and group 6, the respective increase 0.98 ± 0.1 mmol/mg and 1.06 ± 0.05 mmol/mg tissue was estimated (Table 4).
## Effect of Berberis lyceum methanolic extract on serum CAT activity (µmol of H2 O2 consumed/min/mg protein)
The present investigation illustrated that the CAT activity in the doxorubicin-treated group was significantly (*$p \leq 0.05$) decreased (0.287 ± 0.13 µmol/min/mg) than the control group (0.470 ± 0.12 µmol/min/mg). Berberis lyceum methanolic extract groups (MEBL-200 and MEBL-400) showed an insignificant change in the CAT activity as compared to the control. The parallel administration of the doxorubicin and *Berberis lyceum* methanolic extract significantly (*$p \leq 0.05$) decreased the CAT activity in comparison to the doxorubicin-treated group. For groups 5 and 6, the respective decrease in CAT activity was 0.3691 ± 0.085 µmol/min/mg and 0.484 ± 0.160 µmol/min/mg. The concurrent administration of the doxorubicin with vitamin E also exhibited a significant decrease (0.447 ± 0.068 µmol/min/mg) in the CAT activity when compared with the doxorubicin-treated group (Table 3).
The CAT activity in the cisplatin-treated group was significantly (*$p \leq 0.05$) decreased (0.32 ± 0.02 µmol/min/mg) than the control group (0.66 ± 0.02 µmol/min/mg). Berberis lyceum methanolic extract groups (MEBL-200 and MEBL-400) demonstrated an insignificant change in CAT activity as compared to the control. The co-administration of the cisplatin and *Berberis lyceum* methanolic extract treated groups significantly decreased the CAT activity than the cisplatin-treated group. For group 5 and group 6, the respective decrease was 0.59 ± 0.01 µmol/min/mg and 0.61 ± 0.02 µmol/min/mg. The concomitant administration of the cisplatin with vitamin E produced a significant decrease (0.64 ± 0.03 µmol/min/mg) in the CAT activity as compared to the cisplatin-treated group (Table 4).
## Effect of Berberis lyceum methanolic extract on serum MDA content (µmol/g tissue)
The study results manifested that the MDA level in the doxorubicin-treated group was significantly (*$p \leq 0.05$) increased (38.01 ± 0.2 µmol/g) than the control group (14.2 ± 0.3 µmol/g). The methanolic extract groups (MEBL-200 and MEBL-400) showed an insignificant change in the MDA level as compared to the control. The concurrent administration of the doxorubicin with *Berberis lyceum* methanolic extract significantly decreased the MDA level in comparison to the doxorubicin treated group. For groups 5 and 6, the MDA level dropped to 22.3 µmol/g ± 0.46 and 18.76 ± 0.3 µmol/g, respectively. The synchronous administration of the doxorubicin with vitamin E also exhibited a significant decrease (18.4 ± 0.34 µmol/g) in the MDA level as compared to the doxorubicin-treated group (Table 3).
The MDA level in cisplatin-treated group was significantly (*$p \leq 0.05$) increased (183.9 ± 3.3 µmol/g) than control group (88.4 ± 2.9 µmol/g). The methanolic extract groups (MEBL-200 and MEBL-400) showed an insignificant change in the MDA level against the control. The co-administration of the cisplatin with *Berberis lyceum* methanolic extract significantly decreased the MDA level as compared to the cisplatin-treated group. For group 5 and group 6, the respective decrease was 93.4 ± 0.6 and 89 ± 2.6 and the results are presented in Table 4.
## Histo-pathological examination of heart and brain tissues
The histopathological examination of the control (normal) heart tissue showed the normal architecture of myocardiocytes like well-arranged fibers and nucleus with no vacuolization (Figure 3A). In the doxorubicin-treated group (20 mg/ kg), severely degenerated myocardiocytes were noticed with vacuolization of cytoplasm, loss of nuclei, sarcoplasm fragmentation, increased infiltration, coagulated necrosis of cardiac cells, and vascular congestion (Figure 3B). MEBL-200 mg/ kg and MEBL-400 mg/ kg treated groups exhibited histology similar to the control (Figures 3C and 3D). In *Berberis lyceum* methanolic extract (200 mg/kg) co-administered with the doxorubicin, the morphological observation of cardiac tissue showed an adequate degeneration of myocytes, moderate vacuolization of cytoplasm, pyknotic nuclei, less necrosis, and vascular congestion (Figure 3E). In *Berberis lyceum* methanolic extract (400 mg/kg) co-administered with the doxorubicin, morphological study of cardiac tissue illustrated a moderate degeneration of myocytes with less vacuolated cytoplasm and pyknotic nuclei. Further, remnants of necrosis along with little vascular congestion were present (Figure 3F). Vitamin E co-administered with doxorubicin manifested a moderate degeneration of myocytes with few cytoplasmic vacuolization and pyknotic nuclei. The remnants of necrosis and little congestion were also noticed. However, decreased infiltration of inflammatory cells was examined as well (Figure 3G). The histopathological analysis of the methanol extract at two doses (200 and 400 mg/ kg) showed improved morphology of the brain tissue, which was damaged due to the cisplatin administration as shown in Figures 4A and 4B. Moreover, the methanolic extract prevented the formation of pyknotic nuclei with the reduced vacuolization of neurons as illustrated in Figures 4C, 4D, 4E, and 4F, respectively. The study outcome has confirmed that both methanolic extract (200 and 400 mg/kg) have the potential to reverse the neurotoxicity caused by the anticancer drug.
**Figure 3:** *H&E stained light photomicrograph of rat heart treated by Berberis lyceum methanolic extract treated groups, 20X magnification. A: Control heart, A1: normal nuclei, A2: well-formed fibers, B: Doxorubicin (20 mg/kg BW) treated group, B1: vacuolization, B2: pyknotic nuclei, B3: vascular congestion, B4: coagulative necrosis, B5: hemorrhage, C: MEBL (200 mg/ kg BW), D: MEBL (400 mg/kg BW), E: DOX + MEBL (200 mg/kg BW), E1: vascular congestion, F: DOX + MEBL (400 mg/kg BW), F1: hemorrhage, G: DOX + vit-E (100 mg/kg BW), G1: pyknotic nucleu.* **Figure 4:** *H&E stained light photomicrograph of rat brain treated by Berberis lyceum methanolic extract treated groups, 20X magnification. A: control brain, B: cisplatin (4 mg/kg BW), B1: liquefactive necrosis, B2: inflammatory cells, B3: vacuolization, C: MEBL (200 mg/kg BW), D: MEBL (400 mg/kg BW), D1: well formed nuclei, E: cisplatin + (MEBL 200 mg/kg BW), Fcisplatin + MEBL (400 mg/kg BW).*
## Discussion
Doxorubicin-induced cardiotoxicity and cisplatin-induced neurotoxicity are foremost challenges for the current chemotherapy practices and need to be addressed on an urgent basis. Doxorubicin is one of the widely prescribed broad-spectrum anti-cancer drugs. Doxorubicin causes severe cardiotoxicity that limits its clinical use.32 Doxorubicin induces cardiotoxicity due to the formation of free radicals and oxidative stress. Doxorubicin alters the serum enzymes level (ALT, AST, LDH and CK) and also produces marked morphological changes in cardiac tissue including necrosis, intravascular hemolysis, and congestion of the vessels.33 Likewise, cisplatin is widely used to treat cancer patients and induces neurotoxicity as a side effect.34 Many studies have demonstratedthat cisplatin causes brain damage via several mechanisms, i.e. increase lipid peroxidation, amplify the formation of free radicals and elevate the MDA level.35 Globally, medicinal plants are used to treat numerous acute and chronic illnesses because these are considered safe, effective, easily accessible, and inexpensive source of therapy.36,37 *Berberis lyceum* is a medicinal plant that is indigenous to India and Pakistan. It contains valuable bioactive chemicals and widely used to treat many disorders, i.e. diarrhea, inflammation, diabetes, gingivitis, jaundice, and ophthalmic disorders. It has strong antioxidant properties and quite beneficial in reducing the generation of free radicals. In this study, the pre-treatment of rats with the *Berberis lyceum* methanolic extract can mitigate doxorubicin and cisplatin-induced oxidative stress and prevent cardio and neurotoxicity. Since antioxidants can decrease oxidative stress by impeding the progression of reactive species production and lowering lipid peroxidation, this fact may be taken as a promising aspect by which *Berberis lyceum* defenses against unwanted side effects related to doxorubicin and cisplatin.15,38,39 In the present investigation, it is proved that the administration of *Berberis lyceum* root bark methanolic extract can improve or reverse the doxorubicin-induced and cisplatin-induced changes. Earlier studies have confirmed that the release of ALT, AST, LDH, CK, and CK-MB will increase from myocardial tissues with doxorubicin use. These enzymes are valuable tools to assess doxorubicin-induced myocardial toxicity. This study found that the tissue levels of ALT, AST, LDH, CK, and CK-MB were increased in doxorubicin-treated rats, and pre-treatment with *Berberis lyceum* root bark methanolic extract significantly decreased the levels of these enzymes.40 Further, doxorubicin and cisplatin considerably reduce the total protein content, GST specific activity, tGSH content, and CAT activity. In addition, doxorubicin and cisplatin appreciably (*$p \leq 0.05$) elevated the level of MDA. Berberis lyceum root bark methanolic extract significantly augmented the total protein content, GST specific activity, tGSH level and CAT activity when administered with anticancer drugs, and MDA level was considerably reduced.41 The histopathological examination of the heart and brain tissues revealed that doxorubicin and cisplatin both caused the damage of tissue morphology and resulted in cellular death. The histopathological evaluation of methanolic extract treated showed marked improvement in both organ tissues with moderate to mild degeneration. The results have demonstrated that the response of methanolic extract against anticancer drugs induced toxicity is dose-dependent as evident by improving enzymes level and tissue histopathology, i.e. mild protection is attained at a dose of 200 mg/kg while a significant defense against toxicity is achieved at an effective dose of 400 mg/kg.42,43 The plant phytochemicals are responsible for the protective effects against cardio and neurotoxicity induced by the chemotherapeutic agents. These phytochemicals can shield without decreasing the efficacy of synthetic drugs (doxorubicin and cisplatin) and are safe to use for a longer period.44,45 This study manifested, that *Berberis lyceum* is enriched with various phytochemicals such as flavonoids, polyphenols, saponins, and alkaloids. In a previous investigation, HPLC-UV characterization was performed on the plant root extract, and the presence of many chemical compounds (quercetin, chlorogenic acid, berberine, rutin, mandelic acid, and hydroxybenzoic acid) was reported. The chemical characterization of *Berberis lyceum* was also done by NMR and many important compounds were identified, such as berberine, β-sitosterol, 4-methyl, 7-hydroxy coumarin, and butyl-3-hydroxypropyl-phthalate. *The* generation of free radicals led to oxidative stress, which is a possible mechanism behind the induction of cardiotoxicity and neurotoxicity. These phytochemicals reduce oxidative stress by suppressing the generation of free radicals and hence protect against harmful effects of the anticancer drug.46 Additionally, doxorubicin and cisplatin are extensively metabolized in the liver and cause hepatotoxicity which is a serious side effect and frequently leads to stop therapy in cancer patients. Earlier experiments have proved that *Berberis lyceum* protects against hepatotoxicity by bringing the liver enzymes to their normal levels.47 Berberine which is an alkaloid present in this plant has the promising effect to reduce oxidative stress and proven beneficial against drug-induced liver toxicity.48 A study demonstrated that berberine possesses in vivo anti-inflammatory activity and inhibits the activity of activator protein 1, which triggers inflammatory cytokines such as interleukin-6. Many studies have documented that berberine is extremely target-specific with meager cytotoxicity on normal cells. It has tremendous ability to stop cell cycle at G1 phase specifically in tumor cells at lower concentration while arrest cell cycle at G2/M phase at higher concentration and stimulating apoptosis. The co-administration of berberine and anticancer drugs not only reduces cytotoxic effects of chemotherapeutic agents but also improves their therapeutic efficacy.49,50 However, further studies are required to translate *Berberis lyceum* protective mechanism, safety, and practical implications in clinical use.
## Conclusion
Berberis lyceum exhibited significant cardio and neuroprotection against anticancer drugs. The protective activity can be related to the plant antioxidant potential and its membrane-stabilizing effect by reducing lipid peroxidation. It is suggested that further research on *Berberis lyceum* Royle should be carried out to develop new and effective therapeutic agents to treat toxicity induced by anticancer drugs.
## Declaration of interest statement
There is no conflict of interest among the authors.
## Author's Contributions
QAS, RA, IST conceived and designed the study. QAS and IST performed the experiments, curated the data, and wrote the manuscript. RA performed the statistical analysis. RA and KA contributed to interpreting the analyzed data, provision of resources, structured and edited the manuscript. RA supervised and verified the results of this work. All authors participated in this research equally and approved the final manuscript.
## Funding
None
## References
1. Mollakhalili Meybodi N, Mortazavian AM, Bahadori Monfared A, Sohrabvandi S, Aghaei Meybodi F. **Phytochemicals in cancer prevention: a review of the evidence**. *Iranian J Cancer Prev* (2017) **10** e7219. DOI: 10.17795/ijcp-7219
2. Shahbaz M, Kamran SH, Anwar R. **Amelioration of Bleomycin and Methotrexate-Induced Pulmonary Toxicity by Serratiopeptidase and Fisetin**. *Nutr Cancer* (2020) 1-10. DOI: 10.1080/01635581.2020.1860242
3. Bashash D, Zareii M, Safaroghli-Azar A, Omrani MD, Ghaffari SH. **Inhibition of telomerase using BIBR1532 enhances doxorubicin-induced apoptosis in pre-B acute lymphoblastic leukemia cells**. *Hematol* (2017) **22** 330-340. DOI: 10.1080/10245332.2016.1275426
4. Biondo LA, Silveira LS, de Souza Teixeira AA, Neto JCR. **White adipose tissue and cancer: Impacts of doxorubicin and potential co-therapies**. *Immunometabolism* (2020) **4** 1-26. DOI: 10.20900/immunometab20200030
5. Arshad S, ur Rehman M, Abid F, Yasir S, Qayyum M, Ashiq K. **Current situation of breast cancer in Pakistan with the available interventions**. *Int J Biosci* (2019) **11** 232-240. DOI: 10.12692/ijb/14.6.232-240
6. Srivastava A, Amreddy N, Babu A, Panneerselvam J, Mehta M, Muralidharan R. **Nanosomes carrying doxorubicin exhibit potent anticancer activity against human lung cancer cells**. *Sci Rep* (2016) **6** 38541. DOI: 10.1038/srep38541
7. Shi Y, Moon M, Dawood S, McManus B, Liu P. **Mechanisms and management of doxorubicin cardiotoxicity**. *Herz* (2011) **36** 296-305. DOI: 10.1007/s00059-011-3470-3
8. Govender J, Loos B, Marais E, Engelbrecht AM. **Mitochondrial catastrophe during doxorubicin-induced cardiotoxicity: a review of the protective role of melatonin**. *J Pineal Res* (2014) **57** 367-380. DOI: 10.1111/jpi.12176
9. Mobaraki M, Faraji A, Zare M, Dolati P, Ataei M, Manshadi HD. **Molecular mechanisms of cardiotoxicity: a review on major side-effect of doxorubicin**. *Indian J Pharm Sci* (2017) **79** 335-344. DOI: 10.4172/pharmaceutical-sciences.1000235
10. Zhang Y-Y, Yi M, Huang Y-P. **Oxymatrine ameliorates doxorubicin-induced cardiotoxicity in rats**. *Cell Physiol Biochem* (2017) **43** 626-635. DOI: 10.1159/000480471
11. Schaupp CM, White CC, Merrill GF, Kavanagh TJ. **Metabolism of doxorubicin to the cardiotoxic metabolite doxorubicinol is increased in a mouse model of chronic glutathione deficiency: A potential role for carbonyl reductase 3**. *Chem Biol Interact* (2015) **234** 154-161. DOI: 10.1016/j.cbi.2014.11.010
12. Aldossary SA. **Review on pharmacology of cisplatin: clinical use, toxicity and mechanism of resistance of cisplatin**. *Biomed Pharmacol J* (2019) **12** 7-15. DOI: 10.13005/bpj/1608
13. Lomeli N, Di K, Czerniawski J, Guzowski JF, Bota DA. **Cisplatin-induced mitochondrial dysfunction is associated with impaired cognitive function in rats**. *Free Radic Biol Med* (2017) **102** 274-286. DOI: 10.1016/j.freeradbiomed.2016.11.046
14. Avan A, Postma TJ, Ceresa C, Avan A, Cavaletti G, Giovannetti E. **Platinum-induced neurotoxicity and preventive strategies: past, present, and future**. *Oncologist* (2015) **20** 411. PMID: 25765877
15. Ahmed S, Shuaib M, Ali K, Ali S, Hussain F. **Evaluation of different parts of Berberis lyceum and their biological activities: a review**. *Pure Appl Biol* (2017) **6** 897-907
16. Chand N, Durrani F, Qureshi M, Durrani Z. **Role of Berberis lycium in reducing serum cholesterol in broilers**. *Asian-Australasian J Anim Sci* (2007) **20** 563-568. DOI: 10.5713/ajas.2007.563
17. Aslam H, Jehangir A, Naeem U. **Comparison of Hypoglycemic Activity of Berberis Lycium Royle Stem Bark and Glimepiride in Type 2 Diabetes**. *J Islamic In Med Coll* (2015) **92** 117-120
18. Singh A, Gupta M. **Phytochemical Investigation of Different Solvent Extracts Of Berberis Lycium Fruits**. *Rasayan J Chem* (2018) **11** 228-231. DOI: 10.7324/RJC.2018.1111999
19. Anwar R, Sultan R, Batool F. **Ameliorating effect of Berberis lycium root bark extracts against cisplatin-induced nephropathy in rat**. *Bangladesh Journal of Pharmacology* (2018) **13** 248-254. DOI: 10.3329/bjp.v13i3.36705
20. Khan I, Najeebullah S, Ali M, Shinwari ZK. **Phytopharmacological and ethnomedicinal uses of the Genus Berberis (Berberidaceae): A review**. *Trop J Pharm Res* (2016) **15** 2047-2057. DOI: 10.4314/tjpr.v15i9.33
21. Lowry O. **Protein determination with the phenol reagent**. *J biol Chem* (1951) **193** 265-275. PMID: 14907713
22. Besbes S, Blecker C, Deroanne C, Drira NE, Attia H. **Date seeds: chemical composition and characteristic profiles of the lipid fraction**. *Food Chem* (2004) **84** 577-584. DOI: 10.1016/S0308-8146(03)00281-4
23. Al-Hooti S, Sidhu SS, Gabazard H. **Chemical composition of seeds of date fruit cultivars of United Arab Emirates**. *J Food Sci Technol* (1998) **35** 44-46
24. Slinkard K, Singleton VL. **Total phenol analysis: automation and comparison with manual methods**. *Am J Enol Vitic* (1977) **28** 49-55
25. Chang CC, Yang MH, Wen HM, Chern JC. **Estimation of total flavonoid content in propolis by two complementary colorimetric methods**. *J Food Drug Ana* (2002) **10**
26. Hussain K, Ismail Z, Sadikun A, Ibrahim P. **Analysis of proteins, polysaccharides, glycosaponins contents of Piper sarmentosum Roxb. and anti-TB evaluation for bio- enhancing/interaction effects of leaf extracts with Isoniazid (INH)**. *Nat Prod Rad* (2008) **7** 402-408
27. Anwar R, Hussain A, Ismail S, Mansor S. **In vitro effect of mitragynine on activity of drug metabolizing enzymes, n-demethylase and glutathione s-transferase in streptozotocin-induced diabetic rats**. *Pharmacologyonline* (2012) **1** 65-68
28. Sedlak J, Lindsay RH. **Estimation of total, protein-bound, and nonprotein sulfhydryl groups in tissue with Ellman's reagent**. *Anal Biochem* (1968) **25** 192-205. PMID: 4973948
29. Sinha AK. **Colorimetric assay of catalase**. *Anal Biochem* (1972) **47** 389-394. PMID: 4556490
30. Ohkawa H, Ohishi N, Yagi K. **Assay for lipid peroxides in animal tissues by thiobarbituric acid reaction**. *Anal Biochem* (1979) **95** 351-358. PMID: 36810
31. Thenmozhi AJ, Raja TRW, Janakiraman U, Manivasagam T. **Neuroprotective effect of hesperidin on aluminium chloride induced Alzheimer's disease in Wistar rats**. *Neurochem Res* (2015) **40** 767-776. DOI: 10.1007/s11064-015-1525-1
32. Songbo M, Lang H, Xinyong C, Bin X, Ping Z, Liang S. **Oxidative stress injury in doxorubicin-induced cardiotoxicity**. *Toxicol Lett* (2019) **307** 41-48. DOI: 10.1016/j.toxlet.2019.02.013
33. Razavi BM, Karimi G. **Protective effect of silymarin against chemical-induced cardiotoxicity**. *Iranian J Basic Med Sci* (2016) **19** 916. DOI: 10.22038/ijbms.2016.7590
34. dos Santos NAG, Ferreira RS, dos Santos AC. **Overview of cisplatin-induced neurotoxicity and ototoxicity, and the protective agents**. *Food Chem Toxicol* (2020) **136** 111079. DOI: 10.1016/j.fct.2019.111079
35. Turan M, Cayir A, Cetin N, Suleyman H, Turan IS, Tan H. **An investigation of the effect of thiamine pyrophosphate on cisplatin-induced oxidative stress and DNA damage in rat brain tissue compared with thiamine: thiamine and thiamine pyrophosphate effects on cisplatin neurotoxicity**. *Human Exp Toxicol* (2014) **33** 14-21
36. El-Dahiyat F, Rashrash M, Abuhamdah S, Farha RA. **Herbal medicines: a cross-sectional study to evaluate the prevalence and predictors of use among Jordanian adults**. *J Pharm Pol Pract* (2020) **13** 2. DOI: 10.1186/s40545-019-0200-3
37. Latif A, Ashiq K, Qayyum M, Ashiq S, Ali E, Anwer I. **Phytochemical and pharmacological profile of the medicinal herb: Bryophyllum pinnatum**. *J Anim Plant Sci* (2019) **29** 1528-1534
38. Yu J, Wang C, Kong Q, Wu X, Lu J-J, Chen X. **Recent progress in doxorubicin-induced cardiotoxicity and protective potential of natural products**. *Phytomed* (2018) **40** 125-139. DOI: 10.1016/j.phymed.2018.01.009
39. Latif A, Ashiq K, Ashiq S, Ali E, Anwer I, Qamar S. **Phytochemical analysis and in vitro investigation of anti-inflammatory and xanthine oxidase inhibition potential of root extracts of Bryophyllum pinnatum**. *J Anim Plant Sci* (2020) **30** 219-228. DOI: 10.36899/JAPS.2020.1.0025
40. Sergazy S, Shulgau Z, Fedotovskikh G, Chulenbayeva L, Nurgozhina A, Nurgaziyev M. **Cardioprotective effect of grape polyphenol extract against doxorubicin induced cardiotoxicity**. *Sci Rep* (2020) **10** 1-12. DOI: 10.1038/s41598-020-71827-9
41. Saadati H, Noroozzadeh S, Esmaeili H, Amirshahrokhi K, Shadman J, Niapour A. **The Neuroprotective Effect of Mesna on Cisplatin-Induced Neurotoxicity: Behavioral, Electrophysiological, and Molecular Studies**. *Neurotox Res* (2020) 1-15. DOI: 10.1158/1541-7786.mcr-19-0024
42. Afsar T, Razak S, Almajwal A, Shabbir M, Khan MR. **Evaluating the protective potency of Acacia hydaspica R. Parker on histological and biochemical changes induced by Cisplatin in the cardiac tissue of rats**. *BMC Complement Altern Med* (2019) **19** 182. DOI: 10.1186/s12906-019-2575-8
43. Aziz MM, Abd El Fattah MA, Ahmed KA, Sayed HM. **Protective effects of Olmesartan and L-carnitine on doxorubicin-induced Cardiotoxicity in rats**. *Canadian J Physiol Pharmacol* (2020) **98** 183-193. DOI: 10.1139/cjpp-2019-0299
44. Abushouk AI, Ismail A, Salem AMA, Afifi AM, Abdel-Daim MM. **Cardioprotective mechanisms of phytochemicals against doxorubicin-induced cardiotoxicity**. *Biomed Pharmacother* (2017) **90** 935-946. DOI: 10.1016/j.biopha.2017.04.033
45. Hosseini A, Sahebkar A. **Reversal of doxorubicin-induced cardiotoxicity by using phytotherapy: a review**. *J Pharmacopuncture* (2017) **20** 243. PMID: 30151294
46. Nazir N, Rahman A, Uddin F, Khan Khalil AA, Zahoor M, Nisar M, Ullah S, Ullah R, Ezzeldin E, Mostafa GA. **Quantitative Ethnomedicinal Status and Phytochemical Analysis of Berberis lyceum Royle**. *Agronomy* (2021) 130. DOI: 10.3390/agronomy11010130
47. Sabir S, Tahir K, Rashid N, Naz S, Masood B, Shah MA, Sualeh M. **Phytochemical and antioxidant studies of Berberis lycium**. *Pak J Pharm Sci* (2013) **26** 1165-1172. PMID: 24191322
48. Shah FA, Shakir S, Rafique S, Shuaib A. **Reduction of liver enzyme ALT by berberis lycium in acetaminophen induced hepatic damage in mice liver**. *Prof Med J* (2021) **28** 287-292. DOI: 10.29309/TPMJ/2021.28.03.6009
49. Chen X, Zhang Y, Zhu Z, Liu H, Guo H, Xiong C, Xie K, Zhang X, Su S. **Protective effect of berberine on doxorubicin induced acute hepatorenal toxicity in rats**. *Mol Med Rep* (2016) **13** 3953-3960. DOI: 10.3892/mmr.2016.5017
50. Jagetia GC, Baliga MS. **Effect of Alstonia scholaris in enhancing the anticancer activity of berberine in the Ehrlich ascites carcinoma-bearing mice**. *J Med Food* (2004) **7** 235-244. DOI: 10.1089/1096620041224094
51. Iqbal MJ, Quispe C, Javed Z, Sadia H, Qadri QR, Raza S, Salehi B, Cruz-Martins N, Mohamed ZA, Jaafaru MS, Razis AF. **Nanotechnology-based strategies for berberine delivery system in cancer treatment: pulling strings to keep berberine in power**. *Front Mol Biosci* (2021) **7**. DOI: 10.3389/fmolb.2020.624494
|
---
title: 'The effect of liraglutide on renal function in type 2 diabetes: a meta-analysis
of randomized controlled studies'
authors:
- Cheng Luo
- Dongjuan He
- HongBin Yang
- Chunyan Zhu
- Jiajun Zhu
- Zhaohui Hu
journal: African Health Sciences
year: 2022
pmcid: PMC9993257
doi: 10.4314/ahs.v22i3.28
license: CC BY 4.0
---
# The effect of liraglutide on renal function in type 2 diabetes: a meta-analysis of randomized controlled studies
## Abstract
### Introduction
The efficacy of liraglutide on renal function in type 2 diabetes remains controversial. We conduct a systematic review and meta-analysis to explore the influence of liraglutide versus placebo on renal function in type 2 diabetes.
### Methods
We search PubMed, EMbase, Web of science, EBSCO, and Cochrane library databases through March 2020 for randomized controlled trials (RCTs) assessing the effect of liraglutide versus placebo on renal function in type 2 diabetes. This meta-analysis is performed using the random-effect model.
### Results
Seven RCTs are included in the meta-analysis. Overall, compared with control group in type 2 diabetes, liraglutide treatment shows no obvious effect on GFR (SMD=0.02; $95\%$ CI=-0.43 to 0.47; $$P \leq 0.94$$), RBF (SMD=-0.28; $95\%$ CI=-0.80 to 0.24; $$P \leq 0.29$$) or death (RR=1.93; $95\%$ CI=0.71 to 5.21; $$P \leq 0.20$$), but is associated with significantly decreased ACR (SMD=-0.82; $95\%$ CI=-1.39 to -0.26; $$P \leq 0.004$$) and systolic blood pressure (MD=-9.60; $95\%$ CI=-17.46 to -1.73; $$P \leq 0.02$$), as well as increased heart rate (MD=5.39; $95\%$ CI=3.26 to 7.52; $P \leq 0.00001$).
### Conclusions
Liraglutide treatment may provide some benefits for protecting renal function in type 2 diabetes.
## Introduction
As the increase in diabetes pandemic, diabetic kidney disease has emerged as the leading cause of chronic kidney disease, which may cause end-stage kidney disease, cardiovascular events, and premature death1–4. Early detection of albuminuria and decline of glomerular filtration rate (GFR) help to treat diabetic kidney disease5, 6. It is also important to control renal risk factors such as hyperglycemia, obesity, systemic hypertension, glomerular hyper filtration, albuminuria, and dislipidemia1.
Glucagon-like peptide 1 (GLP-1)-based therapies including dipeptidyl peptidase-4 inhibitors (DPP-4Is) and GLP-1 receptor agonists (GLP-1Ras such as liraglutide), have been widely used for type 2 diabetes by improving pancreatic islet cell function, and reducing glucagon secretion7–10. In experimental models of diabetes and hypertension, GLP-1-based therapies was documented to prevent the onset and progression of renal disease, renal morphological abnormalities of diabetic kidney disease11.
Recently, several studies have investigated the efficacy of liraglutide on renal function for type 2 diabetes, but the results are conflicting12–15. This systematic review and meta-analysis of RCTs aims to assess the impact of liraglutide versus placebo on the renal function in patients with type 2 diabetes.
## Materials and methods
This systematic review and meta-analysis are performed based on the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-analysis statement and Cochrane Handbook for Systematic Reviews of Interventions16,17. No ethical approval and patient consent are required because all analyses are based on previous published studies.
## Literature search and selection criteria
We systematically search several databases including PubMed, EMbase, Web of science, EBSCO, and the Cochrane library from inception to March 2020 with the following keywords: “liraglutide”, and “diabetes”, and “renal function” or “kidney function”. The reference lists of retrieved studies and relevant reviews are also hand-searched and the process above is performed repeatedly in order to include additional eligible studies.
The inclusion criteria are presented as follows: [1] study design is RCT, [2] patients are diagnosed with type 2 diabetes, [3] intervention treatments are liraglutide versus placebo, and [4] outcomes should involve the effect on renal outcomes.
## Data extraction and outcome measures
Some baseline information is extracted from the original studies, and they include first author, number of patients, age, female, body mass index, duration of diabetes, and detail methods in two groups. Data are extracted independently by two investigators, and discrepancies are resolved by consensus. We have contacted the corresponding author to obtain the data when necessary.
The primary outcomes are glomerular filtration rate (GFR) and renal blood flow (RBF). Secondary outcomes include albumin-to-creatinine ratio (ACR), systolic blood pressure, diastolic blood pressure, heart rate and death.
## Quality assessment in individual studies
The methodological quality of each RCT is assessed by the Jadad Scale which consists of three evaluation elements: randomization (0–2 points), blinding (0–2 points), dropouts and withdrawals (0–1 points)18. One point would be allocated to each element if they have been conducted and mentioned appropriately in the original article. The score of Jadad Scale varies from 0 to 5 points. An article with Jadad score≤2 is considered to be of low quality. The study is thought to be of high quality if Jadad score≥319.
## Statistical analysis
We assess mean difference (MD) or standard mean difference (SMD) with $95\%$ confidence interval (CI) for continuous outcomes (GFR, RBF, ACR, systolic blood pressure, diastolic blood pressure and heart rate), and risk ratio (RR) with $95\%$ CI for dichotomous outcome (death). Heterogeneity is evaluated using the I2 statistic, and I2 > $50\%$ indicates significant heterogeneity20. The random-effects model is used for all meta-analysis. We search for potential sources of heterogeneity for significant heterogeneity. Sensitivity analysis is performed to detect the influence of a single study on the overall estimate via omitting one study in turn or performing the subgroup analysis. Owing to the limited number (<10) of included studies, publication bias is not assessed. Results are considered as statistically significant for $P \leq 0.05.$ All statistical analyses are performed using Review Manager Version 5.3 (The Cochrane Collaboration, Software Update, Oxford, UK).
## Literature search, study characteristics and quality assessment
Figure 1 shows the detail flowchart of the search and selection results. 229 potentially relevant articles are identified initially and 91 duplicates are excluded. Then, 138 papers are removed after checking the titles ($$n = 32$$)/abstracts ($$n = 106$$). Three studies are removed because of the study design after reading the full articles, and seven RCTs are finally included in the meta-analysis12–15, 21–23.
**Figure. 1:** *Flow diagram of study searching and selection process.*
The baseline characteristics of seven included RCTs are shown in Table 1. These studies are published between 2016 and 2017, and the total sample size is 9766. Among seven included RCTs, liraglutide is administered at the dose ranging from 0.3 mg/day to 1.8 mg/day. The treatment duration ranges from 12 to 24 weeks.
**Table 1**
| NO. | Author | Liraglutide group | Liraglutide group.1 | Liraglutide group.2 | Liraglutide group.3 | Liraglutide group.4 | Liraglutide group.5 | Control group | Control group.1 | Control group.2 | Control group.3 | Control group.4 | Control group.5 | Jada scores |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| NO. | Author | Number | Age (years) | Female (n) | Body mass index (kg/m2) | Duration of diabetes | Methods | Number | Age (years) | Female (n) | Body mass index (kg/m2) | Duration of diabetes | Methods | Jada scores |
| 1 | Von Scholten 2017 | 27 | 65±7 | 5 | 31.9±5.0 | 15±7 | liraglutide (1.8 mg/d) for 12 weeks | 27 | 65±7 | 5 | 31.9±5.0 | 15±7 | placebo | 4 |
| 2 | Mann 2017 | 4668 | 64.2 | 1657 | 32.5 | 12.8 | liraglutide at 0.6 mg daily for 1 week, 1.2 mg for an additional week, and a potential maximum dosage thereafter of 1.8 mg based on tolerance, as determined by the investigator for 6 months | 4672 | 64.4 | 1680 | 32.5 | 12.9 | placebo | 5 |
| 3 | Tonneijck 2016 | 19 | 60.5±7.2 | 5 | 32.0 (30.9–35.9), median (interquartile range) | 7 (4–13), median (interquartile range) | liraglutide (1.8 mg/day) for 12 weeks | 17 | 65.8 6 5.8 | 4 | 30.8 (28.9 – 31.5) | 8 (5–12) | placebo | 4 |
| 4 | Skov 2016 | 11 | 54 ± 5 | 0 | 29 ± 3 | 2.9 ± 1.7 | a single dose of 1.2 mg liraglutide daily | 11 | 54 ± 5 | 0 | 29 ± 3 | 2.9 ± 1.7 | placebo | 3 |
| 5 | Idorn 2016 | 10 | 60.7±3.2 | 3 | 30.2±1.3 | - | titrated to a maximum dose of 1.8 mg daily for 12 weeks | 10 | 63.1±2.1 | 2 | 30.8±1.0 | - | placebo | 3 |
| 6 | Davies 2016 | 140 | 68.0±8.3 | 65 | 33.4±5.4 | 15.9 (8.9) | a single dose of 1.8 mg liraglutide daily for 26 weeks | 137 | 66.3±8.0 | 72 | 34.5±5.4 | 14.2±7.5 | placebo | 5 |
| 7 | Bouchi 2016 | 8 | 57±16 | 3 | 27.7±2.5 | - | Liraglutide administered from 0.3 mg/day, increased to 0.6 mg after one week and 0.9 mg after a further week, up to 24 weeks | 9 | 60±22 | 6 | 28.2 ± 2.5 | - | placebo | 3 |
Among seven included RCTs, two trials report GFR12, 15, two trials report RBF14, 15, two trials report ACR14, 23, four trials report systolic blood pressure and diastolic blood pressure12, 14, 15, 23, three trials report heart rate12, 14, 15, and two trials report death13, 22. Jadad scores of the seven included studies vary from 3 to 5, and all seven studies have high-quality based on the quality assessment.
## Primary outcomes: GFR and RBF
The random-effect model is used for the analysis of primary outcomes. The results find that compared to control group in type 2 diabetes, liraglutide treatment shows no significant effect on GFR (SMD=0.02; $95\%$ CI=-0.43 to 0.47; $$P \leq 0.94$$) with no heterogeneity among the studies (I2=$0\%$, heterogeneity $$P \leq 0.65$$, Figure 2), or RBF (SMD=-0.28; $95\%$ CI=-0.80 to 0.24; $$P \leq 0.29$$) with no heterogeneity among the studies (I2=$0\%$, heterogeneity $$P \leq 0.51$$, Figure 3).
**Figure. 2:** *Forest plot for the meta-analysis of GFR.* **Figure. 3:** *Forest plot for the meta-analysis of RBF.*
## Sensitivity analysis
There is no heterogeneity for the primary outcome, and thus we do not perform the meta-analysis via omitting one study or subgroup analysis to detect the heterogeneity.
## Secondary outcomes
In comparison with control intervention in type 2 diabetes, liraglutide treatment is associated with the substantial decrease in ACR (SMD=-0.82; $95\%$ CI=-1.39 to -0.26; $$P \leq 0.004$$; Figure 4) and systolic blood pressure (MD=-9.60; $95\%$ CI=-17.46 to -1.73; $$P \leq 0.02$$; Figure 5), but has no obvious influence on diastolic blood pressure (MD=-1.18; $95\%$ CI=-4.00 to 1.64; $$P \leq 0.41$$; Figure 6). In addition, liraglutide treatment can result in the increase in heart rate (MD=5.39; $95\%$ CI=3.26 to 7.52; $P \leq 0.00001$; Figure 7) than placebo, but shows no effect on death (RR=1.93; $95\%$ CI=0.71 to 5.21; $$P \leq 0.20$$; Figure 8) in patients with type 2 diabetes.
**Figure. 4:** *Forest plot for the meta-analysis of ACR.* **Figure. 5:** *Forest plot for the meta-analysis of systolic blood pressure (mmHg).* **Figure. 6:** *Forest plot for the meta-analysis of diastolic blood pressure (mmHg).* **Figure. 7:** *Forest plot for the meta-analysis of heart rate (bpm).* **Figure. 8:** *Forest plot for the meta-analysis of death.*
## Discussion
Diabetes has become the most common cause of end-stage renal disease24–28, and a robust relationship is observed between magnitude of short term albuminuria reduction and long-term slowing of chronic kidney disease progression as well as reduced cardiovascular event rates29, 30. Short-term albuminuria reduction can lead to long-term renal protection across different interventions and populations, and a 30 % reduction in albuminuria seemed to confer a detectable reno-protective treatment effect31.
GLP-1 agonist liraglutide has been widely used for the treatment of type 2 diabetes and lowering HbA 1c3234. Reductions in systolic blood pressure, HbA 1c, GFR and body weight may contribute in lowering albuminuria and protecting renal function. In a 12-week, randomized, double-blind trial involving 55 patients with type 2 diabetes, treatment with liraglutide showed no substantial effect on measured renal hemodynamics or renal damage markers of tubular functions or alteration14. Our meta-analysis suggests that compared to placebo in type 2 diabetes, liraglutide treatment had no beneficial effect on GFR, RBF or death, but is associated with the decrease in ACR and systolic blood pressure.
One RCT aimed to investigate the effect of liraglutide treatment on renal function in type 2 diabetic patients with persistent albuminuria, and the results found that liraglutide treatment was associated with a statistically and clinically significant reduction in albuminuria. This beneficial effect on albuminuria may be attributed by the reductions in Ang II of 43 % and renin concentrations of 37 % after liraglutide treatment compared with reductions of 28 % and 27 %, respectively, with placebo treatment12. This beneficial effect of liraglutide on albuminuria was also confirmed by another RCT involving 9340 patients with type 2 diabetes and high cardiovascular risk. The new onset of persistent macroalbuminuria occurred in fewer participants in the liraglutide group than in the placebo group (161 vs. 215 patients; hazard ratio, 0.74; $95\%$ CI, 0.60 to 0.91; $$P \leq 0.004$$)13.
## Limitations
Our analysis is based on only seven RCTs, and more RCTs with large sample size should be conducted to explore this issue. Next, although there is no significant heterogeneity, different doses and treatment duration of liraglutide may produce some bias. Finally, various stages of diabetic kidney disease may have some effect on efficacy evaluation, but it is not feasible to perform their subgroup analysis based on current included RCTs.
## Conclusion
Liraglutide treatment may provide some benefits for the protection of renal function in type 2 diabetes, but more studies should be conducted to explore this issue.
## Disclosure of potential conflicts of interest
The authors declare no conflict of interest.
## Research involving human participants and/or animals
Not applicable.
## Conflicts of Interest and Source of Funding
None.
## References
1. Muskiet MH, Tonneijck L, Smits MM, Kramer MH, Heerspink HJL, van Raalte DH. **Pleiotropic effects of type 2 diabetes management strategies on renal risk factors**. *The Lancet Diabetes & Endocrinology* (2015) **3** 367-381. PMID: 25943756
2. Alicic RZ, Rooney MT, Tuttle KR. **Diabetic kidney disease: challenges, progress, and possibilities**. *Clinical Journal of the American Society of Nephrology* (2017) **12** 2032-2045. PMID: 28522654
3. Qi H, Casalena G, Shi S, Yu L, Ebefors K, Sun Y, Zhang W, D'Agati V, Schlondorff D, Haraldsson B. **Glomerular endothelial mitochondrial dysfunction is essential and characteristic of diabetic kidney disease susceptibility**. *Diabetes* (2017) **66** 763-778. PMID: 27899487
4. Anders H-J, Huber TB, Isermann B, Schiffer M. **CKD in diabetes: diabetic kidney disease versus nondiabetic kidney disease**. *Nature Reviews Nephrology* (2018) **14** 361. PMID: 29654297
5. MacIsaac RJ, Jerums G. **Diabetic kidney disease with and without albuminuria**. *Current Opinion in Nephrology and Hypertension* (2011) **20** 246-257. PMID: 21422923
6. Takagi M, Babazono T, Uchigata Y. **Differences in risk factors for the onset of albuminuria and decrease in glomerular filtration rate in people with type 2 diabetes mellitus: implications for the pathogenesis of diabetic kidney disease**. *Diabetic Medicine* (2015) **32** 1354-1360. PMID: 25968955
7. Meier JJ. **GLP-1 receptor agonists for individualized treatment of type 2 diabetes mellitus**. *Nature Reviews Endocrinology* (2012) **8** 728
8. Kristensen SL, Rørth R, Jhund PS, Docherty KF, Sattar N, Preiss D, Køber L, Petrie MC, McMurray JJ. **Cardiovascular, mortality, and kidney outcomes with GLP-1 receptor agonists in patients with type 2 diabetes: a systematic review and meta-analysis of cardiovascular outcome trials**. *The Lancet Diabetes & Endocrinology* (2019) **7** 776-785. PMID: 31422062
9. Brooks A, Langer J, Tervonen T, Hemmingsen MP, Eguchi K, Bacci ED. **Patient Preferences for GLP-1 Receptor Agonist Treatment of Type 2 Diabetes Mellitus in Japan: A Discrete Choice Experiment**. *Diabetes Therapy* (2019) **10** 735-749. PMID: 30847838
10. Bonaventura A, Carbone S, Dixon D, Abbate A, Montecucco F. **Pharmacologic strategies to reduce cardiovascular disease in type 2 diabetes mellitus: focus on SGLT-2 inhibitors and GLP-1 receptor agonists**. *Journal of Internal Medicine* (2019) **286** 16-31. PMID: 30888088
11. Muskiet MH, Smits MM, Morsink LM, Diamant M. **The gut-renal axis: do incretin-based agents confer renoprotection in diabetes?**. *Nature Reviews Nephrology* (2014) **10** 88. PMID: 24375052
12. Von Scholten BJ, Persson F, Rosenlund S, Hovind P, Faber J, Hansen TW, Rossing P. **The effect of liraglutide on renal function: a randomized clinical trial**. *Diabetes, Obesity and Metabolism* (2017) **19** 239-247
13. Mann JF, Ørsted DD, Brown-Frandsen K, Marso SP, Poulter NR, Rasmussen S, Tornøe K, Zinman B, Buse JB. **Liraglutide and renal outcomes in type 2 diabetes**. *New England Journal of Medicine* (2017) **377** 839-848. PMID: 28854085
14. Tonneijck L, Smits MM, Muskiet MH, Hoekstra T, Kramer MH, Danser AJ, Ter Wee PM, Diamant M, Joles JA, Van Raalte DH. **Renal effects of DPP-4 inhibitor sitagliptin or GLP-1 receptor agonist liraglutide in overweight patients with type 2 diabetes: a 12-week, randomized, double-blind, placebo-controlled trial**. *Diabetes Care* (2016) **39** 2042-2050. PMID: 27585605
15. Skov J, Pedersen M, Holst JJ, Madsen B, Gøtze JP, Rittig S, Jonassen T, Frøkiaer J, Dejgaard A, Christiansen J. **Short-term effects of liraglutide on kidney function and vasoactive hormones in type 2 diabetes: a randomized clinical trial**. *Diabetes, Obesity and Metabolism* (2016) **18** 581-589
16. Moher D, Liberati A, Tetzlaff J, Altman DG. **Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement**. *BMJ* (2009) **339** b2535. PMID: 19622551
17. HigginsJPT G. *Cochrane handbook for systematic reviews of interventions version 5.1. 0updated March 2011* (2011)
18. Jadad AR, Moore RA, Carroll D, Jenkinson C, Reynolds DJM, Gavaghan DJ, McQuay HJ. **Assessing the quality of reports of randomized clinical trials: Is blinding necessary?**. *Controlled Clinical Trials* (1996) **17** 1-12. PMID: 8721797
19. Kjaergard LL, Villumsen J, Gluud C. **Reported Methodologic Quality and Discrepancies between Large and Small Randomized Trials in Meta-Analyses**. *Annals of Internal Medicine* (2001) **135** 982-989. PMID: 11730399
20. Higgins JP, Thompson SG. **Quantifying heterogeneity in a meta-analysis**. *Statistics in Medicine* (2002) **21** 1539-1558. PMID: 12111919
21. Idorn T, Knop FK, Jørgensen MB, Jensen T, Resuli M, Hansen PM, Christensen KB, Holst JJ, Hornum M, Feldt-Rasmussen B. **Safety and efficacy of liraglutide in patients with type 2 diabetes and end-stage renal disease: an investigator-initiated, placebo-controlled, double-blind, parallel-group, randomized trial**. *Diabetes Care* (2016) **39** 206-213. PMID: 26283739
22. Davies MJ, Bain SC, Atkin SL, Rossing P, Scott D, Shamkhalova MS, Bosch-Traberg H, Syrén A, Umpierrez GE. **Efficacy and safety of liraglutide versus placebo as add-on to glucose-lowering therapy in patients with type 2 diabetes and moderate renal impairment (LIRA-RENAL): a randomized clinical trial**. *Diabetes Care* (2016) **39** 222-230. PMID: 26681713
23. Bouchi R, Nakano Y, Fukuda T, Takeuchi T, Murakami M, Minami I, Izumiyama H, Hashimoto K, Yoshimoto T, Ogawa Y. **Reduction of visceral fat by liraglutide is associated with ameliorations of hepatic steatosis, albuminuria, and micro-inflammation in type 2 diabetic patients with insulin treatment: a randomized control trial**. *Endocrine Journal* (2016)
24. Ingelfinger JR, Rosen CJ. **Clinical credence—SGLT2 inhibitors, diabetes, and chronic kidney disease**. *N Engl J Med* (2019) **380** 2371-2373. PMID: 30990261
25. Chagnac A, Zingerman B, Rozen-Zvi B, Herman-Edelstein M. **Consequences of glomerular hyperfiltration: the role of physical forces in the pathogenesis of chronic kidney disease in diabetes and obesity**. *Nephron* (2019) **143** 38-42. PMID: 30947190
26. Mosenzon O, Wiviott SD, Cahn A, Rozenberg A, Yanuv I, Goodrich EL, Murphy SA, Heerspink HJ, Zelniker TA, Dwyer JP. **Effects of dapagliflozin on development and progression of kidney disease in patients with type 2 diabetes: an analysis from the DECLARE-TIMI 58 randomised trial**. *The Lancet Diabetes & Endocrinology* (2019) **7** 606-617. PMID: 31196815
27. Ravizza S, Huschto T, Adamov A, Böhm L, Büsser A, Flöther FF, Hinzmann R, König H, McAhren SM, Robertson DH. **Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data**. *Nature Medicine* (2019) **25** 57-59
28. Charytan DM, Solomon SD, Ivanovich P, Remuzzi G, Cooper ME, McGill JB, Parving HH, Parfrey P, Singh AK, Burdmann EA. **Metformin use and cardiovascular events in patients with type 2 diabetes and chronic kidney disease**. *Diabetes, Obesity and Metabolism* (2019) **21** 1199-1208
29. De Zeeuw D, Remuzzi G, Parving H-H, Keane WF, Zhang Z, Shahinfar S, Snapinn S, Cooper ME, Mitch WE, Brenner BM. **Albuminuria, a therapeutic target for cardiovascular protection in type 2 diabetic patients with nephropathy**. *Circulation* (2004) **110** 921-927. PMID: 15302780
30. Roscioni SS, Heerspink HJL, De Zeeuw D. **Microalbuminuria: target for renoprotective therapy PRO**. *Kidney International* (2014) **86** 40-49. PMID: 24759154
31. Heerspink HJL, Kröpelin TF, Hoekman J, De Zeeuw D. **Drug-induced reduction in albuminuria is associated with subsequent renoprotection: a meta-analysis**. *Journal of the American Society of Nephrology* (2015) **26** 2055-2064. PMID: 25421558
32. Tamborlane WV, Barrientos-Pérez M, Fainberg U, Frimer-Larsen H, Hafez M, Hale PM, Jalaludin MY, Kovarenko M, Libman I, Lynch JL. **Liraglutide in children and adolescents with type 2 diabetes, New England**. *Journal of Medicine* (2019) **381** 637-646
33. Meneghini L, Doshi A, Gouet D, Vilsbøll T, Begtrup K, Őrsy P, Ranthe MF, Lingvay I. **Insulin degludec/liraglutide (ID egLira) maintains glycaemic control and improves clinical outcomes, regardless of pre-trial insulin dose, in people with type 2 diabetes that is uncontrolled on basal insulin**. *Diabetic Medicine* (2019)
34. Vadini F, Simeone PG, Boccatonda A, Guagnano MT, Liani R, Tripaldi R, Di Castelnuovo A, Cipollone F, Consoli A, Santilli F. **Liraglutide improves memory in obese patients with prediabetes or early type 2 diabetes: a randomized, controlled study**. *International Journal of Obesity* (2020) 1-10
|
---
title: Examination of exercise benefit/barrier perceptions of individuals with diabetes
and affecting factors
authors:
- Fatma Ersin
- Derya Tülüce
- Fatih Enzin
journal: African Health Sciences
year: 2022
pmcid: PMC9993258
doi: 10.4314/ahs.v22i3.29
license: CC BY 4.0
---
# Examination of exercise benefit/barrier perceptions of individuals with diabetes and affecting factors
## Abstract
### Background
Exercise, which is one of the health promotion behaviors, is extremely important in healthy life. This study was conducted to examine exercise benefit/barrier perceptions of individuals with diabetes and influencing factors.
### Method
This descriptive study was conducted in the Endocrine Polyclinics of a University Hospital with 285 individuals with Type 2 Diabetes between January and June 2020.
### Results
In this study, the average score of the exercise benefits subscale was 61.69 +14.79, the barriers subscale was 35.83 + 5.99, and the total score of the exercise benefits/barriers scale was 99.79 + 12.58. The total self-efficacy scale score was reported to be 59.74 + 9.46. A significant relationship was reported between the total mean score of the exercise benefits/barriers scale and having the opportunity to exercise, exercising regularly, and having a disease that prevents exercising. A significant difference was reported between the total mean score of the self-efficacy scale and the regular exercise status.
### Conclusion
Because of this study, the number of individuals who regularly exercised is insufficient, the mean exercise benefits/barriers scale score is not at the desired level, and exercise benefit/barrier perceptions are positively affected by the self-efficacy level.
## Introduction
Health promotion is defined as a process that helps individuals change their lifestyles to achieve the highest level of health. Positive health behaviors require to be acquired and maintained to protect and improve health. Exercise, which is one of the health promotion behaviors, is extremely important in healthy life1. Exercise is effective in preventing many chronic diseases2. When the risk factors of diabetes, which is one of the chronic diseases, are examined, combating physical inactivity has a critical importance in controlling the disease, treatment and management of risk factors3.
Exercise behaviors of individuals are closely related to their perception of benefits and barriers related to that behavior. A high perception of benefits and a low perception of barriers of the individual increase the possibility of performing the behavior4. Therefore, it is extremely important for individuals diagnosed with diabetes to be aware of the benefit and barrier perceptions of exercising. In a study using the health belief model scale, individuals with diabetes who exercise have higher benefit, lower barrier perceptions than those who do not exercise5.
Exercise behaviors of individuals are affected by multiple factors and one of the most important determinants is the self-efficacy level, which plays an important role in initiating behavior change and maintaining the behavior4. People with high levels of self-efficacy participate more in exercise programs adequately and regularly to reach a certain point in their behavior2,6,7. In another study, age, sex, and physical activity level affect the benefit and barrier perceptions of people with chronic diseases to a limited extent8.
There are exercise-related studies in individuals with diabetes9–14. A limited number of studies examining individuals' exercise benefit/barrier perceptions and their self-efficacy perceptions have been reviewed15. However, there are no studies investigating the exercise benefit/barrier perceptions and self-efficacy of individuals with diabetes. As a result, in protecting and improving the health of individuals with diabetes and keeping the disease under control, it is important to determine exercise benefit/barrier perceptions and self-efficacy of individuals. The results obtained will be effective in planning additional interventional studies to be performed. Therefore, this study was conducted to examine the exercise benefit/barrier perceptions of individuals with diabetes and influencing factors.
## Materials and Methods
The study was carried out at the Endocrine Polyclinics of a University Hospital between January and June 2020. It is a descriptive study.
## Population Sample
The population of the study comprised individuals with diabetes who applied to the Endocrine Polyclinics between January and June 2020. The sample comprised 285 diabetic individuals with a diagnosis of type 2 diabetes, at least primary school graduate and over 18 years of age, who voluntarily agreed to participate in the study.
## Data Collection Tools
The data were collected by the researchers using a face-to-face interview method. An introductory information form, the Exercise Benefits/Barriers Scale and the Self-Efficacy Scale were used to collect data.
Introductory Information Form: The introductory information form was prepared by researchers in line with the literature. The form comprised 16 questions concerning socio-demographic characteristics and exercise-related characteristics of participants2,13,14.
Exercise Benefits/Barriers Scale: The Exercise Benefits/Barriers Scale was developed by Sechrist, Walker and Pender [1987] to determine exercise benefit and barrier perceptions of individuals who will participate in exercise16. The validity and reliability of this scale in Turkey was conducted by Ortabağ, Ceylan, Akyüz and Bebiş [2010]17. The scale comprises a total of 43 items. The scale has four responses from four (strongly agree) to one (strongly disagree) on a forced Likert-type scale. The scale has two subscales: exercise barriers scale and exercise benefits scale. Each subgroup can be independently used. The barriers scale items are 4, 6, 9, 12, 14, 16, 19, 21, 24, 28, 33, 37, 40 and 42; the benefits scale items are items are 1, 2, 3, 5, 7, 8, 10, 11, 13, 15, 17, 18, 20, 22, 23, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 38, 39, 41 and 43. The lowest score that can be obtained from the scale is 43 and the highest score is 172. The score range of the benefits scale is between 29 and 116, and the score range of the barriers scale is between 14 and 56. The sum of all items in the scale gives the total Exercise Benefits/Barrier scale score. The higher the total scale score, the more the individual understands the benefits of exercise. It is thought that the higher the score on the benefit scale, the higher the perceived benefit of the individuals. The higher the score on the disability scale, the higher the perceived disability of individuals. The Cronbach's alpha coefficient of the scale was determined to be 0.9517. In this study, the Cronbach's alpha coefficient of the scale was reported to be 0.87.
Self-Efficacy Scale: The scale is a self-assessment scale developed by Sherer and Maddux [1982] to evaluate behaviors and behavioral changes18. The validity and reliability of the Turkish form was made by Gözüm and Aksayan19. The scale comprises 23 items. For each item on a five-point Likert-type scale, the participants are asked to select one of the options: “it does not define me at all” [1], “it defines me a little” [2], “I am indecisive” [3], “it defines me well” [4], and “it defines me very well” [5]. The scale consists of four subscales: initating a behavior subscale, maintaining a behavior subscale, completing a behavior subscale, persistence in the face of obstacle subscale. A minimum of 23 points and a maximum of 115 points can be obtained from the scale. A high total score obtained from the scale indicates that the individual's self-efficacy perception is at a good level. The Cronbach's alpha internal consistency coefficient, which includes all the statements of the scale, was 0.81, and the test-retest reliability was 0.92. In this study, the Cronbach's alpha coefficient of the scale was reported to be 0.65.
## Research Variables
The mean exercise benefits/barriers scale score was the dependent variables of the scale. The individuals' self-efficacy perception levels, age, sex, marital status, employment status, habits, social insurance status, economic status perception, family structure, exercise status, frequency of exercise, duration of exercise, presence of environment to exercise, and presence of a barrier to exercise were the independent variables of the scale.
## Data Analysis
The data were evaluated in the SPSS 17.0 package program. Number, percentage, mean, standard deviation, and maximum-minimum values were used in evaluating descriptive data. Whether the data showed normal distribution was determined by the Kolmogorov-Smirnov test. Because the data did not conform to normal distribution, the Mann-Whitney U test and the Kruskal-*Wallis analysis* were used in the analysis of the data. Because the mean scores of exercise benefits/barriers scale and self-efficacy scale were not normally distributed, the Spearman's correlation analysis was performed in the correlation between scales. Moreover, 0.05 was used as the significance level.
## Ethical Permissions
Before starting the collection of research data, written permissions were obtained from the relevant Clinical Research Ethics Committee (decision dated 27.01.2020 and numbered HRU / 20.02.27). Then, the permissions of the diabetic individuals included in the study were obtained, and all the data in the study were obtained according to the patients' statements.
## Results
The mean age of the participants was 51.47 ± 12.7, $36.8\%$ of the participants were primary school graduate, $82\%$.5 were married, $66.3\%$ were unemployed and $82.8\%$ did not have social insurance. Moreover, $62.1\%$ of the participants stated that their economic status was moderate, $79.3\%$ lived mostly in the city, and $55.8\%$ had a nuclear family. Note that $37.5\%$ of the participants used cigarettes and $7.4\%$ used alcohol (Table 1). The average year of diagnosis of the participants was 9.23 ± 4.73.
**Table 1**
| Characteristics | X ± SD | X ± SD.1 |
| --- | --- | --- |
| Age | 51.47 ± 12.78 | 51.47 ± 12.78 |
| | n | % |
| Education status Primary school Middle School High school University and above | 105 47 53 80 | 36.8 16.5 18.6 28.1 |
| Marital status Married Single | 235 50 | 82.5 17.5 |
| Employment status Employed Unemployed | 96 189 | 33.7 66.3 |
| Social insurance Yes No | 236 49 | 82.8 17.2 |
| Economic status Poor Middle Good | 44 177 64 | 15.4 62.1 22.5 |
| Longest living place Villige City | 59 226 | 20.7 79.3 |
| Family structure Nuclear family Extend family Fragmented family | 159 117 9 | 55.8 41.1 3.1 |
| Total | 285 | 100.0 |
When the exercise characteristics of participants were examined, $62.8\%$ had the opportunity to exercise, $20.7\%$ regularly exercised, and $39\%$ of those who regularly exercised every day of the week and $27.1\%$ exercised 75 -150 min a week, $80.4\%$ did not have a barrier to exercise (Table 2).
**Table 2**
| Characteristic | n | % |
| --- | --- | --- |
| Exercise opportunity (n=285) Yes No | 179 106 | 62.8 37.7 |
| Regularly exercise (n=285) Yes No | 59 226 | 20.7 79.3 |
| Exercise freguency (n=59) Everday of the week 1–2 time in week 3–4 time in week 5 time in week | 23 20 13 3 | 39.0 33.9 22.0 5.1 |
| Exercise duratin in week (n=59) 45 minutes in week 60 minutes in week 75–150 minutes in week Other | 8 17 16 18 | 13.6 28.8 27.1 30.5 |
| Have a disease barrier to exercise(n=285) Yes No | 56 229 | 19.6 80.4 |
In the study, the mean total exercise benefits/barriers scale score was 99.79 + 12.58, the mean exercise benefits subscale score was 61.69 + 14.79, and the mean exercise barriers scale score was 35.83 + 5.99. In terms of self-efficacy scale scores, the mean “initiating a behavior change” subscale score was 18.15 + 6.34, the mean “maintaining a behavior” subscale score was 16.15 + 5.24, the mean “completing a behavior” subscale score was 17.06 + 4.34 and the mean “persistence in the face of obstacles” subscale score was 8.38 + 2.45 and the mean total self-efficacy scale score was 59.74 + 9.46 (Table 3).
**Table 3**
| Scales | X+SD (min-max) |
| --- | --- |
| Exercise benefit/barriers scale | |
| Exercise benefit subscale (EBS) | 61.69±14.79 (29–101) |
| Exercise barriers subscale (EBS+) | 35.83±5.99 (20–51) |
| Exercise benefit/barriers scale total (EBBS) | 99.79±12.58 (60–139) |
| Self Efficay Scale | |
| Initating a behavior subscale (IB) | 18.15±6.34 (8–39) |
| Maintaining a behavior subscale (MB) | 16.15±5.24 (7–30) |
| Completing a behavior subscale (CB) | 17.06±4.34 (5–25) |
| Persistence in the face of obstacle subscale (PFO) | 8.38±2.45 (3–15) |
| Self efficacy scale total (SES) | 59.74±9.46 (25–84) |
There was a negative, moderately significant correlation between the mean exercise benefits subscale score and the mean exercise barriers subscale score (r = -. 480, $$p \leq .000$$) and there was a strong positive correlation between the mean exercise benefits subscale score and the mean total benefits/barriers scale score ($r = .870$, $$p \leq .000$$). There was a positive, very weak, significant correlation between the mean exercise benefits/barriers scale score and the mean “initiating a behavior change” subscale score of the self-effic and there was a positive, weak, significant correlation between the mean exercise benefits/barriers scale score and the mean “maintaining a behavior” subscale score ($r = .280$, $$p \leq .000$$). There was a negative, weak, significant correlation between the mean exercise benefits/barriers scale score and the mean “completing a behavior” subscale score of the self-efficacy scale (r = -. 200, $$p \leq .001$$) and there was a negative, very weak, significant correlation between the mean exercise benefits/barriers scale score and the mean “persistence in the face of obstacles” subscale score (r = -. 197, $$p \leq .001$$). There was a positive, very weak, significant correlation between the mean total exercise benefits/barriers scale score and the mean total self-efficacy scale score ($r = .156$, $$p \leq .000$$) (Tablo 4)
**Table 4**
| Scales and subscale | EBS | EBS+ | EBBS | IB | MB | CB | PFO | SES |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| EBS | 1 | | | | | | | |
| EBS+ | -.480 .000 | 1 | | | | | | |
| EBBS | .870 .000 | -.065 .273 | 1 | | | | | |
| IB | .312 .000 | -.299 .000 | .187 .002 | 1 | | | | |
| MB | .385 .000 | -.260 .000 | .280 .000 | .756 .000 | 1 | | | |
| CB | -.319 .000 | .296 .000 | -.200 .001 | -.484 .000 | -.458 .000 | 1 | | |
| PFO | -.227 .000 | .149 .012 | -.197 .001 | -.302 .000 | -.222 .000 | .488 .000 | 1 | |
| SES | .226 .000 | -.140 .004 | .156 .008 | .770 .000 | .770 .000 | -.012 .843 | .135 .022 | 1.0 |
A statistically significant difference was reported between the mean exercise benefits/barriers scale score and education status (KW = 22.014, $$p \leq .000$$), employment status ($U = 6762.50$, $$p \leq .000$$), social insurance status ($U = 4645.00$, $$p \leq .030$$), and family structure (KW = 14.628, $$p \leq .001$$) (Table 5).
**Table 5**
| Unnamed: 0 | EBS | EBS+ | EBBS | IB | MB | CB | PFO |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Education status Primary school Middle School High school University and above | 65.44±14.36 63.34±17.15 62.68±14.18 55.14±12.11 | 35.16±5.81 35.06±6.58 36.42±6.89 36.78±5.09 | 102.56±12.52 100.38±14.28 101.83±12.33 94.41±10.06 | 19.20±6.97 18.57±6.17 18.34±5.75 16.40±5.63 | 16.80±5.72 17.11±4.83 16.25±4.66 14.69±4.96 | 16.37±4.46 16.83±5.07 17.47±3.98 17.83±3.85 | 8.03±2.52 8.47±2.41 8.72±2.51 8.58±2.31 |
| | K-W=26.321 p=0.000 | K-W= 1.665 p=0.645 | K-W=22.014 p=0.000 | K-W=9.065 p=0.028 | K-W=10.828 p=0.013 | K-W=4.379 p=0.223 | K-W=3.303 p=0.347 |
| Marital status Married Single | 61.39±14.95 63.08±14.08 | 35.72±6.12 36.34±5.34 | 99.42±12.68 101.48±12.07 | 18.00±6.23 18.84±6.86 | 16.08±5.21 16.50±5.45 | 17.06±4.49 17.08±3.57 | 8.53±2.49 7.68±2.11 |
| | U=5490.50 p=0.467 | U=5596.50 p=0.598 | U=5450.50 p=0.422 | U=5548.50 p=0.537 | U=5643.50 p=0.661 | U=5826.50 p=0.927 | U=4801.50 p=0.041 |
| Employment status Employed Unemployed | 58.35±14.39 63.38±14.73 | 35.46±5.81 36.02±6.08 | 96.36±12.38 101.51±12.35 | 17.35±5.75 18.56±6.59 | 15.53±4.86 16.47±5.41 | 17.74±4.01 16.71±4.47 | 8.97±2.55 8.08±2.35 |
| | U= 7265.50 p=0.006 | U= 8456.00 p=0.348 | U= 6762.50 p=0.000 | U= 8229.00 p=0.199 | U= 8299.50 p=0.239 | U= 8005.00 p=0.104 | U= 7292.50 p=0.006 |
| Social insurance Yes No | 60.71±15.08 66.37±12.41 | 36.14±5.96 34.37±5.98 | 99.12±12.59 102.94±12.14 | 18.35±6.53 17.18±5.29 | 16.31±5.24 15.39±5.22 | 17.14±4.39 16.65±4.09 | 8.39±2.46 8.31±2.39 |
| | U= 4459.00 p=0.012 | U= 4744.00 p=0.048 | U=4645.00 p=0.030 | U= 5310.00 p=0.368 | U= 5195.00 p=0.262 | U= 5274.00 p=0.332 | U= 5667.50 p=0.826 |
| Economic status | Economic status | Economic status | Economic status | Economic status | Economic status | Economic status | Economic status |
| Poor Middle Good | 60.02±13.52 60.71±14.67 60.03±15.04 | 33.77±4.95 35.83±5.89 37.25±6.55 | 104.34±11.78 98.72±12.55 99.56±12.68 | 19.89±5.84 17.82±6.27 17.88±6.74 | 16.84±5.43 15.87±5.06 16.47±5.61 | 15.73±3.76 17.33±4.15 17.21±5.08 | 8.18±2.45 8.27±2.40 8.84±2.55 |
| | KW=9.438 p=0.009 | KW=7.891 p=0.019 | KW=5.721 p=0.057 | KW=4.900 p=0.086 | KW=1.648 p=0.439 | KW=5.828 p=0.054 | KW=3.204 p=0.202 |
| Longest living place Villige City | 65.81±15.81 60.63±14.36 | 35.14±5.97 36.01±5.99 | 102.97±12.79 98.96±12.42 | 19.64±7.17 17.77±6.07 | 16.89±6.23 15.96±4.96 | 16.21±4.18 17.28±4.37 | 8.21±2.59 8.43±2.41 |
| | U= 5445.50 p=0.042 | U=6078.50 p=0.367 | U= 5545.00 p=0.064 | U= 5666.50 p=0.101 | U= 6138.50 p=0.426 | U= 5655.50 p=0.097 | U= 6205.50 p=0.497 |
| Family structure Immediate family Extend family Fragmented family | 58.57±13.05 65.21±16.32 71.00±8.09 | 36.45±6.02 35.12±5.90 34.11±5.86 | 97.53±11.00 102.27±14.07 107.22±10.13 | 17.85±6.14 18.76±6.68 15.56±4.45 | 15.75±4.89 16.71±5.77 16.00±3.74 | 17.25±4.22 16.86±4.51 16.33±4.66 | 8.45±2.30 8.29±2.66 8.33±2.18 |
| | KW= 18.222 p=0.000 | KW=3.442 p=0.179 | KW=14.628 p=0.001 | KW=2.248 p=0.325 | KW=1.734 p=0.420 | KW=0.441 p=0.802 | KW=0.238 p=0.888 |
There was a statistically significant correlation between the mean total exercise benefits/barriers scale score and the ability to exercise ($U = 7140.00$, $$p \leq 0.000$$), exercising regularly ($U = 4802.00$, $$p \leq 0.001$$) and having a disease that prevents exercise ($U = 4732.00$, $$p \leq 0.002$$) (Table 6).
**Table 6**
| Unnamed: 0 | EBS | EBS+ | EBBS | IB | MB | CB | PFO |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Exercise opportunity | Exercise opportunity | Exercise opportunity | Exercise opportunity | Exercise opportunity | Exercise opportunity | Exercise opportunity | Exercise opportunity |
| Yes No | 57.86±13.94 68.15±13.96 | 37.36±5.60 33.25±5.74 | 97.64±12.47 104.39±11.97 | 17.41±6.36 19.39±6.13 | 15.69±5.25 16.92±5.16 | 17.66±4.22 16.04±4.38 | 8.54±2.61 8.11±2.13 |
| | U= 5627.00 p=0.000 | U=5672.00 p=0.000 | U=7140.00 p=0.000 | U=7465.50 p=0.006 | U=7959.50 p=0.023 | U=7505.50 p=0.003 | U=8714.0p=0.246 |
| Regularly exercise | Regularly exercise | Regularly exercise | Regularly exercise | Regularly exercise | Regularly exercise | Regularly exercise | Regularly exercise |
| Yes No | 53.95±11.42 63.71±14.92 | 38.63±5.93 35.10±65.79 | 95.19±9.99 100.98±12.92 | 15.53±6.14 18.84±6.22 | 14.31±4.99 16.64±5.21 | 17.98±4.55 16.82±4.27 | 8.71±2.54 8.29±2.42 |
| | U=4019.50 p=0.000 | U=4397.00 p=0.000 | U=4802.00 p=0.001 | U=4519.50 p=0.000 | U=4810.50 p=0.001 | U=5649.50 p=0.070 | U=5964.0p=0.070 |
| Have a disease barrier to exercise | Have a disease barrier to exercise | Have a disease barrier to exercise | Have a disease barrier to exercise | Have a disease barrier to exercise | Have a disease barrier to exercise | Have a disease barrier to exercise | Have a disease barrier to exercise |
| Yes No | 69.14±14.78 59.86±14.24 | 34.04±5.64 36.27±5.99 | 104.89±12.13 98.53±12.39 | 20.04±6.09 17.69±6.32 | 17.29±5.26 15.88±5.21 | 15.21±4.36 17.51±4.23 | 7.43±2.21 8.62±2.45 |
| | U=4258.50 p=0.000 | U=5305.00 p=0.045 | U=4732.00 p=0.002 | U=4949.50 p=0.008 | U=5350.00 p=0.054 | U=4577.50 p=0.001 | U=4540.0p=0.001 |
## Discussion
Because of the restrictions during the Covid-19 pandemic, the intensive use of technology has led to an increase in the number of physically inactive individuals and the maintenance of health-enhancing behaviors has been negatively affected. For this reason, health-enhancing behaviors are very important for individuals diagnosed with diabetes to keep their disease under control and to adapt to the treatment process. This study was performed to determine exercise benefits/barriers perceptions of diabetic individuals and the factors affecting them.
Because the studies conducted with the exercise benefits/barriers scale in diabetic individuals are limited, the results of this study were discussed with the results of the studies conducted in different sample groups.
Exercise is considered an important treatment parameter for diabetes mellitus20. Participants in our study were reported to regularly exercise at a low level ($20.7\%$). Moreover, $27.1\%$ of the participants who exercised regularly exercised at the desired level. In a study, researchers reported that despite the positive effects of exercise, only few patients with diabetes maintained physical activity and in those who exercised, the intensity of exercise was extremely low10. For the positive effects aimed in diabetic patients to occur, the recommended exercise prescriptions should comprise aerobic exercise workouts to be performed at least 3–7 days a week (two days in a row) in combination with resistance training and normal range of motion exercises to be performed 2–3 days a week21. In order for the exercise to be effective in patients with diabetes, it is important to do it at a sufficient intensity, frequency and awareness. The fact that the ratio of the participants who exercised in this study was not at the desired level can be explained by both the restrictions experienced during the pandemic and the insufficient level of awareness. Moreover, the mean exercise benefits subscale score of the exercise benefits/barriers scale may have affected regular execise status.
In this study, the mean exercise benefits/barriers scale score was 99.79 + 12.58. In a study conducted with nursing and medical students, the researchers reported a higher mean exercise benefits/barriers scale score than this study (122.98 + 15:47)22. Because the highest score that can be obtained from the exercise benefits/barriers scale is 172, the benefit perceptions of the participants in this study are not at a sufficient level.
Exercise benefit perception means subjective evaluation derived from exercise behavior4. In this study, the mean exercise benefits subscale score of the participants was reported to be 61.69 + 14.79. In a study conducted by Ransdell et al. with women in 2004, the mean exercise benefits subscale score of the participants was higher than our study (92.71 ± 8.30)23. In another study conducted with nursing students, the mean exercise benefits subscale score was reported to be 90.68 ± 12.9817. In the study conducted by Doğan and Ayaz with nurses, the mean exercise benefits subscale score was 89.3 ± 11.6 2. Because the highest score that can be obtained from the mean exercise benefits subscale score of the exercise benefits/barriers scale is 116, the mean score of the participants in the exercise benefits subscale in this study is low. Furthermore, this result suggests that the participants do not have sufficient awareness of exercise.
Perceived barrier to an action is related to the barriers encountered in doing that action. Perceived barriers may prevent starting a new activity or reduce commitment to continued activity4. In Doğan and Ayaz's study, perceived barriers are an important factor affecting exercise2. In this study, the mean exercise barriers subscale score of the exercise benefits/barriers scale was 38.12 + 6.51. The mean exercise barriers subscale score was 50.17 ± 4.79 in the study of Ransdell et al. and 28.66 ± 5.50 in the study of Ortabağ [2009]17,23. In another study conducted with nurses, the mean exercise barriers subscale score of the participants was 31.4 ± 5.4 2. Because the highest score that can be obtained from the mean exercise barriers subscae score of the exercise benefits/barriers scale is 56, the mean score of the participants in the exercise barriers subscale in this study is not at the desired level. The reason for this result suggests that individuals with diabetes do not have suitable conditions for exercise because of measures taken during the Covid-19 pandemic.
One of the important factors affecting developing health-enhancing behaviors is self-efficacy level. Self-efficacy expresses individuals' belief in performing a behavior2,4. In this study, the mean self-efficacy scale score was reported to be 59.55 + 9:45.
In the study of Doğan and Ayaz, the mean self-efficacy scale score of the nurses was reported to be 74.1 ± 12.0 2. In this study, the mean self-efficacy scale score of the participants is not at the desired level. Furthermore, a positive, very weak, significant correlation was reported between the mean self-efficacy scale score and the mean exercise benefits/barriers scale score of diabetic individuals ($r = .157$, $$p \leq 0.009$$). The obtained results indicate that the Covid-19 pandemic has lowered the participants' belief in performing health-protective and health-enhancing behaviors.
In this study, the education status, employment status, social insurance status, economic status, place of residence they lived the longest, family type, opportunity to exercise, regular exercise status, presence of a disease barrier to exercise, smoking status affected the mean exercise benefits subscale score of the exercise benefits/barriers scale of the participants. Furthermore, the economic status, place of residence they lived the longest, opportunity to exercise, regular exercise status, presence of a disease barrier to exercise, and smoking status affected the mean exercise barriers subscale score of the exercise benefits/barriers scale of the participants. In a study, it was reported that, unlike this study, marital status affects the mean exercise barriers subscale score 15.
In this study, the mean exercise benefits subscale score of the exercise benefits/barriers scale of the participants who were married was higher than single individuals; however, the mean exercise barriers subscale score of the exercise benefits/barriers scale of the participants who were married was lower than single individuals. Unlike the mentioned study, in the study conducted by Bakır and Hisar, the mean exercise barriers subscale score of the single individuals was lesser than that of the married participants 15. The results of the study demonstrate that married individuals are aware of the benefits and barriers of exercise behavior that are included in healthy lifestyle behaviors, which is an expected result.
In this study, it was determined that the mean exercise benefits subscale score of the exercise benefits/barriers scale of the participants who exercised regularly was low while the mean exercise barriers subscale score was high. In a study, the mean exercise benefits subscale score and barriers subscale score of nurses who exercised regularly were reported to be statistically significantly high 2. In a study conducted by Arısoyusing the health belief model scale, the benefit and barrier perceptions of individuals who exercised were reported to be higher than those who did not exercise 5. Regular exercise has an effect on glycemic results, weight loss and cardiovascular risk factors in individuals with diabetes 24. However, in a qualitative study, while it was emphasized that exercise is a source of motivation for a healthy life, patients experienced certain negative situations after exercise (experiencing hypoglycemia, increasing carbohydrate intake.). These results cause uncertainty in patients about benefits of exercise 25. The low-level exercise benefit perceptions of participants who regularly exercised in the mentioned study suggests that they may have faced different problems after exercise.
## Limitations of the study
During the Covid-19 pandemic, the restrictions on individuals with chronic diseases and the fear of individuals with diabetes getting infected caused a decrease in the number of diabetic individuals coming to the outpatient clinic. Therefore, the limited number of diabetic individuals reached is a limitation of the study.
## Conclusion and Recommendations
In the study, the number of individuals who regularly exercise is not sufficient. Therefore, it is recommended to conduct interventional studies that will enable individuals with diabetes to understand the importance of exercise in controlling their disease.
Moreover, considering the fact that the mean exercise benefits/barriers scale score is not at the desired level, and that the exercise benefit/barrier perception is affected by multiple factors, additional studies should be conducted to increase exercise benefit/barrier perception.
Furthermore, it was seen in the study that the exercise benefit/barrier perceptions of the participants were affected by their self-efficacy level. Therefore, training and exercise programs should be developed to identify, initiate and maintain behaviors that will increase the level of self-efficacy of individuals with diabetes and to identify barriers to these behaviors.
## Author Contributions
All authors have read and agreed with the content of the manuscript. Each author has participated sufficiently in the work to take public responsibility for appropriate portions of the content.
## Conflicts of Interest
The authors do not have any conflict of interest or financial disclosure. No funding was received for the study. The article or the content is not under consideration or has not been published by any other journal.
## Ethical Approval
Before starting the collection of research data, written permissions were obtained from the relevant Clinical Research Ethics Committee (decision dated 27.01.2020 and numbered HRU / 20.02.27).
## References
1. Maville JA, Huerta CG. *Health Promotion in Nursing* (2008) 42-45
2. Doğan R, Ayaz S. **Exercise behaviours, self sufficiency levels of nurses and related factors**. *Journal of Anatolia Nursing and Health Sciences* (2015) **18** 287-295. DOI: 10.17049/ahsbd.28746
3. Polat MG. **The physical activity/exercise in type II diabetes**. *Physiother Rehabil-Special Topics* (2016) **2** 57-62
4. Champion VL, Skinner CS, Glanz K, Rimer BK, Viswanath K. **The health belief model**. *Health behavior and health education: theory, research, andpractice* (2008) 45-50
5. Arısoy H. *Attitudes of elderly patients with diabetes towards illness and health* (2013)
6. Fallon EA, Hausenblas HA. **Transtheoretical model: is termination applicable to exercise?**. *American Journal of Health Studies* (2004) **19** 35-44
7. Spencer L, Adams TB, Malone S, Roy L, Yost E. **Applying the transtheoretical model to exercise: a systematic and comprehensive review of the literature**. *Health Promotion Practice* (2006) **7** 428-443. DOI: 10.1177/1524839905278900
8. Malone LA, Barfield JP, Brasher JD. **Perceived benefits and barriers to exercise among persons with physical disabilities or chronic health conditions with in action or maintenance stages of exercise**. *Disability and Health Journal* (2012) **5** 254-260. DOI: 10.1016/j.dhjo.2012.05.004
9. Silvano ZA, Jimenez G, Pugliese G, Corigliano S. **Exercise for the management of type 2 diabetes: a review of the evidence**. *Acta Diabetologica* (2010) **47** 15-22. DOI: 10.1007/s00592-009-0126-3
10. Thomas N, Alder E, Leese GP. **Barriers to physical activity in patients with diabetes**. *Postgraduate Medical Journal* (2004) **80** 287-291. DOI: 10.1136/pgmj.2003.010553
11. Pati S, Lobo E, Pati S, Desaraju S, Mahapatra P. **Type 2 diabetes and physical activity: barriers and enablers to diabetes control in Eastern India**. *Primary Health Care Research & Development* (2019) **20** e44. DOI: 10.1017/S146342361800097X
12. Advika TS, Idiculla J, Kumari SJ. **Exercise in patients with Type 2 diabetes: facilitators and barriers a qualitative study**. *Journal of Family Medicine and Primary Care* (2017) **6** 288292. DOI: 10.4103/2249-4863.219998
13. Mo P, Blake H, Batt M. **Getting health care staff more active: The mediating role of self-efficacy**. *British Journal of Health Psychology* (2011) **16** 690-706. DOI: 10.1111/j.2044-8287.2010.02007.x
14. Pedersen MM, Zebis MK, Langberg H, Poulsen OM, Mortensen OS. **Influence of self-efficacy on compliance to work place exercise**. *International Journal of Behavioral Medicine* (2013) **20** 365-370. DOI: 10.1007/s12529-012-9239-0
15. Bakır H, Hisar KM. **Bir üniversitesi hastanesinde çalışan hemşirelerin egzersize ilişkin yarar ve engel algıları ve öz yeterlilik durumu**. *Genel Tıp Dergisi* (2016) **26** 84-91
16. Sechrist R, Walker S, Pender J. **Development and psychometric evaluation of the exercise benefit barriers scale**. *Research in Nursing & Health* (1987) **10** 357-365. DOI: 10.1002/nur.4770100603
17. Ortabağ T, Ceylan S, Akyuz A, Bebiş H. **The validity and reliability of the exercise benefits/barriers scale for Turkish military nursing students**. *South African Journal for Research in Sport, Physical Education and Recreation* (2010) **32** 55-70. DOI: 10.4314/sajrs.v32i2.59297
18. Sherer M, Maddux J. **The selfefficacy scale construction and validation**. *Psychological Reports* (1982) **51** 663-671. DOI: 10.2466/pr0.1982.51.2.663
19. Gözüm S, Aksayan S. **The reliabillty and valldity of Turkish form of the self-efficacy scale**. *Atatürk Üniversitesi Hemşirelik Yüksekokulu Dergisi* (1999) **2** 21-34
20. Colberg SR, Sigal RJ, Fernhall B, Regensteiner JG, Blissmer BJ, Rubin RR. **Exercise and type 2 diabetes: the American College of Sports Medicine and the American Diabetes Association: joint position statement**. *Diabetes Care* (2010) **33** 147-167. DOI: 10.2337/dc10-9990
21. Jonathan J, Gordon P, Visich P, Keteyian S, Arıkan H, Ergün N, Özdinçler AR, Tuğay BU. *Clinical exercise physiology* (2018) 91-111
22. Blake H, Stanulewicz N, Mcgill F. **Predictors of physical activity and barriers to exercise in nursing and medical students**. *Journal of Advanced Nursing* (2018) **73** 917-929. DOI: 10.1111/jan.13181
23. Ransdell LB, Detling N, Hildebrand K, Moyer-Mileur L, Shultz B. **Can Physical Activity Interventions Change Perceived Exercise Benefits and Barriers?**. *American Journal of Health Studies* (2004) 195-204
24. Pan B, Ge L, Xun YQ, Chen YJ, Gao CY. **Exercise training modalities in patients with type 2 diabetes mellitus: a systematic review and network meta-analysis**. *The International Journal of Behavioral Nutrition and Physical Activity* (2018) **15** 72. DOI: 10.1186/s12966-018-0703-3
25. Kennedy A, Narendran P, Andrews RC, Daley A, Greenfield SM. **Attitudes and barriers to exercise in adults with a recent diagnosis of type 1 diabetes: a qualitative study of participants in the exercise for type 1 diabetes (EXTOD) study**. *BMJ Open* (2018) **8** e017813. DOI: 10.1136/bmjopen-2017-017813
|
---
title: Impact of exercise on renal function, oxidative stress, and systemic inflammation
among patients with type 2 diabetic nephropathy
authors:
- Mohamed H Saiem Aldahr
- Shehab M Abd El-Kader
journal: African Health Sciences
year: 2022
pmcid: PMC9993259
doi: 10.4314/ahs.v22i3.30
license: CC BY 4.0
---
# Impact of exercise on renal function, oxidative stress, and systemic inflammation among patients with type 2 diabetic nephropathy
## Abstract
### Background
Diabetic nephropathy (DN) is a prevalent microvascular diabetic complication all over the world.
### Objective
This study was designed to measure oxidative stress, systemic inflammation and kidney function response to exercise training in patients with type 2 diabetic (T2DM) nephropathy.
### Material and Methods
Eighty obese T2DM patients (50 males and 30 females), their body mass index (BMI) mean was 33.85±3.43 Kg/m2 and the mean of diabetes chronicity was 12.53±2.64 year participated in the present study and enrolled two groups; group I: received aerobic exercise training and group II: received no training intervention.
### Results
The mean values of creatinine, interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α) and malondialdehyde (MDA) were significantly decreased, while the mean values of interleukin-10 (IL-10), glutathione peroxidase (GPx) and glutathione (GSH) were significantly increased in group (A) after the aerobic exercise training, however the results of the control group were not significant. In addition, there were significant differences between both groups at the end of the study ($P \leq 0.05$).
### Conclusion
There is evidence that aerobic exercise training modulated oxidative stress and inflammatory cytokines and improved renal function among patients with diabetic nephropathy.
## Introduction
Diabetic nephropathy (DN) considered as the most serious diabetic complication; while renal replacement is required for the majority of subjects with chronic renal disease among patients with T2DM1,2, where poor glycemic control3 is related to abnormal oxidative stress and systemic inflammation that induce progressive diabetic renal lesion4,5. Hyperglycemia induces oxidative stress and inflammation6. In addition, poor glycemic control induces abnormal level of oxidative stress markers7. In the other hand, oxidative stress induce dysfunction of β-cell that lead to insulin resistance development, diabetes and its associated microvascular complications8,9, so that patients with T2DM are under oxidative stress because of prolonged exposure to hyperglycemia10.
Researches proved that hyperglycemia that induced systemic inflammation and oxidative stress which induce DN11,12. Hyperglycemia in diabetic patients leads to mitochondrial dysfunction, advanced glycation end processes and other factors, and generate the reactive free radicals, then triggers the DNA fragmentation that lead to cell death13. However, Navarro et al. found an increase in the gene expression for pro-inflammatory cytokine in patients with DN14. Several studies reported that there was a significant elevation in inflammatory cytokines in T2DM with DN and there is an association between their levels and the incidence & the course of renal lesion among diabetics15–17.
Hyperglycemia also causes oxidative stress, decreases the regeneration of glutathione (GSH) from oxidized GSH and reduces the availability of nicotinamide adenine dinucleotide phosphate18,19. However, several reports stated that there was reduced level of GSH in diabetes associated with systemic inflammation20–22. In addition, in β-cell dysfunction may be related to abnormal GSH level induce long-term complications of diabetes23. Moreover, low GSH is related to DNA oxidative damage in T2DM24. Many studies reported decline in the level of SOD in diabetic tissue and blood25,26. While, study performed by Lucchesi and colleagues to observe the oxidative balance of diabetic rats reported diminished activity of SOD and other antioxidative enzymes in the liver tissue27. In the other hand, several studies reported an increased MDA level in patients with T2DM28,29. In addition, Baynes and Ramesh et al. reported that lipid peroxidation in diabetes induced many secondary chronic complications including atherosclerosis and neural disorders30,31.
Physical activity has several health benefits and plays an important role in treatment of chronic disorders. However, regular physical activity improves glucose control, blood lipid profile, insulin sensitivity and endothelial function that help to prevent diabetic complications32. Moreover, physical activity may reduce the risk and progression for diabetic nephropathy33.
This study was designed to measure oxidative stress, systemic inflammation and kidney function response to exercise training in patients with type 2 diabetic nephropathy.
## Subjects
Eighty obese T2DM patients (50 males and 30 females), their body mass index (BMI) mean was 33.85±3.43 Kg/m2and the mean of diabetes chronicity was 12.53±2.64 year participated in the present study and enrolled two groups; group I: received aerobic exercise training and group II: received no training intervention. Exclusion criteria included smokers, kidney insufficiency, congestive heart failure, pregnant female patients, hepatitis and respiratory failure. Clinical evaluations and laboratory analysis were performed by independent assessors who were blinded to group assignment and not involved in the routine treatment of the patients. The CONSORT diagram outlining the details of the screening, run-in and randomization phases of the study and reasons for participant exclusion can be found in figure [1]. Informed consent was obtained from all participants. This study was approved by the Scientific Research Ethical Committee, Faculty of Applied Medical Sciences at King University.
**Figure 1:** *Subjects screening and recruitment CONSORT diagram.*
## A. Measurement of oxidative stress markers and anti-oxidant status
For all participants serum (from 10 ml blood in plain vial) and plasma (from 5 ml blood in EDTA vial) were separated from the sample within 30 min of collection and was stored in pyrogen free polypropylene cryo-tubes at (-80°C) until analysis. Assessment of lipid markers for peroxidation as malondialdehyde (MDA) was determined according to Buege and Aust34. However, Anti-oxidant status, glutathione (GSH) that was determined by the method of Beutler and colleagues35, in the other hand, glutathione peroxidase (GPx) was measured by the method of Nishikimi and colleagues36.
## B. Measurement of inflammatory cytokines and serum creatinine
Blood samples were drained from the antecubital vein after a 12-hour fasting, the blood samples were centrifuged at + 4 °C (1000 = g for 10 min). Interleukin-6 (IL-6) and Interleukin-10 (IL-10) levels were analyzed by “Immulite 2000” immunassay analyzer (Siemens Healthcare Diagnostics, Deerfield, USA). However, tumor necrosis factor-alpha (TNF-α) was measured by ELISA kits (ELX 50) in addition to ELISA microplate reader (ELX 808; BioTek Instruments, USA). However, serum creatinine was measured with a kit obtained from Stanbio Laboratory (USA).
## C. Aerobic exercise training program
Patients in group (A) were submitted to a 40 min aerobic session on a treadmill (the initial, 5-minute warm-up phase performed on the treadmill at a low load, each training session lasted 30 minutes and ended with 5-minute recovery and relaxation phase) either walking or running, based on heart rate, until the target heart rate was reached, according to American College of Sport Medicine guidelines. The program began with 10 min of stretching and was conducted using the maximal heart rate index (HRmax) estimated by: 220-age, with exercise intensity was 70–$80\%$ of HRmax34.
## Statistical analysis
The mean values of the investigated parameters obtained before and after three months in both groups were compared using paired “t” test. Independent “t” test was used for the comparison between the two groups ($P \leq 0.05$).
## Results
Eighty obese patients with type 2 diabetes mellitus completed the screening evaluation. The baseline characteristics of the participants are shown in table [1]. Most participants ($60\%$) were men. Forty participants were assigned group (A) ($$n = 40$$; 24 males and 16 females) and group (B) ($$n = 40$$, 26 males and 14 females). None of the baseline characteristics differed significantly between the two groups is listed in table [1].
**Table (1)**
| Unnamed: 0 | Group (A) | Group (B) | Significance |
| --- | --- | --- | --- |
| Age (year) | 48.34 ± 6.91 | 47.65 ± 7.28 | P > 0.05 |
| Gender (male/female) | 24/16 | 26/14 | P > 0.05 |
| BMI (kg/m2) | 34.15 ± 3.39 | 33.82 ± 3.47 | P > 0.05 |
| Duration of diabetes (year) | 13.12 ± 2.56 | 11.94 ± 2.72 | P > 0.05 |
| SBP (mmHg) | 148.53 ± 12.16 | 145.81 ± 13.44 | P > 0.05 |
| DBP (mmHg) | 92.62 ± 8.75 | 90.25 ± 7.28 | P > 0.05 |
| HBA1c (%) | 8.52 ± 2.43 | 8.37 ± 2.21 | P > 0.05 |
| Glucose (mmol/L) | 5.71 ±1.65 | 5.42 ± 1.48 | P > 0.05 |
| QUICKI | 0.149 ± 0.017 | 0.158 ± 0.016 | P > 0.05 |
| HOMA-IR | 5.13 ± 1.45 | 4.71 ± 1.32 | P > 0.05 |
The mean values of creatinine, interleukin-6 (IL-6), tumor necrosis factor- alpha (TNF-α) and malondialdehyde (MDA) were significantly decreased, while the mean values of interleukin-10 (IL-10), glutathione peroxidase (GPx) and glutathione (GSH) were significantly increased in group (A) after the aerobic exercise training(table 2), however the results of the control group were not significant (table 3). In addition, there were significant differences between both groups at the end of the study (table 4).
## Discussion
Diabetic nephropathy (DN) is a worldwide prevalent medical problem affecting 20–$40\%$ of T2DM and characterized with high rate of morbidity and mortality as the DN is a principal etiology of renal failure35–37. Poor metabolic control, diabetes duration, race, heredity, life style, diet composition, aging, hypertension, systemic inflammation and oxidative stress are the common risk factors of DN38,39.
Our results demonstrate that aerobic exercise training reduced levels of TNF-α and IL-6, in addition to increased level of IL-10 that indicated reduced systemic inflammation. our results agreed with several studies have shown that aerobic exercise training promotes modulation of inflammatory cytokines40–42. Several large cohort studies have found a relationship between self-reported physical activity levels and systemic markers of inflammation: higher levels of physical activity are coupled to lower levels of circulating inflammatory markers in elderly individuals43–45. While, Nicklas et al. showed that regular aerobic exercise training was efficient in lowering IL-6 levels even without weight loss46. In addition, Santos and colleagues had twenty-two male, sedentary, healthy, elderly volunteers performed moderate aerobic exercise training for 60 min/day, 3 days/week for 24 week and concluded that 6 months of aerobic exercise training can improve sleep in the elderly via anti-inflammatory effect of aerobic training which modiies cytokine profiles (reduced IL-6 and TNF-α and increased IL-10)47. However, Kohut et al. reported that 10-months of aerobic, but not resistance exercise, significantly reduces serum inflammatory mediators in older adults48. Moreover, Bote et al. demonstrated that 8-months (2 sessions/week, 60-min/session) of aquatic-based exercise training tempered neutrophil activation (chemotaxis) and decreased systemic levels of IL-8 and noradrenalin compared to controls49. Similarly, Ploeger et al. reported that moderate aerobic exercise training has been recommended as an anti-inflammatory therapy50. The three possible mechanisms of exercise anti-inflammatory effects include reduction in visceral fat mass51; reduction in the circulating numbers of pro-inflammatory monocytes52 and an increase in the circulating numbers of regulatory T cells53. Moreover, Hong and colleagues show that cardiorespiratory fitness is associated with reduced low grade inflammation which may in part be mediated by enhancing the ability of immune cells to suppress inflammatory responses via adrenergic receptors54.
Concerning results of oxidative stress markers, results of our study agreed with other authors who reported that a six-months aerobic exercise was able to decrease lipid peroxidation, as well as to increase GSH and catalase activity in T2DM patients55,56. A similar study in obese individuals reported attenuation in exercise induced lipid peroxidation following 24 weeks of a moderate intensity resistance training57. In addition, Oliveira et al. compared the effects of 12 weeks training with three different types of exercise (aerobic training, strength training and combined training) on T2DM male and female human subjects, demonstrating that the aerobic exercise may help in minimizing oxidative stress and the development of the chronic complications of diabetes58. However, Vinetti et al. randomly assigned twenty male subjects with T2DM to an intervention group in a supervised exercise training (SET) consisted of a 12-month supervised aerobic, resistance and flexibility training. They concluded that SET was effective in improving cardiorespiratory fitness, cardiometabolic risk and oxidative stress status in T2DM59. While, Farinha et al. completed a 12-week treadmill exercise training, without modifications on dietary pattern in twenty-three women metabolic syndrome who had improved systemic oxidative stress and inflammatory biomarkers60. Similarly, Nojima et al. reported that 103 patients with type 2 diabetes mellitus were instructed to exercise at $50\%$ of peak oxygen uptake for more than 30 minutes on at least 3 days per week over a 12 month period, their results proved that aerobic exercise training improved glycemic control and reduced oxidative stress in patients with type 2 diabetes mellitus61. Moreover, Gordon et al. reported that 3 months of *Hatha yoga* exercise and conventional exercise may have therapeutic preventative and protective effects on diabetes mellitus by decreasing oxidative stress and improving antioxidant status62. There are 2 mechanisms that underlie the anti-oxidative of aerobic exercise training. The first mechanism is that improvement in glycemic control associated with aerobic exercise training may result in a decrease in oxidative stress. Aerobic exercise training improves insulin sensitivity63 and glycemic control64. Hyperglycemia can induce oxidative stress via several mechanisms including glucose autoxidation, formation of advanced glycation end products, and activation of the polyol pathway65. Chugh et al. reported previously that 6 weeks of glycemic control with sulfonylurea resulted in an improvement of glycemic control and a reduction in serum malondialdehyde, a reliable measure of lipid peroxidation66. The other mechanism is that a decrease in oxidative stress caused by aerobic exercise training may lead to an improvement in glycemic control. Aerobic exercise may increase antioxidant activity and reduce oxidative stress. Elosua et al. reported that aerobic exercise training increased the activity of the endogenous antioxidants, glutathione peroxidase, and glutathione reductase and decreased oxidized low-density lipoprotein concentration67. There is evidence that oxidative stress is associated with insulin resistance, as Urakawa et al. demonstrated that plasma isoprostane levels were negatively correlated with glucose infusion rates in men68. These results therefore indicate that improved insulin sensitivity and glycemic control induced reduction in oxidative stress caused by aerobic exercise training.
Concerning renal function, results of the present study proved that aerobic exercise training improved creatinine in patients with DN, the possible cause for improving renal function following aerobic training may be due modulation of inflammatory cytokines and oxidative. Our results consistent with the studies of Chen et al., Shikano et al. and, Kafle et al. who confirmed the possible role of IL-6 and TNF-α and Gpx in diabetic renal damage progress69–71. While, Xu et al. conducted a cohort study on 176 patients with chronic kidney disease and 67 healthy controls and reported increased level of CRP, IL-6 and MDA in addition to decreased levels of SOD and GSHPX (glutathione peroxidase) along with inverse relationship between estimated glomerular filtration rate (eGFR) and MDA associated with positive relationship with SOD and GSH-PX among patients with chronic kidney disease (CKD)72. Moreover, Aslan et al. reported significant correlations between oxidative stress and microalbuminuria levels in patients with diabetic nephropathy73. However, Sreeram et al. reported that among 108 CKD patients, as the renal damage progressed the values of MDA & CRP increased while the values of GPx and SOD decreased74. The current study has important strengths and limitations. The major strength is the supervised nature of the study. However, all exercise sessions were supervised. Moreover, the study was randomized; hence, we can extrapolate adherence to the general population. In the other hand, the major limitations is only obese type 2 diabetic patients enrolled in the study, so the value of this study only related to obese patients with type 2 diabetic nephropathy, also small sample size in both groups may limit the possibility of generalization of the findings in the present study. Finally, within the limit of this study, aerobic exercise training is recommended for modulation of oxidative stress and inflammatory cytokines and improved renal function among patients with diabetic nephropathy. Further researches are needed to explore the impact of weight reduction on quality of life and other biochemical parameters among obese patients with type 2 diabetic nephropathy.
## Conclusion
The current study provides evidence that aerobic exercise training modulated oxidative stress and inflammatory cytokines and improved renal function among patients with diabetic nephropathy.
## References
1. Bukhari SA, Shamshari WA, Ur-Rahman M, Zia-Ul-Haq M, Jaafar HZ. **Computer aided screening of secreted frizzled related protein 4 (SFRP4): a potential control for diabetes mellitus**. *Molecules* (2014) **19** 10129-10136. PMID: 25019556
2. Grace BS, Clayton P, McDonald SP. **Increases in renal replacement therapy in Australia and New Zealand: understanding trends in diabetic nephropathy**. *Nephrology (Carlton)* (2012) **17** 76-84. PMID: 21854505
3. Schaffer SW, Jong CJ, Mozaffari M. **Role of oxidative stress in diabetes mediated vascular dysfunction: Unifying hypothesis of diabetes revisited**. *Vascular Pharmacology* (2012) **57** 139-139. PMID: 22480621
4. Kotur-Stevuljevic J, Simic-Ogrizovic S, Dopsaj V. **A hazardous link between malnutrition, inflammation and oxidative stress in renal patients**. *Clin Biochem* (2012) **45** 1202-1205. PMID: 22580395
5. Massy ZA, Stenvinkel P, Drueke TB. **The role of oxidative stress in chronic kidney disease**. *Semin Dial* (2009) **22** 405-408. PMID: 19708991
6. Dandona P, Mohanty P, Chaudhuri A, Garg R, Aljada A. **Insulin infusion in acute illness**. *The Journal of Clinical Investigation* (2005) **115** 2069-2072. PMID: 16075050
7. Monnier L, Mas E, Ginet C, Michel F, Villon L, Cristol JP, Colette C. **Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes**. *Journal of the American Medical Association* (2006) **295** 1681-1687. PMID: 16609090
8. Pan HZ, Zhang L, Guo MY, Sui H, Li H, Wu WH, Qu NQ, Liang MH, Chang D. **The oxidative stress status in diabetes mellitus and diabetes nephropathy**. *Acta Diabetol* (2010) **47** 71-76. PMID: 19475334
9. Shi YC, Pan TM. **Red mold, diabetes, and oxidative stress: A review**. *Appl Microbiol Biotechnol* (2012) **94** 47-55. PMID: 22382167
10. Likidlilid A, Patchanans N, Peerapatdit T, Sriratanasathavorn C. **Lipid peroxidation and antioxidant enzyme activities in erythrocytes of type 2 diabetes patients**. *J Med Assoc Thai* (2010) **93** 682-693. PMID: 20572373
11. Bhattacharya S, Manna P, Gachhui R, Sil PC. **D-saccharic acid 1,4-lactone protects diabetic rat kidney by ameliorating hyperglycemia-mediated oxidative stress and renal inflammatory cytokines via NF-kappaB and PKC signaling**. *Toxicol Appl Pharmacol* (2013) **267** 16-29. PMID: 23261973
12. Chow F, Ozols E, Nikolic-Paterson DJ, Atkins RC, Tesch GH. **Macrophages in mouse type 2 diabetic nephropathy: correlation with diabetic state and progressive renal injury**. *Kidney Int* (2004) **65** 116-128. PMID: 14675042
13. Maritim AC, Sanders RA, Watkins JB. **Diabetes, oxidative stress, and antioxidants: a review**. *J Biochem Mol Toxicol* (2003) **17** 24-38. PMID: 12616644
14. Navarro JF, Milena FJ, Mora C, Leon C, Garcı´a J. **Renal pro-inflammatory cytokine gene expression in diabetic nephropathy: Effect of angiotensin-converting enzyme inhibition and pentoxifylline administration**. *Am J Nephrol* (2006) **26** 562-570. PMID: 17167242
15. Moriwaki Y, Yamamoto T, Shibutani Y, Aoki E, Tsutsumi Z, Takahashi S. **Elevated levels of interleukin-18 and tumor necrosis factor-alpha in serum of patients with type 2 diabetes mellitus: relationship with diabetic nephropathy**. *Metabolism* (2003) **52** 605-608. PMID: 12759891
16. Navarro JF(a), Mora C, Muros M, Garc´ıa J. **Urinary tumour necrosis factor-alpha excretion independently correlates with clinical markers of glomerular and tubulointerstitial injury in type 2 diabetic patients**. *Nephrology Dialysis Transplantation* (2006) **21** 3428-3434
17. El Mesallamy HO, Ahmed HH, Bassyouni AA, Ahmed AS. **Clinical significance of inflammatory and fibrogenic cytokines in diabetic nephropathy**. *Clin Biochem* (2012) **45** 646-650. PMID: 22421318
18. Vander Jagt DL, Hassebrook RK, Hunsaker LA, Brown WM, Royer RE. **Metabolism of the 2-oxoaldehyde methylglyoxal by aldose reductase and by glyoxalase-I: roles for glutathione in both enzymes and implications for diabetic complications**. *Chem Biol Interact* (2001) **130e132** 549-562
19. Ayalasomayajula SP, Kompella UB. **Subconjunctivally administered celecoxib-PLGA microparticles sustain retinal drug levels and alleviate diabetes-induced oxidative stress in a rat model**. *Eur J Pharmacol* (2005) **511** 191-198. PMID: 15792788
20. Rahigude A, Bhutada P, Kaulaskar S, Aswar M, Otari K. **Participation of antioxidant and cholinergic system in protective effect of naringenin against type-2 diabetes-induced memory dysfunction in rats**. *Neuroscience* (2012) **226** 62-72. PMID: 22999973
21. Calabrese V, Cornelius C, Leso V. **Oxidative stress, glutathione status, sirtuin and cellular stress response in type 2 diabetes**. *Biochimica et Biophysica Acta* (2012) **1822** 729-736. PMID: 22186191
22. Das J, Vasan V, Sil PC. **Taurine exerts hypoglycemic effect in alloxan-induced diabetic rats, improves insulin-mediated glucose transport signaling pathway in heart and ameliorates cardiac oxidative stress and apoptosis**. *Toxicology and Applied Pharmacology* (2012) **258** 296-308. PMID: 22138235
23. Livingstone C, Davis J. **Targeting therapeutics against glutathione depletion in diabetes and its complications**. *British Journal of Diabetes and Vascular Disease* (2007) **7** 258-265
24. Dinc er Y, Akc ay T, Alademir Z, Ilkova H. **Assessment of DNA base oxidation and glutathione level in patients with type 2 diabetes**. *Mutation Research* (2002) **505** 75-81. PMID: 12175907
25. Shukla K, Dikshit P, Tyagi MK, Shukla R, Gambhir JK. **Ameliorative effect of With anticoagulants on dyslipidemia and oxidative stress in nicotinamide streptozotocin induced diabetes mellitus**. *Food and Chemical Toxicology* (2012) **50** 3595-3599. PMID: 22842119
26. Kim CH. **Expression of extracellular superoxide dismutase protein indiabetes**. *Archives of Plastic Surgery* (2013) **40** 517-521. PMID: 24086803
27. Lucchesi AN, Freitas NT, Cassettari LL, Marques SF, Spadella CT. **Diabetes mellitus triggers oxidative stress in the liver of all oxan-treated rats: a mechanism for diabetic chronic liver disease**. *Acta Cirurgica Brasileira* (2013) **28** 502-508. PMID: 23842931
28. Moussa SA. **Oxidative stress in diabetes mellitus**. *Romanian Journal of Biophysics* (2008) **18** 225-236
29. de M Bandeira S, da S Guedes G, da Fonseca LJS, Pires A S, Gelain DP, Moreira JC. **Characterization of blood oxidative stress in type 2 diabetes mellitus patients: increase in lipid peroxidation and SOD activity**. *Oxidative Medicine and Cellular Longevity* (2012) **2012**
30. Baynes JW. **Role of oxidative stress in development of complications in diabetes**. *Diabetes* (1991) **40** 405-412. PMID: 2010041
31. Ramesh B, Karuna R, Sreenivasa RS. **Effect of Commiphora mukul gum resin on hepatic marker enzymes, lipid peroxidation and antioxidants status in pancreas and heart of streptozotocin induced diabetic rats**. *Asian Pacific Journal of Tropical Biomedicine* (2012) **2** 895-900. PMID: 23569867
32. Amaral LS, Silva FA, Correia VB, Andrade CE, Dutra BA, Oliveira MV. **Beneficial effects of previous exercise training on renal changes in streptozotocin-induced diabetic female rats**. *Exp Biol Med (Maywood)* (2016) **241** 437-445. PMID: 26490345
33. Jerums G, MacIsaac RJ. **Diabetic nephropathy: how does exercise affect kidney disease in T1DM?**. *Nat Rev Endocrinol* (2015) **11** 324-325. PMID: 25781858
34. Buege JA, Aust SD. **Microsomal lipid peroxidation**. *Methods Enzymol* (1978) **52** 302-310. PMID: 672633
35. Whiting DR, Guariguta L, Weil C, Shaw J. **IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030**. *Diabetes research and clinical practice* (2011) **94** 311-321. PMID: 22079683
36. Nishikimi M, Appaji Rao N, Yagi K. **The occurrence of superoxide anion in the reaction of reduced phenazine methosulfate and molecular oxygen**. *Biochem Biophys Res Commun* (1972) **46** 849-854. PMID: 4400444
37. Adeshara KA, Diwan AG, Tupe RS. **Diabetes and Complications: Cellular Signaling Pathways, Current Understanding and Targeted Therapies**. *Curr Drug Targets* (2016) **17** 1309-1328. PMID: 26648059
38. Badal SS, Danesh FR. **Diabetic Nephropathy: Emerging Biomarkers for Risk Assessment**. *Diabetes* (2015) **64** 3063-3065. PMID: 26294427
39. Rahimi Z, Mansouri Zaveleh O, Rahimi Z, Abbasi A. **AT2R -1332 G: A polymorphism and diabetic nephropathy in type 2 diabetes mellitus patients**. *J Renal Inj Prev* (2013) **2** 97-101. PMID: 25340140
40. Donges CE, Duffield R, Drinkwater EJ. **Effects of resistance or aerobic exercise training on interleukin-6, C-reactive protein, and body composition**. *Med Sci Sports Exerc* (2010) **42** 304-313. PMID: 20083961
41. Balducci S, Zanuso S, Nicolucci A. **Anti-inflammatory effect of exercise training in subjects with type 2 diabetes and the metabolic syndrome is dependent on exercise modalities and independent of weight loss**. *Nutr Metab Cardiovasc Dis* (2010) **20** 608-617. PMID: 19695853
42. Libardi CA, Souza GV, Cavaglieri CR. **Effect of resistance, endurance, and concurrent training on TNF-a, IL-6, CRP**. *Med Sci Sports Exerc* (2012) **44** 50-56. PMID: 21697747
43. Geffken DF, Cushman M, Burke GL, Polak JF, Sakkinen PA, Tracy RP. **Association between physical activity and markers of inflammation in a healthy elderly population**. *Am. J. Epidemiol* (2001) **153** 242-250. PMID: 11157411
44. Colbert LH, Visser M, Simonsick EM, Tracy RP, Newman AB, Kritchevsky SB, Pahor M, Taaffe DR, Brach J, Rubin S, Harris TB. **Physical activity, exercise, and inflammatory markers in older adults: findings from the Health, Aging and Body Composition Study**. *J. Am. Geriatr. Soc* (2004) **52** 1098-1104. PMID: 15209647
45. Yu Z, Ye X, Wang J, Qi Q, Franco OH, Rennie KL, Pan A, Li H, Liu Y, Hu FB, Lin X. **Associations of physical activity with inflammatory factors, adipocytokines, and metabolic syndrome in middle-aged and older chinese people**. *Circulation* (2009) **119** 2969-2977. PMID: 19487597
46. Nicklas BJ, Hsu FC, Brinkley TJ, Church T, Goodpaster BH, Kritchevsky SB, Pahor M. **Exercise training and plasma C-reactive protein and interleukin- 6 in elderly people**. *J. Am. Geriatr. Soc* (2008) **56** 2045-2052. PMID: 19016938
47. Santos R, Viana V, Boscolo R, Marques V, Santana M, Lira F, Tufik S, de Mello M. **Moderate exercise training modulates cytokine profile and sleep in elderly people**. *Cytokine* (2012) **60** 731-735. PMID: 22917967
48. Kohut ML, McCann DA, Russell DW, Konopka DN, Cunnick JE, Franke WD, Vanderah E. **Aerobic exercise, but not flexibility/resistance exercise, reduces serum IL-18, CRP, and IL-6 independent of beta-blockers, BMI, and psychosocial factors in older adults**. *Brain Behav. Immun* (2006) **20** 201-209. PMID: 16504463
49. Bote ME, Garcia JJ, Hinchado MD, Ortega E. **An exploratory study of the effect of regular aquatic exercise on the function of neutrophils from women with fibromyalgia: role of IL-8 and noradrenaline**. *Brain Behav. Brain Behav Immun* (2014) **39** 107-112. PMID: 24257330
50. Ploeger HE, Takken T, de Greef MH, Timmons BW. **The effects of acute and chronic exercise on inflammatory markers in children and adults with a chronic inflammatory disease: a systematic review**. *Exerc Immunol Rev* (2009) **15** 6-41. PMID: 19957870
51. Mathur M, Pedersen B. **Exercise as a mean to control low-grade inflammation**. *Mediators Inflamm* (2008) **2008** 109502. PMID: 19148295
52. Timmerman K, Flynn M, Coen P, Markofski M, Pence B. **Exercise training-induced lowering of inflammatory (CD14+CD16+) monocytes: a role in the anti-inflammatory influence of exercise?**. *Leukoc Biol* (2008) **84** 1271-1278
53. Wang J, Song H, Tang X, Yang Y, Vieira VJ, Niu Y, Ma Y. **Effect of exercise training intensity on murine T regulatory cells and vaccination response**. *Scand J Med Sci Sports* (2012) **22** 643-652. PMID: 21410542
54. Hong S, Dimitrov S, Pruitt C, Shaikh F, Beg N. **Benefit of physical fitness against inflammation in obesity: role of beta adrenergic receptors**. *Brain Behav Immun* (2014) **39** 113-120. PMID: 24355098
55. Lazarevic G, Antic S, Cvetkovic T, Vlahovic P, Tasic I, Stefanovic V. **A physical activity programme and its effects on insulin resistance and oxidative defense in obese male patients with type 2 diabetes mellitus**. *Diabetes and Metabolism* (2006) **32** 583-590. PMID: 17296511
56. Rector RS, Warner SO, Liu Y. **Exercise and diet induced weight loss improves measures of oxidative stress and insulin sensitivity in adults with characteristics of the metabolic syndrome**. *American Journal of Physiology* (2007) **293** E500-E506. PMID: 17473052
57. Vincent HK, Bourguignon C, Vincent KR. **Resistance training lowers exercise-induced oxidative stress and homocysteine levels in overweight and obese older adults**. *Obesity* (2006) **14** 1921-1930. PMID: 17135607
58. Oliveira VN, Bessa A, Jorge ML. **The effect of different training programs on antioxidant status, oxidative stress, and metabolic control in type 2 diabetes**. *Applied Physiology, Nutrition, and Metabolism* (2012) **37** 334-344
59. Vinetti G, Mozzini C, Desenzani P, Boni E, Bulla L, Lorenzetti I, Romano C, Pasini A, Cominacini L, Assanelli D. **Supervised exercise training reduces oxidative stress and cardiometabolic risk in adults with type 2 diabetes: a randomized controlled trial**. *Sci Rep* (2015) **18** 9238
60. Farinha JB, Steckling FM, Stefanello ST, Cardoso MS, Nunes LS, Barcelos RP, Duarte T, Kretzmann NA, Mota CB, Bresciani G, Moresco RN, Duarte MM, Dos Santos DL, Soares FA. **Response of oxidative stress and inflammatory biomarkers to a 12-week aerobic exercise training in women with metabolic syndrome**. *Sports Med Open* (2015) **1** 3. PMID: 27747840
61. Nojima H, Watanabe H, Yamane K, Kitahara Y, Sekikawa K, Yamamoto H, Yokoyama A, Inamizu T, Asahara T, Kohno N. **Effect of aerobic exercise training on oxidative stress in patients with type 2 diabetes mellitus**. *Metabolism* (2008) **57** 170-176. PMID: 18191045
62. Gordon L, Morrison E, McGrowder D, Young R, Fraser Y, Zamora E, Alexander-Lindo R, Irving R. **Effect exercise therapy on lipid profile and oxidative stress indicators in patients with type 2 diabetes**. *BMC Complement Altern Med* (2008) **8** 21. PMID: 18477407
63. Vanninen E, Uusitupa M, Siitonen O, Laitinen J, Lansimies E. **Habitual physical activity, aerobic capacity and metabolic control in patients with newly diagnosed type 2 (non-insulin-dependent) diabetes mellitus: effect of 1-year diet and exercise intervention**. *Diabetologia* (1992) **35** 340-346. PMID: 1516762
64. Devlin JT, Hirshman M, Horton ED, Horton ES. **Enhanced peripheral and splanchnic insulin sensitivity in NIDDM men after single bout of exercise**. *Diabetes* (1987) **36** 434-439. PMID: 3102297
65. Jay D, Hitomi H, Griendling KK. **Oxidative stress and diabetic cardiovascular complications**. *Free Radic Biol Med* (2006) **40** 183-192. PMID: 16413400
66. Chugh SN, Dhawan R, Kishore K, Sharma A, Chugh K. **Glibenclamide vs gliclazide in reducing oxidative stress in patients of noninsulin dependent diabetes mellitus— a double blind randomized study**. *J Assoc Physicians India* (2001) **49** 803-807. PMID: 11837468
67. Elosua R, Molina L, Fito M, Arquer A, Sanchez-Quesada JL, Covas MI. **Response of oxidative stress biomarkers to a 16-week aerobic physical activity program, and to acute physical activity, in healthy young men and women**. *Atherosclerosis* (2003) **167** 327-334. PMID: 12818416
68. Urakawa H, Katsuki A, Sumida Y, Gabazza EC, Murashima S, Morioka K. **Oxidative stress is associated with adiposity and insulin resistance in men**. *J Clin Endocrinol Metab* (2003) **88** 4673-4676. PMID: 14557439
69. Chen S, Cohen MP, Lautenslager GT, Shearman CW, Ziyadeh FN. **Glycated albumin stimulates TGF-beta 1 production and protein kinase C activity in glomerular endothelial cells**. *Kidney International* (2001) **59** 673-668. PMID: 11168949
70. Shikano M, Sobajima H, Yoshikawa H, Toba T, Kushimoto H, Katsumata H, Tomita M, Kawashima S. **Usefulness of a highly sensitive urinary and serum IL-6 assay in patients with diabetic nephropathy**. *Nephron* (2000) **85** 81-85. PMID: 10773760
71. Prasad Kafle Mukunda, Singh Shah Dibya, Shrestha Shailendra, Raj Sigdel Mahesh, Bahadur Raut Kanak. **Prevalence of specific types of kidney disease in patients undergoing kidney bopsy: a single centre experience**. *Journal of Advances in Internal Medicine* (2014) **03** 5-10
72. Xu Hong, Carrero Juan J. **Insulin resistance in chronic kidney disease**. *Nephrology* (2017) **22** 31-34
73. Aslan JE, David LL, McCarty OJ. **Data detailing the platelet acetyl-lysine proteome**. *Data Brief* (2015) **5** 368-371. PMID: 26904711
74. Ramagopalan Sreeram, Leahy Thomas P, Stamp Elaine, Sammon Cormac. **Approaches for the identification of chronic kidney disease in CPRD-HES-linked studies**. *J Comp Eff Res* (2020) **9** 441-446. PMID: 32148084
|
---
title: Thiol-disulfide balance and trace element levels in patients with seasonal
allergic rhinitis
authors:
- Hasan Basri Savas
- Huseyin Gunizi
journal: African Health Sciences
year: 2022
pmcid: PMC9993264
doi: 10.4314/ahs.v22i3.34
license: CC BY 4.0
---
# Thiol-disulfide balance and trace element levels in patients with seasonal allergic rhinitis
## Abstract
### Background
The prevalence of allergic diseases is gradually increasing worldwide. The most common such allergic disease is allergic rhinitis (AR).
### Objective
The present study investigated the possible relationship between seasonal AR and the thiol-disulfide balance and zinc and copper levels in adult individuals.
### Study Design and Methods
130 male and female adults were included in the study. The participants' serum thiol-disulfide balance and zinc and copper levels were measured spectrophotometrically using commercial kits. Statistical significance was accepted as $p \leq 0.05$ between the groups.
### Results
The serum copper ($$p \leq 0.001$$), native thiol ($$p \leq 0.006$$), reduced thiol ($p \leq 0.001$), and thiol oxidation reduction ratio ($p \leq 0.001$) levels were significantly lower in the seasonal AR group than in the control group.
### Conclusion
In AR patients, the low level of copper, which is an important trace element, the deterioration of the thiol-disulfide balance, which represents a unique indicator of the oxidant-antioxidant balance, the increased disulfide level caused by oxidative stress, and the decreased native thiol level can all serve as important biochemical markers.
## Introduction and background
The prevalence of allergic diseases is gradually increasing worldwide. The most common such allergic disease is allergic rhinitis (AR), which has been found to be between $10\%$ and $40\%$ in adults1, 2. Modern life in the industrialized world is characterized by increased exposure to chemicals and increased exposure to environmental allergens, which contribute significantly to the increase seen in the prevalence of AR. In terms of the mechanism of action behind AR, histamine, interleukin, and prostaglandins all play a role following contact between an allergen substance and the nasal mucosa. More specifically, AR is an inflammatory disease. Indeed, inflammation in the nasal mucosa resulting from the immunoglobulin E (IgE)-dependent hypersensitivity reaction leads to AR. The development of IgE antibodies initiates inflammation by binding to receptors on basophil and mast cells3. When the lower and upper respiratory tract is considered whole, allergy can be considered a single disease affecting the respiratory tract. Nasal itching, nasal congestion, and a runny nose represent the classic symptoms of AR. If the symptoms of AR last for more than four weeks in a year and the complaints associated with AR occur for more than four days a week, the condition is known as persistent AR4. A significant relationship has been found between seasonal AR in adult individuals and obstructive sleep apnea syndrome, characterized by recurrent upper airway obstruction during sleep. Sleep apnea syndrome can lead to much permanent health problems5. AR has significant economic and social impacts due to its high prevalence. It can impair sleep quality. It can also result in a reduced capacity for work. Moreover, its effects may increase seasonally. Steroids and antihistamines can be used to treat AR, although they only provide symptomatic improvement6.
The level of reactive oxygen species within the body constantly increases due to the numerous factors that increase oxidative stress. In the human body, any increase in oxidative stress needs to be balanced by increasing the antioxidant capacity. Under normal conditions, reactive oxygen species are deactivated by the components of the antioxidant system, which results in health being maintained. Yet, reactive oxygen species begin to accumulate if there is an excessive increase in oxidative stress, or a marked decrease in the antioxidant capacity, and the balance is broken. The accumulation of the products of oxidative stress can cause permanent damage to the cell membrane, nuclear membrane, organelles, and genetic material. Copious serious diseases occur because of the cell, tissue, and organ damage. Many parameters can be measured to evaluate the degree of oxidative stress and antioxidant capacity. The balance between native thiol and disulfide is considered a good indicator of the oxidant-antioxidant balance. An unbalanced increase in the reactive oxygen species increases the conversion of native thiol into disulfide7–16. Although it is associated with asthma, the thioldisulfide balance has not yet been adequately studied concerning seasonal AR, despite the disease being quite common in adults17. In addition, the levels of zinc and copper, which are both important trace elements and cofactors of many antioxidant enzymes, are likely to affect the thiol-disulfide balance in patients with seasonal AR. The present study investigated the possible relationship between seasonal AR and the thiol-disulfide balance and zinc and copper levels in adult individuals.
## Study Design
A total of 130 male and female adults were included in the present study. The experimental group was formed by obtaining voluntary informed consent to participate from 65 adult patients who had been diagnosed with seasonal AR. The control group was formed by obtaining voluntary informed consent to participate from 65 healthy adults who did not suffer from any allergic diseases. Sera leftover from routine tests were collected and stored at-80°C. The sera were dissolved by bringing them up to room temperature during the study time. The sera were mixed using a vortex device. Simultaneously, the thiol-disulfide balance and zinc and copper levels were measured spectrophotometrically in all the samples using commercial kits. The remaining descriptive and clinical information were retrieved from the patients' files.
## Ethical Issues
All the procedures applied in the present study were conducted in accordance with the ethical requirements of the Declaration of Helsinki. In addition, permission to conduct the research was granted by the ALKU Clinical Research Ethics Committee (date: 22.04.2020, number: 18-4).
## Thiol-Disulfide Balance
The serum thiol-disulfide balance was examined spectrophotometrically. The measurement process was performed according to a new method described in the literature16. If the method for thiol measurement is briefly described, the disulfide bonds are first reduced to form free functional thiol groups. Meanwhile, the reducing sodium borohydride not used in the reaction was removed with formaldehyde. Then, all thiol groups containing natural thiol groups were determined as a result of the reaction with DTNB (5, 5-dithiobis-2-nitrobenzoic acid).16 Commercial kits were used (Rel Assay Diagnostics, Gaziantep, TURKEY) for all the measurements. The natural (native) thiol (reduced thiol) and total thiol (reduced thiol and oxidized disulfide bonds) levels were also measured. The amount of dynamic disulfide is equivalent to the difference between half the total thiol level and the natural thiol level16. The following rates were then calculated. Reduced Thiol = (Native Thiol / Total Thiol) * 100. Oxidized Thiol = (Disulfide / Total Thiol) * 100. Thiol Oxidation Reduction Ratio = (Native Thiol / Disulfide) * 100. Units: Disulfide levels (µmol / l). Total thiol (µmol / l) / alb (g / l). Native thiol (µmol / L) / alb (g / l). Disulfide (µmol / l) / alb (g / l)16.
## Zinc Levels
Serum zinc levels were measured with the colorimetric method defined in the literature with the Rel Assay Diagnostics (Gaziantep, TURKEY) brand commercial kit. Briefly, the color of 5-Br-PAPS turned from red-orange to light pink caused by the zinc present in the specimens under alkaline conditions. The absorbance level proportionally alters with the overall zinc content in the samples when at 548 nm. The experimental calibration was carried out by dissolution of zinc sulfate in deionized water. The units are given as µg/dl 18.
## Copper Levels
Serum copper levels were measured with the colorimetric method defined in the literature with a commercial kit from Rel Assay Diagnostics (Gaziantep, TURKEY). Briefly, the color of DiBr-PAESA turned from copper found red-orange to violet by copper present in specimens under acidic conditions. The absorbance level proportionally alters with the overall copper content in the samples when at 572 nm. The experimental calibration was carried out by dissolution of copper sulfate in deionized water. The units are given as µg/dl19.
## Statistical analysis
Thiol balance, disulfide, and trace element levels were statistically compared between the control and seasonal allergic rhinitis groups. All necessary statistical analyse were performed using the IBM Statistical Package Social Sciences (SPSS) 21.0 (IBM, New York, USA) computer program. Categorical variables were given as numbers and percentages. Continuous variables were given as mean and standard deviation. ANOVA test was applied. The statistical significance level was accepted as 0.05 for all tests.
## Results
The age distribution of the groups in mean ± standard deviation was control ($$n = 65$$): 35.4 ± 13.76 and seasonal allergic rhinitis ($$n = 65$$): 35.69 ± 13.68. The seasonal allergic rhinitis group consisted of a total of 65 patients, 39 women, and 26 men. Control group; It consisted of a total of 65 patients, 38 women, 27 men.
Serum copper ($$p \leq 0.001$$), native thiol ($$p \leq 0.006$$), reduced thiol ($p \leq 0.001$), thiol oxidation-reduction ratio ($p \leq 0.001$) levels were significantly lower in the seasonal allergic rhinitis group compared to the control group. Serum disulfide ($p \leq 0.001$) and oxidized thiol ($p \leq 0.001$) levels were significantly higher in the seasonal allergic rhinitis group compared to the control group. Descriptive statistics and ANOVA test results are shown in table 1.
**Table 1**
| Parameters / Units | Parameters / Units.1 | N | Mean | Std. Deviation | Std. Error | 95% Confidence Interval for Mean | 95% Confidence Interval for Mean.1 | Anova |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Parameters / Units | Parameters / Units | N | Mean | Std. Deviation | Std. Error | Lower Bound | Upper Bound | p |
| Age | Control | 65 | 35.40 | 13.76 | 1.71 | 31.99 | 38.81 | .904 |
| Age | Seasonal Allergic Rhinitis | 65 | 35.69 | 13.68 | 1.70 | 32.30 | 39.08 | .904 |
| Age | Total | 130 | 35.55 | 13.67 | 1.20 | 33.17 | 37.92 | .904 |
| Zn | Control | 65 | 84.86 | 8.07 | 1.00 | 82.86 | 86.86 | .246 |
| Zn | Seasonal Allergic Rhinitis | 65 | 87.08 | 13.07 | 1.62 | 83.85 | 90.32 | .246 |
| Zn | Total | 130 | 85.97 | 10.88 | 0.95 | 84.09 | 87.86 | .246 |
| Cu | Control | 65 | 134.08 | 43.74 | 5.43 | 123.24 | 144.92 | .001 * |
| Cu | Seasonal Allergic Rhinitis | 65 | 109.74 | 37.22 | 4.62 | 100.51 | 118.96 | .001 * |
| Cu | Total | 130 | 121.91 | 42.26 | 3.71 | 114.57 | 129.24 | .001 * |
| Total Thiol | Control | 65 | 612.11 | 86.61 | 10.74 | 590.65 | 633.57 | .522 |
| Total Thiol | Seasonal Allergic Rhinitis | 65 | 601.94 | 93.88 | 11.64 | 578.68 | 625.21 | .522 |
| Total Thiol | Total | 130 | 607.03 | 90.11 | 7.90 | 591.39 | 622.66 | .522 |
| Native Thiol | Control | 65 | 481.88 | 85.16 | 10.56 | 460.77 | 502.98 | .006 * |
| Native Thiol | Seasonal Allergic Rhinitis | 65 | 437.44 | 94.90 | 11.77 | 413.92 | 460.95 | .006 * |
| Native Thiol | Total | 130 | 459.66 | 92.54 | 8.12 | 443.60 | 475.72 | .006 * |
| Disulfide | Control | 65 | 65.12 | 21.55 | 2.67 | 59.78 | 70.45 | .000 * |
| Disulfide | Seasonal Allergic Rhinitis | 65 | 82.25 | 20.18 | 2.50 | 77.25 | 87.25 * | .000 * |
| Disulfide | Total | 130 | 73.68 | 22.50 | 1.97 | 69.78 | 77.59 | .000 * |
| Reduced Thiol | Control | 65 | 78.58 | 6.87 | 0.85 | 76.87 | 80.28 | .000 * |
| Reduced Thiol | Seasonal Allergic Rhinitis | 65 | 72.16 | 7.53 | 0.93 | 70.30 | 74.03 | .000 * |
| Reduced Thiol | Total | 130 | 75.37 | 7.87 | 0.69 | 74.00 | 76.74 | .000 * |
| Oxidized Thiol | Control | 65 | 10.70 | 3.44 | 0.43 | 9.84 | 11.55 | .000 * |
| Oxidized Thiol | Seasonal Allergic Rhinitis | 65 | 13.92 | 3.76 | 0.47 | 12.99 | 14.85 | .000 * |
| Oxidized Thiol | Total | 130 | 12.31 | 3.94 | 0.35 | 11.63 | 12.99 | .000 * |
| Thiol Oxid. Red. Ratio | Control | 65 | 824.58 | 307.22 | 38.11 | 748.45 | 900.70 | .000 * |
| Thiol Oxid. Red. Ratio | Seasonal Allergic Rhinitis | 65 | 575.36 | 232.70 | 28.86 | 517.70 | 633.02 | .000 * |
| Thiol Oxid. Red. Ratio | Total | 130 | 699.97 | 298.90 | 26.22 | 648.10 | 751.83 | .000 * |
In addition, routine biochemistry parameters belonging to the seasonal allergic rhinitis group were taken from their files. Routine biochemical parameters were analyzed according to reference ranges. Ig E levels were above the reference range. The results of routine biochemical parameters are given in table 2. ROC Curve for laboratory parameters is shown in figure 1. The area under the ROC Curve for laboratory parameters is shown in table 3.
ROC Curve for laboratory parameters is shown in figure 1. The area under the ROC Curve for laboratory parameters is shown in table 3.
The area under the receiver operating curve (ROC) curve (AUC-ROC)is used as a method to show the accuracy of diagnostic tests. The larger the area under the curve, the better the test at distinguishing patients. The ideal value for AUC is 1. The line drawn at an angle of 45 degrees from the zero point is considered the reference line.
## Discussion
As a result of our research, a significant decrease in native thiol ($$p \leq 0.006$$) and copper ($$p \leq 0.001$$) levels and a significant increase in disulfide ($p \leq 0.001$) levels in seasonal adult allergic rhinitis patients indicate an increase in oxidative stress.
Aerobic organisms constantly use oxygen for the continuation of life. During oxygen consumption, the most important free radicals in the body are formed. Reactive oxygen species are released in the form of superoxide, hydrogen peroxide, and hydroxyl radical. These free radicals are highly reactive and can cause many cellular damages, including the genetic material. Various effects occur on biomolecules in the form of nonenzymatic lipid peroxidation, structural and functional changes in amino acids and proteins, oxidative DNA damage and mutations, the formation of oxidized monosaccharides, and oxoaldehydes. To prevent this oxidative stress increase process from turning into disease and damage, the antioxidant system components are very important and continuously work to balance and neutralize oxidative stress. The most important enzymatic components of the antioxidant system are superoxide dismutase (SOD), glutathione peroxidase (GPx), glutathione S-transferase (GST), catalase (CAT), paraoxonase (PON), and mitochondrial cytochrome oxidase system. In addition, there are vitamins such as alpha-tocopherol, ascorbic acid, beta carotene, and folic acid, and various nonenzymatic antioxidants such as melatonin, ferritin, albumin, glutathione, myoglobin, and hemoglobin. If the activities of all these antioxidants are not enough to neutralize oxidative stress, many serious and deadly diseases can result7–16. Thiol-disulfide balance is one of the unique and new biochemical markers that can show the oxidant-antioxidant balance16. Allergic rhinitis is an important disease that can reach $40\%$ in adults and negatively affect the quality of life3. However, there is not enough comprehensive clinical research in the literature on the relationship between thiol-disulfide balance and adult allergic rhinitis. For this reason, the disulfide increase and native thiol decrease revealed in allergic rhinitis patients in our research are very important. There are various studies in the literature investigating the role of oxidative stress in the etiology of ear, nose, and throat (ENT) diseases. It has been reported to play a role in the etiology of hearing loss, rhinosinusitis, otitis media, chronic tonsillitis, and laryngeal cancer20–23. They suggested that oxidative stress increases, and benign paroxysmal positional vertigo (BPVV) develop in patients with vitamin D deficiency24.
Anti-inflammatory agents are given to end the developing inflammatory process also function on this basis. Sino-nasal Outcome Test (SNOT)-22 score is a subjective test used to evaluate allergic rhinitis. In a study, they found a lower SNOT-22 score and serum oxidative stress level after AR attack compared to pre-attack25. Ulusoy et al. found that the disulfide level was significantly higher in patients during the attack. In their study on plasma thiol level in 32 adult patients with AR they found that the native thiol level was significantly higher in the asymptomatic period23. This study supports our research results. In addition, our results are statistically more valuable since the number of participants in the patient and control groups in our study is higher.
Serum Zn and Cu values can give information about antioxidant capacity. For example, Liu et al., in their studies on ARs, found serum Zn levels significantly lower in the patient group than in the control group26. In another study, they found that the Znspan level was statistically significantly lower in the patient group and the Cu level was higher in the patient group27.
In our research results, Ig E values above the reference range in the AR group confirm the diagnosis of allergic rhinitis. Therefore, evidence-based diagnosis of AR should be considered in terms of the value of the study when interpreting the change in thiol-disulfide balance1–3. Other routine biochemical laboratory parameters are within reference ranges. For this reason, they can be considered normal. The results are shown in table 2.
Studies investigating thiol-disulfide balance and trace element levels in allergic rhinitis patients are scarce in the literature. However, in the current literature, it has been shown that thiol-disulfide balance may be a clinical indicator in many diseases with uncertain etiology28, 29. Our study showed that blood copper levels and thiol thiol-disulfide balance might be a clinical laboratory indicator in allergic rhinitis patients. New clinical studies should confirm thiol-disulfide balance and trace element changes in AR patients.
## Conclusion, recommendations, and future directions
In AR patients, the low level of copper, which is an important trace element, the deterioration of the thiol-disulfide balance, which represents a unique indicator of the oxidant-antioxidant balance, the increased disulfide level caused by oxidative stress, and the decreased native thiol level can all serve as important biochemical markers. Thus, copper, disulfide, and thiol levels can be practical biochemical indicators in diagnosis, treatment, and follow-up in allergic rhinitis patients. In addition, nutritional and lifestyle changes in allergic rhinitis patients may provide significant clinical benefits for increasing antioxidant capacity.
## Conflict of interest
No conflict of interest has been declared between the authors.
The authors declare that they have no conflict of interest.
## Author's Contribution
Conception and design of the research: Savas HB, Gunizi H; Acquisition of data: Savas HB, Gunizi H; Analysis and interpretation of the data: Savas HB; Statistical analysis: Savas HB; Writing of the manuscript: Savas HB, Gunizi H; Critical revision of the manuscript for intellectual content: Savas HB.
## Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee.
## Statement of informed consent
Informed consent term was obtained from every participant included in this study.
## References
1. Incorvaia C, Cavaliere C, Frati F, Masieri S. **Allergic rhinitis**. *J Biol Regul Homeost Agents* (2018) **32** 61-66. PMID: 29552875
2. Agnihotri NT, McGrath KG. **Allergic and nonallergic rhinitis**. *Allergy Asthma Proc* (2019) **40** 376-379. DOI: 10.2500/aap.2019.40.4251
3. Bernstein DI, Schwartz G, Bernstein JA. **Allergic Rhinitis: Mechanisms and Treatment**. *Immunol Allergy Clin North Am* (2016) **36** 261-278. DOI: 10.1016/j.iac.2015.12.004
4. Chaplin DD. **Overview of the human immune response**. *J Allergy Clin Immunol* (2006) **117** S430-S435. DOI: 10.1016/j.jaci.2005.09.034
5. Chirakalwasan N, Ruxrungtham K. **The linkage of allergic rhinitis and obstructive sleep apnea**. *Asian Pac J Allergy Immunol* (2014) **32** 276-286. PMID: 25543037
6. Hoyte FCL, Nelson HS. **Recent advances in allergic rhinitis**. (2018) **23**. DOI: 10.12688/f1000research.15367.1
7. Ozturk SA, Ceylan C, Serel TA, Doluoglu OG, Soyupek AS, Guzel A. **Protective effect of theophylline on renal functions in experimental pneumoperitoneum model**. *Ren Fail* (2015) **37** 1044-1049. DOI: 10.3109/0886022X.2015.1040706
8. Gumral N, Saygin M, Asci H, Uguz AC, Celik O, Doguc DK. **The effects of electromagnetic radiation (2450 MHz wireless devices) on the heart and blood tissue: role of melatonin**. *Bratisl Med J* (2016) **117** 665-671. DOI: 10.4149/BLL_2016_128
9. Canbolat MF, Savas HB, Gultekin F. **Improved catalytic activity by catalase immobilization using c-cyclodextrin and electrospun PCL nanofibers**. *J. Appl. Polym. Sci* (2016) **134** 1-7. DOI: 10.1002/app.44404
10. Altuntaş A, Yiğit A, Uz E, İnal S, Kidir V, Aydin B. **The relationship between serum fetuin a levels and fetuin gene polymorphism in hemodialysis patients**. *Biomedical Research* (2017) **28** 495-502
11. Cankara FN, Özmen Ö, Savas HB, Şahin Y, Günaydın C. **Gastroprotective effect of tarantula cubensis extract in the indomethacin-induced peptic ulcer model in rats**. *Acta Med Alanya* (2020) **4** 278-284. DOI: 10.30565/medalanya.797406
12. Savas HB, Gultekin F, Ciris İM. **Positive effects of meal frequency and calorie restriction on antioxidant systems in rats**. *North Clin Istanbul* (2017) **4** 109-116. DOI: 10.14744/nci.2017.21548
13. Yiğit A, Savaş HB. **Glutathione S-Transferase Enzyme Gene Polymorphisms and Cardiovascular Diseases**. *J Clin Anal Med* (2016) **7** 749-752. DOI: 10.4328/JCAM.4323
14. Türkkan A, Savas HB, Yavuz B, Yiğit A, Uz E, Bayram NA. **The Prophylactic Effects of Viscum Album in Streptozotocin-Induced Diabetic Rats**. *North Clin Ist* (2016) **3** 83-89. DOI: 10.14744/nci.2016.22932
15. Gultekin F, Nazıroğlu M, Savas HB, Çiğ B. **Calorie restriction protects against apoptosis, mitochondrial oxidative stress and increased calcium signaling through inhibition of TRPV1 channel in the hippocampus and dorsal root ganglion of rats**. *Metab Brain Dis* (2018) **33** 1761-1774. DOI: 10.1007/s11011-018-0289-0
16. Erdem D, Savas HB, Erdem N, Kuloglu Genc S, Avcioglu G. **Ischemia Modified Albumin as a New Marker for Diagnosis of Early Pregnancy Losses**. *Int J Acad Med Pharm* (2020) **2** 222-227. DOI: 10.29228/jamp.44816
17. Babaoglu E, Kilic H, Hezer H, Dag O, Parlak E, Senturk A. **Comparison of thiol/disulphide homeostasis parameters in patients with COPD, asthma and ACOS**. *Eur Rev Med Pharmacol Sci* (2016) **20** 1537-1543. PMID: 27160126
18. Homsher R, Zak B. **Spectrophotometric Investigation of Sensitive Complexing Agents for the Determination of Zinc in Serum**. *Clin Chem* (1985) **31** 1310-1313. PMID: 4017236
19. El-Masry HMA, Sadek AA, Hassan MH, Ameen HH, Ahmed HA. **Metabolic profile of oxidative stress and trace elements in febrile seizures among children**. *Metab Brain Dis* (2018) **33** 1509-1515. DOI: 10.1007/s11011-018-0258-7
20. Güçlütürk MT, Ünal ZN, İsmi O, Çimen MB, Ünal M. **The Role of Oxidative Stress and Inflammatory Mediators in Benign Paroxysmal Positional Vertigo**. *J Int Adv Otol* (2016) **12** 101-105. DOI: 10.5152/iao.2015.1412
21. Unal M, Tamer L, Pata YS, Kilic S, Degirmenci U, Akbaş Y. **Serum levels of antioxidant vitamins, copper, zinc and magnesium in children with chronic rhinosinusitis**. *J Trace Elem Med Biol* (2004) **18** 189-192. DOI: 10.1016/j.jtemb.2004.07.005
22. Doğruer ZN, Unal M, Eskandari G, Pata YS, Akbaş Y, Cevik T. **Malondialdehyde and antioxidant enzymes in children with obstructive adenotonsillar hypertrophy**. *Clin Biochem* (2004) **37** 718-721. DOI: 10.1016/j.clinbiochem.2004.01.004
23. Ulusoy S, Ayan NN, Dinc ME, Is A, Bicer C, Erel O. **A new oxidative stress marker for thiol-disulphide homeostasis in seasonal allergic rhinitis**. *Am J Rhinol Allergy* (2016) **30** 53-57. DOI: 10.2500/ajra.2016.30.4308
24. Gent JF, Koutrakis P, Belanger K, Triche E, Holford TR, Bracken MB. **Symptoms and medication use in children with asthma and traffic-related sources of fine particle pollution**. *Environ Health Perspect* (2009) **117** 1168-1174. DOI: 10.1289/ehp.0800335
25. Göker AE, Alagöz MH, Kumral TL, Karaketir S, Yilmazer A, Tutar B. **An Evaluation of Oxidative Stress with Thiol/Disulfide Homeostasis in Patients With Persistent Allergic Rhinitis**. *Ear, Nose & Throat Journal* (2020) 1-5. DOI: 10.1177/0145561320926336
26. Liu Y C, Hou L, Wang WC, Cao L, Ma RX. **The relationship between IL-4 and trace elements in patients with allergic rhinitis of hui and han populations in Ningxia**. (2016) **5** 715-717. DOI: 10.13201/j.issn.1001-1781.2016.09.011
27. Ma R, Shen Y, Hou Li, Yang Z, Feng N, Yan X. **The correlation of allergic rhinitis and trace elements in Ningxia region**. *Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi* (2014) **49** 1017-1020. PMID: 25623870
28. Savas HB, Sayar E. **Oxidant Antioxidant Balance and Trace Elements In Children with Functional Dyspepsia**. *Turkiye Klinikleri J Med Sci* (2021) **41** 70-79. DOI: 10.5336/medsci.2020-79596
29. Etli M, Savas HB. **Ischemia Modified Albumin as a Novel Biochemical Indicator in Peripheral Artery Patients**. *J Clin Exp Invest* (2021) **12** em00774. DOI: 10.29333/jcei/10960
|
---
title: Assessment of electrolytes, markers of glycaemic control and renal dysfunction
among adult Nigerians recently diagnosed with type 2 diabetes mellitus
authors:
- Oloruntoba A Ekun
- Oloruntoba F Fagbemi
- Esther N Adejumo
- Oyeronke O Ekun
- Kehinde S Wojuade
- Folu M Oshundun
- Florence O Adefolaju
- Sade R Oyegbami
journal: African Health Sciences
year: 2022
pmcid: PMC9993265
doi: 10.4314/ahs.v22i3.31
license: CC BY 4.0
---
# Assessment of electrolytes, markers of glycaemic control and renal dysfunction among adult Nigerians recently diagnosed with type 2 diabetes mellitus
## Abstract
### Background
Diabetes mellitus is a chronic and progressive endocrine disorder that may result in macro and microvascular complications.
### Objective
This study assessed some biochemical analytes in Nigerians who were recently (≤ 6 months) diagnosed with Type 2 diabetes mellitus (T2DM).
### Methods
160 T2DM and 90 non-diabetic control participated in this study. Blood samples were collected and analyzed for Heart-type fatty acid-binding protein (HFABP), high sensitivity C-reactive protein (hs-CRP), electrolytes, lipid and renal profile parameters, glycated haemoglobin (HBA1C) and fasting blood glucose (FBG), using standard guidelines.
### Result
The body mass index (BMI) of the T2DM volunteers was higher than control ($P \leq 0.001$). The lipid profile, potassium, glucose, HBA1C, urea and creatinine values were elevated ($P \leq 0.001$) while estimated glomerular filtration rate (eGFR) was lower ($P \leq 0.05$) in diabetes. The median HFABP and hs-CRP were raised ($P \leq 0.05$) in T2DM. Positive associations existed between FBG and urea ($P \leq 0.001$), Creatinine and HBAIC ($P \leq 0.001$). A logistic regression analysis, shows that an increased BMI, HBA1C, FBG, Cholesterol, urea and creatinine were associated with higher odds ($p \leq 0.001$) of cardiovascular and renal complications.
### Conclusion
Elevated hs-CRP, glycated haemoglobin, urea and creatinine among T2DM increase the odds of cardiovascular and renal insults in this population.
## Introduction
Diabetes mellitus (DM) is one of the largest global health emergencies of the 21st century1. There are about 415 million people living with diabetes mellitus worldwide, with type 2 diabetes (T2DM) accounting for more than $90\%$ of diabetic patients2. Diabetes mellitus is a major risk factor for cardiovascular disease (CVD), which is the most common cause of death among adults with DM3. Besides the well-recognized microvascular complications of DM, such as nephropathy and retinopathy, there is a growing epidemic of macrovascular complications, including diseases of coronary arteries, peripheral arteries, and carotid vessels, particularly in the burgeoning type 2 DM populations4.
DM is a group of metabolic diseases marked by high levels of blood glucose resulting from problems in insulin production, insulin use, or both. The data from the International Diabetes Federation indicated that an estimated 415 million adults aged 20–79 years worldwide have DM in 2015 and the number will project to 642 million in 2040, with the prevalence increasing from 8.8 to $10.4\%$. Despite the high prevalence of diagnosed DM, as many as 193 million people representing close to half of all people with DM are unaware of their disease. The prevalence of Diabetes Mellitus continues to increase. The current prevalence of diabetes mellitus in *Nigeria is* between 5–$6\%$5 with a current African Region prevalence of between 2.1–$6.7\%$6. Regionally, the age-adjusted prevalence of DM is $7.3\%$ in Europe, $10.7\%$ in the Middle East and North Africa, $11.5\%$ in North America and Caribbean, $9.6\%$ in South and Central America, $9.1\%$ in Southeast Asia, and $8.8\%$ in Western Pacific. China, India, and the USA remain the top three countries with the largest number of people with Diabetes mellitus4.
The two main types of DM are type 1 DM, type 2 DM. Type 1 DM is one of the most common chronic autoimmune disorders that typically manifests in early childhood and adolescence7. Gestational DM is a form of glucose intolerance diagnosed during the second or third trimester of pregnancy. Type 2 DM is the most common type and accounts for about 90–$95\%$ of all diagnosed cases of DM. The number of people with type 2 DM is growing rapidly worldwide. This rise is associated with aging population, economic development, increasing urbanization, less healthy diets, and reduced physical activity1. Many people remain undiagnosed because there are often few symptoms during the early years of type 2 DM or symptoms that do occur may not be recognized as being related to DM. However, during this time the body is already being damaged by excess blood glucose, and as a result, many people are affected by complications even before diagnosed with type 2 DM.
Consistently high blood glucose levels can lead to serious diseases associated with heart, blood vessels, eyes, kidneys, and nerves. The cardiovascular diseases (CVDs) that accompany DM include angina pectoris, myocardial infarction, stroke, peripheral artery disease (PAD), and congestive heart failure (CHD)8,9. It has been estimated that about $53\%$ of life time medical cost of managing type 2 diabetes mellitus is dedicated to managing some of these complications in the USA10; however there is a paucity of such data in many developing countries especially in Nigeria, thus prevention of complications through rigorous glycaemic monitoring may be invaluable.
It has been reported previously that diabetes mellitus is an independent risk factor for cardiovascular diseases (CVD)8. The adverse influence of diabetes extends to all components of the cardiovascular system: the microvasculature, the larger arteries, and the heart, as well as the kidneys11. Patients with diabetes mellitus aggregate other comorbidities such as obesity, hypertension, and dyslipidemia which also contribute to increase the risk for CVD. This study aims to assess Heart-type fatty acid-binding protein (HFABP) (cardiac biomarker), markers of glycaemic control and renal function among individuals recently diagnosed with type 2 diabetes mellitus.
## Study design
This is a cross-sectional study on recently diagnosed type 2 Diabetes Mellitus (within first six months of diagnosis) attending Diabetic Clinic of a General Hospital, Lagos State. A total number of 250 volunteers participated in this study out of which 160 were recently diagnosed with diabetes mellitus. The remaining 90 volunteers were non-diabetic participants. This group served as control.
## Human Subjects
Adults (≥40 years) diagnosed of diabetes mellitus. Studies have shown that type 2 Diabetes *Mellitus is* common among men and women older than 40years. However recent study shows that type 2 diabetes mellitus is becoming common among Children and adolescents5. American Diabetes Association (ADA)12, has also reported that the prevalence by age and sex appeared more between ages 20 – 79 years with a peak age of 50 - 59 years.
## Sample size
The calculation of the sample size for this study was based on the prevalence of between 5–$6\%$ for this disorder in Nigeria based on literature5.
## Inclusion criteria
Adult male and female ≥40 year's old consented patients who were recently registered (not later than 6 months) at the Diabetic Clinic and who do not have any other comorbidity as at the time of this study were recruited.
## Exclusion criteria
Children and teenager were excluded from this study, also pregnant women as well as individuals with any form of malignancies were excluded from this study. Diabetes subjects with history of smoking and malaria parasite infestation or have been treated for malaria in the past one month before this study were excluded from this study. Also this study excluded diabetes mellitus individual with overt evidence of co-morbidity (renal failure and hypertension) as at the time of this study.
## Medication for diabetes volunteers:
Majority of the diabetes participants (Diabetes) were mainly on combination of two of the following; Metformin-a Biguanides, Diamicron MR (Gliclazide)- a Sulfonylureas and Voglinorm (Voglibose)-an α-Glucosidase inhibitors.
## Ethical approval and Informed consent
Approval for this study was obtained from the Health Research Ethics Committee (HREC) (approval number: CMUL/HREC/$\frac{04}{19}$/516) of College of Medicine of the University of Lagos prior to the commencement of the study. Informed consent was obtained from each participant before the commencement of this study after the purpose and the objectives of the study have been explained to them. This study was in total conformity to the declaration of Helsinki.
## Blood sample collection and handling
After 8–12 hours overnight fast, a total of ten [10] mls of venous blood was collected and dispensed into plain, lithium heparinized, K2EDTA and fluoride oxalate bottles. All the bottles except K2EDTA samples were centrifuged at 5,000 rpm for three [3] minutes to separate serum and plasma respectively. The serum and plasma were extracted into Eppendorf tubes and stored at -200C until the day of analysis.
## Methods
Electrolytes were determined using Ion Selective Electrode (ISE), while lipid, urea, creatinine, glucose and glycated haemoglobin (HBA1C) were analyzed using Roche-Cobas 111. High Sensitivity C Reactive Protein (hs-CRP) and Heart Type Fatty Acid Binding Protein (H- FABP) were determined using ELISA method. Estimated glomerular filtration rate (eGFR) was calculated using Modification of Diet in Renal Disease (MDRD) equation13.
## Data analysis
The data were analyzed with Software for Statistics and Data Science [STATA software version 16 (StataCorp) USA]. The mean of age, body mass index, systolic and diastolic blood pressure were compared between type 2 diabetes mellitus and non-diabetic volunteers. Test of normality was conducted on all continuous variable using kurtosis, Skewness, Shapiro wilk and Kolmogrorov-Simrnov test. Variables that were normally distributed were analyzed using parametric test while the skewed continuous variables were analyzed using non-parametric method. Normally distributed variables were presented as mean standard deviation while the skewed variables were presented as median and interquartile ranges. A normally distributed independent continuous variables were analyzed using independent student t test while a non-normally distributed variables were analyzed using Wilcoxon Mann-Whitney U test. The degree of association of the measured parameters were determined using Pearson Correlation. Spearman Rank Correlation was used to analyze the skewed variables. A multivariate logistic regression analysis was conducted to predict the risks of renal and cardiovascular derangement among the study participants. The level of statistical significance was set at probability of less than 0.05 ($p \leq 0.05$).
## Results
The results of the present study are shown in Table 1 to 5. Table 1 presents the Anthropometric parameters and blood pressure of the participants. The mean age (years) of the test and control groups was 59±8.97 and 60±10.97 respectively. The mean weight (Kg) and BMI (Kg/m2) of the participants were: 79.59±9.83; 70.62±4.73; 31.76±4.32 and 27.94±3.39 for test and control group respectively. Systolic and diastolic blood pressure (mmHg) measurements were 141.09±10.98; 129.07±4.21, 88.71±9.21 and 77.81±6.67 for type-2 diabetes mellitus and control groups respectively. The electrolytes and renal function parameters were estimated for test and control groups.
The (Table 2) mean sodium (mmol/l), potassium (mmol/l) chloride (mmol/l), bicarbonate (mmol/l), urea (mg/dL) and creatinine (mg/dL) were: 139.89 (mmol/l), 4.56 (mmol/l), 104.64 (mmol/l), 22.12 (mmol/l), 32.45 (mg/dL), 1.06(mg/dl) respectively for type-2 diabetes mellitus volunteers. The mean cholesterol, Triglyceride, HDL and LDL were: 190.44±35.59 and 167.09±17.22, 105.69±31.52 and 79.52±12.02, 41.65±8.53 and 34.79±5.16, 127.73±25.22 and 109.59±10.85 for diabetic group and control group respectively. A comparative evaluation of markers of cardiovascular dysfunction and glycaemic control were presented in Table 3. Plasma fasting blood glucose (FBG), Glycated Haemoglobin (HBA1C), HFABP and hs-CRP were evaluated. The Plasma HFABP and hs-CRP distribution were skewed among the participants and were presented as median and interquartile range (IQR). The mean FBG (mg/dL) and HbA1C (%) for both test and control group were: 163.16±51.28 and 102.66±10.49, 8.79±2.75 and 5.99±0.41 respectively.
Tables 4 shows Pearson Correlation Coefficient and Spearman rank correlation of the measured biomarkers among diabetes mellitus. A positive association existed between blood pressure and markers of glycaemic control and markers of renal function. High sensitivity C- reactive protein associated positively with glycated haemoglobin (HBAIC) and Creatinine. There was no association between hs-CRP and HFABP. A multivariate logistic regression analysis result was presented in table 5. The odds of future complication among test participants were higher with increase in BMI (OR: 1.120), hs-CRP (OR: 1.161), FBG (OR: 1.149), HBA1C (52.717), urea (OR: 1.127), Creatinine (OR: 5.811), Cholesterol (OR: 1.032), Triglyceride (OR: 1.085) and LDL-Cholesterol. Whereas a higher eGFR value was associated with a lower odd (0.968) of renal complication.
## Discussion
In this study, biochemical markers of glycaemic control, and renal dysfunction were measured. It was observed that in the overall, $56\%$ of the volunteers were female. The mean age of the test and control was not significantly different from each other (Table 1). However, the mean weight and body mass index (BMI) (an indication of obesity) of the diabetic volunteers were significantly higher when compared with the control group. Previous study has opined that as BMI increases, insulin resistance also increases which results in increased blood glucose level in body. An increase in BMI as observed in test volunteers is in consonance with several previous studies. Of a particular interest were the studies by Bjorntorp14, Mckeigue et al. ,15 Eckel et al. ,16 Al-Goblan17, that reported the influence of obesity on type 2 diabetes risk and its association with metabolic syndrome, and cardiovascular disease. Mckeigue et al., 15 and Al-Goblan, et al. ,17 linked Obesity to many medical, psychological, and social conditions, the most devastating of which may be type 2 diabetes.
Thus, there is a strong relationship between obesity and type 2DM. Also in Table 1, we observed that the mean blood pressure (systolic and diastolic) of the T2DM individuals was significantly higher than that of the control group. This observation agrees with previous studies that suggested that T2DM is a member of metabolic syndrome that is also referred to as syndrome X 16. Previous study has also demonstrated a strong link between T2DM and hypertension 18; this was clearly demonstrated in this study.
Furthermore, an evaluation of electrolyte and markers of renal function among the volunteers shows that T2DM volunteers demonstrated some levels of electrolytes imbalance when compared with the apparently healthy control group (Table 2). In this study it was observed that the test group presented with the plasma potassium that was significantly higher than the control group. This observation regarding plasma potassium is in consonance with some previous studies. Thus, a higher mean potassium as presented by the diabetic volunteers in this study agreed with the previous observations by Alexopoulou et al. ,19 Kim et al. ,20. Alexopoulou et al. ,19 reported that hyperkalemia whenever it occurs among T2DM may be suggestive of the presence of microvascular complications of diabetes mellitus; whereas Nzerue and Jackson21 presented the possible mechanism and causes of hyperkalemia in T2DM. It has been suggested that patients with diabetes constitute a unique high-risk group for hyperkalemia, in that they develop defects in all aspects of potassium metabolism 22. Thus, diabetes mellitus should be considered as an independent possible cause of hyperkalemia19. A significantly raised urea and creatinine as well as a decrease in the estimated glomerular filtration rate (eGFR) (markers of renal function) was observed among diabetic volunteers when compared with control. This might possible suggest an underlying renal dysfunction among these set of volunteers. This observation is in concordance with the previous studies which reported an underlying renal disease among diabetes mellitus population. However, it is interesting to note that the test group had presented with a lower eGFR at the early stage of type 2 diabetes mellitus presentation. This group of individuals often develop hyporeninemic hypoaldosteronism and impaired renal excretion of potassium23,24.
Moreover, in Table 3, the plasma glucose, glycated haemoglobin, Heart-type fatty acid binding protein (HFABP) and high-sensitivity C-reactive protein (hs-CRP) were significantly raised in T2DM volunteers when compared with the control volunteers. HFABP and hs-CRP have been considered as markers of cardiovascular impairment and inflammation respectively. HFABP has been shown to be released from the injured myocardium and is detected in blood within 1 hour after the onset of ischemia. Heart type fatty acid binding protein (HFABP) has been demonstrated to be a sensitive early marker of myocardial injury. Previous study had used HFABP to demonstrate the incidence of early-period cardiac ischemia in children and adolescents with diabetic-keto acidosis (DKA)25. However, there is a paucity of information regarding HFABP in adult onset of diabetes mellitus. Thus, elevated HFABP in type 2 diabetes mellitus volunteers may have resulted from either lower eGFR or as an indication of cardiovascular involvement. However, in this study HFABP did not produce any significant odds of future complications among adult type 2 diabetes mellitus thus possibly limiting its predictive usefulness among adult DM. Our study also demonstrated a significant increase in the value of hs-CRP among T2DM.
Previous studies have suggested that serum hs-CRP levels are higher in T2DM patients with complications than in patients without T2DM. Also, it has been shown that T2DM is associated with a low-grade inflammation26. Thus, it has been reported that DM and insulin resistance are associated with the overexpression of many cytokines by adipose tissue including tumor necrosis factor-α, interleukin (IL)-1, IL-6, leptin, resistin Monocyte Chemo-attractant (MCP-1), Plasminogen Activator (PAI-1), fibrinogen and angiotensin 27. The overexpression of these cytokines contributes to increased inflammation and lipid accumulation; this might have contributed to dyslipidemia observed among T2DM in this study. It has been demonstrated that CRP impairs endothelial production of nitric oxide (NO) and prostacyclin, which are vital to vessel compliance. CRP has also been shown to increase the uptake of oxidized low-density lipoprotein (LDL) in coronary vasculature walls, which can contribute to endothelial dysfunction as well as the development of atherosclerotic plaques 28. Martin-Timon et al. ,29 reported that increased levels of hs-CRP are related with the presence and severity of coronary artery disease (CAD) and renal impairment in individuals with T2DM.
The mean values of FBS and HbA1C in T2DM patients was significantly higher when compared with the mean value of control group. A meta-analysis of previous studies among individuals with T2DM showed that an increase in glycated haemoglobin by $1\%$ leads to about 17–$18\%$ increase risk of CVD events 30,31. Thus Hyperglycemia in T2DM encourages the activation of oxidative stress and overproduction of mitochondrial superoxide, which trigger various metabolic pathways of glucose-mediated vascular damage 32,33. Glucose which is overtly abundant in T2DM reacts with various proteins leading to an accumulation of cross-linked proteins. This cross-linked proteins damage cells and tissues and may contribute to long-term complications in diabetes, plaque formation, and atherosclerosis34. Previous study supports that cardiovascular mortality is significantly increased when HbA1C levels are above $8.0\%$ in the population with diabetes 35. It must be noted that the mean glycated haemoglobin observed in this study was $8.79\%$ among T2DM. Also, a multivariate analysis involving glycated haemoglobin in this study produced a significant odd of complications (higher risk of cardiovascular dysfunction/complications) among T2DM group studied.
Moreover, the mean lipid profile was significantly raised in T2DM. Total Cholesterol, Triglyceride and LDL -C levels were increased in T2DM, when compared with controls. These lipid profile components presented with higher odds of future complications among diabetes participants as observed from this study. Our observations agree with the previous studies by Ejuoghanran et al. ,36 and Srinidhi et al. ,37. Srinidhi et al. ,37 reported that the common lipid abnormalities associated with patients with T2DM are hypercholesterolemia and hypertriglyceridemia.
Furthermore, the levels of associations that exist among parameters measured in Type 2 Diabetes Mellitus were evaluated (Table 4). The level of Fasting Blood glucose correlates positively with urea. This observation might suggest that diabetes mellitus may precipitate renal pathology and these two together are additive risk factors for CVD 29. Also in this study, there was a strong positive correlation between weight, and Blood pressure. This observation corroborates the previous observation by Vuvor38 who reported that overweight and high BP have independent fatal health consequences as they carry serious risk factors for several non-communicable diseases such as type 2 diabetes, heart disease, stroke, and even death. In addition to this, a positive and significant association was observed between urea and glycated haemoglobin. Our observation regarding markers of glycaemic control and renal function agreed with the previous study by Sivasubramanian et al. ,39. A positive and significant association was observed between Creatinine and glycated haemoglobin. This observation suggests that diabetes mellitus could provide a veritable template for renal pathology. The finding from this study agreed with the previous study by Sivasubramanian et al. ,39. Also there were significant associations between creatinine and hs-CRP as well as HBA1C and hs-CRP. These observations also agreed with the previous studies by Shaheer et al. ,40, and Sultania et al. ,41. These findings suggest the possible link between inflammation and diabetes mellitus as well as pathogenesis of renal disease.
A multivariate analysis study (table 5) to predicts the risk for renal and cardiovascular involvement in type 2 diabetes mellitus using logistic regression analysis indicated that an increase in body mass index, high-sensitivity C-reactive protein (hs-CRP), fasting blood glucose (FSG), glycated haemoglobin (HBA1C), urea and Creatinine as well as total cholesterol, triglyceride and LDL-cholesterol were associated with significant odds of complications among adult diabetes volunteers.
## Conclusion
From the outcome of this study, markers of renal function and glyceamic control were elevated in type 2 diabetes mellitus. Thus, rigorous glycaemic control through effective and efficient monitoring of markers of glycaemic control, could possibly prevent or delay renal impairment. This may possibly prevent or delay the onset of micro and macro-vascular complications in type 2 *Diabetes mellitus* in the long run.
## Human rights statement and informed consent
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and/or with the Helsinki Declaration of 1964 and later versions. Informed consent was obtained from all patients before the commencement of this study.
## Conflict of Interest Statement
The authors declared no conflict of interest.
## Data availability statement
All datasets regarding this study can be obtained from the corresponding author upon reasonable request.
## Funding
The authors did not receive any financial support for this study.
## Conceptualization
Oloruntoba Ayodele Ekun, Oloruntoba Festus Fagbemi.
## Methodology
Oloruntoba Ayodele Ekun, Oloruntoba Festus Fagbemi, Esther Ngozi Adejumo, Oyeronke Olufemi Ekun, Kehinde Samuel Wojuade, Folu Mary Oshundun. Florence Oreetan Adefolaju, Sade Ruth Oyegbami
## Formal analysis and investigation
Oloruntoba Ayodele Ekun, Oloruntoba Festus Fagbemi, Esther Ngozi Adejumo, Oyeronke Olufemi Ekun, Kehinde Samuel Wojuade, Folu Mary Oshundun, Florence Oreetan Adefolaju, Sade Ruth Oyegbami
## Writing original draft preparation
Oloruntoba Ayodele Ekun.
## Writing review and editing
Oloruntoba Ayodele Ekun, Esther Ngozi Adejumo, Folu Mary Oshundun.
## Funding acquisition
Oloruntoba Ayodele Ekun, Oloruntoba Festus Fagbemi, Esther Ngozi Adejumo, Oyeronke Olufemi Ekun, Kehinde Samuel Wojuade, Folu Mary Oshundun, Florence Oreetan Adefolaju, Sade Ruth Oyegbami.
## Resources
Oloruntoba Ayodele Ekun, Oloruntoba Festus Fagbemi, Florence Oreetan Adefolaju, Sade Ruth Oyegbami
## References
1. 1Centres for Disease Control and PreventionNational diabetes statistics report: Estimates of diabetes and its burden in the United States2014Atlanta, GAUS Department of Health and Human Services. *National diabetes statistics report: Estimates of diabetes and its burden in the United States* (2014)
2. Goyal R, Jialal I. **Diabetes Mellitus Type 2. [Updated 2020 Feb 28]**. *StatPearls [Internet]* (2020)
3. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M. **Executive summary: heart disease and stroke statistics-2016 update: a report from the American Heart Association**. *Circulation* (2016) **133** 447. PMID: 26811276
4. Wenjun F. **Epidemiology in diabetes mellitus and cardiovascular disease**. *Cardiovascular Endocrinology* (2017) **6** 8-16. PMID: 31646113
5. 5International Diabetic FederationRegional overviews of prevalence of Diabetes Mellitus20136th Edition5470Chapter 3. *Regional overviews of prevalence of Diabetes Mellitus* (2013) 54-70
6. 6International Diabetic FederationDiabetes by Region20157th Edition6797Chapter 4. *Diabetes by Region* (2015) 67-97
7. de Ferranti SD, de Boer IH, Fonseca V, Fox CS, Golden SH, Lavie CJ. **Type 1 diabetes mellitus and cardiovascular disease: a scientific statement from the American Heart Association and American Diabetes Association**. *Circulation* (2014) **130** 1110-1130. PMID: 25114208
8. 8National Center for Chronic Disease Prevention and Health Promotion, Division of Diabetes TranslationNational diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States2011Centers for Disease Control and Preventionhttps://www.cdc.gov/diabetes/pubs/pdf/methods11.pdf (2011). *National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States* (2011)
9. Zimmet PZ, Magliano DJ, Herman WH, Shaw JE. **Diabetes: a 21st century challenge**. *Lancet Diabetes Endocrinol* (2014) **2** 56-64. PMID: 24622669
10. Zhuo X, Zhang P, Hoerger TJ. **Lifetime direct medical costs of treating type 2 diabetes and diabetic complications**. *Am. J. Prev. Med* (2013) **45** 253-261. PMID: 23953350
11. Grundy SM, Benjamin IJ, Burke GL, Chait A, Eckel RH, Howard BV. **Diabetes and cardiovascular disease: a statement for health professionals from the American Heart Association**. *Circulation* (1999) **100** 1134-1146. PMID: 10477542
12. **Classification and diagnosis of diabetes**. *Diabetes Care* (2017) **40** S11-S24. DOI: 10.2337/dc17-S005
13. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. **A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group**. *Ann Intern Med* (1999) **130** 461-470. PMID: 10075613
14. Björntorp P. **Metabolic implications of body fat distribution**. *Diabetes Care* (1991) **14** 1132-1143. PMID: 1773700
15. McKeigue PM, Shah B, Marmot MG. **Relation of central obesity and insulin resistance with high diabetes prevalence and cardiovascular risk in South Asians**. *Lancet* (1991) **337** 382-386. PMID: 1671422
16. Eckel RH, Kahn SE, Ferrannini E, Goldfine AB, Nathan DM, Schwartz MW. **Obesity and Type 2 Diabetes: What Can Be Unified and What Needs to Be Individualized?**. *Diabetes care* (2011) **34** 1424-1430. PMID: 21602431
17. Al-Goblan AS, Al-Alfi MA, Khan MZ. **Mechanism linking diabetes mellitus and obesity**. *Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy* (2014) **7** 587-591. PMID: 25506234
18. Masuo K, Tuck ML, Lambert G. **Hypertension and diabetes in obesity**. *International Journal of Hypertension* (2011)
19. Alexopoulou C, Fourlas C, Dourakis SP. **Hyperkalemia in Patient with diabetes mellitus**. *Lancet* (2002) **359** 628
20. Kim HW, Lee DH, Lee SA, Koh GA. **relationship between serum potassium concentration and insulin resistance in patient with Type 2 DM**. *Int Urol Nephrol* (2015) **47** 991-999. PMID: 25966806
21. Nzerue CM, Jackson K. **Intractable Life threatening Hyperkalemia in a diabetic patient**. *Nephrol Dial Transplant* (2000) **15** 113-114. PMID: 10607780
22. Raphael MA, Ross C, Xu S. **Diabetes and Drug Associated Hyperkalemia and Effect of Potassium monitoring**. *Journal Gen Intern Med* (2010) **36** 26-29
23. Preston RA, Afshartous D, Garg D. **Mechanism of Impaired Potassium handling with dual Renin-Angiotensin-Aldosterone blockage in Chronic Disease Hypertension**. *Journal Mol Cell Cardiol* (2009) **45** 270-280
24. Khanna A, White W. **The management of Hyperkalemia in Patients with Cardiovascular disease**. *Am. Journal. Med* (2009) **122** 215-221. PMID: 19272479
25. Yılmaz FH, Yuca SA, Vatansev H, Cimbek EA, İsa YS, Akyürek YF. **A.. Heart-Type Fatty Acid Binding Protein Level as a Tool in Identification of Early Cardiac Effects of Diabetic Ketoacidosis**. *Journal Clin. Res Pediatric Endocrinol* (2017) **9** 118-123
26. Duncan BB, Schmidt MI, Pankow JS, Ballantyne CM, Couper D, Vigo A. **Atherosclerosis Risk in Communities Study. Low-grade systemic inflammation and the development of type 2 diabetes**. *Diabetes* (2003) **52** 1799-1805. PMID: 12829649
27. Shoelson SE, Lee J, Goldfine AB. **Inflammation and insulin resistance**. *Journal Clin Invest* (2006) **116** 1793-1801
28. Chait A, Han CY, Oram JF, Heinecke JW. **Thematic review series: The immune system and atherogenesis. Lipoprotein-associated inflammatory proteins: Markers or mediators of cardiovascular disease?**. *Journal Lipid Res* (2005) **46** 389-403. PMID: 15722558
29. Martín-Timón I, Sevillano-Collantes C, Segura-Galindo A, Del Cañizo-Gómez FJ. **Type 2 diabetes and cardiovascular disease: Have all risk factors the same strength?**. *World J Diabetes* (2014) **5** 444-470. DOI: 10.4239/wjd.v5.i4.444
30. Selvin E, Marinopoulos S, Berkenblit G, Rami T, Brancati FL, Powe NR. **Meta-analysis: glycosylated hemoglobin and cardiovascular disease in diabetes mellitus**. *Ann Intern Med* (2012) **141** 421-431
31. Zhang Y, Hu G, Yuan Z, Chen L. **Glycosylated Hemoglobin in Relationship to Cardiovascular Outcomes and Death in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis**. *PLoS ONE* (2012) **7** e42551. DOI: 10.1371/journal.pone.0042551
32. Brownlee M, Hirsch IB. **Glycemic variability: a hemoglobin A1cindependent risk factor for diabetic complications**. *JAMA* (2006) **295** 1707-1708. PMID: 16609094
33. Monnier L, Mas E, Ginet C, Michel F, Villon L, Cristol JP. **Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes**. *JAMA* (2006) **295** 1681-1687. PMID: 16609090
34. Sheetz MJ, King GL. **Molecular understanding of hyperglycemia's adverse effects for diabetic complications**. *JAMA* (2002) **288** 2579-2588. PMID: 12444865
35. Cavero-Redondo I, Peleteiro B, Álvarez-Bueno C, Rodriguez-Artalejo F, Martínez-Vizcaíno V. **Glycated haemoglobinA1c as a risk factor of cardiovascular outcomes and all-cause mortality in diabetic and non-diabetic populations: a systematic review and meta-analysis**. *BMJ Open* (2017) **7** e015949. DOI: 10.1136/bmjopen-2017-015949
36. Ejuoghanran O, Chukwu OE, Christopher SL. **The effect of diabetic nephropathy on the lipid profile of diabetics in southern Nigeria**. *Journal of Medical sciences* (2011) **11** 198-202
37. Srinidhi R, Prajna K, Tirthal R. **Lipid profile in Type 2 diabetes mellitus and in diabetic nephropathy**. *International Journal of Clinical Biochemistry and Research* (2017) **4** 379-382
38. Vuvor F. **Correlation of body mass index and blood pressure of adults of 30-50 years of age in Ghana**. *J Health Res Rev* (2017) **4** 115-121
39. Sivasubramanian V, Jetty K, Kumar S. **Correlation of HbA1c with urinary ACR, serum creatinine and eGFR in type-2 diabetes mellitus at Puducherry, South India**. *International Journal of Research in Medical Science* (2019) **7** 1924-1928
40. Shaheer AK, Tharayil JK, Krishna PW. **A Comparative Study of High Sensitivity C - reactive protein and Metabolic Variables in Type 2 Diabetes Mellitus with and without Nephropathy**. *J Clin Diagn Res* (2017) **11** BC01-BC04. DOI: 10.7860/JCDR/2017/30272.10528
41. Sultania S, Thakur D, Kulshreshtha M. **Study of Lipid Profile in Type 2 Diabetes Mellitus Patients and its Correlation with HbA1c**. *International Journal of Contemporary Medical Research* (2017) **4** 2
|
---
title: Impact of COVID-19 on non-communicable disease management services at selected
government health centers in Addis Ababa, Ethiopia
authors:
- Abiyu Mekonnen
- Zelalem Destaw
- Dejene Derseh
- Eshetu Gadissa
- Solomon Ali
journal: African Health Sciences
year: 2022
pmcid: PMC9993267
doi: 10.4314/ahs.v22i3.57
license: CC BY 4.0
---
# Impact of COVID-19 on non-communicable disease management services at selected government health centers in Addis Ababa, Ethiopia
## Abstract
### Background
The global pandemic of COVID-19 forced the world to divert resources and asked the public to shelter-in-place, so the diagnosis surveillance system and management of non-communicable diseases has become more challenging.
### Objective
To identify the impact of COVID-19 on non-communicable diseases management services at government health centers in Addis Ababa, Ethiopia.
### Methods
Health facility based cross-sectional study was conducted from August to September, 2020. A total of 30 health centers were included in this study. Bivariate and multiple logistic regression models were used to assess association between the outcome and independent variables
### Results
The majority, 24 ($80\%$), of the study participants perceived that the COVID-19 pandemic severely disrupted the non-communicable disease management services. There was a statistically significant association between a decrease in outpatient volume at non communicable disease (NCD) management services (25 ($83.3\%$), P-value: 0.006), closure of population level screening programs of NCDs (22 ($73.3\%$), P-value: 0.007), and closure of disease specific NCD clinics and the occurrence of the COVID-19 pandemic (23 ($76.7\%$), P-value: 0.013).
### Conclusion
The most critical health-care services for non-communicable diseases management were severely disrupted by the COVID-19 pandemic. Therefore, during public health emergencies, policymakers should ensure continuation of critical clinical services and inform the public about proper service utilization.
## Introduction
Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been declared as a public health emergency of international concern by the World Health Organization (WHO) 1. Since then, COVID-19 has spread outside China to all continents causing death and economic disruption2.
The existing link between non-communicable diseases (NCDs), communicable diseases and health emergencies is exposed by the COVID-19 pandemic and emphasize the need to stop addressing health issues in siloes 3. Non-communicable diseases and communicable diseases like COVID-19 reinforce one another and disproportionally impacting the poorest segment of the society and the most vulnerable people around the globe 3.
In sub-Saharan Africa the magnitude of NCDs is increased over the past thirty years. The disability adjusted life years (DALYs) attributed to NCDs rose by $67\%$ from 1990 to 2017 4; The non-communicable diseases responsible for the majority of deaths in African region were Cardiovascular disease and cancer 5, but the magnitude of diabetes was also found to be increased in the western, eastern and central Africa, while it was more prevalent in Southern Africa 6. Studies also showed that non communicable diseases such as hypertension, chronic obstructive pulmonary disease, cardiovascular disease or diabetes are each associated with increased risk of either severe disease or death due to COVID-19 7–9.
The Ministry of Health of Ethiopia has reported the first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), in Addis Ababa on March 13, 2020 10; then, administrative measures including closure of schools and implementation of infection prevention measures by the public were instructed. However, the number of COVID-19 cases was increasing and a total of 132,000 cases and 2,057 deaths were reported up to January 21, 2021 11.
COVID-19 causes death, economic distraction and disruption to health services globally. Financial and human resources of the health system were mobilized to the management of COVID-19 pandemic. So, the health system responsible to manage non-communicable diseases could be interrupted. Hence, this study was intended to identify evidence about the impact of the pandemic on the management of non-communicable diseases at selected government health facilities in Addis Ababa, Ethiopia.
## Study setting and design
The study was conducted in Addis Ababa, the capital city of Ethiopia. Addis *Ababa is* administratively divided into 10 sub-cities and 117 districts/Woredas. The Addis Ababa Health Bureau encompasses ninety-eight government health centers under its administration. Health facility based cross-sectional study was conducted from August to September, 2020.
## Sample size and sampling technique
The sample size for this study was determined based on recommendations from a previous study 12. Thirty or about thirty percent of the government health centers available under the administration of the Addis Ababa Health Bureau were covered. The selection of the health facilities included in the study was done by simple random sampling by using the sampling frame.
## Data collection and Statistical analysis
Data were collected by trained Nurses, Health Officers or Health Information Management System (HMIS) professionals accordingly. Nurses and Health Officers were involved in face-to-face interview, while HMIS professionals extract data from HMIS registry. A standard questionnaire designed was used to extract data from HMIS registry and face-to-face interview were conducted with the respective head of the health facility called medical director or their delegates during the data collection period. The non-communicable diseases: hypertension, diabetes mellitus and Asthma were included in this study based on the high prevalence data and availability of reasonably complete information documented in the health facilities than the other non-communicable diseases.
Face-to-face interview were conducted to obtain perceived impact and disruption of the non-communicable disease management services due to the COVID-19 pandemic. Whereas data related to the burden of non-communicable diseases in the years 2018, 2019 and 2020 and in the respective months from March 15 to July 30 were extracted from the Health Information Management System (HMIS) departments of the respective health facilities and the District Health Information Software 2 (DHIS2). DHIS2 is a free and open source health management data platform for health data collection, validation, analysis, and presentation of individual and aggregated data 13.
Descriptive statistics for predictor and outcome variables have been employed. Bivariate and multivariate logistic regression models have been used to assess the level of disruption of the health facilities non communicable diseases management services due to the COVID-19 pandemic. Furthermore, the trend of the most prevalent non communicable diseases before and after the onset of the COVID-19 pandemic was compared using figures. Data were entered into Microsoft excel and exported to IBM SPSS version-23 for statistical analysis. P-value <0.05 was considered statistically significant. Multiple regression model was fitted and Adjusted Odds Ratio (AOR) were reported with their $95\%$ confidence interval ($95\%$ CI) in order to evaluate the level of associations between the dependent variable and the covariates.
## Ethical consideration
The study was ethically approved by the Addis Ababa Health Bureau-Public Health and Emergency Management-Ethical Review Committee (A.A.H.B.P.H.E.MERC), Reference number A/A/H/B/$\frac{453}{227}$, dated $\frac{29}{07}$/2020. Based on the ethical clearance, permission was obtained from the respective health institutions before data collection. Each participant of the study was informed about the objectives of the study, and provided their written consent before included in the study; furthermore, no personal identifier was used during data analysis.
## Respondents' demographic characteristics
In the 30 health centers included in the study, medical directors or their delegates who participated in the study were BSc nurses or public health officers in their field of specialty. Most of the respondents were male, 20 ($68.7\%$), with a mean age of 33 years (SD: + 4.4). The mean service year of the study participants were 4.2 years with a minimum of 2 years and a maximum of 12 years (Table-1).
**Table 1**
| Unnamed: 0 | Number | Percent |
| --- | --- | --- |
| Gender | | |
| Male | 20 | 66.7 |
| Female | 10 | 31.3 |
| Age group | | |
| ≤30 | 8 | 26.7 |
| 31–35 | 15 | 50.0 |
| 36–40 | 6 | 20.0 |
| ≥40 | 1 | 3.3 |
| Respondents service year in the health facility | Respondents service year in the health facility | Respondents service year in the health facility |
| 2–5 Years | 20 | 66.7 |
| 6–10 years | 8 | 26.7 |
| >10 years | 2 | 6.7 |
| Respondents field of specialty | Respondents field of specialty | Respondents field of specialty |
| Public Health Officer | 21 | 70.0 |
| BSc Nurse | 9 | 30.0 |
The majority, 24 ($80\%$) of the study participants perceived that the COVID-19 pandemic severely disrupted the non-communicable disease (NCD) management services at the study health centers. At the same time, 25 ($83.3\%$) described there was a decrease in the volume of outpatients for non-communicable disease management service at the health facilities. On the other hand, only about $33\%$ stated that there were unavailability of essential medicines and diagnostics at NCD management clinics (Table-2).
**Table 2**
| Variable | Number (%) |
| --- | --- |
| Perceived disruption rate of NCD management services due to the COVID-19 pandemic | Perceived disruption rate of NCD management services due to the COVID-19 pandemic |
| Severe | 24 (80.0) |
| Moderate | 6 (20.0) |
| Is there a decrease in outpatient volume at NCD services? | Is there a decrease in outpatient volume at NCD services? |
| Yes | 25(83.3) |
| No | 5 (16.7 |
| Is there insufficient staff to provide NCD as deployed to COVID-19? | Is there insufficient staff to provide NCD as deployed to COVID-19? |
| Yes | 16 (53.3) |
| No | 14 (46.7) |
| Is there closure of population level screening programs of NCD? | Is there closure of population level screening programs of NCD? |
| Yes | 22 (73.3) |
| No | 8 (26.7) |
| Is there transportation issue hindering NCD services? | Is there transportation issue hindering NCD services? |
| Yes | 18 (60.0) |
| No | 12 (40.0) |
| Is there closure of disease specific NCD clinics? | Is there closure of disease specific NCD clinics? |
| Yes | 23 (76.7) |
| No | 7 (23.3) |
| Is there closure of NCD services as per government directions? | Is there closure of NCD services as per government directions? |
| Yes | 15 (50.0) |
| No | 15 (50.0) |
| Is there unavailability of essential medicines and Diagnostics at NCD services? | Is there unavailability of essential medicines and Diagnostics at NCD services? |
| Yes | 10 (33.3) |
| No | 20 (66.7) |
There was a statistically significant association between a decrease in outpatient volume at non communicable disease management services, closure of population level screening programs of NCDs, and closure of disease specific NCD clinics and the occurrence of the COVID-19 pandemic [Crude Odds Ratio (COR) 46.0, 35.0, and 14.0, and $95\%$ Confidence Interval (CI) of (3.33, 634.88), (2.98, 411.46) and (1.74, 112.55)], respectively (Table-3).
**Table 3**
| Variable | Disruption level | Disruption level.1 | COR (95% CI) | P-Value | AOR (95%CI) | P-Value.1 |
| --- | --- | --- | --- | --- | --- | --- |
| Variable | Severe No. (%) | Moderate No. (%) | COR (95% CI) | P-Value | AOR (95%CI) | P-Value |
| Is there a decrease in outpatient volume at NCD services? | Is there a decrease in outpatient volume at NCD services? | Is there a decrease in outpatient volume at NCD services? | Is there a decrease in outpatient volume at NCD services? | Is there a decrease in outpatient volume at NCD services? | Is there a decrease in outpatient volume at NCD services? | Is there a decrease in outpatient volume at NCD services? |
| Yes | 23 (76.7) | 2 (6.7) | 46.0 (3.33, 634.88) | 0.004 | 48.0 (3.09, 750.03) | 0.006 |
| No | 1 (3.3) | 4 (13.3) | R | | | |
| Is there insufficient staff to provide NCD as deployed to COVID-19? | Is there insufficient staff to provide NCD as deployed to COVID-19? | Is there insufficient staff to provide NCD as deployed to COVID-19? | Is there insufficient staff to provide NCD as deployed to COVID-19? | Is there insufficient staff to provide NCD as deployed to COVID-19? | Is there insufficient staff to provide NCD as deployed to COVID-19? | Is there insufficient staff to provide NCD as deployed to COVID-19? |
| Yes | 14 (46.7) | 2 (6.7) | 0.357 (0.05,2.34) | 0.283 | 0.32 (0.03, 4.11) | 0.388 |
| No | 10 (33.3) | 4 (13.3) | R | | | |
| Is there closure of population level screening programs of NCD? | Is there closure of population level screening programs of NCD? | Is there closure of population level screening programs of NCD? | Is there closure of population level screening programs of NCD? | Is there closure of population level screening programs of NCD? | Is there closure of population level screening programs of NCD? | Is there closure of population level screening programs of NCD? |
| Yes | 21 (70.0) | 1(3.3) | 35 (2.98, 411.46) | 0.005 | 42.2 (2.78, 642.25) | 0.007 |
| No | 3 (10.0) | 5 (16.7) | R | | | |
| Is there transportation issue hindering NCD services? | Is there transportation issue hindering NCD services? | Is there transportation issue hindering NCD services? | Is there transportation issue hindering NCD services? | Is there transportation issue hindering NCD services? | Is there transportation issue hindering NCD services? | Is there transportation issue hindering NCD services? |
| Yes | 16 (53.3) | 2 (6.7) | 0.25 (0.04, 1.67) | 0.152 | 0.18 (0.02, 2.46) | 0.11 |
| No | 8 (26.7) | 4 (13.3) | R | | | |
| Is there closure of disease specific NCD clinics? | Is there closure of disease specific NCD clinics? | Is there closure of disease specific NCD clinics? | Is there closure of disease specific NCD clinics? | Is there closure of disease specific NCD clinics? | Is there closure of disease specific NCD clinics? | Is there closure of disease specific NCD clinics? |
| Yes | 21 (70.0) | 2 (6.7) | 14 (1.74, 112.55) | 0.013 | 14.7 (1.75, 123.89) | 0.013 |
| No | 3 (10.0) | 4 (13.3) | R | | | |
| Is there closure of NCD services as per government directions? | Is there closure of NCD services as per government directions? | Is there closure of NCD services as per government directions? | Is there closure of NCD services as per government directions? | Is there closure of NCD services as per government directions? | Is there closure of NCD services as per government directions? | Is there closure of NCD services as per government directions? |
| Yes | 12 (40.0) | 3 (10.0) | 1.00 (0.17, 5.99) | 0.999 | 1.32 (0.16, 10.91 | 0.799 |
| No | 12 (40.0) | 3 (10.0) | R | | | |
| Is there unavailability of essential medicines and Diagnostics at NCD services? | Is there unavailability of essential medicines and Diagnostics at NCD services? | Is there unavailability of essential medicines and Diagnostics at NCD services? | Is there unavailability of essential medicines and Diagnostics at NCD services? | Is there unavailability of essential medicines and Diagnostics at NCD services? | Is there unavailability of essential medicines and Diagnostics at NCD services? | Is there unavailability of essential medicines and Diagnostics at NCD services? |
| Yes | 8 (26.7) | 2 (6.7) | 0.99 (0.15, 6.67) | 0.988 | 1.23 (0.13, 11.39) | 0.858 |
| No | 16 (53.3) | 4 (13.3) | R | | | |
The trend of number of patients who obtained non-communicable disease management services (hypertension, diabetes mellitus and Asthma) at the study health facilities showed a steady decline in their number in 2020 when compared with the number of cases who obtained the respective services during the same period in the last two years (2018 and 2019) (Fig 2–4).
**Figure 2:** *Trend of hypertensive cases in the months March-July in the respective years at selected health centers in Addis Ababa.* **Figure 3:** *Trend of Diabetes Mellitus cases in the months March-July in the respective years at selected health centers in Addis Ababa* **Figure 4:** *Trend of Asthmatic cases in the months March-July in the respective years at selected health centers in Addis Ababa*
Compared with 2019 in the same months (March 15 to July 30), the decrease in the number of patients who obtained the respective services in 2020 showed a decrease in $69\%$, $65\%$ and $57\%$ for hypertension, Diabetes Mellitus and Asthma cases, respectively.
## Discussion
This is the first large scale data to report the impact of the COVID-19 pandemic on the clinical services of non-communicable diseases in Ethiopia. In the present study we found that the health facilities medical services were severely or moderately affected by the COVID-19 pandemic. The under-utilization of important medical services and patients delay despite life threatening symptoms with non-COVID-19 urgent and emergent health needs is an important problem for health care systems 14. Hogan et al showed that, in the era of the COVID-19 pandemic, the lack of proper attention towards non-communicable diseases will lead to greater devastation 15. In the present study, the vast majority of the study participants at the study health centers perceived that the COVID-19 pandemic severely disrupted the non-communicable diseases management services; which is in agreement with a previous finding from Ethiopia 16, which showed that the flow of cases in almost all essential healthcare services were declined as preventive measures against the COVID-19 pandemic.
The decrease in outpatient volume and closure of population level screening programs of non-communicable diseases showed a statistical significant association with the occurrence of the COVID-19 pandemic. This finding showed that the hindrance of the public from regular health service utilization and also outreach community services to find individuals with chronic diseases and conditions but didn't seek health services were aimed at awareness creation as early as possible and making appropriate preventive measures were hampered by the COVID-19 pandemic. A study also indicated that the health status monitoring strategy to identify and solve community health problems have the potential to improve health care delivery adapted to local situation driven by effective health care workers surveillance of households around the world 17.
In a similar fashion, non-communicable disease specific clinical services like the diabetic clinics were closed in some facilities and individuals who were trying to get the consultation and treatment services didn't get access and forced to suffer from the ailments. On the contrary, it is well established that individuals with chronic co-morbidity with diseases and conditions like diabetes were with increased chance of death due to infection with the COVID-19 7,9.
In the current COVID-19 pandemic situation, where accessibility of clinical services is diminished, other healthcare service provision strategies, which seem rewarding in other countries like telehealth service delivery which lacks infrastructure, and inter-practice medical consultation service which is again not well established in Ethiopia need appropriate consideration by responsible institutions in Ethiopia.
Furthermore, managing healthcare crisis associated with shortage of health care professionals and lack of personal protective equipment to provide clinical services for non-communicable diseases in such pandemic situations demand strong collaboration between governmental and non-governmental organizations in Ethiopia.
## Conclusion and recommendations
The most critical health-care services and prevention activities for non-communicable diseases were severely disrupted by the COVID-19 pandemic. Responsible institutions striving against non-communicable diseases control including the Ministry of Health of Ethiopia and nongovernmental organizations should provide appropriate attention for the management of non-communicable diseases in the current situation of the COVID-19 pandemic. These responsible bodies should devise a mechanism to ensure availability of clinical services for these diseases regularly, and make the public aware for health service utilization while maintaining universal infection prevention precautions at the health facilities.
## References
1. 1World Health OrganizationCoronavirus disease (COVID-19) outbreak(https://www.who.int). *Coronavirus disease (COVID-19) outbreak*
2. 2World Health OrganizationCoronavirus diseases (COVID-19) situation reports2020May 2020Geneva, SwitzerlandWHO
https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports. *Coronavirus diseases (COVID-19) situation reports* (2020.0)
3. Reddy K. **15 April 2020, Who all are COVID's fellow conspirators?**. *The New Indian Express*
4. Gouda H, Charlson F, Sorsdahl K. **Burden of non-communicable diseases in sub- Saharan Africa, 1990-2017: results from the Global Burden of Disease Study 2017**. *Lancet Glob Health* (2019.0) **7** e1375-e1387. PMID: 31537368
5. 5Institute for Health Metrics and EvaluationGlobal Health Data Exchange: GBD Results Tool
http://ghdx.healthdata.org/gbd-results-tool. *Global Health Data Exchange: GBD Results Tool*
6. **Trends in obesity and diabetes across Africa from 1980 to 2014: an analysis of pooled population-based studies**. *Int J Epidemiol* (2017.0) **46** 1421-1432. PMID: 28582528
7. Roncon L, Zuin M, Rigatelli G, Zuliani G. **Diabetic patients with COVID-19 infection are at higher risk of ICU admission and poor short-term outcome**. *J Clin Virol* (2020.0) **127** 104354. PMID: 32305882
8. Singh A, Gupta R, Misra A. **Comorbidities in COVID-19: Outcomes in hypertensive cohort and controversies with renin angiotensin system blockers**. *Diabetes Metab Syndr* (2020.0) **14** 283-287. PMID: 32283499
9. Wang B, Li R, Lu Z, Huang Y. **Does comorbidity increase the risk of patients with COVID- 19: evidence from meta-analysis**. *Aging (Albany NY)* (2020.0) **12** 6049-6057. PMID: 32267833
10. 10WHOFirst case of COVID-19 confirmed in Ethiopia2020313
https://www.afro.who.int/news/first-case-covid-19-confirmed-ethiopia. *First case of COVID-19 confirmed in Ethiopia* (2020.0)
11. 11Ethiopian government COVID-19 UpdateJune 11, 2020
https://www.covid19.et/covid-19/. *Ethiopian government COVID-19 Update*
12. Donabedian A. **Methods for deriving criteria for assessing the quality of medical care**. *Medical Care Review* (1980.0) **37** 653-698. PMID: 10298019
13. Gathogo J. *A model for post-implementation valuation of health information systems: the case of the DHIS 2 in Kenya* (2014.0)
14. Guo H, Zhou Y, Liu X, Tan J. **The impact of the COVID-19 epidemic on the utilization of emergency dental services**. *J Dent Sci* (2020.0)
15. Hogan A, Jewell B, Sherrard-Smith E, Vesga J, Watson O, Whittaker C. **Potential impact of the COVID-19 pandemic on HIV, tuberculosis, and malaria in low-income and middle-income countries: A modelling study**. *Lancet Glob Health* (2020.0) **8** e1132-e1141. PMID: 32673577
16. **Dessie study: Abdela S, Berhanu A, Ferede L, Griensven J. Essential Healthcare Services in the Face of COVID-19 Prevention: Experiences from a Referral Hospital in Ethiopia**. *Am. J. Trop. Med. Hyg* (2020.0) **103** 1198-1200. DOI: 10.4269/ajtmh.20-0464
17. Tulenko K, Vervoort D. **Cracks in the System: The Effects of the Coronavirus Pandemic on Public Health Systems**. *The American Review of Public Administration* (2020.0). DOI: 10.1177/0275074020941667
|
---
title: 'Effect of heavy cigarette and water pipe smoking on antioxidants and lipids
in Sudanese male smokers: a case-control study'
authors:
- Ahmed M Ahmed
- Amna M Ibrahim
journal: African Health Sciences
year: 2022
pmcid: PMC9993274
doi: 10.4314/ahs.v22i3.15
license: CC BY 4.0
---
# Effect of heavy cigarette and water pipe smoking on antioxidants and lipids in Sudanese male smokers: a case-control study
## Abstract
### Background
Tobacco smoking is a source of many toxins such as free radicals, mutagenic substances as well as cause for developing cardiovascular diseases (CVD), particularly atherosclerosis. This study aims to assess the impact of smoking on antioxidants in Sudanese male smokers.
### Methods
Cases were 85 and 48 men who smoke cigarettes (CS) and water pipe (WPS) respectively and they were compared with matching 50 non-smoking controls. Blood samples were collected and following parameters: Glutathione peroxidase, Superoxide dismutase, Total cholesterol, Triglyceride, LDL, HDL, Paraoxinase, and Malondialdehyde were measured.
### Results
There were no significant differences in biochemical parameters between light CS and WPS compared to controls. In heavy smokers of both WPS and CS, the TC, TG, LDL, and MDA were higher than controls ($p \leq 0.05$), GPx, SOD, HDL, and PON were lower in smokers than controls ($p \leq 0.05$). In both groups of smokers; HDL, GPx, SOD, and PON were inversely correlated with duration of smoking ($p \leq 0.05$), also, HDL was positively correlated with SOD and GPx ($p \leq 0.05$). Moreover, GPx and SOD were correlated with each other in both groups of smokers ($p \leq 0.05$).
### Conclusion
In Sudanese male smokers' biochemical profile disturbances suggest that heavy smoking was leading to developing CVD, particularly WPS.
## Introduction
Tobacco smoking is a significant reason for death and disability internationally. WHO counts 5 million deaths annually because of tobacco use1. Death caused by smoking regarding CVD might have been preceded by subclinical cardiovascular abnormalities, for example, injury and raised biomarkers in asymptomatic subjects2 3,4.
Tobacco use poses the greatest hazards and challenges worrying public health in Africa, because of the expansion in utilizing Cigarette smoking (CS) and Water Pipe Smoking (WPS), both of which have a significant influence on public health and are considered causative factors of chronic diseases such as cancer and coronary artery disease 5.
Since cigarette smoking is expanding in sub-Saharan Africa, especially in men, which increments many dangers compromising public health6.
In Sudan, about $20\%$ of people use various types of tobacco, with about $8\%$ of them smoking cigarettes. Tobacco use was found to be $2\%$ among children and young adults, and cigarette smoking was found to be $12\%$ among adults aged 18 and up. The majority of cases were found in urban areas rather than rural areas7. Moreover, the utilization of tobacco in the form of CS, WPS, and Tombak (snuff) is largely spread among Sudanese young adults and adolescents8–10.
CS is associated with the progression of the pathogenesis of numerous illnesses, including atherosclerosis and cancer, because it produces a significant amount of free radicals and reactive oxygen species (ROS)11, These free radicals and ROS can damage tissues through oxidative pressure due to an imbalance between the reduced amount of antioxidant and raised free radicals12.
WPS is a popular form of smoking in Sudan now and inquisitively among people who smoke because of its varied flavors types, and misperception that it is less risky to diverge from cigarette smoking considering the way that the usage of water as a filter13. It was named locally as Shisha and in a recent cross-sectional study done on school students; the rate of smokers was equivalent to $13.4\%$ over both sexes14. New research details that WPS could cause oxidative, inflammatory, and mutagenic effects on human health that might prompt chronic sicknesses. Moreover, WPS contains a high proportion of nicotine and tar that causes serious and consistent CVD15.
The current investigation was undertaken to determine the association between CS and WPS on the levels of lipids, Glutathione peroxidase (GPx), Superoxide dismutase (SOD), Malondialdehyde (MDA), and Paraoxinase enzyme (PON), which compared with non-smoking subjects.
## Participants
In this case-control study, eighty-five cigarette smokers' (CS) men (age 32±0.25 years), with various categories of smoking; sixty-five were light smokers and twenty were heavy smokers (for the mean period of 12.6±0.46 years). Light and heavy smokers were classified as follows: light smoking has been described as smoking less than 10 cigarettes/day16, while heavy smoking might have been described as smoking ≥25 cigarettes/day17. Compared to forty-eight water pipe smoker (WPS) men (age 33±0.98 years), with different categories of smoking; thirty-six were light smokers and twelve were heavy smokers. Light smoking was smoking about one time/week, and heavy smoking was smoking 1–2 times/day18. Both groups (CS and WPS) were compared with fifty nonsmoking healthy men as a control group with age ranged between 20–50 years with a mean age of 33±0.6 years. Controls were close associates of the smokers who sit with them during the smoking period, therefore sometimes they may be passive smokers. They were of comparable age and gender to the cases. The clearance for this study was taken from the Institutional Review Board (IRB) of the faculty of applied medical sciences at Taibah University, Madinah, Saudi Arabia, which follows the measures of the declaration of Helsinki and all of its amendments. All participants were informed of the aim of this study and then they concurred as volunteers and signed consent. Exclusion criteria included diabetes mellitus, thyroid diseases, patients with chronic renal/liver disease, cancerous diseases, anemia, and those who use antioxidant/vitamin or mineral supplements.
## Biochemical parameters
Fasting blood samples were taken from study groups in plain tubes and the serum was separated close to collection time. Total Cholesterol (TC), Triglyceride (TG), Low density lipoprotein (LDL), and High density lipoprotein (HDL) were measured with a full auto-analyzer (Hitachi 704, Roche Diagnostics Switzerland). Serum antioxidant, GPx level was measured according to changes in nicotinamide adenine dinucleotide phosphate (NADPH) which was read at 340 nm spectrophotometrically, Paglia et al19, and SOD was determined by using the principle of nitro blue tetrazolium (NBT) reduction rate (Durak et al20. The Paraoxonase (PON) enzyme level was measured by Elabscience's ELISA kit (Sandwich-ELISA principle); and we follow full ELISA protocol which was previously described by Ahmed21. Malondhyde (MDA) was measured using thiobarturic acid reactive substances (TBARS) according to Ahmed et al22.
## Statistical analysis
The data was analyzed with SPSS version 21 (IBM Corporation, Armonk, NY, USA). The normality of our data was tested using the Shapiro-Wilk Test, and the result was greater than 0.05, indicating that our data was normally distributed. For comparisons of three groups, ANOVA followed by Tukey's post-hoc test was conducted. An unpaired t-test was used to determine the difference between groups and Pearson correlation were used to determine the correlation between two sets of data. For comparisons of frequencies data, the chi-square/Fisher's exact test was applied. A $P \leq 0.05$ indicated significant differences.
## Results
A total of one hundred and thirty-three male smokers were included in this study, eighty-five subjects were CS and forty-eight were WPS. Table 1 shows the socio-demographic characteristics of smokers, isolated and altogether. In which there was no significant difference. Table 2 shows the demographic and biochemical profile of smokers contrasted with controls. The duration of smoking in CS was higher than that of WPS ($p \leq 0.001$). In addition, the mean level of antioxidants GPx and SOD were lower in both groups of smokers compared to controls ($p \leq 0.001$), and lower in WPS compared to CS ($p \leq 0.001$). Moreover, PON was lower in both groups of smokers than controls ($p \leq 0.001$), and lower in WPS than in CS ($p \leq 0.01$). Table 3 shows the comparison of the mean levels of lipid profiles and antioxidants between heavy and light smokers in both CS and WPS, in which TC, TG, and LDL were higher in heavy smokers than in light smokers ($p \leq 0.05$). In addition, HDL was lower in heavy smokers compared to light smokers in CS ($p \leq 0.05$). Moreover, with respect to antioxidants mean levels, GPx and SOD were lower in heavy smokers compared to light smokers, ($p \leq 0.05$). Table 4 shows the correlation between age, duration of smoking and antioxidant levels. HDL, GPx, SOD, and PON were contrarily correlated with the duration of smoking in both CS and WPS ($p \leq 0.05$). In addition, HDL was positively correlated with SOD and GPx in both groups of smokers ($p \leq 0.05$). Moreover, GPx and SOD were positively correlated with each other in both groups of smokers ($p \leq 0.05$). Figure 1 shows the comparison of MDA level in heavy and light smokers; MDA was higher in heavy than light smokers ($p \leq 0.01$).
## Discussion
In the current study, the modulation of lipid profiles (TC, TG, LDL, and HDL), two antioxidants (GPx, and SOD), oxidative biomarker (MDA), and PON enzyme among CS and WPS were examined. The most striking results were higher levels of TC, TG, LDL, and MDA concomitant with low levels of HDL, GPx, SOD, and PON in heavy smokers of both CS and WPS than in light smokers. In the current finding, low antioxidants and PON enzyme reflect the high amounts of free radicals which may generate oxidative stress. Cigarette smoking is a leading factor for oxidative stress and cytolysis23. This finding is supported by other studies around the world11,24–26. The molecular mechanism of association between cigarette smoking and CVD is not known yet, but it could be due to the induction of oxidative stress in the cardiovascular system, leading to many bad effects 27,28.
In both cigarettes and WPS, many toxins could be delivered harmful to human health, such as carbon monoxide, nicotine, polyaromatic hydrocarbons, volatile aldehydes, and tobacco-specific nitrosamines29, one session smoke of WPS (30–60 min) the subject was inhaled over ≤40-liter smoke compared to ≤1-liter for cigarette smoker15. In our study, we found that heavy WPS were more affected compared to CS based on the evidence of biochemical outcomes such as antioxidants, lipids, PON, and MDA, which are very high in WPS groups. This proposed WPS is a serious form of smoking because of the quantity of bad materials, long period of smoking sitting, and more amount of smoke breathed; this is agreed with the finding of Pratiti and Mukherjee15 who showed WPS induces oxidative stress by impairing the function of endothelial vasodilator and its repairing mechanisms, that elevated transcriptional expression of matrix metalloproteinase an immune response regulator thereby inducing inflammatory and inactivation of cellular growth. Oxidative stress plays a key role in the progression of chronic diseases such as CVD, particularly atherosclerosis, which is developed by an imbalance between smoking-induced free radicals (reactive oxygen species) and antioxidant defense mechanisms, and our outcome documented this finding and suggested the serious risk of WPS more than non-smokers and decreased of antioxidants levels which correlated with the period of smoking, and Yalcin et al, supported that30. In agreement with the previous review concerning the association between WPS and coronary artery disease (CAD), the author found positive evidence of developing CAD and other CVD in smokers. Our study supports this finding because of positive evidence of CVD including high lipids, high oxidative biomarker (MDA), low antioxidants, PON and HDL suggested the risk of our both smokers groups CS and WPS31. In agreement with the previous prospective cohort study done in Sudanese CS, the authors concluded an association between CS and myocardial infarction and a correlation between the risk of myocardial infarction and smoking duration32. Among the limitations of the current research include, besides low sample size, comparison of period and amount of smoking for both CS and WPS to estimate the real differences between exposure to CS and WPS in biochemical indicators, and estimate the toxins materials in both smoker's groups such as carbon monoxide, nicotine, polyaromatic hydrocarbons, volatile aldehydes, and tobacco-specific nitrosamines. So we recommend another study with a large sample size.
## Conclusion
Sudanese heavy smokers' males had higher levels of lipids, “including bad lipoprotein (LDL)” and an oxidation marker (MDA). However, they had lower levels of good lipid (HDL), PON enzyme, and antioxidants (SOD and GPx), indicating that they were at risk of developing CVD, particularly in WPS. The progression of the smoking period was directly correlated with these disturbances, and WPS were at higher risk compared to CS.
## Conflict of Interest
Authors have nothing to declare.
## References
1. **Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015**. *Lancet* (2016) **388** 1659-1724. PMID: 27733284
2. Marano KM, Kathman SJ, Jones BA, Nordskog BK, Brown BG, Borgerding MF. **Study of cardiovascular disease biomarkers among tobacco consumers. Part 3: evaluation and comparison with the US National Health and Nutrition Examination Survey**. *Inhal Toxicol* (2015) **27** 167-173. PMID: 25787702
3. Zatu MC, Van Rooyen JM, Schutte AE. **Smoking and vascular dysfunction in Africans and Caucasians from South Africa**. *Cardiovasc J Afr* (2011) **22** 18-24. PMID: 21298201
4. Reichert V, Xue X, Bartscherer D. **A pilot study to examine the effects of smoking cessation on serum markers of inflammation in women at risk for cardiovascular disease**. *Chest* (2009) **136** 212-219. PMID: 19225057
5. 5Organization WHOThe WHO framework convention on tobacco control: 10 years of implementation in the African region2015World Health Organization. *The WHO framework convention on tobacco control: 10 years of implementation in the African region* (2015)
6. Brathwaite R, Addo J, Smeeth L, Lock K. **A systematic review of tobacco smoking prevalence and description of tobacco control strategies in Sub-Saharan African countries; 2007 to 2014**. *PloS one* (2015) **10** e0132401. PMID: 26162085
7. Idris AM, Ibrahim Y, Warnakulasuriya K, Cooper D, Johnson N, Nilsen R. **Toombak use and cigarette smoking in the Sudan: estimates of prevalence in the Nile state**. *Preventive medicine* (1998) **27** 597-603. PMID: 9672954
8. El-Amin SE-T, Nwaru BI, Ginawi I, Pisani P, Hakama M. **The role of parents, friends and teachers in adolescents' cigarette smoking and tombak dipping in Sudan**. *Tobacco Control* (2011) **20** 94-99. PMID: 20943827
9. Almahdi HM, Åstrøm AN, Ali RW, Nasir EF. **School workers' knowledge, attitude and behaviour related to use of Toombak: a cross sectional study from Khartoum state, Sudan**. *BMC oral health* (2017) **17** 160. PMID: 29268746
10. El Amin SET. **School smoking policies and health science students' use of cigarettes, shisha and dipping tombak in Sudan**. *Frontiers in public health* (2019) **7** 290. PMID: 31681722
11. Chávez J, Cano C, Souki A. **Effect of cigarette smoking on the oxidant/antioxidant balance in healthy subjects**. *American journal of therapeutics* (2007) **14** 189-193. PMID: 17414589
12. Mahmood IH, Abdullah KS, Othman SH. **The total antioxidant status in cigarette smoking individuals**. *The Medical Journal of Basrah University* (2007) **25** 46-50
13. Rababa'h AM, Bsoul RW, Alkhatatbeh MJ, Alzoubi KH, Khabour OF. **Waterpipe tobacco smoke distresses cardiovascular biomarkers in mice: alterations in protein expression of metalloproteinases, endothelin and myeloperoxidase**. *Inhalation toxicology* (2019) **31** 99-106. PMID: 31050300
14. Othman M, Aghamohammadi N, Farid NDN. **Determinants of shisha use among secondary school students in Sudan**. *BMC public health* (2019) **19** 1390. PMID: 31660934
15. Pratiti R, Mukherjee D. **Epidemiology and Adverse Consequences of Hookah/Waterpipe Use: A Systematic Review**. *Cardiovascular & Hematological Agents in Medicinal Chemistry (* (2019) **17** 82-93
16. Husten CG. **How should we define light or intermittent smoking? Does it matter?**. *Nicotine & Tobacco Research* (2009) **11** 111. PMID: 19246425
17. Killen JD, Fortmann SP, Telch MJ, Newman B. **Are heavy smokers different from light smokers? A comparison after 48 hours without cigarettes**. *JAMA* (1988) **260** 1581-1585. PMID: 3411738
18. Haik MY, Ashour AA, Alahmad YF. **Water-pipe smoking and serum testosterone levels in adult males in Qatar**. *Tobacco Induced Diseases* (2019) **17**
19. Paglia DE, Valentine WN. **Studies on the quantitative and qualitative characterization of erythrocyte glutathione peroxidase**. *The Journal of laboratory and clinical medicine* (1967) **70** 158-169. PMID: 6066618
20. Durak I, Canbolat O, Kavutçu M, Öztürk HS, Yurtarslani Z. **Activities of total, cytoplasmic, and mitochondrial superoxide dismutase enzymes in sera and pleural fluids from patients with lung cancer**. *Journal of clinical laboratory analysis* (1996) **10** 17-20. PMID: 8926562
21. Ahmed AM. **Correlation of Paraoxonase-1 with glycated hemoglobin and lipid profile among Sudanese diabetic patients**. *Pakistan journal of medical sciences* (2019) **35** 1050. PMID: 31372141
22. Ahmed AM, Ozbak HA, Hemeg HA. **Effect of essential oil of traditional two Saudi mint types and its possible role in cardiovascular and throat health**. *International journal of clinical and experimental medicine* (2015) **8** 8060. PMID: 26221371
23. Al Rifai M, DeFilippis AP, McEvoy JW. **The relationship between smoking intensity and subclinical cardiovascular injury: The Multi-Ethnic Study of Atherosclerosis (MESA)**. *Atherosclerosis* (2017) **258** 119-130. PMID: 28237909
24. McEvoy JW, Nasir K, DeFilippis AP. **Relationship of cigarette smoking with inflammation and subclinical vascular disease: the Multi-Ethnic Study of Atherosclerosis**. *Arteriosclerosis, thrombosis, and vascular biology* (2015) **35** 1002-1010. PMID: 25745060
25. Ambrose JA, Barua RS. **The pathophysiology of cigarette smoking and cardiovascular disease: an update**. *Journal of the American college of cardiology* (2004) **43** 1731-1737. PMID: 15145091
26. Messner B, Bernhard D. **Smoking and cardiovascular disease: mechanisms of endothelial dysfunction and early atherogenesis**. *Arteriosclerosis, thrombosis, and vascular biology* (2014) **34** 509-515. PMID: 24554606
27. Jain A, Agrawal B, Varma M, Jadhav A. **Antioxidant status and smoking habits: relationship with diet**. *Singapore medical journal* (2009) **50** 624. PMID: 19551318
28. Lymperaki E, Makedou K, Iliadis S, Vagdatli E. **Effects of acute cigarette smoking on total blood count and markers of oxidative stress in active and passive smokers**. *Hippokratia* (2015) **19** 293. PMID: 27688691
29. Jacob P, Raddaha AHA, Dempsey D. **Comparison of nicotine and carcinogen exposure with water pipe and cigarette smoking**. *Cancer Epidemiology and Prevention Biomarkers* (2013) **22** 765-772
30. Yalcin FK, Er M, Hasanoglu HC. **Deteriorations of pulmonary function, elevated carbon monoxide levels and increased oxidative stress amongst water-pipe smokers**. *International journal of occupational medicine and environmental health* (2017) **30** 731. PMID: 29093578
31. Almedawar MM, Walsh JL, Isma'eel HA. **Waterpipe smoking and risk of coronary artery disease**. *Current opinion in cardiology* (2016) **31** 545-550. PMID: 27428005
32. Elkhader BA, Abdulla AA, Omer MAA. **Correlation of smoking and myocardial infarction among sudanese male patients above 40 years of age**. *Polish journal of radiology* (2016) **81** 138. PMID: 27081418
|
---
title: Association between serum lipid profile, body mass index and osteoporosis in
postmenopausal Sudanese women
authors:
- Asgad Osman Alfahal
- Abdalla Eltoum Ali
- Gadallah Osman Modawe
- Wael Mohialddin Doush
journal: African Health Sciences
year: 2022
pmcid: PMC9993279
doi: 10.4314/ahs.v22i3.43
license: CC BY 4.0
---
# Association between serum lipid profile, body mass index and osteoporosis in postmenopausal Sudanese women
## Abstract
### Background
Epidemiological observations suggest links between osteoporosis and the risk of acute cardiovascular events. Whether the two clinical conditions are linked by common pathogenic factors or atherosclerosis per se remains incompletely understood. The reduction of bone density and osteoporosis in postmenopausal women contributes to elevated lipid parameters and body mass index (BMI).
### Objective
To investigate the relationship between serum lipid profile, BMI and osteoporosis in postmenopausal women.
### Materials and Methods
A prospective analytical case control-study conducted in Khartoum north hospital at Khartoum city, capital of the Sudan from April 2017 to March 2018 after ethical approval obtained from the local Research Ethics Committee of Faculty of Medical Laboratories, Alzaeim Alazhary University on the committee meeting number [109] on Wednesday 15th February 2017. A written informed consent was obtained from all participants to participate in the study.
Two hundred postmenopausal women were enrolled in the study. The age was studied in one hundred osteoporosis postmenopausal women as a case group and one hundred non-osteoporosis postmenopausal women as control group. The serum lipid profiles were estimated using spectrophotometers (Mandry) and BMI calculated using Quetelet index formula. The data were analysed using SPSS version 16.
### Results
The BMI, serum total cholesterol, triglyceride, HDL and LDL in case group respectively were (24.846±2.1647, 251.190±27.0135 mg/dl, 168.790 ±45.774 mg/dl, 50.620 ± 7.174 mg/dl, 166.868 ±28.978 mg/dl). While the BMI, serum total cholesterol, triglyceride, HDL and LDL in control group respectively were (25.378 ±3.8115, 187.990 ± 26.611 mg/dl, 139.360±20.290 mg/dl, 49.480 ±4.659 mg/dl, 111.667 ±28.0045 mg/dl). All serum lipid profiles significantly increased ($$p \leq 0.000$$) in the case group compared to the control group, except serum HDL was insignificant different between the case and control group and also BMI was insignificant different between the case and control group. There was a positive Pearson's correlation between BMD and serum total cholesterol ($r = 0.832$, $P \leq 0.01$), serum LDL ($r = 0.782$, $P \leq 0.01$) and serum triglyceride ($r = 0.72$, $P \leq 0.01$).
### Conclusions
Osteoporotic postmenopausal women had a significant increase in serum lipid profile and BMI. Moreover, we found a positive link between women with cardiovascular diseases and stroke.
## Introduction
Epidemiological studies suggested a relation between cardiovascular diseases and osteoporosis.1, 2 The intravascular deposition of lipids is a strong risk factor for cardiovascular disease. Studies evaluating the relationship between lipid parameters and bone mineral density (BMD) in healthy adults and those with metabolic syndrome revealed inconsistent results.3, 4 Most of these studies were performed in women more than men, mainly in adolescents.3, 5, 6 *Osteoporosis is* one of the most common systemic skeletal disorders characterized by micro-architectural changes and low bone mass which increase the risk of bone fracture, (Figure 1).7 The fracture risk depends on bone strength, which is determined by the bone quality and Bone Mineral Density (BMD).8,9 Moreover, osteoporosis is considered as a metabolic bone disorder accompanied by low mass and weakness of the bones. Plain x-ray images can help the clinicians in the diagnosis of osteoporosis, (Figure 2). Some studies revealed a relationship between dyslipidaemia and low bone mineral density; while other studies found no relationship between total serum cholesterol levels and bone mineral density (BMD).10, 5 *Osteoporosis is* a major health problem in postmenopausal women and is associated with a high risk of cardiovascular disease and stroke due to raised atherogenic lipid levels.11 Body weight is one of the strongest positive predictors of bone mass. There is a positive link between body weight and bone mass in all age groups.12, 13 Because of the controversy in the previous studies and lack of data in Sudanese postmenopausal women, we investigated the relationship between serum lipid profile, BMI and osteoporosis in postmenopausal women.
**Figure 1:** *Osteoporosis inside the femoral bone.* **Figure 2:** *Plain X-ray image of right hip joint showed osteoporosis inside the head of femoral bone.*
## Materials and Methods
This was a prospective analytical case-control hospital-based study conducted in Khartoum north hospital at Khartoum city, capital of the Sudan from April 2017 to March 2018. Two hundred postmenopausal women were enrolled in this study. The age and sex matched, one hundred osteoporosis postmenopausal women as the case group and one hundred non-osteoporosis postmenopausal women as a control group.
## Inclusion and exclusion criteria
Postmenopausal women with an osteoporosis group and postmenopausal women without osteoporosis group were included in this study. Obese women, those with malignant disease, diabetes mellitus, thyroid diseases, parathyroid diseases, adrenal glands diseases, chronic renal failure, inflammatory arthritis, statins usage in the treatment of dyslipidaemia, corticosteroids, hormones and diuretics for more than three months, secondary osteoporosis due to endocrine diseases, gastrointestinal tract diseases such as (Crohn's disease, malabsorption), peptic ulcer surgery, chronic liver disease and osteoporosis induced by medications were excluded from the study.
## Data collection and sampling
The blood samples were taken from a peripheral vein after twelve hours of fasting and were immediately centrifuged at 4°C for 10 min to obtain serum. The fluorescence was measured by automated spectrofluorometer (Mandry, Germany) at 350 nm (Ex) / 420 nm (Em). The obtained values with the usage of enzymatic methods were ascribed the lipid profiles in the serum.
## Data analysis
SPSS version 16 was used for data analysis. The data are presented as the (mean ± standard deviation). The t-test was used to compare the lipid profile and BMI between the study and control group. P-value of <0.05 was considered statistically significant
## Results
The result data of BMI, serum total cholesterol, triglyceride, HDL and LDL in case group and control group were showed in (Table 1) respectively.
**Table 1**
| Parameters | Case group No.=100 | Control group No.=100 | P-value |
| --- | --- | --- | --- |
| BMI | 24.846±-2.1647 | 25.378 ±3.8115 | 0.226 |
| T-CH | 251.190±27.0135 | 187.990 ± 26.611 | 0.0 |
| TG | 168.790 ±45.774 | 139.360±20.290 | 0.0 |
| HDL | 50.620 ± 7.174 | 49.480 ±4.659 | 0.184 |
| LDL | 166.868 ±28.978 | 111.667 ±28.0045 | 0.0 |
In addition, all serum lipid profiles significantly increased ($$p \leq 0.000$$) in the case group compared to the control group, except serum HDL was insignificant different between the case and control group and also BMI was insignificantly difference between case and control group, (Table 1). Pearson's correlation showed a positive correlation between BMD and serum total cholesterol ($r = 0.832$, $P \leq 0.01$), (Figure 3). Also, the Pearson's correlation revealed a positive correlation between serum LDL and BMI ($r = 0.782$, $P \leq 0.01$), (Figure 4). In Pearson's correlation, there was a positive correlation between serum triglyceride ($r = 0.72$, $P \leq 0.01$), (Figure 5).
**Figure 3:** *Correlations between TC and BMD (r= 0.832, P<0.01).* **Figure 4:** *Correlation between LDL and BMD (r = 0.782, P<0.01).* **Figure 5:** *Correlation between TG and BMD (r = 0.72, P<0.01).*
## Discussion
Because of the controversy on the previous studies and lack of data in Sudanese postmenopausal women, this study investigated the relationship between serum lipid profile, BMI and osteoporosis in postmenopausal women. Two hundred postmenopausal women were enrolled in the study. One hundred osteoporosis postmenopausal women as case group and one hundred non-osteoporosis postmenopausal women as a control group. In this present study, all serum lipid profile was significantly increased in osteoporotic postmenopausal women compared to the control group except serum HDL showed the insignificant difference between case and control group. Also, BMI showed an insignificant difference between the case and control group. Pearson's correlation found a positive correlation between BMD, total serum cholesterol (TC), serum LDL and serum triglyceride (TG). Dyslipidaemia has been associated with BMD in some studies, but other studies revealed no relationship between total serum cholesterol levels and bone mineral density.10, 5 A positive association between atherosclerotic CVD and osteoporosis was supported by epidemiological studies.14, 15 Researchers also showed BMD in postmenopausal women was quantitatively associated with high lipid levels in the blood.16, 5 This study disagrees with Li et al.17 who reported that HDL was positively correlated with postmenopausal osteoporosis, but not LDL, TG and TC. Moreover, Sivas et al.18 supported this study and found a positive correlation of LDL, TC and TG with postmenopausal osteoporosis. Furthermore, our study disagrees with Wang et al.19 who reported that a negative correlation between LDL, TC in postmenopausal osteoporosis, but there was no significant correlation between HDL and TG in postmenopausal osteoporosis. The dyslipidaemia increases after menopause. A significant increase in TC, LDL, and TG levels has been demonstrated. HDL data have been controversial. Some authors indicated a lack of any change in HDL values, while others reported decreased or increased HDL levels.20–25 Previously, TC was thought to be associated with cardiovascular diseases and osteoporosis, and subsequent studies investigating the correlation between TC, LDL, TG, and BMD were performed. However, these studies yielded varying outcomes. Some studies found no relationship between them, while others reported a positive or negative correlation.26–30 *In this* study the TC, LDL, TG levels were higher in the control group compared with the osteoporotic group and there is a positive correlation with BMD. Lipid disorders have been associated with BMD in some studies.31 The mechanism of this relationship may be directly related to the cholesterol biosynthetic pathway which determines cholesterol levels and contributes to the activity of the osteoclast.32 Beneficial effects of lipid reducing drugs such as statins on BMD has been seen in most of previous studies.33, 31 Furthermore, these findings proposed the probable association between serum lipid profile and BMD especially among patients with increased risk of osteoporosis other than healthy persons.34, 18
## Conclusions
Osteoporotic postmenopausal women had a significant increase in serum lipid profile and BMI. Moreover, we found a positive link between these women with cardiovascular diseases and stroke.
## Ethics approval and consent to participate
The study received no grant funding and our research complies with the guidelines for human studies and was conducted ethically in accordance with the World Medical Association Declaration of Helsinki. Ethical approval was obtained from the local Research Ethics Committee of Faculty of Medical Laboratories, Alzaeim Alazhary University on the committee meeting number [109] on Wednesday 15th February 2017. A written informed consent was obtained from all participants to participate in the study.
## Conflict of interest
The authors declare that they have no conflict of interest.
## Funding sources
There is no financial support and sponsorship from any institute.
## Author contributions
WMD is responsible for original article manuscript drafting and critical revision of contents. AOA, AEA and GOM are responsible for data collection, data analysis and manuscript design. WMD and GOM are responsible for manuscript drafting and revision. All authors read and gave the final approval of the manuscript to be published.
## ORCID
Dr.Wael Doush https://orcid. org/0000-0002-4099-5255
## References
1. Kiel DP, Kauppila LI, Cupples LA, Hannan MT, O'donnell CJ, Wilson PW. **Bone loss and the progression of abdominal aortic calcification over a 25-year period: the Framingham heart study**. *Calcified Tissue International* (2001) **68** :271-276. DOI: 10.1007/BF02390833
2. McFarlane SI, Muniyappa R, Shin JJ, Bahtiyar G, Sowers JR. **Osteoporosis and cardiovascular disease: brittle bones and boned arteries, is there a link?**. *Endocrine* (2004) **23** 1-10. DOI: 10.1385/ENDO:23:1:01
3. Lawlor DA, Sattar N, Sayers A, Tobias JH. **The association of fasting insulin, glucose, and lipids with bone mass in adolescents: findings from a cross-sectional study**. *The Journal of Clinical Endocrinology & Metabolism* (2012) **97** 2068-2076. DOI: 10.1210/jc.2011-2721
4. Xue P, Gao P, Li Y. **The association between metabolic syndrome and bone mineral density: a meta-analysis**. *Endocrine* (2012) **42** 546-554. DOI: 10.1007/s12020-012-9684-1
5. Wu LY, Yang TC, Kuo SW. **Correlation between bone mineral density and plasma lipids in Taiwan**. *Endocrine Research* (2003) **29** 317-325. DOI: 10.1081/ERC-120025039
6. Dennison EM, Syddall HE, Aihie Sayer A, Martin HJ, Cooper C. **Lipid profile, obesity and bone mineral density: the Hertfordshire cohort study**. *Journal of the association of physicians* (2007) **100** 297-303. DOI: 10.1093/qjmed/hcm023
7. Hennemann A. **Osteoporosis: prevention, diagnosis and therapy**. *Med Monatsschr Pharm* (2002) **25** 164-167. PMID: 12050922
8. Diab DL, Watts NB. **Diagnosis and treatment of osteoporosis in older adults**. *Endocrinology and Metabolism Clinics* (2013) **42** 305-317. DOI: 10.1016/j.ecl.2013.02.007
9. Oleson CV, Morina AB. **Causes and risk factors of osteoporosis**. *Osteoporosis Rehabilitation* (2017) 5-14. DOI: 10.1007/978-3-319-45084-1_2
10. Orozco P. **Atherogenic lipid profile and elevated lipoprotein (a) are associated with lower bone mineral density in early postmenopausal overweight women**. *European Journal of Epidemiology* (2004) **19** 1105-1112. DOI: 10.1007/s10654-004-1706-8
11. Makovey J, Macara M, Chen JS, Hayward CS, March L, Sambrook PN. **High osteoporotic fracture risk and CVD risk co-exist in postmenopausal women**. *Bone* (2013) **52** 120-125. DOI: 10.1016/j.bone.2012.09.025
12. Hannan MT, Felson DT, Anderson JJ. **Bone mineral density in elderly men and women: results from the Framingham osteoporosis study**. *Journal of Bone and Mineral Research* (1992) **7** 547-553. DOI: 10.1002/jbmr.5650070511
13. Nguyen TV, Sambrook PN, Eisman JA. **Bone loss, physical activity, and weight change in elderly women: the Dubbo osteoporosis epidemiology study**. *Journal of Bone and Mineral Research* (1998) **13** 1458-1467. DOI: 10.1359/jbmr.1998.13.9.1458
14. Buizert PJ, van Schoor NM, Lips P, Deeg DJ, Eekhoff EM. **Lipid levels: a link between cardiovascular disease and osteoporosis?**. *Journal of Bone and Mineral Research* (2009) **24** 1103-1109. DOI: 10.1359/jbmr.081262
15. Lian XL, Zhang YP, Li X, Jing LD, Cairang ZM, Gou JQ. **Exploration on the relationship between the elderly osteoporosis and cardiovascular disease risk factors**. *Eur Rev Med Pharmacol Sci* (2017) **21** 4386-4390. PMID: 29077156
16. Orozco P. **Atherogenic lipid profile and elevated lipoprotein (a) are associated with lower bone mineral density in early postmenopausal overweight women**. *European Journal of Epidemiology* (2004) **19** 1105-1112. DOI: 10.1007/s10654-004-1706-8
17. Li S, Guo H, Liu Y. **Relationships of serum lipid profiles and bone mineral density in postmenopausal Chinese women**. *Clinical Endocrinology* (2015) **82** 53-58. DOI: 10.1111/cen.12616
18. Sivas F, Alemdaroglu E, Elverici E, Kulug T, Ozoran K. **Serum lipid profile: its relationship with osteoporotic vertebrae fractures and bone mineral density in Turkish postmenopausal women**. *Rheumatology International* (2009) **29** 885-890. DOI: 10.1007/s00296-008-0784-4
19. Wang J, li Xm, hong y. **The study of correlation between serum adiponectin and the levels of lipid metabolism and bone metabolism biochemical marker in postmenopausal osteoporosis**. *J Pract Obstet Gynecol* (2014) **30** 828-830
20. Ramos RG, Olden K. **The prevalence of metabolic syndrome among US women of childbearing age**. *American Journal of Public Health* (2008) **98** 1122-1127. DOI: 10.2105/AJPH.2007.120055
21. Davis CE, Pajak A, Rywik S, Williams DH, Broda G, Pazucha T, Ephross S. **Natural menopause and cardiovascular disease risk factors The Poland and US collaborative study on cardiovascular disease epidemiology**. *Annals of Epidemiology* (1994) **4** 445-448. DOI: 10.1016/1047-2797(94)90003-5
22. Bonithon-Kopp C, Scarabin PY, Darne B, Malmejac A, Guize L. **Menopause-related changes in lipoproteins and some other cardiovascular risk factors**. *International Journal of Epidemiology* (1990) **19** 42-48. DOI: 10.1093/ije/19.1.42
23. Poehlman ET, Toth MJ, Ades PA, Rosen CJ. **Menopause-associated changes in plasma lipids, insulin-like growth factor I and blood pressure: a longitudinal study**. *European Journal of Clinical Investigation* (1997) **27** 322-326. DOI: 10.1046/j.1365-2362.1997.1160662.x
24. Matthews KA, Meilahn E, Kuller LH, Kelsey SF, Caggiula AW, Wing RR. **Menopause and risk factors for coronary heart disease**. *New England Journal of Medicine* (1989) **321** 641-646. DOI: 10.1056/NEJM198909073211004
25. WU Z, WU X, Zhang Y. **Relationship of menopausal status and sex hormones to serum lipids and blood pressure**. *International Journal of Epidemiology* (1990) **19** 297-302. DOI: 10.1093/ije/19.2.297
26. Yamaguchi T, Sugimoto T, Yano S. **Plasma lipids and osteoporosis in postmenopausal women**. *Endocrine Journal* (2002) **49** 211-217. DOI: 10.1507/endocrj.49.211
27. Samelson EJ, Cupples LA, Hannan MT. **Long-term effects of serum cholesterol on bone mineral density in women and men: the Framingham osteoporosis study**. *Bone* (2004) **34** 557-561. DOI: 10.1016/j.bone.2003.11.024
28. Adami S, Braga V, Zamboni M, Gatti D, Rossini M, Bakri J, Battaglia E. **Relationship between lipids and bone mass in 2 cohorts of healthy women and men**. *Calcified Tissue International* (2004) **74** 136-142. DOI: 10.1007/s00223-003-0050-4
29. Brownbill RA, Ilich JZ. **Lipid profile and bone paradox: higher serum lipids are associated with higher bone mineral density in postmenopausal women**. *Journal of Women's Health* (2006) **15** 261-270. DOI: 10.1089/jwh.2006.15.261
30. Tanko LB, Bagger YZ, Nielsen SB, Christiansen C. **Does serum cholesterol contribute to vertebral bone loss in postmenopausal women?**. *Bone* (2003) **32** 8-14. DOI: 10.1016/S8756-3282(02)00918-3
31. Hernandez JL, Olmos JM, Romana G. **Bone mineral density in statin users: a population-based analysis from a Spanish cohort**. *Journal of Bone and Mineral Metabolism* (2014) **32** 184-191. DOI: 10.1007/s00774-013-0481-6
32. Bauer DC. **HMG CoA reductase inhibitors and the skeleton: a comprehensive review**. *Osteoporosis International* (2003) **14** 273-282. DOI: 10.1007/s00198-002-1323-x
33. Wada Y, Nakamura Y, Koshiyama H. **Lack of positive correlation between statin use and bone mineral density in Japanese subjects with type 2 diabetes**. *Archives of internal medicine* (2000) **160** 2865
34. Prasad GR, Chiu R, Nash MM, Zaltzman JS. **Statin use and bone mineral density in renal transplant recipients**. *American Journal of Transplantation* (2003) **3** 1320-1321. DOI: 10.1046/j.1600-6143.2003.00209.x
|
---
title: 'Association between physical exercise and all-cause and CVD mortality in patients
with diabetes: an updated systematic review and meta-analysis'
authors:
- Xinmin Liu
- Zhen Wu
- Ning Li
journal: African Health Sciences
year: 2022
pmcid: PMC9993283
doi: 10.4314/ahs.v22i3.27
license: CC BY 4.0
---
# Association between physical exercise and all-cause and CVD mortality in patients with diabetes: an updated systematic review and meta-analysis
## Abstract
### Objectives
Physical activity is recommended in guidelines for treatment for diabetes, but the association between physical activity and mortality among diabetic patients has not been extensively studied.
### Methods
Databases were searched from inception to July 10, 2020. Prospective studies were selected to evaluate the association between physical activity and risk for total and cardiovascular diseases (CVD) mortality among diabetic patients. Data were pooled using random-effect model to calculate the relative risks (RRs) with $95\%$ confidence intervals (CIs).
### Results
We included 16 eligible studies involving with 155,203 diabetic participants and 13,821 cases of death. Our study suggested that physical activity in diabetic patients may decrease risk for all-cause (RR 0.57, $95\%$ CI 0.49–0.67) and CVD mortality (RR 0.55, $95\%$ CI 0.34–0.68). The summary RR for CVD events was 0.65 ($95\%$ CI 0.41–1.03). Furthermore, the reductions in all-cause mortality were more significant in diabetic patients with old age (> 60 years) (RR 0.46, $95\%$ CI 0.29 -0.75), higher body mass index (BMI ≥ 28) (RR 0.53, $95\%$ CI 0.42–0.69) and shorter duration of diabetes (RR 0.45, $95\%$ CI 0.24–0.84).
### Conclusion
Physical activity reduced the risk of total and CVD mortality among patients with diabetes, in particular in diabetic patients with old age (> 60 years), obesity and shorter duration of diabetes.
## Background
Diabetes was recognized as a substantial threat to public health with the fastest increasing morbidity worldwide 1. It was reported that 415 million people were estimated to have diabetes in 2015, and will increase to 642 million by 2040 2,3. Patients with diabetes are at high risk for adverse outcomes from its macro vascular and micro vascular complications, which account for more than 2 million deaths every year 4 and constitute the seventh most common cause of disability worldwide 5. The absolute number of deaths from diabetes increased between 2006 and 2016 by $31.1\%$ 6. Among adults in China, diabetes was associated with 2-fold increased mortality compared with adults without diabetes 7. Cardiovascular diseases (CVD) is the most common cause of morbidity and mortality among subjects with type 2 diabetes mellitus (T2DM) 8. Body mass index (BMI) is an independent risk factor of diabetes and CVD 9, which were closely related with physical exercise. Physical activity is important in the prevention of the development of T2DM in people with impaired glucose tolerance (IGT) and for the control of glycaemia and related CVD complications 10,11. Several studies have indicated that high leisure-time physical activity is associated with reduced total and CVD mortality among patients with diabetes 12–17. However, results were not consistent 14,17. In diabetic patients with heart diseases or other serious complication, physical activity may worsen their health conditions. Furthermore, how diabetes severity, patients' age or BMI influence this association has not been extensively studied. Several newly studies on the association between physical activity and mortality have been published. The aim of this study was to examine associations of physical activity with risk total and CVD mortality among diabetic patients. Furthermore, we analyzed the association in diabetic patients with different characteristics such as age, BMI, glycated haemoglobin A1C (HbA1c), types and duration of diabetes.
## Materials and methods
This meta-analysis is reported according to Meta-analysis Of Observational Studies in Epidemiology (MOOSE) Reporting Guidelines 18.
## Search strategy
A systematic literature search for relevant studies was conducted in the databases of Pub Med and EMBASE from inception to January 15th, 2020. The search strategy was as follows: (((((((Diabetes Mellitus [MeSH Terms])) OR (diabetes)) OR (type 2 diabetes)) OR (type 1 diabetes) [All Fields])) AND ((((physical activity[All Fields])) OR (exercise)) OR (sports))) AND ((((death[All Fields])) OR (mortality)) OR (fetal)). In addition, we reviewed the references from relevant articles to identify additional relevant studies. Authors were contacted and requested to provide further data if required.
## Study selection
The following inclusion criteria were required to be eligible for the meta-analysis: [1] cohort, prospective or longitudinal study with more than 5-year follow up; [2] diabetic patients with active and inactive physical activity; [3] reported relative risk (RR) estimates of mortality, such as relative risks (RR), odds ratios (OR), hazard ratios (HR) or incidence with $95\%$ confidence intervals (CIs) for 2 or more categories of physical activity. In multiple same-population studies, we selected and included the one study with longest follow-up time.
## Data extraction
For each eligible study, we recorded the following data: name of the first author, year of publication, study name, study location, participants, age at baseline, and measurement for physical activity, number of diabetes and death, years of follow-up and outcomes.
## Statistical Methods
The pooled analyses were performed using the random-effects model to calculate RRs with $95\%$ CIs). If several estimates were reported in the same article, we chose the most fully adjusted RR of the top category vs. the lowest category of physical activity. If the reference category used in the analyses was not the lowest category, we used the method described was by Hamling et al 19 to convert risk estimates. Study quality was assessed using Newcastle-Ottawa scale (NOS) 20. Heterogeneity was quantified using the I2 test, where I2 > $50\%$ indicated significant heterogeneity 21. Publication bias was evaluated by the Egger's and Begg's test 22,23. Sensitivity analyses were performed to assess the robustness of the findings by omitting each study from the analyses and then summarized the remains. Subgroup analyses were conducted to investigate the impact of age, study location, number of participants and case, follow-up of cohort studies, HbA1c, type and duration of diabetes on the association between physical activity and risk of total and CVD mortality among diabetes.
All the analyses were conducted using Stata statistical software (version 16.0). A 2-sided P value of less than 0.05 was considered statistically significant.
## Study Selection
Of 5,766 studies identified by the initial search, 178 were selected for full-text review; 162 of these were excluded, leaving 16 (Figure S1). Two studies were from the same study 16,24, and we included the one with larger sample size 16.
**Figure S1:** *Study selection.*
## Study characteristics
After ineligible studies were excluded, 16 cohort studies were included in our meta-analysis. ( Descriptive characteristics of studies and outcomes are shown in Table S1)12,13,29–34,14–17,25–28. It involved with 155,203 diabetic participants and 13,821 cases of death. Participants were aged 25 to 80 years, with more than half being middle-aged or older. The duration of cohort studies ranged from 5.7 to 23.8 years, with a median year of 8.7. Results of study quality assessment (score 0–9) yielded a score of 7.0 or above for 15 studies (Table S2).
## Overall analyses
Fifteen observational studies were included in the analysis of diabetes and all-cause mortality. Significant reductions in all-cause mortality were observed for diabetic patients with physical activity (RR 0.57, $95\%$ CI 0.49–0.67; $P \leq 0.001$; Figure 1). Obvious heterogeneity was detected among these studies (I2 = $83.0\%$; $P \leq 0.001$ for heterogeneity; Figure 1). Furthermore, pooled estimates of seven studies showed that diabetic patients with physical activity had lower risk for CVD mortality (RR 0.55, $95\%$ CI 0.44–0.68; $P \leq 0.001$; Figure 2) with moderate heterogeneity (I2 = $68.1\%$; $$P \leq 0.003$$ for heterogeneity; Figure 2). There were four studies reported CVD evens in diabetic patients with active or inactive physical activity and the pooled RR for CVD was 0.58 ($95\%$ CI 0.49–0.69; Figure S2) by a random effects model (I2 = $84.9\%$; Figure S2).
**Figure 1:** *Forest plot of physical activity and risk of all-cause mortality.Abbreviations: RR, risk ratios; CI, confidence interval.* **Figure 2:** *Forest plot of physical activity and risk of CVD mortality.Abbreviations: RR, risk ratios; CI, confidence interval; Moe-1 means outcomes for diabetes patients without medication, and Moe-2 means outcomes for diabetic patients with medication.* **Figure S2:** *Forest plot of physical activity and risk of CVD.*
## Sensitivity and subgroup analyses
Sensitivity and subgroup analyses were conducted to examine the stability of the primary results. In the sensitivity analyses, through omission of any individual study, the results for all-cause and CVD mortality were not significantly altered (Figure S3–S4). However, the results for CVD were changed in the sensitivity analysis (Figure S5), which may due to limited studies were included. We further conducted subgroup analyses for all-cause mortality stratified by age, study location, number of participants and case, follow-up of cohort studies, HbA1c, type and duration of diabetes. Results of any subgroup were consistent with our overall findings (Table S3). Importantly, the reductions in all-cause mortality were more significant in diabetic patients with old age (> 60 years) (RR 0.46, $95\%$ CI 0.29–0.75; $$P \leq 0.002$$; Table S3), higher BMI (BMI ≥ 28) (RR 0.53, $95\%$ CI 0.42–0.69; $P \leq 0.001$; Table S3) and shorter duration of diabetes (RR 0.45, $95\%$ CI 0.24–0.84; $P \leq 0.001$; Table S3). There were eight studies conducted in T2DM and one study conducted in type 1 diabetes mellitus (T1DM). The remains didn't provide the information of types of diabetes. The summary RR for T2DM was 0.56 ($95\%$ CI 0.46–0.68; $P \leq 0.001$; Table S3) and the RR for T1DM was 0.66 ($95\%$ CI 0.42–1.03; $$P \leq 0.007$$; Table S3). Furthermore, there were four studies provided data of HbA1c and the average of HbA1c in these studies were more than $7\%$. The pooled RR of these 4 studies was 0.54 ($95\%$ CI 0.35–0.84; $P \leq 0.001$; data was not shown in Table). We didn't perform subgroup analysis for CVD mortality and events because the limited studies were included in each subgroup, which may provide some misleading information.
**Figure S3:** **Sensitivity analysis* of studies for all-cause mortality.* **Figure S4:** **Sensitivity analysis* of studies for CVD mortality.* **Figure S5:** **Sensitivity analysis* of studies for CVD.* TABLE_PLACEHOLDER:Table S3
## Publication bias
As shown in Table S4, no publication bias was observed according to Egger's and Begg's test in studies of analysis for mortality from all causes and CVD, and CVD events (all $P \leq 0.05$).
**Table S4**
| Unnamed: 0 | Unnamed: 1 | Tests for Heterogeneity | Tests for Heterogeneity.1 | Unnamed: 4 | Unnamed: 5 | Tests for Publication Bias | Tests for Publication Bias.1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | N | RR (95% CI) by random-effect model | RR (95% CI) by fixed-effect model | P value for heterogeneity | I2 (%) | P value of the Egger test | P value of the Begg test |
| All-cause mortality | 14 | 0.57 (0.49–0.67) | 0.69 (0.66–0.73) | <0.001 | 83.0 | 0.13 | 0.51 |
| CVD mortality | 8 | 0.55 (0.44–0.68) | 0.61 (0.55–0.68) | 0.003 | 68.1 | 0.06 | 0.54 |
| CVD | 4 | 0.65 (0.41–1.03) | 0.58 (0.49–0.69) | <0.001 | 84.9 | 0.31 | 1.00 |
## Discussion
In this meta-analysis involving 155,203 diabetic patients, we concluded that physical activity reduced risk for total and CVD mortality among patients with diabetes. Furthermore, the reductions were more significant in diabetic patients with old age (> 60 years), obesity and shorter duration of diabetes.
Several high-quality meta-analyses summarized the studies conducted in general population and conducted that physical activity may reduce the risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes35–41. We firstly summarized the studies conducted in diabetic patients and conducted that physical activity could reduce the risk of total and CVD mortality, as well as CVD events among diabetes. It may contributed by the beneficial effect of physical activity on several indices of cardio metabolic diseases including body weight42, body fat distribution 42–44, blood pressure 45, blood lipids43,46, insulin resistance47,48, endothelial function 49 and cardio-respiratory fitness42,44. It was also reported that moderate or high levels of physical activity were associated with a significantly reduced risk of total and CVD mortality among adults with diabetes, independent of age, education, BMI, blood pressure, total cholesterol, and smoking12. Aerobic and resistance training improve insulin action and plasma glucose (PG), lipids, blood pressure and cardiovascular risk 50. Regular exercise is necessary for continuing benefit. Therefore, diet control and lifestyle interventions are recommended as the first-line treatments for diabetes 11. Schellenberg et al also found that lifestyle interventions effectively decrease the incidence of type 2 diabetes in high-risk patients 51. However, in patients who already have type 2 diabetes, there is no evidence of reduced all-cause mortality and insufficient evidence to suggest benefit on cardiovascular and micro vascular outcomes 51. We, therefore, tried to further analyze the association in diabetic patients stratified by age, BMI, HbA1c and duration of diabetes. The results showed that the reductions of total mortality were more significant in diabetic patients with old age (> 60 years), obesity and shorter duration of diabetes. It was reported that older adults> 65 years old) are the least physically active age group 52. In advanced age, physical activity is effective at mitigating sarcopenia, restoring robustness, and preventing/delaying the development of disability 53. As we discussed previously, BMI is an independent risk factor of diabetes and CVD 9, therefore physical activity may have more protective effects on diabetic patients with obesity.
The significant reduction of total mortality in the patients with shorter duration of diabetes could, in part, be due to the protection of physical activity on β cell from further failure induced by lipotoxicity in early stage. Besides, the physical activity did not reduce the total mortality in patients with T1DM 17, which may due to the hypoglycemia during the immediate post exercise period 54.
There are several limitations for the current study. Firstly, our meta-analysis was performed on summary data, thus leading to a relatively poor accuracy of assessment compared with individual-level analyses. Secondly, our study was limited to studies reported in English. Furthermore, significant heterogeneity was detected among all studies, may due to the different measurements of physical activity. Finally, we cannot identify the best way and the most appropriate amount physical activity for diabetes to achieve the greatest benefit.
## Conclusion
The present study added to the literature by confirming the association between physical activity and risk for total and CVD mortality among diabetic patients, in particular in diabetic patients with old age (> 60 years), obesity and shorter duration of diabetes. It may provide some information for policymakers and future guidelines. Future study is needed to summarize the dose-response association of different kinds of physical activity and health outcomes in patients with diabetes.
## Ethics approval and consent to participate
Not applicable.
## Consent for publication
Not applicable.
## Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
## Conflicts of interest
The authors declare no conflicts of interest.
## Findings
This study received no specific funding for this work.
## Author's contribution
Mr. Liu a had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. XL and ZW selected relevant studies and extracted data from included studies. Any disagreement was resolved by discussion with a third review author to reach a consensus (NL). XL analyzed data and drafted the manuscript. NL and ZW reviewed and provided suggestions. All authors reviewed the manuscript and approved the final manuscript.
## References
1. Zhou B, Lu Y, Hajifathalian K. **Worldwide trends in diabetes since 1980: A pooled analysis of 751 population-based studies with 4.4 million participants**. *The Lancet* (2016) **387** 1513-1530
2. Tricco AC, Ivers NM, Grimshaw JM. **Effectiveness of quality improvement strategies on the management of diabetes: A systematic review and meta-analysis**. *The Lancet* (2012) **379** 2252-2261
3. Chatterjee S, Khunti K, Davies MJ. **Type 2 diabetes**. *The Lancet* (2017) **389** 2239-2251
4. Danaei G, Lu Y, Singh GM. **Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: A comparative risk assessment**. *The Lancet Diabetes and Endocrinology* (2014) **2** 634-647. PMID: 24842598
5. Vos T, Barber RM, Bell B. **Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: A systematic analysis for the Global Burden of Disease Study 2013**. *The Lancet* (2015) **386** 743-800
6. Naghavi M, Abajobir AA, Abbafati C. **Global, regional, and national age-sex specifc mortality for 264 causes of death, 1980-2016: A systematic analysis for the Global Burden of Disease Study 2016**. *The Lancet* (2017) **390** 1151-1210
7. Bragg F, Holmes M V, Iona A. **Association between diabetes and cause-specific mortality in rural and urban areas of China**. *JAMA* (2017) **317** 280-289. PMID: 28114552
8. Lorber D. **Importance of cardiovascular disease risk management in patients with type 2 diabetes mellitus**. *Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy* (2014) **7** 169-183. PMID: 24920930
9. **Health Effects of Overweight and Obesity in 195 Countries over 25 Years**. *New England Journal of Medicine* (2017) **377** 13-27. PMID: 28604169
10. Amadid H, Johansen NB, Bjerregaard AL. **The role of physical activity in the development of first cardiovascular disease event: A tree-structured survival analysis of the Danish ADDITION-PRO cohort**. *Cardiovascular Diabetology* (2018) **17** 1-12. PMID: 29301528
11. Rydén L, Grant PJ, Anker SD. **ESC guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD**. *European Heart Journal* (2013) **34** 3035-3087. PMID: 23996285
12. Hu G, Jousilahti P, Barengo NC, Qiao Q, Lakka TA, Tuomilehto J. **Physical activity, cardiovascular risk factors, and mortality among finnish adults with diabetes**. *Diabetes Care* (2005) **28** 799-805. PMID: 15793176
13. Batty GD, Shipley MJ, Marmot M, Smith GD. **Physical activity and cause-specific mortality in men with Type 2 diabetes / impaired glucose tolerance**. *Diabetic Medicine* (2002) **19** 580-588. PMID: 12099962
14. Tanasescu M, Leitzmann MF, Rimm EB, Hu FB. **Physical Activity in Relation to Cardiovascular Disease and Total Mortality Among Men With Type 2 Diabetes**. *Circulation* (2003) **107** 2435-2439. PMID: 12719277
15. Sone H, Tanaka S, Tanaka S, Suzuki S, Seino H. **Leisure-time physical activity is a significant predictor of stroke and total mortality in Japanese patients with type 2 diabetes : analysis from the Japan Diabetes Complications Study (JDCS)**. *Diabetologia* (2013) **56** 1021-1030. PMID: 23443242
16. Moe B, Augestad LB, Nilsen TIL. **Diabetes severity and the role of leisure time physical exercise on cardiovascular mortality : the Nord-Trøndelag Health study (HUNT), Norway**. *Cardiovascular Diabetology* (2013) **12** 83. PMID: 23738538
17. Tielemans SMAJ, Neve M De, Toeller M, Chaturvedi N, Fuller JH, Stamatakis E. **Association of physical activity with all-cause mortality and incident and prevalent cardiovascular disease among patients with type 1 diabetes : the EURODIAB Prospective Complications Study**. *Diabetologia* (2013) **56** 82-91. PMID: 23052062
18. Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, Moher D, Becker BJ, Sipe TA TS. **Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group**. *JAMA* (2010) **283** 2008-2012
19. Hamling J, Lee P, Weitkunat R, Ambuhl M. **Facilitating meta-analyses by deriving relative effect and precision estimates for alternative comparisons from a set of estimates presented by exposure level or disease category**. *Statistics in Medicine* (2008) **27** 954-997. PMID: 17676579
20. Wells GA SBOC. *The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomized studies in meta-analyses* (2011)
21. Higgins JP, Thompson SG, Deeks JJ AD. **Measuring inconsistency in meta-analyses**. *BMJ* (2003) **327** 557-660. PMID: 12958120
22. Begg CB, Mazumdar M. **Operating Characteristics of a Rank Correlation Test for Publication Bias**. *Biometrics* (1994) **50** 1088. PMID: 7786990
23. Egger M, Smith GD, Schneider M, Minder C. **Bias in meta-analysis detected by a simple, graphical test**. *BMJ* (1997) **315** 629-634. PMID: 9310563
24. Moe B, Midthjell K, Nilsen TIL. **Glycaemic control in people with diabetes influences the beneficial role of physical activity on cardiovascular mortality. Prospective data from the HUNT Study, Norway**. *Primary Care Diabetes* (2015) **9** 451-457. PMID: 26001311
25. JONKER JT, LAET C DE, FRANCO OH. **Physical Activity and Life Expectancy With Life table analysis of the Framingham Heart Study**. *Diabetes Care* (2006) **29** 38-43. PMID: 16373893
26. Trichopoulou A, Psaltopoulou T, Orfanos P, Trichopoulos D. **Diet and physical activity in relation to overall mortality amongst adult diabetics in a general population cohort**. *Journal of Internal Medicine* (2006) **259** 583-591. PMID: 16704559
27. Blomster JI, Chow CK, Zoungas S. **The influence of physical activity on vascular complications and mortality in patients with type 2 diabetes mellitus**. *Diabetes, Obesity and Metabolism* (2013) **15** 1008-1012
28. Williams PT. **Reduced total and cause-specific mortality from walking and running in diabetes**. *Medicine and Science in Sports and Exercise* (2014) **46** 933-939. PMID: 24968127
29. Zethelius B, Gudbjörnsdottir S, Eliasson B, Eeg-Olofsson K, Cederholm J. **Level of physical activity associated with risk of cardiovascular diseases and mortality in patients with type-2 diabetes: Report from the Swedish National Diabetes Register**. *European Journal of Preventive Cardiology* (2014) **21** 244-251. PMID: 24227183
30. Martínez-Gómez D, Guallar-Castillon P, Mota J, Lopez-Garcia E, Rodriguez-Artalejo F. **Physical activity, sitting time and mortality in older adults with diabetes**. *International Journal of Sports Medicine* (2015) **36** 1206-1211. PMID: 26332898
31. Lear SA, Hu W, Rangarajan S. **The effect of physical activity on mortality and cardiovascular disease in 130 000 people from 17 high-income, middle-income, and low-income countries: the PURE study**. *The Lancet* (2017) **390** 2643-2654
32. Shin WY, Lee T, Jeon DH, Kim HC. **Diabetes, frequency of exercise, and mortality over 12 years: Analysis of the national health insurance service-health screening (NHIS-HEALS) database**. *Journal of Korean Medical Science* (2018) **33** 1-9
33. Li CL, Chang HY, Hsu CC, Lu J, fen R, Fang HL. **Joint predictability of health related quality of life and leisure time physical activity on mortality risk in people with diabetes**. *BMC Public Health* (2013) **13** 67. PMID: 23343405
34. Zhao M, Veeranki SP, Magnussen CG, Xi B. **Recommended physical activity and all cause and cause specific mortality in US adults: Prospective cohort study**. *The BMJ* (2020) **370** m2031. PMID: 32611588
35. Blond K, Brinkløv CF, Ried-Larsen M, Crippa A, Grøntved A. **Association of high amounts of physical activity with mortality risk: A systematic review and meta-analysis**. *British Journal of Sports Medicine* (2020) **54** 1195-1201. PMID: 31406017
36. Ekelund U, Tarp J, Steene-Johannessen J. **Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: Systematic review and harmonised meta-analysis**. *The BMJ* (2019) **366** l4570. PMID: 31434697
37. Patterson R, McNamara E, Tainio M. **Sedentary behaviour and risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes: a systematic review and dose response meta-analysis**. *European Journal of Epidemiology* (2018) **33** 811-829. PMID: 29589226
38. Wahid A, Manek N, Nichols M. **Quantifying the Association Between Physical Activity and Cardiovascular Disease and Diabetes: A Systematic Review and Meta-Analysis**. *Journal of the American Heart Association* (2016) **5** e002495. PMID: 27628572
39. Xu C, Furuya-Kanamori L, Liu Y. **Sedentary Behavior, Physical Activity, and All-Cause Mortality: Dose-Response and Intensity Weighted Time-Use Meta-analysis**. *Journal of the American Medical Directors Association* (2019) **20** 1206-1212.e3. PMID: 31272857
40. Ekelund U, Brown WJ, Steene-johannessen J. **Do the associations of sedentary behaviour with cardiovascular disease mortality and cancer mortality differ by physical activity level? A systematic review and harmonised meta-analysis of data from 850 060 participants**. *British journal of sports medicine* (2019) **53** 886-894. PMID: 29991570
41. Ekelund U, Steene-johannessen J, Brown WJ. **Does physical activity attenuate , or even eliminate , the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women**. *The Lancet* (2016) **388** 1302-1310
42. Walker KZ, Piers LS, Putt RS, Jones JA, O'Dea K. **Effects of regular walking on cardiovascular risk factors and body composition in normoglycemic women and women with type 2 diabetes**. *Diabetes Care* (1999) **22** 555-561. PMID: 10189531
43. Ostman C, Smart NA, Morcos D, Duller A, Ridley W, Jewiss D. **The effect of exercise training on clinical outcomes in patients with the metabolic syndrome: A systematic review and meta-analysis**. *Cardiovascular Diabetology* (2017) **16** 110. PMID: 28854979
44. Lehmann R, Vokac A, Niedermann K, Agosti K, Spinas GA. **Loss of abdominal fat and improvement of the cardiovascular risk profile by regular moderate exercise training in patients with NIDDM**. *Diabetologia* (1995) **38** 1313-1319. PMID: 8582541
45. Dun Y, Thomas RJ, Smith JR. **High-intensity interval training improves metabolic syndrome and body composition in outpatient cardiac rehabilitation patients with myocardial infarction**. *Cardiovascular Diabetology* (2019) **18** 104. PMID: 31412869
46. Price HC, Tucker L, Griffin SJ, Holman RR. **The impact of individualised cardiovascular disease (CVD) risk estimates and lifestyle advice on physical activity in individuals at high risk of CVD: A pilot 2 × 2 factorial understanding risk trial**. *Cardiovascular Diabetology* (2008) **7** 21. PMID: 18637168
47. Gill JMR, Malkova D. **Physical activity, fitness and cardiovascular disease risk in adults: Interactions with insulin resistance and obesity**. *Clinical Science* (2006) **110** 409-425. PMID: 16526946
48. Grace A, Chan E, Giallauria F, Graham PL, Smart NA. **Clinical outcomes and glycaemic responses to different aerobic exercise training intensities in type II diabetes: A systematic review and meta-analysis**. *Cardiovascular Diabetology* (2017) **16** 37. PMID: 28292300
49. Qiu S, Cai X, Yin H. **Exercise training and endothelial function in patients with type 2 diabetes: A meta-analysis**. *Cardiovascular Diabetology* (2018) **17** 64. PMID: 29720185
50. Vanhees L, Geladas N, Hansen D. **Importance of characteristics and modalities of physical activity and exercise in the management of cardiovascular health in individuals with cardiovascular risk factors: Recommendations from the EACPR (Part II)**. *European Journal of Preventive Cardiology* (2012) **19** 1005-1033. PMID: 22637741
51. Schellenberg ES, Dryden DM, Vandermeer B, Ha C, Korownyk C. **Lifestyle Interventions for Patients With and at Risk for Type 2 Diabetes**. *Annals of Internal Medicine* (2013) **159** 543. PMID: 24126648
52. Van Cauwenberg J, Nathan A, Barnett A, Barnett DW, Cerin E. **Relationships Between Neighbourhood Physical Environmental Attributes and Older Adults' Leisure-Time Physical Activity: A Systematic Review and Meta-Analysis**. *Sports Medicine* (2018) **48** 1635-1660. PMID: 29721838
53. Marzetti E, Calvani R, Tosato M. **Physical activity and exercise as countermeasures to physical frailty and sarcopenia**. *Aging Clinical and Experimental Research* (2017) **29** 35-42
54. Colberg SR, Laan R, Dassau E, Kerr D. **Physical activity and type 1 diabetes: Time for a rewire?**. *Journal of Diabetes Science and Technology* (2015) **9** 609-618. PMID: 25568144
|
---
title: 'Factors associated with death among hospitalized COVID-19 patients in Lagos
State, Nigeria: a retrospective cross-sectional study'
authors:
- Olusola Adedeji Adejumo
- Tope Ogunniyan
- Adeife Valentina Adetola
- Sandra Chizoba Mba
- Olakunle Ogunbayo
- Oluwaseun David Oladokun
- Oluwayemisi Bamidele Oluwadun
- Olufemi Erinoso
- Sunday Adesola
- Abimbola Bowale
journal: African Health Sciences
year: 2022
pmcid: PMC9993304
doi: 10.4314/ahs.v22i3.52
license: CC BY 4.0
---
# Factors associated with death among hospitalized COVID-19 patients in Lagos State, Nigeria: a retrospective cross-sectional study
## Abstract
### Background
Lagos State has the highest burden of COVID-19 in Nigeria. We assessed associated factors with death from COVID-19 among hospitalized patients in Lagos, Nigeria.
### Methods
A retrospective cross-sectional study was conducted using de-identified records of laboratory-confirmed COVID-19 patients admitted into 15 isolation centers in Lagos State between February 27, 2020, and September 30, 2020.
### Results
A total of 2,858 COVID -19 patients were included in this study. The mean age of the patients was 41.9±15.5 years. A higher proportion of patients were males ($65.8\%$), asymptomatic ($55.5\%$), had no comorbid condition ($72.2\%$) and had the mild disease ($73.8\%$). The case fatality rate was $6.5\%$. The odds of death from COVID-19 infection increased by $4\%$ with every increase in age (AOR 1.04, $95\%$CI 1.03–1.05, $p \leq 0.001$). The chance of dying was $50\%$ fold more among males (AOR 1.5, $95\%$CI 1.0 – 2.2, $$p \leq 0.042$$), $60\%$ fold more among patients with comorbidity (AOR 1.6, $95\%$CI 1.3 – 2.4, $$p \leq 0.037$$) and 9 fold more among patients with severe COVID-19 infection (AOR 9.6, $95\%$ CI 4.9 – 19.1, $p \leq 0.001$).
### Conclusion
The odds of dying was higher among males, the elderly, patients with comorbidity and severe COVID-19.
## Introduction
Corona virus Disease 2019 (COVID-19) gained global attention when the World Health Organization (WHO) declared it a pandemic on March 11 2020. As of December 2020, there were over 62 million cases and 1.4 million deaths reported globally.1 In Africa, the total number of COVID-19 cases reported was over 2 million, with about 52,000 deaths as of December 2020, while Nigeria recorded 67,557 confirmed cases and 1,173 deaths.2 Lagos state, is the epicenter of the disease in Nigeria has the highest burden of COVID-19 confirmed cases in the country.3 Various factors have been identified as influencing poor outcomes among COVID-19 patients; these include but are not limited to being male, older age group, comorbidities, obesity, severe symptoms at presentation, and social deprivation.4–6 A systematic review involving 114 studies reported that older age, hypertension and diabetes increased the mortality risk of COVID-19 patients.7 Another systemic review and meta-analysis opined that mortality from COVID-19 was predicated on the mean age of patients.8 In Nigeria, a study conducted among 2184 patients with COVID-19 showed that the case fatality rate was $4.3\%$, the severity of symptoms and clinical signs at presentation were associated with mortality.9 Another Nigerian study among COVID-19 patients with comorbidity showed that the risk of death was highest among patients with hypertension and diabetes.10 *Unlike previous* studies from Lagos, Nigeria, the present study assessed the factors associated with death from COVID-19 in a larger and more diverse cohort of hospitalized patients, allowing for more generalizability of the study findings.
## Study design
A retrospective cross-sectional study was conducted using de-identified medical records of laboratory-confirmed COVID-19 patients admitted into 15 isolation centers in Lagos State between February 27, 2020, and September 30, 2020.
## Study sites
The study was conducted in four private and eleven public treatment centers in Lagos established to handle inpatient care for COVID-19 patients in Lagos. Mainland hospital was the first treatment centre to manage COVID-19 patients being the only infectious disease hospital in Lagos state.11 Other centers were established as the number of COVID-19 cases increased. The private providers were involved in managing COVID-19 as alternatives for patients who prefer to be managed by private medical practitioners. All COVID-19 isolation centers had a standard treatment protocol developed by the Lagos State Government Incident Command System and the National Center for Disease Control (NCDC).12
## Study population
Based on the World Health Organization guidelines, all COVID-19 patients diagnosed using the real-time reverse transcription-polymerase-chain-reaction (RT-PCR) assay and admitted for treatment in Lagos State isolation centers were included in this study.13 Excluded from the study were suspected COVID-19 patients initially admitted into the isolation centers but later found to be negative for COVID-19 after RT-PCR assay, patients with inconclusive and missing results. COVID-19 positive patients on home-based care were also excluded from the study.
## Assessment of disease severity
Assessment of the severity of the disease was based on the NCDC guidelines.12 Mild cases are either asymptomatic or present with non-specific symptoms such as fever, cough, sore throat, nasal congestion, malaise, headache, muscle pain, loss of smell, loss of taste, diarrhea, vomiting and abdominal pain; with no evidence of viral pneumonia or hypoxia.12 Severe presentation in adults is characterized by high-grade fever (>380C) or suspected respiratory infection and one of the following: respiratory rate >30 breath/minute, severe respiratory distress, Spo2 < $90\%$ in room air and any risk factor for severe infection. The risk factors for severe infection include being elderly (≥ 60 years), diabetes, hypertension, cardiac diseases, chronic lung disease, cerebrovascular disease, chronic kidney disease, immunosuppression, cancer and smoking.14 Severe infection in children is characterized by cough or difficulty in breathing and at least one of the following: central cyanosis or spo2 <$92\%$, grunting respiration or severe respiratory distress, very severe in-drawing or signs of pneumonia.
## Testing practices
In Lagos, Nigeria, testing for COVID-19 was based on a strong suspicion of COVID-19 infection. Testing was done routinely for suspected cases of COVID-19. According to the NCDC, a suspect was any person with acute respiratory illness or new respiratory symptoms in the absence of an alternative diagnosis that explains the clinical presentation and a history of travel to or residence in a country reporting COVID-19 within 14 days prior to symptom onset. A COVID-19 suspect is any person with new respiratory symptoms who had contact with a confirmed or probable COVID-19 case in the last 14 days prior to symptom onset.12
## Admission and discharge criteria
The admission and discharge criteria changed over time with protocol reviews. At the initial stages, all diagnosed COVID-19 patients were admitted and discharged based on 2 consecutive negative samples and one negative sample later. After that, patients with mild symptoms were managed at home, provided the home environment was conducive for home treatment while the severe cases were managed in the hospital. Patients under home-based care were discharged after 14 days of isolation and treatment. All patients discharged after hospital care were asked to do self-isolation at home for one week.
Hospitalized patients were managed with Lopinavir-Ritonavir, Vitamin C, Vitamin D Zinc, blood thinners such as clopidogrel, Aspirin or Clexane, dexamethasone and intranasal oxygen depending on the severity. Patients were admitted into the intensive care unit when the Spo2 <$90\%$. In addition, the patient's comorbid conditions were also managed along with the treatment for COVID-19.
The outcome variables were discharge or death. “ Discharge" was based on the resolution of symptoms and a negative PCR-based SARS-CoV-2 virus test. Patients that died from the complications of COVID-19 while on admission were classified as “dead”. Discharged patients were seen at the clinic monthly for at least three months to ascertain their health status before discharge from follow-up.
## Data analysis
Patients' age, gender, presence and types of comorbidity, symptoms and risk factors, and outcome (Discharged or dead) were extracted from the central electronic medical records of all the isolation centers in Lagos State. Data was transported to IBM statistics for analysis. The severity of patients' presentation was determined using the NCDC guidelines. Continuous variables were presented as means and standard deviation, while percentages and frequencies were used to present categorical variables. The student ‘t’ test was used to compare the numerical variables of two independent groups, while the chi-squared test was used to compare proportions. Logistic regression was used to assess the factors associated with mortality among COVID-19 patients. The effect of possible confounders (symptoms and severity of disease) was reduced using regression analysis. Missing and incomplete data were excluded before data analysis. The confidence interval was set at $95\%$ and $p \leq 0.05$ was considered significant. IBM statistics version 26 was used for data analysis.
## Ethical Approval
The ethical approval was obtained from the Health Research Ethics Committee of the Lagos State University Teaching Hospital.
## Results
A total of 3157 patients were admitted into Lagos isolation centers within the study period, out of which 299 ($9.5\%$) were excluded. Of those excluded from the analysis, 281 ($94.0\%$) were COVID-19 negative, 6 ($2.0\%$) had an inconclusive result, and 12($4.0\%$) had no laboratory result (Figure 1). A total of 2,858 COVID -19 patients were included in this study. The mean age of the patients was 41.9 ±15.5 years, 42.0 % were aged between 20 - 39 years, while the proportion of patients aged below ten years and above 60 years was $2.0\%$ and $13.4\%$, respectively. A higher proportion were male ($65.8\%$), asymptomatic ($55.5\%$), had no comorbid condition ($72.2\%$) and had the mild disease ($73.8\%$). The proportion of patients that died among those admitted was $6.5\%$ (Table 1). The factors associated with mortality from COVID-19 are highlighted in Table 2. Mortality rates were highest among patients aged > 60 and 50–59 years ($26.2\%$ and $9.8\%$ respectively), while there were no deaths in those less than ten years. The mean age of patients that died was higher (59.5± 14.5 years) than patients that survived (40.7±14, 8 years) ($p \leq 0.001$). The mortality rate was higher among the males ($7.3\%$ vs. $5.1\%$, $$p \leq 0.026$$), symptomatic patients ($13.0\%$ vs. $1.3\%$, $p \leq 0.001$), patients with comorbidity ($16.5\%$ vs. $2.7\%$, $p \leq 0.001$) and patients with severe presentation ($22.3\%$ vs $0.9\%$, $p \leq 0.001$).
**Figure 1:** *Patients' flow chart* TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2 The odds of death from COVID-19 infection increased by $4\%$ with every increase in age (AOR 1.04, $95\%$ CI 1.03 – 1.05, $p \leq 0.001$). Compared with the females, the odds of dying was $50\%$ fold more in males (AOR 1.5, $95\%$ CI 1.0 – 2.2, $$p \leq 0.042$$). In addition, the odds of mortality was $60\%$ fold more among patients with comorbidity (AOR 1.6, $95\%$ CI 1.3 – 2.4, $$p \leq 0.037$$) than patients without comorbidity, while it was over nine-fold more among patients with severe COVID-19 infection than patients with mild presentation (AOR 9.6, $95\%$ CI 4.9 – 19.1, $p \leq 0.001$) (Table 3)
**Table 3**
| Variable | AOR | 95% CI | p-value |
| --- | --- | --- | --- |
| Age (in years) | 1.04 | 1.027 – 1.053 | <0.001 |
| Female | 1.0 | | |
| Male | 1.5 | 1.0 – 2.2 | 0.042 |
| Asymptomatic | 1.0 | | |
| Symptomatic | 2.4 | 0.9 – 5.7 | 0.071 |
| No comorbidity | 1.0 | | |
| Comorbidity | 1.5 | 1.0 – 2.2 | 0.037 |
| Mild | 1.0 | | |
| Severe | 9.6 | 4.9 – 19.1 | <0.001 |
Of the risk factors for severe COVID-19, Age (AOR 1.1, $95\%$ CI 1.0–1.1, $p \leq 0.001$), Diabetes (AOR 2.2, $95\%$ CI 1.5 – 3.3, $p \leq 0.001$), HIV (AOR 7.4, $95\%$ CI 2.7 - 20.3, $p \leq 0.001$) and Cancer (AOR 6.4, $95\%$ CI 1.8 – 22.9, $$p \leq 0.004$$) were associated with mortality. ( Table 4)
**Table 4**
| Variable | AOR | 95% CI | p-value |
| --- | --- | --- | --- |
| Age (in years) | 1.1 | 1.0 – 1.1 | <0.001 |
| Hypertension | 1.4 | 1.0 – 2.1 | 0.075 |
| Diabetes | 2.2 | 1.5 – 3.3 | <0.001 |
| Chronic lung disease | 1.8 | 0.9 – 3.9 | 0.116 |
| Chronic kidney disease | 1.9 | 0.5 – 6.4 | 0.324 |
| Immunosuppression | 7.4 | 2.7 – 20.3 | <0.001 |
| Cancer | 6.4 | 1.8 – 22.9 | 0.004 |
## Discussion
Since the first case of COVID-19 was reported in Nigeria on February 27 2020, Lagos State has actively managed many PCR confirmed COVID-19 cases at its various isolation and treatment centers. In this study, most hospitalized patients were adults aged 20 years and above, with about $5\%$ aged less than 20 years. This finding aligns with other studies15,16 and suggests that older adults are more susceptible to COVID-19. This study also demonstrated that males were predominantly affected, and the majority of the cases were asymptomatic or had mild symptoms. Similar findings have been reported in other studies among hospitalized COVID-19 patients.17–19 In the cases reviewed, comorbidities such as hypertension and diabetes were relatively common. This finding corroborates the current evidence that comorbidities are common in COVID-19 patients and are a reason for hospitalization and increased disease severity.20–22 *In this* study, the mortality rate in the hospitalized COVID-19 cases was $6.6\%$. While varying rates have been documented in different regions of the world, the relatively low proportion, as seen in this study, conforms to the low mortality rates reported on the African continent.2,23 In older patients, COVID-19 has been characterized by disease severity, poor outcome, and higher mortality.15,19,21 Our study likewise supported this fact as most deaths were recorded in the older age groups, especially those aged 60 years and above. Being male has been identified as a factor associated with poor outcomes and mortality from COVID-19.19,22–25 *In this* study, similar results were observed as male patients had higher mortality rates compared to females. This finding can be explained by a decrease in the efficiency of immune response with increasing age and the ability for females to mount more robust adaptive and innate immune responses than males.26 The presence of comorbidities such as cardiovascular diseases, diabetes mellitus, and obesity has been documented widely to be associated with severe illness, poor outcome and mortality among COVID-19 patients.19, 27–29 As in previous studies,19, 27–29 findings from this study showed that the mortality rate was higher among patients with a prior history of hypertension, diabetes, cancer, chronic kidney disease and chronic lung diseases, with the rates increasing in patients with multiple comorbidities. Patients with severe COVID-19 disease have been reported to exhibit acute respiratory distress syndrome (ARDS), acute respiratory failure, acute renal injury, multi-organ failure, and may eventually die.21,30,31 An essential factor associated with the severity and prognosis of COVID-19 disease is the increased release of systemic pro-inflammatory cytokines and other inflammatory markers indicative of a phenomenon called “cytokine storm”. This phenomenon can be exacerbated by underlying systemic illnesses and may explain the poorer prognosis in cases with comorbidities.32, 33 Similar findings were observed in this study as patients with comorbidities had higher mortality rates.
## Limitations
Our study had some limitations. Firstly, the meticulous and thorough collection of information was complex during the first pandemic wave, given the rapid surge of cases, the fear of contracting the disease and the little knowledge about the disease. Second, data on underlying health conditions and comorbidities that could increase the risk for complications and severe illness were self-reported and inaccessible from some critically ill patients. Limited testing and admission process in the State may have resulted in the under-reporting of cases and selection bias. Furthermore, we had no access to the variant typing done in the State. Thus, we could not determine if there was an association between the variant and the severity of the illness.
Nonetheless, the current study provides additional evidence and validates prior findings of risk factors associated with mortality in COVID-19 disease. The
## Conclusion
This study explored the factors associated with poor outcomes among COVID-19 patients in the isolation centers in Lagos State. With poorer outcomes and higher mortality rates among men, older persons, those with comorbidities, and severe COVID-19, public health measures to prevent the transmission of the SARS-CoV-2 virus should be sustained, particularly among these groups. Social distancing, use of facemasks, and regular hand-washing are recommended to slow the spread of the virus and help protect vulnerable older adults. Early vaccination will reduce the risk of severe infection and mortality,
## References
1. 1World Health OrganizationNaming the coronavirus disease (COVID-19) and the virus that causes it2020Available at https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it (Assessed April 2021). *Naming the coronavirus disease (COVID-19) and the virus that causes it* (2020.0)
2. 2African Union and African CDCCoronavirus Disease 2019 (COVID-19). Latest Updates on the COVID-19 Crisis from Africa CDC2020Available at https://africacdc.org/covid-19/ (Assessed December 2020). *Coronavirus Disease 2019 (COVID-19). Latest Updates on the COVID-19 Crisis from Africa CDC* (2020.0)
3. 3Nigeria Center for Disease ControlCOVID-19 tracking dashboard for NigeriaAvailable at https://covid19.ncdc.gov.ng/gis/ (Assessed April 27 2021). *COVID-19 tracking dashboard for Nigeria*
4. Velavan TP, Meyer CG. **The COVID-19 epidemic**. *Trop Med Int Health* (2020.0) **25** 278-280. DOI: 10.1111/tmi.13383
5. Martins-Filho PR, Tavares CSS, Santos VS. **Factors associated with mortality in patients with COVID-19. A quantitative evidence synthesis of clinical and laboratory data**. *Eur J Intern Med* (2020.0) **76** 97-99. PMID: 32345526
6. Grasselli G. **Risk factors associated with mortality among patients with COVID-19 in intensive care units in Lombardy, Italy**. *JAMA Intern Med* (2020.0) **180** 1345-1355. PMID: 32667669
7. Mehraeena E, Karimib A, Barzegaryc A, Vahedib F, Afsahid AM, Dadrase O. **Predictors of mortality in patients with COVID-19-a systematic review**. *Eur J Integr Med* (2020.0) **40** 101226. DOI: 10.1016/j.eujim.2020.101226
8. Mesas AE, Cavero-Redondo I, A’ lvarez-, Bueno C, Sarria’ Cabrera MA, Maffei de Andrade S. **Predictors of in-hospital COVID-19 mortality: A comprehensive systematic review and meta-analysis exploring differences by age, sex and health conditions**. *PLOS ONE* (2020.0) **15** e0241742. DOI: 10.1371/journal.pone.0241742
9. Abayomi A, Odukoya O, Osibogun A, Wright O, Adebayo B, Balogun M. **Presenting symptoms and predictors of poor outcomes among 2,184 patients with COVID-19 in Lagos State, Nigeria**. *Int J Infect Dis* (2021.0) **102** 226-232. DOI: 10.1016/j.ijid.2020.10.024
10. Osibogun A, Balogun M, Abayomi A, Idris J, Kuyinu Y, Odukoya O. **Outcomes of COVID-19 patients with comorbidities in southwest Nigeria**. *PLoS ONE* (2021.0) **16** e0248281. DOI: 10.1371/journal.pone.0248281
11. Bowale A, Abayomi A, Idris J, Omilabu S, Abdus-Salam I, Adebayo B. **Clinical presentation, case management and outcomes for the first 32 COVID-19 patients in Nigeria**. *Pan Afr Med J* (2020.0) **35** 24
12. 12Nigeria Centre for Disease ControlNational Interim Guidelines for Clinical Management of COVID-19April 17, 2021[Published online March 14, 2020]. Available from: https://reliefweb.int/report/nigeria/national-interim-guidelines-clinical-management-covid-19. *National Interim Guidelines for Clinical Management of COVID-19*
13. 13Laboratory testing strategy recommendations for COVID-192020April 17 2021GenevaWorld Health OrganizationAvailable from: https://apps.who.int/iris/bitstream/handle/10665/331509/WHO-COVID-19-lab_testing-2020.1-eng.pdf. *Laboratory testing strategy recommendations for COVID-19* (2020.0)
14. 14World Health OrganisationCOVID-19 Clinical management: living guidianceNovember 3, 2020Available from: https://www.who.int/publications/i/item/clinical-management-of-covid-19. *COVID-19 Clinical management: living guidiance*
15. Guan W, Ni Z-Y, Hu Y, Liang W, Ou C, He J. **Clinical characteristics of coronavirus disease 2019 in China**. *N Engl J Med* (2020.0) **382** 1708-1720. PMID: 32109013
16. Davies NG, Klepac P, Liu Y, Prem K, Jit M, Eggo RM. **Age-dependent effects in the transmission and control of COVID-19 epidemics**. *Nat Med* (2020.0) **26** 1205-1211. DOI: 10.1038/s41591-020-0962-9
17. Channappanavar R, Zhao J, Perlman S. **T cell-mediated immune response to respiratory coronaviruses**. *Immunol. Res* (2014.0) **59** 118-128. PMID: 24845462
18. Nelemans T, Kikkert M. **Viral innate immune evasion and the pathogenesis of emerging RNA virus infections**. *Viruses* (2019.0) **11** 961. PMID: 31635238
19. Zhou F, Yu T, Du R, Ren L, Zhao J, Hu Y. **Clinical course and risk factors for mortality of adult in patients with COVID-19 in Wuhan, China: a retrospective cohort study**. *Lancet* (2020.0) **395** 1054-1062. PMID: 32171076
20. Jordan RE. **Covid-19: risk factors for severe disease and death**. *BMJ* (2020.0) **368** m1198. PMID: 32217618
21. Huang C, Wang Y, Li X. **Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China**. *Lancet* (2020.0) **395** 497-506. PMID: 31986264
22. Liang WH, Guan WJ, Li CC, Li YM, Liang HR, Zhao Y. **Clinical characteristics and outcomes of hospitalised patients with COVID-19 treated in Hubei (epicentre) and outside Hubei (non-epicentre): a nationwide analysis of China**. *Eur Respir J* (2020.0) **55** 2000562. DOI: 10.1183/13993003.00562-2020
23. 23World Health OrganisationCoronavirus Disease (COVID-19) Situation Report -1062020Available at https//apps.who.int/iris/handle/10665/332060. *Coronavirus Disease (COVID-19) Situation Report -106* (2020.0)
24. Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW. **Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalised with COVID-19 in the New York City area**. *JAMA* (2020.0) **323** 2052-2059. PMID: 32320003
25. Jin JM, Bai P, He W, Wu F, Liu XF, Han DM. **Gender Differences in Patients With COVID-19: Focus on Severity and Mortality**. *Front Public Health* (2020.0) **8** 152. DOI: 10.3389/fpubh.2020.00152
26. Rapp JL, Lieberman-Cribbin W, Tuminello S, Taioli E. **Male Sex, Severe Obesity, Older Age, and Chronic Kidney Disease Are Associated With COVID-19 Severity and Mortality in New York City**. *Chest* (2021.0) **159** 112-115. DOI: 10.1016/j.chest.2020.08.2065
27. Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N. **Estimates of the severity of coronavirus disease 2019: a model-based analysis**. *Lancet Infect Dis* (2020.0) **20** 669-677. PMID: 32240634
28. Mehra MR, Desai SS, Kuy SR, Henry TD, Patel AN. **Cardiovascular disease, drug therapy, and mortality in Covid-19**. *N Engl J Med* (2020.0) **382** 102
29. Du RH, Liang LR, Yang CQ, Wang W, Cao TZ, Li M. **Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: a prospective cohort study**. *Eur. Respir. J* (2020.0) **55** 2000524. PMID: 32269088
30. Li L, Huang T, Wang Y. **2019 novel coronavirus patients' clinical characteristics, discharge rate and fatality rate of meta-analysis**. *J Med Virol* (2020.0) **92** 577-583. PMID: 32162702
31. Chen N, Zhou M, Dong X. **Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study**. *Lancet* (2020.0) **395** 507-513. PMID: 32007143
32. Chen G, Wu D, Guo W, Cao Y, Huang D, Wang H. **Clinical and immunological features of severe and moderate coronavirus disease 2019**. *J Clin Invest* (2020.0) **130** 2620-2629. PMID: 32217835
33. Qin C, Zhou L, Hu Z, Zhang H, Yang S, Tao Y. **Dysregulation of Immune Response in Patients with Coronavirus 2019 (COVID-19) in Wuhan, China**. *Clin Infect Dis* (2020.0) **71** 762-768. PMID: 32161940
|
---
title: 'The effect of mobile-based training on maternal breastfeeding self-efficacy:
a randomized clinical trial'
authors:
- Aazam Seddighi
- Zahra Bostani Khalesi
- Soheila Majidi
journal: African Health Sciences
year: 2022
pmcid: PMC9993313
doi: 10.4314/ahs.v22i3.69
license: CC BY 4.0
---
# The effect of mobile-based training on maternal breastfeeding self-efficacy: a randomized clinical trial
## Abstract
### Background
The Aim of this study is to determine the effect of mobile-based training on maternal breastfeeding self-efficacy.
### Materials and Methods
This trial was conducted from November May to December 2020 on 198 women referring to healthcare centers in Guilan, Iran. The samples of this study were selected using the convenience sampling method, and random block sampling was used for the allocation of groups. The data collection tool was a two-part questionnaire including questions about demographic data and Dennis's self-efficacy. The questionnaires were completed before and 8 weeks after the intervention in both groups.
### Results
The mean and standard deviation of self-efficacy before the education in the experiment and control group were 48.26+ 6.49 and 49.11 + 7.36, respectively. After the education, the experimental group was 53.78 + 12.61 and control group was 41.90 + 17.98. The difference between the pretest and posttest scores indicated that the breastfeeding educational intervention increased the women's self-efficacy in breastfeeding ($p \leq 0$/001).
### Conclusion
The results showed that mobile-based training could improve maternal breastfeeding self-efficacy. It is therefore recommended, this training program as an available and convenient method to improve breastfeeding self-efficacy.
## Introduction
Breastfeeding is the natural and safe way of feeding infants and is essential to ensure infant's growth and development1. Because it meets infant needs and establishes a deeper emotional relationship between mother and child2. Breastfeeding is an effective way and beautiful process that helps create intimacy and bonding between mom and baby3. Human milk is as a first vaccination that protects infants against a wide variety of infections; diarrhea; respiratory infection, and sudden death4.
Although most mothers Consider breastfeeding to be the best nutrition for their baby until they are 2 years old, they mostly fail to continue it in the first week after birth5. One of the causes of failure to continue breastfeeding is the low self-efficacy of the mother in breastfeeding6.
Self-efficacy is an important psychological and motivational factor in breastfeeding ability7. According to Albert Bandura's theory of social cognition, Self-efficacy consists of the person's beliefs and ideas toward doing responsibilities8. Many factors affect breastfeeding self-efficacy, including Age, previous experience and mother's attitude, verbal encouragement from family and friends, peer breastfeeding education9. Nipple sores and fissures, flat or sunken nipples, breastfeeding pain, stress and anxiety, low birth weight, premature baby, Cesarean delivery and Working mother have a negative effect on breastfeeding self-efficacy11. Also, many studies have confirmed the positive effect of education on breastfeeding self-efficacy. However, one of the most important factors in the effectiveness of educational programs is choosing the most appropriate method and educational media to transfer information to the audience11.
One of the modern educational methods, which are approved by researchers, is using the developed electronic technology because the traditional methods no longer meet the learner's needs and do not give them enough information12. Following the changing expectations of society, people are looking for a training strategy that is personal, based on competency, and it can happen at any anytime, anywhere8. The need for such a strategy forces designers to try to create effective and efficient systems for the development of electronic learning13. Computer-based training has attracted increasing attention from researchers in the recent decade14. With the development of the use of computers, research more was performed on computer-based training15. This progress helped to access the global goal of education and learning for all16. Mobile phones also such as computers were become a learning tool with great potential17. Learning through mobile apps using mobile devices to obtain learning materials so-called mobile learning18. With mobile learning, there is no limitation to learn and people can be educated without a presence in the classroom16. Another advantage is ease-of-use and flexibility, full-time learning, reduces the pressure for attending courses, and lowering the cost of education19. Since many mothers have expressed a need for suitable information on breastfeeding, using an educational method to achieve this aim seems necessary. Therefore, this study was conducted to effect of mobile-based training on maternal breastfeeding self-efficacy.
## Material & Method
This trial was conducted from November May to December 2020 on 198 women referring to healthcare centers in Guilan, Iran. The samples of this study were selected using the convenience sampling method, and random block sampling was used for the allocation of groups.
The sample size was estimated at 74 eligible women per group based on a study by Wu DS et al., using the sample size formula with a $95\%$ confidence and the power of $90\%$ (, and considering a $25\%$ attrition rate.
According to the following formula: n=(z1−α2+z1−β)2×σ2d2σ2=2×$\frac{7}{42}$×(1−0.5)=$\frac{54}{76}$n=($\frac{1}{96}$+$\frac{0}{84}$)2×$\frac{54}{762}$/42=74 To find the final adjusted sample size, a non-response rate of $25\%$, the adjusted sample size will be: nf=n1−fnf=740.75=99 Participants were all the mothers who meet the inclusion criteria and referring for infants' to comprehensive health centers within 2–4 days after birth. Iranian nationality, ability to read and write, beginning breastfeeding the first 24 hours after birth, no history of underlying or chronic diseases, absence of addiction to alcohol or tobacco or drugs, lack of medical prohibition for breastfeeding, possessing mobile phone with android operating system compatible with intervention application, ability to work with and use mobile phone applications, no history of education or not being a graduate in medical fields. Exclusion criteria were refusal to participate in the study at any time.
Based on the proportional-stratified sampling method, the city was divided into 4 districts (i.e., Northern, Southern, Eastern, and Western districts). The affiliated Health centers of the Guilan University of medical sciences in Rasht were the data extract's centers. After calculating the number of samples in each district, a number of comprehensive health centers were randomly selected, and the researcher referred to them for selecting participants among mothers who were referrng to the centers to receive their postnatal care. Participants were selected using the convenient sampling method. 198 women, who were recruited to the study using the convenience sampling method, were randomly divided into the intervention ($$n = 99$$) and control ($$n = 99$$) groups. Assigning individuals to the groups was carried out through the block randomization method.
The researcher gave participants a number from 1 to 198. By random allocation software, a table with 50 rows of blocks be designed and each block was named A and B. The numbers were placed in each house in order, after that, all numbers were placed in blocks. 50 Quadruple blocks were randomly selected. Random blocks were placed in sealed and locked envelopes and kept at selected health centers and no one knew their sequence. After starting the study, the blocks were randomly selected daily. Those in house A were placed in the test group and those in house B were placed in the control group (Figure1).
**Figure 1:** *Consort flow diagram*
The data collection tool consisted of a demographic questionnaire and breastfeeding self-efficacy scale short form (BSES-SF). The BSES-SF is a 14-item instrument developed to measure breastfeeding confidence. All the items are preceded by the statement ‘I can always' and are anchored by a 5-point Likert-type scale, with 1=not at all confident and 5=always confident. All the items are presented positively and the scores are summed up to produce a final score ranging from 14 to 70, with the higher scores indicating better breastfeeding self-efficacy. The BSES-SF was designed in 199920 and Arban et al21 confirmed its credibility, and its reliability was confirmed with the internal conformity of alpha Cronbach $91\%$. Cronbach's α of the BSES-SF in this study was 0.94.
The content of the mobile-based education app was prepared based on the items of the breastfeeding self-efficacy questionnaire from the breastfeeding promotion educational booklet by the Ministry of Health and Medical Education, which including the benefits, breast milk ingredients, patterns of daily duration and frequency of breastfeeding, common breastfeeding positions, use of a pacifier, signs of being full or hungry in infants, signs of baby's hunger and fullness, different positions of mother in breastfeeding, Identify the indicators of adequacy of breast milk, proper storage and preparation of breast milk, common causes of a breast-feeding strike, cracked nipples, breast engorgement, premature and low weight infants, mother's common cold, taking medications while breastfeeding, infant's diarrhea, ways to way to increase and continue milk supply, supplements to increase milk supply and disadvantages of powdered milk. Mothers the intervention group received the smartphone-based app besides routine care. Mothers were taught how to use the program face-to-face session. Every week for eight consecutive weeks, notifications using SMS were sent to the participants to remind them to use the app. During the app usage period (8 weeks), the participants were supported in terms of how to use the program. To provide support, the researcher's telephone number was provided to the participants for contact purposes, if needed. The control group receiving routine care. After 8 weeks, the breastfeeding self-efficacy questionnaire was completed in both groups.
In the end, out of 198 samples, 3 of them in the experimental group(1 for unwillingness and 2 for not studying the app materials), and 8 people in the control group (3 for unwillingness and 5 due to call failure during 8 weeks of study exited from the study)were excluded from the study. Statistical analysis was performed using descriptive statistics. Shapiro-Wilk and Kolmogorov Smirnov tests were used to evaluate the normality of the data. In order to compare the inferential data in groups before and after the intervention, data were analyzed using spss16 software under version 16 and Mann-Whitney u, Wilcoxon, Kruskal-Wallis, and also chi-square and multiple regression tests. And significance level was considered $P \leq 0.05.$ This study was approved by the ethics committee of Guilan University of Medical Sciences with the code number of “IR.GUMS.REC.1398.106” and clinical trial code IRCT20190615043895N1.
## Result
Based on the findings of the demographic questionnaire, the mean age of participants was 29.58 ± 5.32, most of them ($43.32\%$) had a university education, and homemaker (75.4 %), and their type of delivery was cesarean delivery ($72.73\%$). Most of them had a monthly income of twenty million Iranian Rial and above ($36.90\%$) (Table 1).
**Table 1**
| variable | variable.1 | Experimental Group N = 96 | Control Group N = 91 | P value |
| --- | --- | --- | --- | --- |
| Mother's age | Mean | 29.8±5.06 | 29.3±5.6 | 0.689 |
| Mother's education | primary | 1.04 | 4.4 | 0.361 |
| Mother's education | elementary | 4.17 | 8.79 | 0.361 |
| Mother's education | secondary | 7.29 | 5.49 | 0.361 |
| Mother's education | diploma | 40.62 | 41.76 | 0.361 |
| Mother's education | University education | 46.88 | 39.56 | 0.361 |
| Mother's job | housewife | 68.75 | 82.4 | 0.069 |
| Mother's job | Working in health centers | 3.13 | 3.3 | 0.069 |
| Mother's job | Working in other fields | 28.12 | 14.3 | 0.069 |
| Type of delivery | Normal Vaginal Delivery | 29.17 | 25.27 | 0.55 |
| Type of delivery | Cesarean delivery | 70.83 | 74.73 | 0.55 |
| Number of children | First child | 64.6 | 57.1 | 0.386 |
| Number of children | Second child | 30.2 | 33 | 0.386 |
| Number of children | Third child and more | 5.2 | 9.9 | 0.386 |
| Family income | One million and less | 5.21 | 3.30 | 0.176 |
| Family income | One to one and half million | 22.92 | 29.67 | 0.176 |
| Family income | One and half to two million | 28.12 | 37.36 | 0.176 |
| Family income | Two million and more | 43.75 | 29.67 | 0.176 |
The breastfeeding self-efficacy score before the intervention was 49.11+7.36 and 48.26 ± 6.49 in the control and experimental groups, respectively. After the intervention, this score was 41.90 ± 17.98 in the control group and 53.78 ± 12.61 in the experimental group (Table 2).
**Table 2**
| Breastfeeding self-efficacy | Breastfeeding self-efficacy.1 | Mean | Std. Deviation | Mean difference | SD difference | P value |
| --- | --- | --- | --- | --- | --- | --- |
| Experiment | Before intervention | 48.26 | 6.49 | 5.52 | 12.35 | 0.001>* |
| Experiment | After intervention | 53.78 | 12.61 | 5.52 | 12.35 | 0.001>* |
| Control | Before intervention | 49.11 | 7.36 | -7.21 | 16.82 | 0.024* |
| Control | After intervention | 41.9 | 17.98 | -7.21 | 16.82 | 0.024* |
The difference between the pretest and posttest scores indicated that the breastfeeding educational intervention increased the women's self-efficacy in breastfeeding ($p \leq 0$/001).
## Discussion
The results showed that intervention with the use of mobile phone app is effective in promoting breastfeeding self-efficacy. The participants improved their breastfeeding self-efficacy through the information provided in the breastfeeding education. This improvement was shown by the increase in the BSES-SF instrument's posttest scores after completing the breastfeeding education. Thus, the null hypothesis was accepted. The findings are related to Bandura's social cognitive theory that an individual with a high sense of self-efficacy is likely to anticipate positive performances22. Bandura's social cognitive theory was relevant to this study because the women could access breastfeeding education that contained information on solutions to overcome breastfeeding problems for repeated use whenever desired. Previous research has found higher breastfeeding knowledge to positively affect both breastfeeding outcomes and breastfeeding intention1, 3, 5. In this study, most of the mothers ($29.41\%$) had referred to comprehensive health centers to get information on breastfeeding. $27.27\%$ of mothers received no education on breastfeeding during pregnancy. According to the results of Behzadifar's research, the most sources of information about child nutrition were health care, followed by radio, television, and books23. The study of Khayyati also shows that $51.6\%$ of mothers used health center information while only 2.5 % used media as the most important resource of information in breastfeeding24. In the study of Khosravi et al, the main source of information was family, friends, and acquaintances25.
Also in the study of Poorahmad et al, the biggest resource of information were friends and acquaintances ($47\%$)11. In the study of Mahmuodi et al, the main source of information was the internet and then interaction with friends26. In the study by Gudarzi et al.5, peer education was effective to promote breastfeeding self-efficacy. In the study, a significant relationship was found between breastfeeding self-efficacy and type of delivery, which is consistent with the results of some studies. In Nursan et al.27 study, although the breastfeeding self-efficacy was higher in mothers who had a cesarean section, no significant relationship was observed between breastfeeding self-efficacy and type of delivery. The results of Tokat et al. and Poorshaban et al. indicate a significant relationship between breastfeeding self-efficacy and type of delivery and mothers with a normal delivery had a higher self-efficacy than mothers with cesarean section9, 28. The study of Ahmadi et al showed that there was a significant difference between breastfeeding self-efficacy of women with normal delivery and cesarean section and acknowledged that women with normal delivery had higher breastfeeding self-efficacy compared to women with cesarean delivery29. The number of living children was positively correlated with higher breastfeeding self-efficacy. This is consistent with previous studies30. Similarly, Hinic found the number of livng children to be a predictor of breastfeeding self-efficacy in the immediate postpartum period among a sample of mixed primiparous and multiparous women31.
This study has a number of limitations. This study used a small sample. It was difficult to recruit subjects to this study; most of the women did not intend to breastfeed and therefore, did not meet the inclusion criteria.
## Conclusion
The results of the BSES-SF post-test scores showed that all of the participants had a high level of breastfeeding self-efficacy after accessing breastfeeding education.. Health providers could use this tool to improve their maternal breastfeeding self-efficacy. It is important that all breastfeeding mothers be followed up after delivery. After delivery, mothers often do not have the time to learn all they needed to know about breastfeeding. They often feel overwhelmed with the amount of material they are presented with and may feel tired or uncomfortable comprehending what they have been taught. They often leave the hospital not feeling confident in their abilities to breastfeed.
## References
1. Azhari S, baghani R, akhlaghi F, Ebrahim zade S, Salehi fedri J. **Comparison of two methods of lactation training with and without direct intervention of educator on self-efficacy of lactation in primipara mothers**. *Journal of Mashhad Nursing and Midwifery Faculty* (2010) **4** 244-255
2. Varie SH, Mehrdad N, Bahraii N. **The concept of self-efficacy In Bandura's Social Cognitive Theory**. *Journal of Nursing and Midwifery* (2000) **15** 31-38
3. Moudi A, Tafazoli M, Boskabadi H, Ebrahimzadeh S, Salehiniya H. **Comparing the Effects of Peer Support and Training by Healthcare Providers on Primiparous Women's Breastfeeding Self-Efficacy**. *Journal of Midwifery and Reproductive Health* (2016) **4** 488-497
4. Merdasi F, Araban M, Amal Saki M. **The Effect of Message-Framing on Breastfeeding Self- Efficacy Among Nulliparous Women in Shushtar, Iran**. *Electronic Physician* (2017) **9** 3554-3560. PMID: 28243406
5. Gudarzi Z, Saaidi SM, Daneshvar Ameri Z, Shamshiri A, Sadeghi T. **Effect of Breastfeeding training through peer on breastfeeding self-efficacy in primipara mothers: a random cluster randomized trial**. *Hakim* (2015) **2** 105-113
6. Noel-Weiss J, Rupp A, Cragg B, Bassett V, Woodend AK. **Randomized controlled trial to determine effects of prenatal breastfeeding workshop on maternal breastfeeding self-efficacy and breastfeeding duration**. *Journal of Obstetric Gynecologic Neonatal_Nursing* (2006) **35** 616-624
7. Heidari MH, Amini Zaghani Sh, Shirvani Shiri A. **Role of Self-efficacy, Self-regulatory and Self-steam in Academic Achievement of Third Grades Students in the Field of Experimental Sciences**. *Hakim* (2014) **1** 89-102
8. Soleimani E, Hoveida R. **The concept of self-efficacy In Bandura's Social Cognitive Theory**. *Isfahan University of Medical Sciences* (2013) **63** 91-97
9. Poorshaban F, Pakseresht S, Bostani Khalesi Z, Kazem Nejad Leili E. **Factors Associated with breastfeeding self-efficacy of Mothers Within 6 Weeks of Delivery**. *Journal of Holistic Nursing and Midwifery* (2017) **27** 27-34
10. Guimarães CMS CR, Gomes-Sponholz FA, Oriá MO, Monterio JCS. **Breastfeeding self-efficacy and length of exclusive breastfeeding among adolescent mothers**. *Acta Paul Enferm* (2017) **30** 383-389
11. PourAhmad Garbandi F, Salarzade M, Etehad R. **Evaluation of the causes of early termination of exclusive breastfeeding of mothers referred to health centers in Bandar Abbas**. *Preventive Medicine Journal* (2014) **1** 16-22
12. Badri GR, Fathi AS. **A comparison of the effect of the group problem based learning and traditional teaching on critical thinking of teacher students**. *Students in Education & Psychology Jornal* (2007) **8** 27-42
13. Sinclair P, Kable A, Levett-Jones T. **The effectiveness of internet-based e learning on clinician behavior and patient outcomes: a systematic review protocol**. *JBI Database System Rev Implement Rep* (2015) **13** 52-64
14. Lewis KO, Cidon MJ, Seto TL, Chen H, Mahan JD. **Leveraging e learning in medical education**. *Curr Probl Pediatr Adolesc Health Care* (2014) **44** 150-163. PMID: 24981664
15. Schreurs J, Sammour G, Ehlers U. **ERA-E-learning readiness analysis: A eHealth case study of E-learning readiness**. *International Journal of Knowledge and Learning* (2008) **4** 496-508
16. Marjan T. **The effect of mobile phone education in urinary catheterization on learning motivation, knowledge and practice of nursing and Midwifery students[ MSc Thesis]**. *Rasht: Guilan University of Medical Sciences* (2018)
17. Chuisano SA, Anderson OS. **Assessing Application-Based Breastfeeding Education for Physicians and Nurses: A Scoping Review**. *J Hum Lact* (2020) **36** 699-709. PMID: 31112052
18. Suryavanshi N, Kadam A, Gupte N, Hegde A, Kanade S, Sivalenka S, Kumar VS, Gupta A, Bollinger RC, Shankar A, McKenzie-White J, Mave V. **A mobile health-facilitated behavioural intervention for community health workers improves exclusive breastfeeding and early infant HIV diagnosis in India: a cluster-randomized trial**. *J Int AIDS Soc* (2020) **23** e25555. PMID: 32618115
19. Jerin I, Akter M, Talukder K, Talukder MQEK, Rahman MA. **Mobile phone support to sustain exclusive breastfeeding in the community after hospital delivery and counseling: a quasi-experimental study**. *Int Breastfeed J* (2020) **15** 14. PMID: 32131865
20. Dennis CL, Heaman M, Mossman M. **Psychometric Testing of the breastfeeding self-efficacy Scale-Short Form Among Adolescents**. *Journal of Adolescent Health* (2011) **49** 265-271
21. Araban M, Falahiyan Mehrjardi F, Shahry P, Montazeri A. **The Persian version of breastfeeding self-efficacy Scale-Short Form (breastfeeding self-efficacy -SF): translation and psychometric assessment**. *Journal of the Iranian Institute for Health Sciences Research* (2016) **15** 87-93
22. Bandura A. *Self-efficacy: The exercise of control* (1997)
23. Behzadifar M, Saki M, Behzadifar M, Mardani M, Yari F, Ebrahimzadeh F, Majidi Mehr H, Abdi Bastami S, Bragazzi NL. **Prevalence of exclusive breastfeeding practice in the first six months of life and its determinants in Iran: a systematic review and meta-analysis**. *BMC Pediatr* (2019) **19** 384. PMID: 31656169
24. Khayyati F. **An investigation into the reasons of terminating breastfeeding before the age of two**. *The Journal of Qazvin Univof MedSci* (2007) **11** 25-30
25. Khosravi A, AhmadZade Kh, ArastuoPour Sh, Tahmasebi R. **Assessment of the proportion of health literacy level and readability of educational resources available to diabetic patients referred to Shiraz health centers**. *Library and Information* (2014) **16** 38-57
26. Mahmuodi H, Taheri A. **The Relationship between Information Literacy and Health Literacy in Graduate Students of Ferdowsi University of Mashhad**. *Human interaction and information* (2015) **2** 31-41
27. Nursan C, Dilek K, Sevin A. **breastfeeding self-efficacy of Mothers and the Affecting Factors**. *Aquichan* (2014) **14** 35-327
28. Tokat M, Okumus H, Dennis C. **Translation and psychometric assessment of the Breast-feeding Self-EfficacyScale-Short Form among pregnant and postnatal women in Turkey**. *Midwifery* (2010) **26** 101-108. PMID: 18541350
29. Ahmadi Y, Sharififar ST, Poghogoo SAH, Yari M, Hussein MS, Teymouri F. **Comparison of breastfeeding quality of newborns in normal delivery and cesarean section in selected military hospitals in Tehran**. *Journal of Military Care Sciences* (2017) **4** 70-76
30. Charoghchian Khorasani E, Peyman N, Esmaily H. **Relation between breastfeeding self-efficacy and maternal health literacy among pregnant women**. *Evidence Based Care Journal* (2016) **6** 18-25
31. Hinic K. **Predictors of Breastfeeding Confidence in the Early Postpartum Period**. *J Obstet Gynecol Neonatal Nurs* (2016) **45** 649-660
|
---
title: 'Empagliflozin for the treatment of non-alcoholic fatty liver disease: a meta-analysis
of randomized controlled trials'
authors:
- Xue Tang
- Huaping Zhang
- Xin Wang
- Dan Yang
journal: African Health Sciences
year: 2022
pmcid: PMC9993318
doi: 10.4314/ahs.v22i3.42
license: CC BY 4.0
---
# Empagliflozin for the treatment of non-alcoholic fatty liver disease: a meta-analysis of randomized controlled trials
## Abstract
The efficacy of empagliflozin for non-alcoholic fatty liver disease remains controversial. This meta-analysis aims to explore the influence of empagliflozin versus placebo on the treatment of non-alcoholic fatty liver disease and we have searched PubMed, EMbase, Web of science, EBSCO, and Cochrane library databases through July 2021 for related randomized controlled trials (RCTs). Three RCTs involving 212 patients are included in the meta-analysis. Compared with control group for non-alcoholic fatty liver disease, empagliflozin treatment has no improvement in controlled attenuation parameter (CAP) score, hepatic steatosis and liver stiffness measurement (LSM) score, alanine aminotransferase (ALT), aspartate-aminotransferase (AST), low density lipoprotein (LDL) or triglyceride (TG). These indicate that empagliflozin treatment may be not effective for non-alcoholic fatty liver disease.
## Introduction
Similar to obesity, the prevalence of non-alcoholic fatty liver disease has been increasing worldwide for the past 30 years1–3. Nonalcoholic steatohepatitis is the progressive form of nonalcoholic fatty liver disease and has the features of hepatocellular damage, inflammation, and liver fibrosis4–6. The prevalence of ultrasound-determined fatty liver in patients with type 2 diabetes mellitus is estimated to range from 29.6 to $87.1\%$7. Serious non-alcoholic fatty liver disease can progress to cirrhosis, end-stage liver disease and hepatocellular carcinoma. Liver diseases are anticipated to become the main cause of mortality in the next 20 years and an important cause for liver transplantation in the next few years8, 9. Recent findings suggest that non-alcoholic fatty liver disease is a major cause of cryptogenic cirrhosis10.
Sodium-glucose co-transporter 2 (SGLT2) inhibitors are reported to increase urinary glucose excretion and decrease blood glucose and insulin levels11, 12. They result in a significant increase in fatty acid (FA) mobilization from adipose tissues and FA uptake13. As one important SGLT2 inhibitor, the beneficial effects of empagliflozin on liver are seen in patients with non-alcoholic fatty liver disease14, 15.
However, the benefit of empagliflozin for nonalcoholic fatty liver disease has not been well established and several studies reports the conflicting results15–17. With accumulating evidence, we therefore perform a meta-analysis of RCTs to explore the efficacy of empagliflozin versus placebo for nonalcoholic fatty liver disease.
## Materials and methods
Ethical approval and patient consent are not required because this is a meta-analysis of previously published studies. The meta-analysis are conducted according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)18.
## Search strategy and study selection
Two investigators have independently searched the following databases (inception to July 2021): Pub Med, EMbase, Web of science, EBSCO and Cochrane library databases. The electronic search strategy is conducted using the following keywords: “liver disease” OR “steatohepatitis” AND “empagliflozin”. We also check the reference lists of the screened full-text studies to identify other potentially eligible trials.
The inclusive selection criteria are as follows: (i) patients are diagnosed with non-alcoholic fatty liver disease; (ii) intervention treatments are empagliflozin versus placebo; (iii) study design is RCT.
## Data extraction and outcome measures
We have extracted the following information: author, number of patients, age, female, body mass index, statin use and detail methods in each group etc. Data have been extracted independently by two investigators, and discrepancies are resolved by consensus. The primary outcomes are controlled attenuation parameter (CAP) score to evaluate hepatic steatosis and liver stiffness measurement (LSM) to assess fibrosis. Secondary outcomes include alanine aminotransferase (ALT), aspartate-aminotransferase (AST), low density lipoprotein (LDL) and triglyceride (TG).
## Quality assessment in individual studies
Methodological quality of the included studies is independently evaluated using the modified Jadad scale19. There are 3 items for Jadad scale: randomization (0–2 points), blinding (0–2 points), dropouts and withdrawals (0–1 points). The score of Jadad Scale varies from 0 to 5 points. An article with Jadad score≤2 is considered to have low quality. If the Jadad score≥3, the study is thought to have high quality20.
## Statistical analysis
We estimate the standard mean difference (SMD) with $95\%$ confidence interval (CI) for all continuous outcomes. The random-effects model is used regardless of heterogeneity. Heterogeneity is reported using the I2 statistic, and I2 > $50\%$ indicates significant heterogeneity21. Whenever significant heterogeneity is present, we search for potential sources of heterogeneity via omitting one study in turn for the meta-analysis or performing subgroup analysis. All statistical analyses are performed using Review Manager Version 5.3 (The Cochrane Collaboration, Software Update, Oxford, UK).
## Literature search, study characteristics and quality assessment
A detailed flowchart of the search and selection results is shown in Figure 1. 132 potentially relevant articles are identified initially. Finally, three RCTs are included in the meta-analysis15–17.
**Figure. 1:** *Flow diagram of study searching and selection process.*
The baseline characteristics of three eligible RCTs in the meta-analysis are summarized in Table 1. The three studies are published between 2018 and 2021, and sample sizes range from 50 to 90 with a total of 212. Two RCTs report all included patients with diabetes15, 16, while the remaining RCT report patients without diabetes17. Empagliflozin is administered at the dose of 10 mg daily.
**Table 1**
| NO. | Author | Empagliflozin group | Empagliflozin group.1 | Empagliflozin group.2 | Empagliflozin group.3 | Empagliflozin group.4 | Empagliflozin group.5 | Control group | Control group.1 | Control group.2 | Control group.3 | Control group.4 | Control group.5 | Jada scores |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| NO. | Author | No | Age (years) | Female (n) | Body mass index (kg/m2) | Statin use (n) | Methods | No | Age (years) | Female (n) | Body mass index (kg/m2) | Statin use (n) | Methods | Jada scores |
| 1 | Chehrehgosha 2021 | 35 | 50.5±8.4 | 20 | 30.9±3.3 | 34 | empagliflozin 10 mg daily for 24 weeks | 37 | 51.8±7.8 | 23 | 30.2±4.4 | 35 | placebo | 5 |
| 2 | Taheri 2020 | 43 | 43.8±9.7 | 15 | 30.5±2.3 | 5 | empagliflozin (10 mg/day) for 24 weeks | 47 | 44.1±9.3 | 25 | 30.7±3.5 | 6 | placebo | 5 |
| 3 | Kuchay 2018 | 25 | 50.7±12.8 | 9 | 30.0±3.8 | - | empagliflozin (10 mg/day) for 20 weeks | 25 | 49.1±10.3 | 8 | 29.4±3.1 | - | placebo | 4 |
Among the three studies included here, two studies report CAP score and LSM16, 17, three studies report ALT, AST15–17, while two studies report LDL and TG15, 16. Jadad scores of the three included studies vary from 4 to 5, and all three studies have high quality according to quality assessment.
## Primary outcomes: CAP score and LSM
These outcome data are analyzed with the random-effects model, and compared to control group for non-alcoholic fatty liver disease, empagliflozin treatment has no obvious effect on CAP score (SMD=-0.17; $95\%$ CI=-0.48 to 0.14; $$P \leq 0.29$$) with no heterogeneity among the studies (I2=$0\%$, heterogeneity $$P \leq 0.29$$) (Figure 2) or LSM score (SMD=-0.25; $95\%$ CI=-0.74 to 0.23; $$P \leq 0.30$$) with significant heterogeneity among the studies (I2=$58\%$, heterogeneity $$P \leq 0.30$$) (Figure 3).
**Figure. 2:** *Forest plot for the meta-analysis of CAP score.* **Figure. 3:** *Forest plot for the meta-analysis of LSM score.*
## Sensitivity analysis
Significant heterogeneity is observed among the included studies for LSM score, but there are just two RCTs included. Thus, we do not perform sensitivity analysis via omitting one study in turn to detect heterogeneity.
## Secondary outcomes
In comparison with control group for non-alcoholic fatty liver disease, empagliflozin treatment shows no substantial impact on ALT (SMD=-0.09; $95\%$ CI=-0.37 to 0.18; $$P \leq 0.50$$; Figure 4), AST (SMD=-0.26; $95\%$ CI=-0.53 to 0.02; $$P \leq 0.07$$; Figure 5), LDL (SMD=-0.31; $95\%$ CI=-0.74 to 0.11; $$P \leq 0.15$$; Figure 6) or TG (SMD=-0.17; $95\%$ CI=-0.54 to 0.19; $$P \leq 0.35$$; Figure 7).
**Figure. 4:** *Forest plot for the meta-analysis of ALT.* **Figure. 5:** *Forest plot for the meta-analysis of AST.* **Figure. 6:** *Forest plot for the meta-analysis of LDL.* **Figure. 7:** *Forest plot for the meta-analysis of TG.*
## Discussion
Nonalcoholic fatty liver disease commonly occur in patients with type 2 diabetes mellitus which serves as a leading cause of chronic liver disease22, 23. Type 2 diabetes mellitus is associated with increased risk of cirrhosis, hepatocellular carcinoma, and double the death rate of liver cirrhosis24. Liver fat accumulation may result in triglyceride accumulation (steatosis), nonalcoholic steatohepatitis, cirrhosis, and even hepatocellular carcinoma25. There are still lack of effective pharmacologic agents for the treatment of nonalcoholic fatty liver disease. Several anti-diabetic agents have been explored considering the importance of insulin resistance for non-alcoholic fatty liver disease, but the results are variable26–28.
SGLT2 inhibitors are widely used to prevent glucose re-absorption in renal proximal tubules, leading to increased urinary glucose excretion and decreased blood glucose and insulin levels29, 30. These drugs have the potential in reducing macrovascular events and producing beneficial effects on liver function in both clinical trials and animal models29–32. They also demonstrate the ability to decrease insulin resistance, adipose tissue dysfunction, and inflammation responses2. These provide the theoretical support the benefits of SGLT2 inhibitors to non-alcoholic fatty liver disease. As one important kind of SGLT2 inhibitors, serval studies demonstrated the potential of empagliflozin in treating non-alcoholic fatty liver disease16, 17.
Our meta-analysis concludes that empagliflozin demonstrates no beneficial effect on hepatic steatosis or fibrosis as shown by the LSM and CAP score in patients with non-alcoholic fatty liver disease. In consistent, no improvements is seen in terms of ALT, AST, LDL or TG after empagliflozin treatment. Regarding the sensitivity analysis, significant heterogeneity remains. Several factors may lead to the heterogeneity. Firstly, the treatment duration of empagliflozin ranges from 20 to 24 weeks. Secondly, two RCTs report patients with diabetes15, 16, while the remaining RCT report patients without diabetes17. Thirdly, there may be some confouning factors such as age, obesity, statin use and blood glucose.
The adverse events of empagliflozin are generally mild and mainly include hypoglycemia, urticaria, fatigue, nocturia and polyuria15, 16. Our meta-analysis also has some important limitations. Firstly, our analysis is based on three RCTs, and all of them have a relatively small sample size (n<100). Overestimation of the treatment effect was more likely in smaller trials compared with larger samples. There is significant heterogeneity, and empagliflozin treatment may produce variable impact in patients with the comorbidity of diabetes or not. Finally, treatment duration ranges from 20 weeks to 24 weeks, and the duration may be not sufficient to produce the positive results.
## Conclusions
Empagliflozin treatment may provide no additional benefits for non-alcoholic fatty liver disease and should be not recommended in clinical work.
## Ethics approval and consent to participate
Not applicable.
## Declaration of conflict of interest
None, the authors contribute equally.
## Consent for publication
Not applicable.
## Competing interests
The authors declare that they have no competing interests.
## Funding
Not applicable.
## References
1. **Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants**. *Lancet (London, England)* (2016) **387** 1377-1396. PMID: 27115820
2. Duvnjak M, Tomasic V, Gomercic M, Smircic L, Duvnjak N, Barsic I. **Therapy of nonalcoholic fatty liver disease: current status**. *Journal of Physiology and Pharmacology: an official Journal of the Polish Physiological Society* (2009) **60** 57-66
3. Singh S, Osna NA, Kharbanda KK. **Treatment options for alcoholic and non-alcoholic fatty liver disease: A review**. *World journal of gastroenterology* (2017) **23** 6549-6570. PMID: 29085205
4. Adams LA, Sanderson S, Lindor KD, Angulo P. **The histological course of nonalcoholic fatty liver disease: a longitudinal study of 103 patients with sequential liver biopsies**. *Journal of hepatology* (200) **42** 132-138. PMID: 15629518
5. Park JS, Shim YJ, Kang BH, Lee WK, Min BH. **Hepatocyte-specific clusterin overexpression attenuates diet-induced nonalcoholic steatohepatitis**. *Biochemical and biophysical research communications* (2018) **495** 1775-1781. PMID: 29229391
6. Jahn D, Dorbath D, Kircher S, Nier A, Bergheim I, Lenaerts K, Hermanns HM, Geier A. **Beneficial Effects of Vitamin D Treatment in an Obese Mouse Model of Non-Alcoholic Steatohepatitis**. *Nutrients* (2019) **11**
7. Dai W, Ye L, Liu A, Wen SW, Deng J, Wu X, Lai Z. **Prevalence of nonalcoholic fatty liver disease in patients with type 2 diabetes mellitus: A meta-analysis**. *Medicine* (2017) **96** e8179. PMID: 28953675
8. Ray K. **NAFLD-the next global epidemic, Nature reviews**. *Gastroenterology & hepatology* (2013) **10** 621. PMID: 24185985
9. Cobbina E, Akhlaghi F. **Non-alcoholic fatty liver disease (NAFLD) - pathogenesis, classification, and effect on drug metabolizing enzymes and transporters**. *Drug metabolism reviews* (2017) **49** 197-211. PMID: 28303724
10. Poonawala A, Nair SP, Thuluvath PJ. **Prevalence of obesity and diabetes in patients with cryptogenic cirrhosis: a case-control study**. *Hepatology (Baltimore, Md.)* (2000) **32** 689-692
11. Jung CH, Mok JO. **The Effects of Hypoglycemic Agents on Non-alcoholic Fatty Liver Disease: Focused on Sodium-Glucose Cotransporter 2 Inhibitors and Glucagon-Like Peptide-1 Receptor Agonists**. *Journal of obesity & metabolic syndrome* (2019) **28** 18-29. PMID: 31089576
12. Kahl S, Gancheva S, Straβburger K, Herder C, Machann J, Katsuyama H, Kabisch S, Henkel E, Kopf S, Lagerpusch M, Kantartzis K, Kupriyanova Y, Markgraf D, van Gemert T, Knebel B, Wolkersdorfer MF, Kuss O, Hwang JH, Bornstein SR, Kasperk C, Stefan N, Pfeiffer A, Birkenfeld AL, Roden M. **Empagliflozin Effectively Lowers Liver Fat Content in Well-Controlled Type 2 Diabetes: A Randomized, Double-Blind, Phase 4, Placebo-Controlled Trial**. *Diabetes care* (2020) **43** 298-305. PMID: 31540903
13. Razavi-Nematollahi L, Ismail-Beigi F. **Adverse Effects of Glycemia-Lowering Medications in Type 2 Diabetes**. *Current Diabetes reports* (2019) **19** 132. PMID: 31748838
14. Gharaibeh NE, Rahhal MN, Rahimi L, Ismail-Beigi F. **SGLT-2 inhibitors as promising therapeutics for non-alcoholic fatty liver disease: pathophysiology, clinical outcomes, and future directions, Diabetes, metabolic syndrome and obesity**. *targets and therapy* (2019) **12** 1001-1012
15. Kuchay MS, Krishan S, Mishra SK, Farooqui KJ, Singh MK, Wasir JS, Bansal B, Kaur P, Jevalikar G, Gill HK, Choudhary NS, Mithal A. **Effect of Empagliflozin on Liver Fat in Patients With Type 2 Diabetes and Nonalcoholic Fatty Liver Disease: A Randomized Controlled Trial (E-LIFT Trial)**. *Diabetes care* (2018) **41** 1801-1808. PMID: 29895557
16. Chehrehgosha H, Sohrabi MR, Ismail-Beigi F, Malek M, Reza Babaei M, Zamani F, Ajdarkosh H, Khoonsari M, Fallah AE, Khamseh ME. **Empagliflozin Improves Liver Steatosis and Fibrosis in Patients with Non-Alcoholic Fatty Liver Disease and Type 2 Diabetes: A Randomized, Double-Blind, Placebo-Controlled Clinical Trial, Diabetes therapy**. *research, treatment and education of diabetes and related disorders* (2021) **12** 843-861
17. Taheri H, Malek M, Ismail-Beigi F, Zamani F, Sohrabi M, Reza Babaei M, Khamseh ME. **Effect of Empagliflozin on Liver Steatosis and Fibrosis in Patients With Non-Alcoholic Fatty Liver Disease Without Diabetes: A Randomized, Double-Blind, Placebo-Controlled Trial**. *Advances in therapy* (2020) **37** 4697-4708. PMID: 32975679
18. Moher D, Liberati A, Tetzlaff J, Altman DG. **Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement**. *Journal of clinical epidemiology* (2009) **62** 1006-1012. PMID: 19631508
19. Jadad AR, Moore RA, Carroll D, Jenkinson C, Reynolds DJM, Gavaghan DJ, McQuay HJ. **Assessing the quality of reports of randomized clinical trials: Is blinding necessary?**. *Controlled Clinical Trials* (1996) **17** 1-12. PMID: 8721797
20. Kjaergard LL, Villumsen J, Gluud C. **Reported Methodologic Quality and Discrepancies between Large and Small Randomized Trials in Meta-Analyses**. *Annals of Internal Medicine* (2001) **135** 982-989. PMID: 11730399
21. Higgins JP, Thompson SG. **Quantifying heterogeneity in a meta-analysis**. *Statistics in Medicine* (2002) **21** 1539-1558. PMID: 12111919
22. El-Ashmawy HM, Ahmed AM. **Serum fetuin-B level is an independent marker for nonalcoholic fatty liver disease in patients with type 2 diabetes**. *European journal of Gastroenterology & Hepatology* (2019)
23. Smits MM, Tonneijck L, Muskiet MH, Kramer MH, Pouwels PJ, Pieters-van den Bos IC, Hoekstra T, Diamant M, van Raalte DH, Cahen DL. **Twelve week liraglutide or sitagliptin does not affect hepatic fat in type 2 diabetes: a randomised placebo-controlled trial**. *Diabetologia* (2016) **59** 2588-2593. PMID: 27627981
24. McPherson S, Hardy T, Henderson E, Burt AD, Day CP, Anstee QM. **Evidence of NAFLD progression from steatosis to fibrosing-steatohepatitis using paired biopsies: implications for prognosis and clinical management**. *Journal of Hepatology* (2015) **62** 1148-1155. PMID: 25477264
25. Bugianesi E, Manzini P, D'Antico S, Vanni E, Longo F, Leone N, Massarenti P, Piga A, Marchesini G, Rizzetto M. **Relative contribution of iron burden, HFE mutations, and insulin resistance to fibrosis in nonalcoholic fatty liver**. *Hepatology* (2004) **39** 179-187. PMID: 14752836
26. Sanyal AJ, Chalasani N, Kowdley KV, McCullough A, Diehl AM, Bass NM, Neuschwander-Tetri BA, Lavine JE, Tonascia J, Unalp A, Van Natta M, Clark J, Brunt EM, Kleiner DE, Hoofnagle JH, Robuck PR, Nash CRN. **Pioglitazone, vitamin E, or placebo for nonalcoholic steatohepatitis**. *The New England Journal of Medicine* (2010) **362** 1675-1685. PMID: 20427778
27. Loomba R, Lutchman G, Kleiner DE, Ricks M, Feld JJ, Borg BB, Modi A, Nagabhyru P, Sumner AE, Liang TJ, Hoofnagle JH. **Clinical trial: pilot study of metformin for the treatment of non-alcoholic steatohepatitis**. *Alimentary Pharmacology & Therapeutics* (2009) **29** 172-182. PMID: 18945255
28. Olaywi M, Bhatia T, Anand S, Singhal S. **Novel anti-diabetic agents in non-alcoholic fatty liver disease: a mini-review, Hepatobiliary & pancreatic diseases international**. *HBPD INT* (2013) **12** 584-588. PMID: 24322742
29. Zinman B, Wanner C, Lachin JM, Fitchett D, Bluhmki E, Hantel S, Mattheus M, Devins T, Johansen OE, Woerle HJ, Broedl UC, Inzucchi SE. **Empagliflozin, Cardiovascular Outcomes, and Mortality in Type 2 Diabetes**. *The New England Journal of Medicine* (2015) **373** 2117-2128. PMID: 26378978
30. Wu JH, Foote C, Blomster J, Toyama T, Perkovic V, Sundström J, Neal B. **Effects of sodium-glucose cotransporter-2 inhibitors on cardiovascular events, death, and major safety outcomes in adults with type 2 diabetes: a systematic review and meta-analysis**. *The Lancet. Diabetes & Endocrinology* (2016) **4** 411-419. PMID: 27009625
31. Bailey CJ, Gross JL, Pieters A, Bastien A, List JF. **Effect of dapagliflozin in patients with type 2 diabetes who have inadequate glycaemic control with metformin: a randomised, double-blind, placebo-controlled trial**. *Lancet (London, England)* (2010) **375** 2223-2233. PMID: 20609968
32. Tahara A, Kurosaki E, Yokono M, Yamajuku D, Kihara R, Hayashizaki Y, Takasu T, Imamura M, Li Q, Tomiyama H, Kobayashi Y, Noda A, Sasamata M, Shibasaki M. **Effects of SGLT2 selective inhibitor ipragliflozin on hyperglycemia, hyperlipidemia, hepatic steatosis, oxidative stress, inflammation, and obesity in type 2 diabetic mice**. *European Journal of Pharmacology* (2013) **715** 246-255. PMID: 23707905
|
---
title: 'Quality of life among cancer patients at Queen Elizabeth and Kamuzu Central
Hospitals in Malawi: a cross-sectional double-center study'
authors:
- Jonathan Chiwanda Banda
- Maganizo B Chagomerana
- Michael Udedi
- Adamson Sinjani Muula
journal: African Health Sciences
year: 2022
pmcid: PMC9993320
doi: 10.4314/ahs.v22i3.24
license: CC BY 4.0
---
# Quality of life among cancer patients at Queen Elizabeth and Kamuzu Central Hospitals in Malawi: a cross-sectional double-center study
## Abstract
### Introduction
Many cancer patients experience psychosocial challenges that affect quality of life during the trajectory of their disease process. We aimed at estimating quality of life among cancer patients at two major tertiary hospitals in Malawi.
### Methods
The study was conducted among 398 cancer patients using semi-structured questionnaire. Quality of life was measured using EQ-5D-3L instrument.
### Results
Mean age was 45 years ± 12.77. Pain ($44\%$) was the most prevalent problem experienced by cancer patients. About $23\%$ had worst imaginable health status on the subjective visual analogues scale. Attending cancer services at QECH (AOR= 0.29, $95\%$ CI: 0.17–0.54, $p \leq 0.001$) and having normal weight (AOR=0.25, $95\%$ CI: 0.08–0.74, $$p \leq 0.012$$), were associated with improved quality of life. A history of ever taken alcohol (AOR= 2.36, $95\%$ CI: 1.02–5.44, $$p \leq 0.045$$) and multiple disease comorbidities (AOR= 3.78, $95\%$ CI: 1.08–13.12, $$p \leq 0.037$$) were associated with poor quality of life.
### Conclusion
Loss of earning, pain, marital strife, sexual dysfunction, were among the common psychosocial challenges experienced. History of ever taken alcohol and multiple comorbidities were associated with poor quality of life. There is need to integrate psychosocial solutions for cancer patients to improve their quality of life and outcomes.
## Introduction
Cancer incidence and mortality are on the increase and remain one of the major public health problems worldwide1. According to GLOBOCAN 2020 report, an estimated 19.3 million new cancer cases and 10.0 million cancer deaths occurred in 2020 2. Furthermore, over 36 million people were living with various forms of cancer and the burden disproportionately affected Low- and Middle-Income Countries (LMICs) which contributed $70\%$ of cancer deaths in 2020 3. In Malawi, cancers contributed to $16\%$ of Disability-Adjusted Life Years (DALYs) due to Non-Communicable Diseases (NCDs) in 2015 4, 5. Cancer survival in Malawi was equally poor with median survival time of about 9 months and only $6\%$ of patients surviving for 5 years or more in 2014 6. The top five common cancers in both genders included: cervical cancer ($23.1\%$), esophageal ($9.8\%$), *Kaposi sarcoma* ($9.4\%$), breast cancer ($8.3\%$) and non-Hodgkin's lymphoma ($6.5\%$) 7.
People living with cancer experience several challenges that affect their quality of life (QoL) 8, 9. The World Health organization (WHO) defines QoL as individual's perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concern 10. Therefore, quality of life in cancer is a dynamic, multidimensional concept, referring to all life aspects and needs of the patient, continuously assessing balancing processes between the real and ideal situation at a given time 11. The factors affecting QoL among cancer patients are not only physiological in nature, but also psychological, arising from patient reaction to results of diagnostic tests, the stages of sadness, grief and anger 8.
Although several QoL instruments have been validated and used in different settings, most have reported low scores among cancer patients. A study in India reported $82\%$ of the patients had low scores of QoL which were contributed by pain ($73\%$), depression ($54\%$), and physical deterioration ($86\%$) among other indicators 12. In Poland, $87.5\%$ of the patients had low scores which were attributable to severe problems of self-care ($81\%$), and depression ($63\%$) 11. Similarly in Iran, improved QoL was associated with improved income, higher educational status ($p \leq 0.05$) 8. A similar study in Malaysia had also reported that $70\%$ of the participants were depressed while $93\%$ had anxiety 13. In Ethiopia, QoL was reduced with advanced cancer disease, ageing and low socioeconomic status 14. While in Kenya, marriage and education were associated with improved QoL 15. A multi-center study in South Africa and Uganda has shown that patients receiving palliative care exhibited significantly poorer QoL on function subscale followed by well-being, symptoms, transcendent and interpersonal subscales compared to similar populations in the High-Income Countries (HICs)16. Similarly in Malawi, few studies done on patients with various chronic illnesses under palliative care had shown poor quality of life 17, 18.
The measurement of QoL and follow-up may provide important information to ascertain acceptance, adaptation of cancer disease, treatment and could also represent end-line evaluation of healthcare interventions 9, 19.A rapid review of quality of life studies in Malawi indicated fourteen studies and only two of those focused on palliative care targeting chronic conditions including cancer17, 18. Again, only two other studies from that review had used EuroQol-5 Dimensions-3Levels (EQ-5D-3L) tool for evaluating patients' quality of life in Malawi and these included a validation study by Chokotho et al and a health-related quality of life of inpatients and outpatients treated for tuberculosis in rural Malawi by Jo et al19, 20. Lack of studies in Malawi evaluating QoL among cancer patients limits the possibility of ascertaining type of psychosocial problems experienced by these particular set of patients and how to approach them especially in resource limited settings 19. Therefore, the current study was aimed at estimating quality of life and the psychosocial experiences among cancer patients attending oncology services at Queen Elizabeth (QECH) and Kamuzu Central Hospitals (KCH), the two main referral hospitals in Malawi. The findings could support development of interventions to guide in effective management of psychosocial challenges and consequently helping empowering patients over their illnesses and treatment and possibly improve their QOL 12.
## Study setting and design
We conducted a cross-sectional study at two main referral hospitals of QECH and KCH between January and March, 2021. Basing on monthly clinic data, these two facilities attend to majority of the cancer patients in the country with an average of 3,000 and 2,400 patients reviewed annually at QECH and KCH respectively. The study took place at oncology clinics as specialized centers where all newly diagnosed cancers are referred for further management. In this case we minimized selection bias of study participants. By the time of the conduct of the study, there were two oncologists, four non-specialist medical doctors and 13 nurses at QECH. On other hand at KCH, there was one oncologist, four non-specialist medical doctors and 26 nurses. In both sites, there were no psychosocial counsellors working at study sites and that standard practice was that patients suspected of psychosocial problems would be referred to psychiatric clinics which were situated away from cancer clinic premises.
## Sampling size and sampling technique
This study was embedded in a larger cancer comorbidity prevalence study whose sample size was estimated basing on the Cochran's formula using estimated prevalence of $26\%$ of single count chronic disease comorbidity among cancer patients,$5\%$ precision level and $95\%$ confidence level ($Z = 1.96$) 21. We recruited 398 participants for the study. We used simple random sampling approach using consecutive numbers in recruiting participants aged above 18 years of age attending to adult oncology clinics.
## Ethical Review and Approvals
The College of Medicine Research and Ethics Committee (COMREC) approved the study (P$\frac{.07}{20}$/3085). Approval l tters for conducting the study in the respective sites were obtained from Hospital Directors. Patient identity numbers at the clinics were used as codes to replace actual patient names to maintain confidentiality.
## Data collection and data management
Data were collected through face-to-face interviews using semi-structured questionnaire on socio-demographic and psychosocial factors. Quality of life was measured using EQ-5D-3L tool which had been previously validated in Malawi 19. This instrument was commissioned by the EuroQOL group in 2012 and it is a preference-based measure of health status 19. The instrument has two sections, the first part has 5 dimensions, namely: mobility; self-care; usual activities; pain/discomfort; and anxiety/depression. Each dimension has 3 levels: no problems, some problems, and extreme problems, with scores of 1, 2, and 3 representing each level, respectively. The respondents were asked to choose one level for each of the 5 dimensions that best described their own health state on the day of the interview19. The second section has a visual analogue scale (VAS), whereby patients would self-rate their health state on a scale of 0 to 100, with 0 and 100 as the worst and best imaginable health states, respectively11,19. The collected data was uploaded into Open Data Kit (ODK) on android tablets to minimize data collection errors and also reduce missing data. Data validations and checks were programmed to ensure that much of data capture errors are solved at the data collection point. All data collected on the tablets were being sent to a secured server and routine data quality checks were ran on the server to identify any data inconsistencies and discrepancies which were then sent to the data collection teams for resolutions which later applied to the server. Data were downloaded from the server as a CSV dataset which was imported into Stata for further data preparations and data analysis.
## Outcome variable
The study considered quality of life as the primary outcome while psychosocial factors were the secondary outcome.
## Independent variables
The study had the following explanatory variables [1] sociodemographic characteristics such as; sex, age, marital status, area of residence, education level, occupation, socioeconomic status, [2] behavioral risk factors such as smoking and alcohol [3] cancer diagnosis as it appears in the patient files and the date of diagnosis, [4] cancer stage, [5] intent to treat [6] and treatment options.
## Data Analysis
Stata statistical software version 14 was used for analysis. Socioeconomic status was generated as a single explanatory variable using factor analysis based on a set of variables namely: type of residence, house ownership, energy source; mode of transport, communication facilities, water source and type of toilet (flush toilet) because they were all indicators of socioeconomic profile. In factor analysis, first level explained largest proportion of total variance and assets that were more unequally distributed across the sample had higher weights. Those weights were used for each asset to generate factor scores. Higher score indicated higher wealth status and vice versa. Finally based on quintiles, the scores were converted into five ordered categories from highest (1st quintile) to lowest (5th quintile). Therefore, the new variable SES was categorized into those five categories namely highest, higher, high, middle and low.
We estimated correlation between QoL and various sociodemographic characteristics. Chi-square test was used to assess the association between quality of life and explanatory variables. An unadjusted logistic regression model was used to identify explanatory variables associated with quality of life. All significant explanatory variables ($p \leq 0.05$) in the adjusted model were all fitted into multivariate logistic regression model using forward selection to determine factors independently associated with quality of life at $p \leq 0.05.$ The model was tested for sensitivity by the forward selection procedure (e.g., including and excluding specific variables) with robust standard errors.
## Sociodemographic characteristics of the study participants
A total of 398 participants were included in the analysis and the majority were females ($64\%$). The highest proportions were in the middle age group of 45–54 years ($$n = 136$$, $34\%$) with an average age of 43 years (standard deviation=12.46). Cervical cancer was the commonest malignancy ($30\%$), seconded by Kaposi ‘sarcoma ($24\%$). Other malignancies included the following; breast ($11\%$), esophageal ($4\%$), leukemia ($4\%$) and non-Hodgkin's lymphoma ($3\%$). The most common treatment modalities were; chemotherapy ($99\%$), surgery ($20\%$), radiation ($0.26\%$), herbal remedies ($12\%$), spiritual healing ($24\%$). Most patients ($74\%$) were being managed with curative intent. At least $18\%$ had missed their clinical appointments largely due to high transport costs ($73\%$), disease severity ($29\%$) and long distances of travel ($29\%$) as shown in table 1.
**Table 1**
| Patient Characteristics | QECH: N=205, n (%) | KCH: N=193, n (%) | Total: N=398, n (%) | P-value† |
| --- | --- | --- | --- | --- |
| Gender | | | | |
| Female | 127 (61.95) | 128(66.32) | 255 (64.07) | |
| Male | 78 (38.65) | 65 (33.68) | 143 (35.93) | 0.364 |
| Age (years) | 43 ± 12.46 | 47 ± 12.90 | 45 ± 12.77 | |
| 18–24 | 15 (7.32) | 5 (2.59) | 20 (5.03) | |
| 25–34 | 29 (14.15) | 28 (14.51) | 57 (14.32) | |
| 35–44 | 69 (33.66) | 53 (27.46) | 122 (30.65) | |
| 45–54 | 68 (33.17) | 68 (35.23) | 136 (34.17) | |
| 55–64 | 24 (11.71) | 39 (20.21) | 63 (15.83) | 0.035* |
| Marital status | | | | |
| Never married | 15 (7.32) | 14 (7.25) | 29 (7.29) | |
| Currently married | 137 (66.83) | 126 (65.28) | 263 (66.08) | |
| Divorced | 31 (15.12) | 28 (14.51) | 59 (14.82) | |
| Widow | 22 (10.73) | 25 (12.95) | 47 (11.81) | 0.924 |
| Education status | | | | |
| No education | 35 (17.07) | 39 (20.31) | 74 (18.64) | |
| Primary | 93 (45.37) | 90 (46.88) | 183 (46.10) | |
| Secondary | 64 (31.22) | 52 (27.08) | 116 (29.22) | |
| Tertiary | 13 (6.34) | 11 (5.73) | 24 (6.05) | 0.741 |
| Occupation status | | | | |
| Not employed | 102 (49.76) | 78 (40.63) | 180 (45.34) | |
| Formally employed | 23 (11.22) | 17 (8.85) | 40 (10.08) | |
| Informally employed | 52 (25.37) | 79 (41.15) | 131 (33.00) | |
| Student | 5 (2.44) | 4 (2.08) | 9 (2.27) | |
| Retired | 5 (2.44) | 7 (3.65) | 12 (3.02) | |
| Others | 18 (8.78) | 7 (3.65) | 25 (6.30) | <0.013* |
| Area of residence | | | | |
| Urban | 92 (44.88) | 62 (32.29) | 154 (38.79) | |
| Rural | 113 (55.12) | 130 (67.71) | 243 (61.21) | <0.01* |
| Socioeconomic status | | | | |
| Highest | 59 (28.78) | 37 (19.27) | 96 (24.18) | |
| Higher | 35 (17.07) | 34 (17.71) | 69 (17.38) | |
| High | 27 (13.17) | 51 (26.56) | 78 (19.68) | |
| Middle | 65 (31.71) | 55 (28.65) | 12 (30.23) | |
| Low | 19 (9.27) | 15 (7.81) | 34 (8.56) | <0.01* |
| Smoking history | | | | |
| Never smoked | 121 (83.05) | 168 (87.05) | 339 (85.18) | |
| Ever smoked | 29 (14.15) | 22 (11.40) | 51 (12.81) | |
| Current smokers | 5 (2.44) | 3 (1.55) | 8 (2.01) | 0.569 |
| Alcohol history | | | | |
| Never alcohol | 156 (76.10) | 157 (81.35) | 313 (78.64) | |
| Ever alcohol | 43 (20.98) | 31 (16.06) | 74 (18.59) | |
| Current alcohol | 6 (2.93) | 5 (2.59) | 11 (2.76) | 0.432 |
| Body mass index | | | | |
| Underweight | 32 (15.61) | 23 (11.92) | 55 (13.82) | |
| Normal weight | 98 (47.80) | 128 (66.32) | 226 (56.78) | |
| Over weight | 47 (22.93) | 32 (16.58) | 79 (19.85) | |
| Obesity | 28 (13.66) | 10 (5.18) | 38 (9.55) | <0.001* |
| Cancer diagnosis | | | | |
| Kaposi's sarcoma | 46 (22.44) | 51 (26.42) | 97 (24.37) | |
| Cervical | 52 (25.37) | 69 (35.75) | 121 (30.40) | |
| Breast | 19 (9.27) | 23 (11.92) | 42 (10.55) | |
| Esophageal | 12 (5.85) | 5 (2.59) | 17 (4.27) | |
| Leukemia | 15 (7.32) | 1 (0.52) | 16 (4.02) | |
| Non-Hodgkin's lymphoma | 7 (3.41) | 4 (2.07) | 11 (2.76) | 0.003* |
| Cancer stage | | | | |
| Localized | 186 (90.73) | 128 (66.32) | 314 (78.89) | |
| Lymph node involvement | 17 (8.29) | 51 (26.42) | 68 (17.09) | |
| Distant metastasis | 2 (0.98) | 14 (7.25) | 16 (4.02) | <0.001* |
| Treatment modality | | | | |
| Chemotherapy | 204 (99.51) | 180 (98.36) | 384 (98.97) | 0.262 |
| Surgery | 27 (13.17) | 51 (27.87) | 78 (20.10) | <0.001* |
| Radiation | - | 1 (0.55) | 1 (0.26) | 0.289 |
| Herbal remedies | 5 (2.44) | 42 (22.95) | 47 (12.11) | <0.001* |
| Spiritual healing | 12 (5.85) | 83 (45.36) | 95 (24.48) | <0.001* |
| Intention for treatment | | | | |
| Cure | 133 (77.78) | 76 (67.26) | 209 (73.59) | |
| Palliative | 38 (22.22) | 37 (32.74) | 75 (26.41) | 0.049* |
| Psychosocial experiences | | | | |
| Clinical Appointment | | | | |
| Missed clinical appointment | 39 (19.02) | 34 (17.62) | 73 (18.34) | 0.717 |
| Reasons of missed appointment | | | | |
| Lack of transport | 32 (82.05) | 22(64.71) | 54 (73.97) | 0.092 |
| Disease severity | 15 (44.12) | 6 (15.38) | 21 (28.77) | 0.007* |
| Long distance | 18 (46.15) | 3 (9.09) | 21 (29.17) | <0.001* |
## Psychosocial experiences of the study participants
Patients reported the following psychosocial challenges; pill burden ($58\%$), loss of earning ($78\%$), fear of death ($18\%$). Participants suggested the following responses to solve their psychosocial challenges; reminders using Short Messaging Service (SMS) ($52\%$), formulation of patient support groups ($70\%$), decentralized cancer care ($77\%$) regular health talks ($62\%$) and home visits ($79\%$).
## Quality of life experiences of the study participants
In five dimensions of quality-of-life scale, majority ($75\%$) had no mobility problems while $24\%$ of the participants had some mobility challenges. At least $91\%$ had no problems with self-care and that about $8\%$ of the participants had problems with daily usual activity. Pain was the most prevalent problem experienced by cancer patients as $44\%$ had some pain while $9\%$ had extreme pain. We observed that $27\%$ of the study participants had some anxious perception about their disease situation while $13\%$ had extreme anxiety problems. At most $23\%$ had worst imaginable health status on the subjective visual analogues scale as presented in table 3.
**Table 3**
| Factor | Unadjusted OR (95% CI) | P-value | Adjusted OR (95% CI) | p-value |
| --- | --- | --- | --- | --- |
| Gender | | | | |
| Female | 1.00 | | 1.00 | |
| Male | 0.78 (0.49–1.24) | 0.297 | 0.52 (0.23–1.18) | 0.121 |
| Age | | | | |
| 18–24 | 1.00 | | 1.00 | |
| 25–34 | 1.00 (0.35 –2.84) | 1.00 | 0.67 (0.13 – 3.39) | 0.631 |
| 35–44 | 1.98 (0.74 –5.31) | 0.177 | 1.42 (0.29 – 6.79) | 0.661 |
| 55–64 | 1.85 (0.69 –4.90) | 0.217 | 0.89 (0.18 – 4.51) | 0.894 |
| 55–64 | 1.83 (0.63 – 5.30) | 0.263 | 0.86 (0.16 –4.78) | 0.865 |
| Facility | | | | |
| KCH | 1.00 | | 1.00 | |
| QECH | 0.41 (0.26 – 0.65) | <0.001* | 0.29 (0.17 – 0.54) | <0.001 |
| Education status | | | | |
| No education | 1.00 | | 1.00 | |
| Primary | 1.19 (0.64 – 2.22) | 0.574 | 1.96 (0.91 – 4.26) | 0.087 |
| Secondary | 0.71 (0.37 – 1.37) | 0.31 | 1.67 (0.66 – 4.22) | 0.277 |
| Tertiary | 0.53 (0.20 – 1.38) | 0.193 | 0.93 (0.24 – 3.67) | 0.919 |
| Occupation status | | | | |
| No employment | 1:00 | | 1.00 | |
| Formal employment | 0.65 (0.31 –1.34) | 0.244 | 0.97 (0.37 – 2.53) | 0.944 |
| Informal employment | 0.74 (0.45 – 1.23) | 0.248 | 0.61 (0.33 – 1.13) | 0.115 |
| Socioeconomic status | | | | |
| Highest | 1.00 | | 1.00 | |
| Higher | 0.91 (0.48 –1.74) | 0.774 | 0.72 (0.32–1.63) | 0.434 |
| High | 1.49 (0.77 – 2.92) | 0.234 | 1.09 (0.44 – 2.73) | 0.848 |
| Middle | 2.33 (1.25 – 4.34) | 0.008* | 2.46 (0.99 – 6.10) | 0.051 |
| Low | 2.32 (0.87– 5.65) | 0.093 | 3.39 (0.93 – 12.36) | 0.064 |
| Body mass index | | | | |
| underweight | 1:00 | | 1.00 | |
| Normal weight | 0.26 | 0.007* | 0.25 (0.08 – 0.74) | 0.012* |
| Over weight | 0.15 | <0.001* | 0.17 (0.05 – 1.23) | 0.064 |
| obesity | 0.22 (0.85 –4.93) | 0.009* | 0.29 (0.07 –1.10) | 0.068 |
| Alcohol history | | | | |
| Never alcohol | 1.00 | | 1.00 | |
| Ever alcohol | 1.56 (0.85 –2.86) | 0.154 | 2.36 (1.02–5.44) | 0.045* |
| Current drinker | 2.00 (0.42 – 9.44) | 0.381 | 3.33 (0.47–23.84) | 0.23 |
| Disease comorbidity | | | | |
| No comorbidity | 1.00 | | 1.00 | |
| 1–2 comorbid conditions | 0.72 (0.45 – 1.16 | 0.177 | 1.06 (0.58 – 1.92) | 0.849 |
| 3 or more comorbid conditions | 2.22 (0.81 – 6.13) | 0.123 | 3.78 (1.08 – 13.12) | 0.037* |
| Cancer diagnosis | | | | |
| Kaposi sarcoma | 1.00 | | 1.00 | |
| Cervical | 0.99 (0.55–1.81) | 0.989 | 0.62 (0.25–1.54) | 0.305 |
| Esophageal | 1.88 (0.50–7.07) | 0.35 | 1.69 (0.34 – 8.39) | 0.518 |
| Non-Hodgkin's lymphoma | 0.67 (0.15 – 3.01) | 0.603 | 0.60 (0.09 – 4.10) | 0.604 |
| Leukemia | 0.46 (0.15 – 1.40) | 0.17 | 0.96 (0.26 – 3.61) | 0.954 |
| Breast | 1.06 (0.47–2.43) | 0.886 | 1.20 (0.40 – 3.62) | 0.742 |
## Factors ass ociated with quality of life among cancer patients
In adjusted regression model, being treated at QECH was associated with improved quality of life, 0.29 ($95\%$ CI: 0.17–0.54, $P \leq 0.001$) as well as normal weight, 0.25 ($95\%$ CI: 0.08–0.74, $$P \leq 0.012$$). On the other hand, patients with history of ever taken alcohol, 2.36 ($95\%$ CI: 1.02–5.44, $$P \leq 0.045$$) and having 3 or more comorbid conditions, 3.78 ($95\%$ CI: 1.08–13.12, $$p \leq 0.037$$) were associated with poor quality of life.
## Discussion
Many cancer patients experience both physiological and psychosocial challenges. In this study, we aimed at estimating quality of life with focus on psychosocial well-being among cancer patients in two referral hospitals in Malawi. Cervical cancer remained the leading cause of morbidity among Malawi cancer patients and this was consistent with country GLOBOCAN recent report for 2020 7. Most participants ($74\%$) were managed with intention to cure the disease. This implied that they were in early stages of their cancer disease process. This was an important finding considering that early diagnosis is associated with higher remission rate given availability of all treatment modalities. However, at the time of the study, the country had not commissioned any radiotherapy services22. Yet radiation medicine technology is a critical and indispensable component of comprehensive cancer treatment and care with approximately above $50\%$ of the all cancers requiring radiation for diagnostic, treatment and palliative care services among cancer patients 23–25.
In terms of psychosocial experiences, higher proportions of participants had lost earnings and failed to provide for their dependents and was consistent with findings from other studies 18, 26, 27. Cancer diagnosis and treatment often overshadow the impact of financial burden on QoL due to inability to work and out-of-pocket costs expenditure on livelihood, transport and medical bills 28–30. Marital problems were also common in this present study as well as disfigurement, sexual dysfunction and loss of energy which could impact negatively on self-esteem and general coping mechanisms against the disease 31. Other participants experienced fear of death, a similar experience reported by other studies as well 27, 31, 32.
We also found that significant proportions of the participants complained of pill burden, side effects which had potential of reducing drug compliance. 31. Low compliance is a major barrier to good health outcomes 33. On other hand, participants had proposed some solutions including use of SMS as a temporal reminder to curb low compliance and in other studies this has been successful ($84\%$ versus $77\%$) in achieving good outcomes as clients felt cared 33, 34. However, accessibility to mobile phones could be a challenge among our participants because of low subscription rate of mobile phones in Malawi at $48\%$ in 2019 according to world bank report 35.
Use of support groups has also been proposed by participants in this present study. These are meetings for people with cancer and anyone touched by the disease36. Patients realized that joining support groups with others who have similar cancer experiences could improve their quality of life and survival 36. Support groups can help patients to feel better, give them opportunity to talk about their feelings, help deal with problems and coping to treatment side effects. They are many types of support groups such as: online, peer-to-peer, groups for cancers in general, groups for particular cancer type, groups for patients / or families and caregivers 36, 37. The need for support groups for cancer have also been recommended not only by patients themselves but also with health professionals elsewhere 30. Health education either through clinic-based or home visits have also been proposed as options to improve psychosocial challenges among cancer patients in this current study. In fact, health education as an intervention administered at the clinic has been effective towards improving pain, distress, anxiety, depression, quality of life and performance among cancer patients 38. Participants also proposed that decentralized cancer services to district hospitals would be ideal to cut short of transport costs. Elsewhere, longer distances of travel in seeking health services have been associated with increased burden on value for time, cost implications as well as discomfort to patients 39. Transport support in form of subsidies by government could go a long way in reducing travel cost 18.
On QoL scale, despite having better mobility ($75\%$), self-care ($91\%$) and usual activity ($70\%$) scores on QoL scale, most patients experienced some pain and anxiety and these results were similar to the findings from other non-cancer patients with chronic diseases on palliative care in Malawi 17, 18. Between the two facilities under study, patients from QECH had improved quality of life than KCH. The reasons for these differences remained unknown however, QECH has an older and established cancer unit compared to KCH. Above all, QECH is the main teaching hospital which would benefit from students' allocation in wards from College of Medicine (Kamuzu University of Health Sciences), the only medical institution in the country at the time of the conduct of the study. We also observed that normal weight was associated with improved quality of life and the findings were consistent with other studies 40–43. Having multiple (3 or more) chronic disease comorbidities was significantly associated with poor quality of life. These results were consistent with findings from other studies elsewhere 21,28, 44. A positive history of taking alcohol in the past was also associated with poor quality of life. Despite having different cancers, the study has reported no differences in QoL between cancers, sex distribution and age. The study also did not report any association between quality of life and socioeconomic status, occupation and education among cancer patients. Therefore, in the present study, the determinants of QoL included: facility type, multiple disease comorbidities, ever taken alcohol history, normal weight and overweight. This study was limited by its design as it failed to establish causal relationship between the quality of life and exposure variables. In addition, it failed to capture common side-effects experienced by participants. The study only focused on the QoL for all cancer patients without specifying cancer type yet there could be important variations in QoL between cancers. Furthermore, although the study was conducted on two major referral facilities, the findings may not be representative of all cancer patients' experiences in Malawi however, the findings could provide useful information for further studies. Apart from being conducted on two main tertiary hospitals in Malawi, the other strength derived from the study was its utilization of EQ-5D-3L tool for measuring quality of life which was validated in Malawi. In conclusion, most participants in this study had complained of loss of earning, pain, marital strife, sexual dysfunction, as common psychosocial challenges experienced by cancer patients which affected their quality of life. History of ever taken alcohol and having 3 or more comorbidities were associated with poor quality of life. There is need to integrate mandatory and comprehensive psychosocial education, psychosocial support and psychotherapy for cancer patients and their families to improve their quality of life and outcomes.
## Conflict of interest
The authors had no conflict of interest.
## Data availability
All relevant data are available within the paper. Individual level data are not freely available for ethical reasons as public availability would compromise patient confidentiality. Additional data requests can be sent to the author at [email protected].
## Author's contribution
JCB and ASM were involved in the conceptualization of the study protocol, data collection. JCB, MC and MU analyzed the data. JCB made first manuscript draft. JCB, MC, MU and ASM edited the manuscript. All authors read and approved the final manuscript.
## Funding
This study was funded by Collaboration of Evidence-Based Health Care in Africa (CEBHA+).
## References
1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. **Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries**. *CA: a cancer journal for clinicians* (2018) **68** 394-424. PMID: 30207593
2. Sung H, Ferlay J, Siegel RL. *Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries* (2021)
3. 3World Cancer OrganizationCancer Fact Sheet10/04/20Available on: https://www.who.int/news-room/fact-sheets/detail/cancer. *Cancer Fact Sheet*
4. Masiye J, Wroe E, Ambersir A, Banda G, Berman J, Chilumphe S. *Malawi NCDI Poverty Commission: Commission Report* (2018)
5. 5Malawi Health Sector Strategic Plan HSSP II 2017-22 (2017)10/11/21Available on: https://extranet.who.int/countryplanningcycles/sites/default/files/planning_cycle_repository/malawi/health_sector_strategic_plan_ii_030417_smt_dps. *Malawi Health Sector Strategic Plan HSSP II 2017-22 (2017)*
6. Msyamboza KP, Manda G, Tembo B, Thambo C, Chitete L, Mindiera C. **Cancer survival in Malawi: a retrospective cohort study**. *The Pan African medical journal* (2014) **19** 234. PMID: 25838862
7. 7G. Cancer burden in Malawi202004/11/21Available on: https://gco.iarc.fr/today/data/factsheets/populations/454-malawi-fact-sheets. *G. Cancer burden in Malawi* (2020)
8. Ranjbar M, Siamian H, Fallahpour MJ. **Study on the Quality of Life Among the Cancer Patients at the Sari Comprehensive Cancer Center in 2017**. *Materia socio-medica* (2018) **30** 127-130. PMID: 30061803
9. Singh H, Kaur K, Banipal RP, Singh S, Bala R. **Quality of life in cancer patients undergoing chemotherapy in a tertiary care center in malwa region of punjab**. *Indian journal of palliative care* (2014) **20** 116-122. PMID: 25125867
10. 10World Health OrganisationWHOQOL Measuring Quality of Life1997GenevaWHO(WHO/MSA/MNH/PSF/97.4). *WHOQOL Measuring Quality of Life* (1997)
11. Lewandowska A, Rudzki G. *Quality of Life of Cancer Patients Treated with Chemotherapy* (2020) **17**
12. Nayak MG, George A, Vidyasagar MS, Mathew S, Nayak S, Nayak BS. **Quality of Life among Cancer Patients**. *Indian journal of palliative care* (2017) **23** 445-450. PMID: 29123353
13. Akhtari-Zavare M, Mohd-Sidik S, Periasamy U, Rampal L, Fadhilah SI, Mahmud R. **Determinants of quality of life among Malaysian cancer patients: a cross-sectional study**. *Health and quality of life outcomes* (2018) **16** 163. PMID: 30103759
14. Abegaz TM, Ayele AA, Gebresillassie BM. **Health Related Quality of Life of Cancer Patients in Ethiopia**. *Journal of Oncology* (2018) **2018** 1467595. PMID: 29849628
15. Orindi OT AD, Amimo FA. **Socio-Demographic Characteristics Associated with Quality of Life-Scores among Palliative Care Cancer Patients in Kenya**. *Journal of Community Medicine & Public Health* (2021) **5**
16. Selman LE, Higginson IJ, Agupio G, Dinat N, Downing J, Gwyther L. **Quality of life among patients receiving palliative care in South Africa and Uganda: a multi-centred study**. *Health and quality of life outcomes* (2011) **9** 21. PMID: 21477274
17. 17Bates JBTaMJHospital based palliative care in sub-Saharan. A six-month review from Malawi2011. *Hospital based palliative care in sub-Saharan. A six-month review from Malawi* (2011)
18. Herce ME, Elmore SN, Kalanga N, Keck JW, Wroe EB, Phiri A. **Assessing and Responding to Palliative Care Needs in Rural Sub-Saharan Africa: Results from a Model Intervention and Situation Analysis in Malawi**. *PloS one* (2014) **9** e110457. PMID: 25313997
19. Chokotho L, Mkandawire N, Conway D, Wu HH, Shearer DD, Hallan G. **Validation and reliability of the Chichewa translation of the EQ-5D quality of life questionnaire in adults with orthopaedic injuries in Malawi**. *Malawi medical journal: the journal of Medical Association of Malawi* (2017) **29** 84-88. PMID: 28955412
20. Jo Y, Gomes I, Shin H, Tucker A, Ngwira LG, Chaisson RE. **Health-related quality of life of inpatients and outpatients with TB in rural Malawi**. *The international journal of tuberculosis and lung disease: the official journal of the International Union against Tuberculosis and Lung Disease* (2020) **24** 1165-1171. PMID: 33172524
21. Salako O, Okediji PT, Habeebu MY, Fatiregun OA, Awofeso OM, Okunade KS. **The pattern of comorbidities in cancer patients in Lagos, South-Western Nigeria**. *Ecancermedicalscience* (2018) **12** 843. PMID: 30034520
22. 22Malawi National Cancer Control Strategic Plan2019–202929/03/21Available on: https://www.iccp-portal.org/news/new-nccp-malawi. *Malawi National Cancer Control Strategic Plan* (2019–2029)
23. Abdel-Wahab M, Zubizarreta E, Polo A, Meghzifene A. **Improving Quality and Access to Radiation Therapy— An IAEA Perspective**. *Seminars in Radiation Oncology* (2017) **27** 109-117. PMID: 28325236
24. Atun R, Jaffray DA, Barton MB, Bray F, Baumann M, Vikram B. **Expanding global access to radiotherapy**. *The Lancet Oncology* (2015) **16** 1153-1186. PMID: 26419354
25. Ndlovu N. **Radiotherapy treatment in cancer control and its important role in Africa**. *Ecancermedicalscience* (2019) **13** 942. PMID: 31552115
26. Grassi L, Spiegel D, Riba M. **Advancing psychosocial care in cancer patients**. *F1000Research* (2017) **6** 2083. PMID: 29259774
27. Klikovac T, Djurdjevic A. **Psychological aspects of the cancer patients' education: thoughts, feelings, behavior and body reactions of patients faced with diagnosis of cancer**. *Journal of BUON: official journal of the Balkan Union of Oncology* (2010) **15** 153-156
28. Seol KH, Bong SH, Kang DH, Kim JW. **Factors Associated with the Quality of Life of Patients with Cancer Undergoing Radiotherapy**. *Psychiatry investigation* (2021) **18** 80-87. PMID: 33460533
29. Singer S. **Psychosocial Impact of Cancer. Recent results in cancer research Fortschritte der Krebsforschung**. *Progres dans les recherches sur le cancer* (2018) **210** 1-11. PMID: 28924676
30. Weis J. **Psychosocial Care for Cancer Patients**. *Breast care (Basel, Switzerland)* (2015) **10** 84-86. PMID: 26195935
31. Grassi L, Spiegel D, Riba M. **Advancing psychosocial care in cancer patients [version 1; peer review: 3 approved]**. *F1000Research* (2017) **6**
32. Carlson LE, Bultz BD. **Efficacy and medical cost offset of psychosocial interventions in cancer care: making the case for economic analyses**. *Psycho-oncology* (2004) **13** 837-849. PMID: 15578622
33. da Costa TM BB, Gomes e Costa DA. **Results of a randomized controlled trial to assess the effects of a mobile SMS-based intervention on treatment adherence in HIV/AIDS-infected Brazilian women and impressions and satisfaction with respect to incoming messages**. *Int J Med Inform* (2012) **81** 257-269. PMID: 22296762
34. Volcke D, Snoeck P, Festjens T, Kowalski J, Jones R, Van Hoorde S. **Feasibility and acceptability of short message service (SMS) text messaging to support adherence in patients receiving quetiapine: A pilot study**. *European Psychiatry* (2007) **22** S294-S295
35. 35World BankMobile cellular subscription coverage201909/04/21Available on: https://data.worldbank.org/indicator/IT.CEL.SETS.P2?locations=MW. *Mobile cellular subscription coverage* (2019)
36. 36National Cancer InstituteCancer Support Groups201909/04/21Available on: https://www.cancer.gov/about-cancer/coping/adjusting-to-cancer/support-groups. *Cancer Support Groups* (2019)
37. Smith E SD. *How to choose a support group for cancer patients that's right for you* (2021)
38. Kim HS, Shin SJ, Kim SC, An S, Rha SY, Ahn JB. **Randomized controlled trial of standardized education and telemonitoring for pain in outpatients with advanced solid tumors**. *Supportive care in cancer: official journal of the Multinational Association of Supportive Care in Cancer* (2013) **21** 1751-1759. PMID: 23338230
39. Zullig LL, Jackson GL, Provenzale D, Griffin JM, Phelan S, van Ryn M. **Transportation: a vehicle or roadblock to cancer care for VA patients with colorectal cancer?**. *Clinical colorectal cancer* (2012) **11** 60-65. PMID: 21803001
40. Hageman PA, Mroz JE, Yoerger MA, Pullen CH. **Weight loss is associated with improved quality of life among rural women completers of a web-based lifestyle intervention**. *PloS one* (2019) **14** e0225446. PMID: 31743365
41. Ambak R, Mohamad Nor NS, Puteh N, Mohd Tamil A, Omar MA, Shahar S. **The effect of weight loss intervention programme on health-related quality of life among low income overweight and obese housewives in the MyBFF@home study**. *BMC women's health* (2018) **18** 111. PMID: 30066637
42. Pan HJ, Cole BM, Geliebter A. **The benefits of body weight loss on health-related quality of life**. *Journal of the Chinese Medical Association: JCMA* (2011) **74** 169-175. PMID: 21463847
43. Rothberg AE, McEwen LN, Kraftson AT, Neshewat GM, Fowler CE, Burant CF. **The impact of weight loss on health-related quality-of-life: implications for cost-effectiveness analyses**. *Quality of life research: an international journal of quality-of-life aspects of treatment, care and rehabilitation* (2014) **23** 1371-1376. PMID: 24129672
44. Sogaard M, Thomsen RW, Bossen KS, Sorensen HT, Norgaard M. **The impact of comorbidity on cancer survival: a review**. *Clin Epidemiol* (2013) **5** 3-29. PMID: 24227920
|
---
title: Association of ATM, CDH1 and TP53 genes polymorphisms with familial breast
cancer in patients of Khyber Pakhtunkhwa, Pakistan
authors:
- Abdur Rahim
- Zakiullah
- Asif Jan
- Johar Ali
- Fazli Khuda
- Basir Muhammad
- Hamayun Khan
- Hussain Shah
- Rani Akbar
journal: African Health Sciences
year: 2022
pmcid: PMC9993321
doi: 10.4314/ahs.v22i3.17
license: CC BY 4.0
---
# Association of ATM, CDH1 and TP53 genes polymorphisms with familial breast cancer in patients of Khyber Pakhtunkhwa, Pakistan
## Abstract
### Background
Genetic studies play a significant role in understanding the underlying risk factors of breast cancer. Polymorphism in the tumor suppressor gene TP 53, CDH1 and ATM genes are found to increase susceptibility for breast cancer globally.
### Objective
This study aimed to identify/analyze the contribution of genetic polymorphisms in the breast cancer candidate genes ATM, TP53 and CDH1 that may be associated with familial breast cancer risk in the Khyber Pakhtunkhwa population.
### Subjects and Methods
In the present case-control study, Whole Exome Sequencing (WES) of the 100 breast cancer patients and 100 ethnic controls were performed for the selected genes in the target population.
### Results
Of the studied variants rs3743674 of the CDH1 gene (crude $$P \leq 0.014$$ and adjusted $$p \leq 0.000$$) evident significant association with breast cancer in Pakistani Pashtun population. Whereas TP53rs1042522 (crude $$P \leq 0.251$$ and adjusted $$P \leq 0.851$$) and ATM rs659243 (crude $$p \leq 0.256$$ and adjusted $$p \leq 0.975$$) showed no or negative association with breast cancer in study population.
### Conclusion
The present study demonstrates that CDH1rs3743674 polymorphism is associated with elevated breast cancer risk in the Pashtun ethic population of Khyber Pakhtunkhwa.
## Background
Breast Cancer (BC) is the most frequent type of all cancers reported worldwide. In males it accounts for less than $1\%$ of the total cases reported 1 and less than $0.2\%$ deaths are attributed to male from BC 2. However, BC in the females is more prevalent ($32\%$ of all cancers); and as whole it is the most common type of cancer reported with 1.67 million new expected cases 3, 4. Stated otherwise, 1 in every 4 of all new cases detected in females is BC and its incidence was increased by $20\%$ between 2008 and 2012 throughout the world 5. Its incidence in Pakistani women is 2.5 times more than in other Asian countries such as India and Iran 6, and is the second main cause of death in such patients (American cancer society report 2013), with the proportion increasing further in post menopausal women after the age of 45 years 7. Moreover, the occurrence and death rate are increasing rapidly in developing countries 4.
Maximum cases arise infrequently in individuals with slight or no family antiquity of the disease 8 and only 5–$10\%$ is thought to be of genetic origin, although the core genetic cause is not yet recognized; still most of these cases are the results of alterations in the Breast cancer type 1 & 2 (BRCA1, BRCA2), Phosphatase and tensin homolog (PTEN), Cadherin-1 (CDH1), tumor protein p53 (TP53) or Serine/threonine kinase 11 (STK11) genes due to their association with a distinctive genetic cancer syndrome. In addition, Ataxia Telangiectasia *Mutated* gene (ATM), Checkpoint kinase 2 (CHEK2), BRCA1 Interacting Protein 1(BRIP1) and Recombinase (RAD51) genes association has been reported in certain cases9. Similarly, other less penetrant but more common genes such as ATM may describe the rest of genetic susceptibility to BC 10, 11. On the other hand, around 15–$20\%$ of such patients have a known domestic history with two or more first- and/or second-degree relatives having this disease. Thus a combination of genes along with environmental and lifestyles factors may contribute towards the development of disease12–14. Soaring affiliations have been shown by epidemiological studies of BC with risk factors such as family history of cancer, oral contraceptives, high exposure to estrogen, diet, ecological factors, premature puberty, and socioeconomic status of the patients 15–19.
Emerging evidences suggest that BC can arise due to changes in human cadherin-1 (E-cadherin/ CDH1) gene and tumor suppressor gene (TP53) 20–23. In case of any mutation in TP53 gene, loss of normal functions along with the abilities to produce tumorigenesis may develop24. Expression of E-cadherin either blackout or down regulation interferes with the veracity of intercellular adhesion junctions 25, 26, leading to decrease intercellular adhesion and increase cell motility that may permit cancerous cells to cross the base membrane and diffuse in the neighboring tissues 27. Polymorphic variants have been reported in CDH1 gene in a number of populations 28, 29. E-cadherin supporter consisting a C→A polymorphism associates a decreased in efficiency of the gene transcription 30, 31. Cattaneo et al 32 stated that the transcription factor binding capacity of A allele has a 68 % less compared to the C allele. Its existence shows more vulnerability to breast, colorectal, endometrial, prostate, lung and gastric cancers in various racial groups 30, 32,33.
In this study, the correlation was determined between CDH1 rs3743674, ATM rs659243 and TP53 rs1042522 polymorphisms of breast cancer risk in females of Khyber Pakhtoon Khawa, Pakistan. A large number of studies have been conducted on breast cancer patients globally, but no study on the subject has still been performed in this population. So we suppose to get a good perceptive of risk linked with such polymorphisms in the females of KP, Pakistan.
## Sample collection & study population
A Case-Control study was designed consisting of 100 Familial Breast Cancer patients and 100 gender- and age-matched healthy volunteers, recruited from various Tertiary care hospitals including Institute of Radiotherapy & Nuclear Medicine (IRNUM) hospital, Peshawar, Khyber Teaching Hospital (KTH), Peshawar, and Hayatabad Medical Complex (HMC), Peshawar. Patients were histopathlologically confirmed with breast cancer. Case as well as control samples of whole blood were collected after through physical examination, informed written consent from patients or guardians authorizing the use of blood samples and their clinical data, in a properly labeled EDTA tubes. A detailed and carefully designed patient history proforma having information regarding breastfeeding duration, menarche and the menstrual cycle, use of oral contraceptives, age and demographic characteristics with lifestyle factor data were noted from the medical reports accompanied by a personal interviewer-administered questionnaire that was conducted by nurses in the presence of physicians 15. The study was approved by the ethical committee of the department of pharmacy, University of *Peshawar via* Ref. # 920/PHAR, dated 30th October, 2018.
## Inclusion/Exclusion criteria
Inclusion and exclusion criteria of the patients and healthy individuals were as follow: (i) patients who have histopathologically confirmed BC with at least one first and/or second-degree relatives and (ii) aged between 25 to 65 years were included, while (i) patients who have no family history of BC or (ii) aged below 25 and above 65 years were excluded from the study.
Criteria for control subject's selection: (i) Normal healthy age-matched subjects of similar ethnicity and (ii) aged between 25 & 65 years and free from breast cancer.
The genomic study was conducted at the Genomic Center of Rehman Medical Institute, Hayatabad Peshawar, Pakistan, using Next Generation Sequencing (NGS).
## DNA Extraction
DNA was extracted using standard DNA extraction Kit (NovelGenomic DNA Mini Kit; cat. No. NG-S250), as per manufacturer's instructions. Quality of DNA was confirmed by running on $1\%$ agarose gel and the quantity was checked by Qubit® fluorometer with the aid of dsDNA high sensitivity kit (Qubit, Cat. No. Q32851). After quantification of DNA, each sample was properly labeled, recorded according to the DNA concentration present and stored at –20°C for further analysis 21.
## DNA Quantification
Prior to Library Preparation, quantification of DNA was done using Qubit Fluorometer with the help of dsDNA high sensitivity kit (Qubit, Cat. #. Q32851) and the concentration was adjusted to 10 ng/µL.
## DNA pooling
DNA fractions extracted from of all samples (included in the study) were pooled according to the previously described protocols 34, 35. The process was carried out to simplify the sequencing process and to save the cost and time of analysis. Pooling was done by mixing an equimolar amount of DNA (100ng) from each individual sample and then subjected to further steps for libraries preparation and sequencing.
## Library Preparation
Illumina Nextera XT DNA library kit (Cat. No. FC-142-1123) was used to generate paired-end libraries (2101 bp) by properly following the manufacturer guidelines 36. Initial fragmentation of genomic DNA by Transposome into randomly sized DNA fragments were followed by a cleanup approach to remove transposomes adhering to DNA fragments to minimize interference in the subsequent steps, and DNA amplification using 12 cycles of thermal PCR.
Paramagnetic beads were used to eliminate fragments of less than 150–200 bp (unamplified) after the PCR amplification was completed. The exome amplified pieces of DNA (pre-selected genomic regions of interest) were then maintained, while non-specified DNA fragments were removed using biotinylated probes, as per the capture method plan. Using an Agilent 2100 Bioanalyzer (Agilent 228 Technologies), libraries were measured to confirm final DNA concentration.
Finally, the sequencing of the generated libraries was completed using the Illumina MiSeq NGS Machine. The sequence data generated by the Illumina MiSeq NGS Machine was saved in FASTQ format.
The study was conducted in the Center for Genomics, Rehman Medical Institute Hayatabad, Peshawar Pakistan.
## Bioinformatics Data Analysis
The FAST Q files obtained were subjected to different downstream analysis. These files were filtered on the basis of quality score using ‘Trimmomatic’ software to eliminate the Q30 & Q20 files and analyze Q40 & Q30 files only. For the data analysis and alignment, the newly identified sequence reads were aligned to a reference genome with the help of bioinformatics Burrows-Wheeler Aligner (BWA) software and BAM files were visualized on Integrated Genome Viewer (IGV). After alignment, differences between the reference genome, variant calling (vcf), and the newly sequenced DNA reads, were identified and analyzed.
## Statistical analysis
A chi-square test (χ2) was performed to know the relationship of genotypes of patients and healthy controls with breast cancer risk. Assessment of odd ratio (OR) and 95 % confidence intervals (CIs) was estimated through binary logistic regression. The $P \leq 0.05$ was considered statistically significant. ORs were also calculated for various clinicopathological characteristics for both the mutation carriers and non-carriers. All of the statistical analysis was determined by applying the SPSS software, version 21.00.
## Basic demographic data of patients
Demographic as well as other characteristics such as use of oral contraceptives, duration of breast feeding, area of residence, education and history of breast cancer in the first and/or second degree relatives, were studied as given in the table # 1 & 2. Mean age of patients and control were 43.30 +18.41 and 46.96+18.41 respectively.
Age groups and marital status showed no significant deviation ($P \leq 0.05$) between patients and controls, whereas significant deviation ($P \leq 0.05$) was found between users and non-user of drugs/contraceptives ($$P \leq 0.003$$) and as well as in socioeconomic status groups.
According to different age group, majority of the patients 37 ($21\%$) belongs to age group 46–60 years followed by 31($17.6\%$) [31–45years], 14($8\%$) [15–30years] (t test p value = 0.085).
All of the subjects were females and no male patient throughout the study was observed. Regarding regional distribution of the patients shown in table 1, the majority 53 ($30.1\%$) of the patients belongs to Peshawar division. Patients of other regions like Mardan, Malakand, Kohat and Bannu divisions were also incorporated in the study. Majority of the patients 82 ($93.3\%$) were found married (t test p value = 0.136). According to socio-economic status of the patients, majority of the patients were found in poor category, followed by satisfactory and then well off (t-test p value = 0.000). Highest incidence ($30.1\%$) of BC was observed in Peshawar division, followed by Mardan ($6.8\%$), Malakand, Kohat ($2.8\%$ each) and tribal districts ($3.4\%$).
Different clinicopathological parameters like menarche, age at clinical stages (menopause, first pregnancy), no. of children, duration of breast feeding, enlargement of breast and ulceration, mobility, tenderness of breast, nipple discharge, Peaudorange, cyclical pains, Lymphedema, weight loss and liver changes were also studied (Table. 2). Significant deviation ($P \leq 0.05$) between patients and controls was observed in terms of breast enlargement, cyclical pain, peaudorange, mobility, tenderness, nipple discharge, Lymphedema, weight loss and liver changes, whereas rest of the factors showed no significant deviation ($P \leq 0.05$).
## Risk Variants reported in the study population
Whole Exome Sequencing (WES) results are shown in table 3. The WES reported three missense variants in the selected genes namely TP53rs1042522, CDH1rs3743674 and ATM rs659243 in the study population. WES reported risk variants were validated by using polymerase chain reaction restriction fragment length polymorphism.
**Table 3**
| Unnamed: 0 | Gene | Variant | Homo% | Hetero% | HGVSc | HGVSp | Db SNP ID |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Cases | TP53 | G>G/C | 26 | 78 | NM _ 000546.5:c.215C>G | NP _ 000537.3:p. Arg 72 Pro | rs1042522 |
| Control | TP53 | G>G/C | 66 | 34 | NM _ 000546.5:c.215C>G | NP_000537.3:p. Arg 72 Pro | rs1042522 |
| Cases | CDH1 | C>C/T | 70 | 30 | NM_004360.3:c.48+6C>T | | rs3743674 |
| Control | CDH1 | C>C/T | 75 | 25 | NM_004360.3:c.48+6C>T | | rs3743674 |
| Cases | ATM, C11ORF65 | A>G/G | 55 | 45 | NM _ 000051.3:c.5948A>G | NP _000042.3:p.Asn1983Ser | rs659243 |
| Control | ATM, C11ORF65 | A>G/G | 52 | 48 | NM _ 000051.3:c.5948A>G | NP _000042.3:p.Asn1983Ser | rs659243 |
The allelic and genotypic frequencies of TP53rs1042522, CDH1rs3743674 and ATM rs659243 polymorphism of both controls and cases are given in Table 4. In case of CDH1rs3743674 risk allele was significantly high in Breast cancer patients compared to healthy controls. CDH1rs3743674 polymorphism (crude $$P \leq 0.014$$ and adjusted $$P \leq 0.000$$) was evident as risk variant and increases risk for breast cancer in the Pashtun ethnic population of Pakistan. TP53rs1042522 and ATM rs659243 polymorphism showed No/negative association with breast cancer in the present studied population.
**Table 4**
| Gene | Genotype /Allele | Case N (%) | Control N (%) | OR ( 95%CI ) | P-Value | Adjusted OR ( 95%CI ) | P-Value.1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| TP53Variant | G>C/C | 34(19.3%) | 29(16.5%) | 0.647 (0.308–1.361) | 0.251 | 0.81 (0.1–6.89) | 0.851 |
| TP53Variant | G>G/C | 32(18.2%) | 30(17.0%) | 0.647 (0.308–1.361) | 0.251 | 0.81 (0.1–6.89) | 0.851 |
| TP53Variant | Mutation not reported | 22(12.5%) | 29(16.5%) | Reference | | Reference | -- |
| CDH1.Variant | C>C/T | 28(15.9%) | 14(8.0%) | 0.250 (0.082–0.759) | 0.014 | 1.07 (0.05–22.59) | 0.000 |
| CDH1.Variant | C>T/T | 63(34.1%) | 40(29.1%) | 0.250 (0.082–0.759) | 0.014 | 1.07 (0.05–22.59) | 0.000 |
| CDH1.Variant | Mutation not reported | 07(4.0%) | 14(8.0%) | Reference | | Reference | -- |
| ATM | A>G/C | 55(31%) | 52(29%) | 0.560 (0.212–1.512) | 0.256 | 1.07 (0.05–1.059) | 0.975 |
| ATM | A>G/G | 53(30%) | 58(35%) | 0.560 (0.212–1.512) | 0.256 | 1.07 (0.05–1.059) | 0.975 |
| ATM | Mutation not reported | 0(0.0) | 0(0.0) | Reference | 0.0 | -- | -- |
## Discussion
Genetic modification due to single nucleotide polymorphism in the genomic DNA has an involvement in cancer development 37–39. Polymorphism in the tumor suppressor TP 53 and cell-cell adhesion CDH1 genes is found to increase susceptibility for breast cancer 23,40. In Present study, three variants (TP53rs1042522, CDH1rs3743674 and ATM rs659243) were screened for its association with BC in Pashtun ethnic population. Association of TP53 rs1042522 with breast cancer was reported 23. Previously Sekar et al described that over-expression of TP53 was considerably connected with the breast cancer development41. E-cadherin loss results in the dedifferentiation and incursion of the breast carcinoma 42. The current study concentrates on fascinating results including CDH1 and TP 53 and ATM genes mutations in patients, by comparison, to ethnically matched controls.
TP53 emerges as an essential regulatory protein that acts as a multifunctional transcription factor to control the cell-cycle progression, restore DNA damage to keep the integrity of genome and induces apoptosis where stressors create abnormal and irreversible injury to eliminate the smashed cells 43–45. As the magnitude of cellular stress is linked with post-translational modifications of tumor suppressor gene, TP53 can be considered as a possible molecular signature for the study of breast cancer populations at high-risk 46–48. Literature shows the involvement of heterozygous Arg/Pro variant enhances the risk of breast cancer in the population. From the study it was observed that Arg/Pro incidence was 77.7 % in patients and 33.3 % in controls which shows a major association of this polymorphism with breast cancer. In Japani women, this polymorphism was reported with $48.9\%$ in controls and with 50.0 % of patients for Arg/Pro heterozygosity49. In New York, the ratio of same polymorphism was 42.2 % and 35.4 % in patients and controls respectively. However, increase in breast cancer risk due to heterozygous genotype by 32 % was observed in the same population50. Lum et al. 51 reported a high prevalence of Arg/Pro heterozygosity with 47.5 %f controls and 51.0 % of patients in the Chinese population. All these results are in line with our findings.
In the current study, we found an alliance among the rs3743674 mutant and breast tumor (crude $$P \leq 0.014$$ and adjusted $$P \leq 0.000$$). A high risk of breast cancer for Pro/Pro homozygosity (Odds Ratio = 2.38; $$P \leq 0.046$$) was also found in Austrian52 and Japani population (Odds Ratio = 2.14; 95 % confidence interval=1.21–3.79) 49. It has an important role in the risk of hormone receptor (ER- positive) breast cancer with adjusted OR = 2.04, $$P \leq 0.04$$ in Japanese women 53. A large quantity of genotype carrying the pro allele (Pro/Pro and Pro/Arg) (OR = 1.47, $$P \leq 0.014$$; 95 % CI = 1.08 – 2.00) and its frequency was found in the Swedish population 54. Lum and his colleagues established a biologically relation to the presence of homozygosity for proline with breast cancer patients in Chinese women, with $16.3\%$ for controls and $22.1\%$ for patients 51.
All these results are in line with our findings. Moreover our findings are not in line with those studies which establish a high frequency of homozygosity of Arg in breast cancer patients as reported in Turkish 55, 56, Arab 48, Iranian 57, Greece 58, and Southern Brazilian populations59. Keshava et al found high commonness of Arg allele in Caucasian breast cancer patients 60, and higher prevalence of the Arg allele was found by Ohayon et al.61 in the Ashkenazi and non-Ashkenazi Jews.
Reports have shown that TP 53 Arg 72 variant is more efficient in the initiation of apoptosis 62–64. Therefore the Pro allele is considered to be mostly responsible for reduction in apoptosis leading to breast cancer.
E-cadherin has an important role in cellular differentiation, inter-cellular adhesion and cell signaling. Studies have shown the association of different types of cancers with CDH1 rs3743674 polymorphism, however some other reports have shown no significant relation. The non-significant relation may be due to genetic and ethnic variability of the patients and controls that might be responsible for this inequality among these information. Our findings are in line with the majority of the studies conducted in different populations. *The* genetic changes in CDH1 gene along with cellular polarity and cell-to-cell adhesion, is finally accountable for the metastasis and tumor progression 26, 28, 65.
A previous study relates CDH1rs3743674 and TP53rs1042522 polymorphisms with the risk and breast cancer progression 53, 54, 58, 66, 67. In the present study we have screened the presence of the polymorphism in TP53 codon 72 and CDH1 rs3743674 and ATM rs659243 genes in breast cancer for the first time in patients of Khyber Pakhtunkhwa population to evident the association of the aforementioned gene varints with BC in study population.
## Conclusion
Our results show a significant association of CDH1 rs3743674 polymorphism with increasing risk of breast cancer in the Pashtun population of Pakistan. This study will help to provide a plate form for future diagnosis and treatment of breast cancer patients. Similar projects should be designed by national governmental agencies to screen and pinpoint genetically susceptible individuals and awareness campaigns are needed regarding genetic susceptibility and environmental risk factors be initiated in general public.
## Competing interests
Authors declare no competing interests.
## References
1. Korde LA. **Multidisciplinary meeting on male breast cancer: summary and research recommendations**. *J. Clin. Oncol* (2010) **28** 2114-2122. PMID: 20308661
2. Darkeh MHSE. **Male breast cancer clinical features, risk factors, and current diagnostic and therapeutic approaches**. *Int. J. Clin. Med* (2014) **5** 1068-1086
3. Coley HM. **Mechanisms and strategies to overcome chemotherapy resistance in metastatic breast cancer**. *Cancer Treat Rev* (2008) **34** 378-390. PMID: 18367336
4. Kumar A. **Epidemiology of breast cancer: current figures and trends**. *Preventive oncology for the gynecologist* (2019) 335-339
5. Garbee D. **The impact of side effects on adherence and persistence with oral anti-cancer agents in women diagnosed with early stage breast cancer: a systematic review of quantitative evidence protocol**. *JBI Database Syst Rev Implement Rep* (2014) **12** 27-39
6. Shaukat U. **Epidemiology, major risk factors and genetic predisposition for breast cancer in the Pakistani population**. *Asian Pac. J. Cancer Prev* (2013) **14** 5625-5629. PMID: 24289553
7. Lee SY. **Cost-effective mammography screening in Korea: high incidence of breast cancer in young women**. *Cancer Sci* (2009) **100** 1105-1111. PMID: 19320639
8. Wiseman RA. **Breast cancer hypothesis: a single cause for the majority of cases**. *J. Epidemiol. Community Health* (2000) **54** 851-858. PMID: 11027200
9. Apostolou P. **Hereditary breast cancer: the era of new susceptibility genes**. *Biomed Res. Int* (2013) 1-11
10. Ford D. **The genetics of breast and ovarian cancer**. *Br. J. Cancer* (1995) **72** 805-812. PMID: 7547224
11. Savitsky K. **A single ataxia telangiectasia gene with a product similar to PI-3 kinase**. *Science* (1995) **268** 1749-1753. PMID: 7792600
12. Hesse-Biber S. **Within-gender differences in medical decision making among male carriers of the BRCA genetic mutation for hereditary breast cancer**. *Am.J.Men's Health* (2017) **11** 1444-1459. PMID: 26468160
13. Shiovitz S. **Genetics of breast cancer: a topic in evolution**. *Annals of Oncology* (2015) **26** 1291-1299. PMID: 25605744
14. Economopoulou P. **Beyond BRCA: new hereditary breast cancer susceptibility genes**. *Cancer Treat. Rev* (2014) **41** 1-8. PMID: 25467110
15. Key TJ. **Epidemiology of breast cancer**. *Lancet Oncol* (2001) **2** 133-140. PMID: 11902563
16. DeBruin LS. **Perspectives on the chemical etiology of breast cancer**. *Environ Health Perspect* (2002) **110** 119-128
17. Dumitrescu R. **Understanding breast cancer risk-where do we stand in 2005?**. *J Cell Mol Med* (2005) **9** 208-221. PMID: 15784178
18. Leong SP. **Is breast cancer the same disease in Asian and Western countries?**. *World J Surg* (2010) **34** 2308-2324. PMID: 20607258
19. Amaral P. **Body fat and poor diet in breast cancer women**. *Nutr Hosp* (2010) **25** 456-461. PMID: 20593130
20. Tipirisetti NR. **Association of E-cadherin single-nucleotide polymorphisms with the increased risk of breast cancer: a study in South Indian women**. *Genet Test Mol Biomarkers* (2013) **17** 494-500. PMID: 23551055
21. Ripperger T. **Breast cancer susceptibility: current knowledge and implications for genetic counselling**. *Eur J Hum Genet* (2009) **17** 722-731. PMID: 19092773
22. Soleimani A. **The evaluation of p53 polymorphism at codon 72 and association with breast cancer in iran: a systematic review and meta-analysis**. *JOCP* (2016) **21** 288
23. Arfaoui A. **Role of p53 Codon72 SNP in breast cancer risk and anthracycline resistance**. *Anticancer Res* (2015) **35** 1763-1769. PMID: 25750340
24. Brosh R. **When mutants gain new powers: news from the mutant p53 field**. *Nat Rev Cancer* (2009) **9** 701-713. PMID: 19693097
25. Pećina-Šlaus N. **Tumor suppressor gene E-cadherin and its role in normal and malignant cells**. *Cancer Cell Int* (2003) **3** 1-7. PMID: 12633505
26. Beeghly-Fadiel A. **E-cadherin polymorphisms and breast cancer susceptibility: a report from the Shanghai Breast Cancer Study**. *Breast Cancer Res Treat* (2010) **121** 445-452. PMID: 19834798
27. Weinberg RA. *The biology of cancer* (2006)
28. Jeanes A. **Cadherins and cancer: how does cadherin dysfunction promote tumor progression?**. *Oncogene* (2008) **27** 6920-6929. PMID: 19029934
29. Nakamura A. **Characterization of DNA polymorphisms in the E-cadherin gene (CDH1) promoter region**. *Mutat Res* (2002) **502** 19-24. PMID: 11996968
30. Wang GY. **The E-cadherin gene polymorphism− 160C→ A and cancer risk: a HuGE review and meta-analysis of 26 case-control studies**. *Am J Epidemiol* (2008) **167** 7-14. PMID: 17971340
31. Akbas H. **E-cadherin (CDH1) gene-160C> A promoter polymorphism and Risk of Gastric and Esophageal Cancers**. *Acta Medica Mediterranea* (2013) **29** 671-676
32. Cattaneo F. **Functional analysis and case-control study of-160C/A polymorphism in the E-cadherin gene promoter: association with cancer risk**. *Anticancer Res* (2006) **26** 4627-4632. PMID: 17201188
33. Qiu LX. **The E-cadherin (CDH1)− 160 C/A polymorphism and prostate cancer risk: a meta-analysis**. (2009) **17** 244-249
34. Amos CI. **DNA pooling in mutation detection with reference to sequence analysis**. *Am J Hum Genet* (2000) **66** 1689-1692. PMID: 10733464
35. Carmi R. **Use of a DNA pooling strategy to identify a human obesity syndrome locus on chromosome 15**. *Human Molecular Genetics* (1995) **4** 9-13. PMID: 7711739
36. Jan A. **Evaluation of Type 2 Diabetes Risk Variants (Alleles) in the Pashtun Ethnic Population of Pakistan**. *J ASEAN Fedration of Endocrine Societies* (2022) **37**
37. Pérez LO. **Evaluation of p53 codon 72 polymorphism in adenocarcinomas of the colon and rectum in La Plata, Argentina**. *World J Gastroenterol* (2006) **12** 1426-1429. PMID: 16552814
38. Bojesen SE. **The common germline Arg-72Pro polymorphism of p53 and increased longevity in humans**. *Cell Cycle* (2008) **7** 158-163. PMID: 18256523
39. Katkoori VR. **Prognostic significance of p53 codon 72 polymorphism differs with race in colorectal adenocarcinoma**. *Clin Cancer Res* (2009) **15** 2406-2416. PMID: 19339276
40. Deng QW. **Roles of E-cadherin (CDH1) genetic variations in cancer risk: a meta-analysis**. *Asian Pac J Cancer Prev* (2014) **15** 3705-3713. PMID: 24870781
41. Sekar P. **Evaluation of p53, HoxD10, and E-cadherin status in breast cancer and correlation with histological grade and other prognostic factors**. *J. Oncol* (2014) 7025-7027
42. Blick T. **Epithelial mesenchymal transition traits in human breast cancer cell lines**. *Clin Exp Metastasis* (2008) **25** 629-642. PMID: 18461285
43. Bai L. **p53: structure, function and therapeutic applications**. *J Cancer Mol* (2006) **2** 141-153
44. Zilfou JT. **Tumor suppressive functions of p53**. *Cold Spring Harb Perspect Biol* (2009) **1** a001883. PMID: 20066118
45. Giono LE. **The p53 tumor suppressor participates in multiple cell cycle checkpoints**. *J Cell Physiol* (2006) **209** 13-20. PMID: 16741928
46. Hussain SP. **Increased p53 mutation load in nontumorous human liver of wilson disease and hemochromatosis: oxyradical overload diseases**. *Proc Natl Acad Sci U S A* (2000) **97** 12770-12775. PMID: 11050162
47. Appella E. **Post-translational modifications and activation of p53 by genotoxic stresses**. *Eur J Biochem* (2001) **268** 2764-2772. PMID: 11358490
48. Alawadi S. **P53 gene polymorphisms and breast cancer risk in Arab women**. *Med Oncol* (2011) **28** 709-715. PMID: 20443084
49. Huang XE. **Association of p53 codon Arg72Pro and p73 G4C14-to-A4T14 at exon 2 genetic polymorphisms with the risk of Japanese breast cancer**. *Breast Cancer* (2003) **10** 307-311. PMID: 14634508
50. Gaudet MM. **Genetic variation of TP53, polycyclic aromatic hydrocarbon-related exposures, and breast cancer risk among women on Long Island, New York**. *Breast Cancer Res Treat* (2008) **108** 93-99. PMID: 17624591
51. Lum SS. **MDM2 SNP309 G allele increases risk but the T allele is associated with earlier onset age of sporadic breast cancers in the Chinese population**. *Carcinogenesis* (2008) **29** 754-761. PMID: 18281248
52. Proestling K. **The Pro allele of the p53 codon 72 polymorphism is associated with decreased intratumoral expression of BAX and p21, and increased breast cancer risk**. *PLoS One* (2012) **7** e47325. PMID: 23071787
53. Noma C. **Association of p53 genetic polymorphism (Arg72Pro) with estrogen receptor positive breast cancer risk in Japanese women**. *Cancer Lett* (2004) **210** 197-203. PMID: 15183535
54. Sjalander A. **p 53 polymorphisms and haplotypes in breast cancer**. *Carcinogenesis* (1996) **17** 1313-1316. PMID: 8681448
55. Buyru N. **P53 codon 72 polymorphism in breast cancer**. *Oncol Rep* (2003) **10** 711-714. PMID: 12684648
56. Kara N. **P53 codon 72 and HER2 codon 655 polymorphisms in Turkish breast cancer patients**. *DNA Cell Biol* (2010) **29** 387-392. PMID: 20380571
57. Doosti A. **A p53 codon 72 polymorphism associated with breast cancer in Iranian patients**. *Afr J Pharm Pharmacol* (2011) **5** 1278-1281
58. Papadakis E. **p53 codon 72 polymorphism as a risk factor in the development of breast cancer**. *Mol Cell Biol Res Commun* (2000) **3** 389-392. PMID: 11032762
59. Damin AP. **Evidence for an association of TP53 codon 72 polymorphism with breast cancer risk**. *Cancer Detect Prev* (2006) **30** 523-529. PMID: 17113725
60. Keshava C. **Waf-1 (p21) and p53 polymorphisms in breast cancer**. *Cancer Epidemiol Biomarkers Prev* (2002) **11** 127-130. PMID: 11815410
61. Ohayon T. **The R72P P53 mutation is associated with familial breast cancer in Jewish women**. *Br J Cancer* (2005) **92** 1144-1148. PMID: 15756275
62. Pim D. **p53 polymorphic variants at codon 72 exert different effects on cell cycle progression**. *Int J Cancer* (2004) **108** 196-199. PMID: 14639602
63. Dumont P. **The codon 72 polymorphic variants of p53 have markedly different apoptotic potential**. *Nat Genet* (2003) **33** 357-365. PMID: 12567188
64. Osorio A. **A haplotype containing the p53 polymorphisms Ins16bp and Arg72Pro modifies cancer risk in BRCA2 mutation carriers**. *Hum Mutat* (2006) **27** 242-248. PMID: 16419081
65. Yanjun Xu. **Detection of CDH1 gene variants in early-onset diffuse gastric cancer in Chinese patients**. *Clinical Laboratory* (2014) **60** 1823-1830. PMID: 25648022
66. Kalemi TG. **The association of p53 mutations and p53 codon 72, Her 2 codon 655 and MTHFR C677T polymorphisms with breast cancer in Northern Greece**. *Cancer Lett* (2005) **222** 57-65. PMID: 15837541
67. Zhao L. **Association between E-cadherin (CDH1) polymorphisms and pancreatic cancer risk in Han Chinese population**. *Int J Clin Exp Pathol* (2015) **8** 5753-5760. PMID: 26191293
|
---
title: 'An environmental scan of Ontario Health Teams: a descriptive study'
authors:
- Claire Sethuram
- Tess McCutcheon
- Clare Liddy
journal: BMC Health Services Research
year: 2023
pmcid: PMC9993364
doi: 10.1186/s12913-023-09102-6
license: CC BY 4.0
---
# An environmental scan of Ontario Health Teams: a descriptive study
## Abstract
### Background
Ontario Health Teams (OHTs) are an integrated care system introduced in Ontario, Canada in 2019 after the 14 Local Health Integrated Networks (LHINs) were dissolved. The objective of this study is to give an overview of the current state of the OHT model’s implementation, and what priority populations and transitions of care models were identified by OHTs.
### Methods
This scan involved a structured search for each approved OHT of publicly available resources with three main sources: the full application submitted by the OHT, the OHT website, and a Google search with the name of the OHT.
### Results
As of July 23, 2021, there were 42 approved OHTs and nine transitions of care programs were identified across nine OHTs. Of the approved OHTs, 38 had identified ten distinct priority populations, and 34 reported partnerships with organizations.
### Conclusions
While the approved OHTs currently cover $86\%$ of Ontario’s population, not all OHTs are at the same stage of activity. Several areas for improvement were identified, including public engagement, reporting, and accountability. Moreover, OHTs’ progress and outcomes should be measured in a standardized manner. These findings may be of interest to healthcare policy or decision-makers looking to implement similar integrated care systems and improve healthcare delivery in their jurisdictions.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12913-023-09102-6.
## Background
In 2019, the Ontario government passed the Connecting Care Act, 2019 that initiated a redesign of the existing health system. Considered “one of the largest reforms to the provincial health system” by the Ontario Medical Association [1] (np)], the Act includes dissolving its 14 Local Health Integrated Networks (LHINs) and replacing them with small, local teams within communities, called Ontario Health Teams (OHTs), spread across the province [2, 3]. Similar to other areas of Canada, the country’s most populous province, Ontario [4], was struggling to deliver healthcare that is continuous and accessible to its residents. Whereas the LHINs were regionally based and autonomously run, OHTs are community based and managed by a newly created agency, Ontario Health, with the vision that by transferring the bureaucratic load to a central agency and narrowing their scope, OHTs will be more patient-centered than LHINs and the system more cost effective [5, 6]. Eliminating the silos that exist between the various internal organizations and integrating services would not only create safer and more financially efficient care delivery, but also better patient experiences and healthier communities [2]. Such models of care, where healthcare providers work as a team, rather than separate entities, have already been identified and implemented as a directional goal in healthcare delivery. For example, the College of Family Physicians of Canada’s patient’s medical neighbourhood (PMN) model [7] aims to provide safe, comprehensive, and coordinated quality care that is integrated across an entire care network while remaining patient-centered and rendering services accessible to a specific population [8, 9].
In the case of OHTs, the intent was to expand beyond primary care and encourage groups of healthcare providers, such as physicians, home and community care organizations, and hospitals, defined by existing patterns of access and referral, to formally link within the healthcare system [10–12]. The aim is to design a health system tailored to a defined population and allow patients to access healthcare and transition from provider to provider seamlessly within their community [10]. Aligned with the PMN’s goals of fostering a collaborative environment between healthcare professionals by integrating virtual care, necessary infrastructure, and coordination of care, groups of providers were invited to form a team and submit a proposal describing their plans for transforming care, implementation, and creating community partnerships. In this proposal, teams were also asked to identify a priority population, such as seniors, people living in rural areas, or people living with dementia, to focus care redesign and improvement efforts during the initial implementation of the OHT over its first year [13]. This report aims to give an overview of the current state of the OHT model’s implementation. We were specifically interested in seeing what priority populations and transitions of care models were identified by OHTs, as this was an area of interest identified by our one of our funders, Innovations Strengthening Primary Health Care Through Research (INSPIRE-PHC).
## Setting
The study was conducted in Ontario, Canada. In Canada, public healthcare is managed at the provincial level. When the LHINs came into effect in 2007, Ontario was divided into 14 geographic health regions. These regions stood until the Connecting Care Act was enacted in 2019, creating Ontario Health, which has divided the province into health regions that cover larger areas than the LHINs. At the time of data collection (Summer 2021) there were five health regions: West, Central, Toronto Central, East, and North. Since that time, the North region has split into North West and North East. Ontario Health utilizes the regions to “work with local community and health care partners”, track and evaluate health system performance, and oversee OHTs [14] (np)]. Because Ontario covers approximately 1.1 million km2 with approximately 14.9 million residents [4] unequally distributed across the province, each region has unique needs and challenges in delivering healthcare to its residents. Each region contains multiple OHTs that are healthcare partnerships at the community level that are funded by and report to Ontario Health (Fig. 1).Fig. 1Relationship between Ontario Health, health regions, and OHTs
## Study design and approach
Our team conducted this environmental scan between June and August 2021 based on the process outlined by the Canadian Agency for Drugs and Technologies in Health [15]. First, we found a list of approved OHTs on the Ontario Ministry of Health and Ministry of Long-Term Care webpage [16]. For each approved OHT, we used a structured search of publicly available resources with three main sources:1. The full application submitted by the OHT, to identify the initial plans of the OHT, including Year 1 target populations, attributed population size, and partners;2. The OHT website (if available), to identify any updates on work related to Year 1 targets;3. A Google search with the name of the OHT (e.g., “Burlington OHT”), to identify any updates related to Year 1 targets that may not have been provided on the OHT website. We limited this search to the first five pages of results.
## Inclusion/exclusion criteria for google search
We included the resources that described updates on the OHT’s activities (e.g., programs related to their priority population for Year 1), and those that were publicly available and published after the full application was approved.
We excluded resources regarding COVID-19 activities that were not initially outlined in the full application, such as information about testing sites and vaccine clinics. This exclusion criterion allowed us to focus on the OHT’s implementation of the plans outlined in the full application. While we realize that the pandemic was of great significance, it is also important to ensure that the OHTs are not pausing the work they set out to accomplish, such as increasing resources for mental health and addictions or improving transitions care of the elderly, prior to the declaration of the pandemic [17, 18]. In fact, integrated care with strong partnerships is even more significant during this time [19].
## Data extraction and description
For each approved OHT, we recorded the following information from their full applications in an Excel spreadsheet: name of OHT, date of approval, population(s) of focus, population size, number of affiliated organizations or partners, website, and additional notes. After data extraction, we summarized and tabulated the findings from the full applications, OHT websites, and Google searches of all approved OHTs. Any findings related to transitions of care were also summarized together. We used summary statistics and frequencies to describe commonalities across approved OHTs, including Ontario Health region, priority populations, partnerships, and population size.
## Ethics
Since we collected all materials for data analysis from publicly available resources on the internet, no ethics approval was required, as per the policy of our local research ethics board, the Ottawa Health Sciences Network.
## Results
As of July 23, 2021, the Government of Ontario had approved forty-two OHTs [11]. Twenty-four OHTs were approved before the COVID-19 pandemic was declared in March 2020. There was a seven-month gap between the first and second OHT approval cohorts. Eighteen OHTs were approved after the onset of the pandemic. These approved OHTs currently cover $86\%$ of Ontario’s population [11].
Of the approved OHTs, 38 OHTs had identified ten distinct priority populations. Most OHTs ($66\%$; $$n = 25$$) had listed more than one priority population. The top three populations identified were Mental Health and Addiction ($66\%$; $$n = 25$$), Seniors ($61\%$; $$n = 23$$), and Palliative Care ($29\%$; $$n = 11$$). The remaining priority populations, along with the number of OHTs that identified these groups as a priority for their region, are listed in Table 1.Table 1OHT-identified priority populations ($$n = 38$$)Priority population Identified by OHTNumber of OHTsPercentageMental Health and Addictions$2566\%$Seniors$2361\%$Palliative$1129\%$Chronic conditions$411\%$Homelessness and Precarious Housing$411\%$Dementia$25.3\%$Rural$25.3\%$Acute GI/GU$12.6\%$High Health System Users$12.6\%$Refugee$12.6\%$ All five health regions had approved OHTs. Of the five Ontario Health Regions, the Central Region had the most approved OHTs, with 17 ($40\%$). The West Region had the second most with 12 approved OHTs ($29\%$), surpassing the East and North Regions, with only four approved OHTs each. Of the 42 approved OHTs, the majority ($$n = 32$$) reported their population size in their initial applications and had an average population of 325,902; the Ottawa OHT’s application reported the largest population coverage at 934,242. In comparison, Muskoka and Area OHT reported the smallest population coverage at 64,445 people.
Thirty-four OHTs ($80\%$) provided information on the number and types of partnerships they have developed, including organizations, patient or clinician partners, and community or government agencies. Of the OHTs that had self-reported their number of partnerships, 13 ($38\%$) had 1–20 partners, 12 OHTs ($35\%$) reported 21–40 partners, six OHTs ($18\%$) reported 41–60 partners, one OHT ($3\%$) reported 61–80 partners, and two OHTs ($6\%$) had more than 80 partners. Specific numbers of patient partners were not available. We found limited information on the patient and community engagement initiatives across OHTs and could not find a website for eight of the OHTs ($19\%$).
## Transitions of care programs
We identified transitions of care models during the environmental scan (Table 2). Many of these programs were identified in OHTs’ full applications as Year 1 projects, and they have been implemented in various OHTs to improve transitions of care. However, we found no evidence regarding their utilization, impact, and effectiveness. Brief descriptions of these innovative approaches to transitions of care can be found in Appendix A.Table 2Transitions of care modelsTransitions of care resourceOntario Health Team(s)Ontario Health RegionCommunity paramedicine programChatham-KentWestSurgical transitions virtual careHamilton Health TeamWestThree-page form improving communication during transfersEastern York Region and North DurhamCentralHigh-Intensity Supports at Home (HISH) ProgramConnected Care Halton, Eastern York Region and North DurhamCentralSeniors Home Support (SHS) ProgramEastern York Region and North DurhamCentralNorth York Community Access to Resources Enabling Support (North York CARES)North York Toronto Health PartnersCentralSouthlake@home and COVID@home programsSouthlake CommunityCentralCOVID-19 hospital-to-home transitionsHills of HeadwatersCentralNP-led clinic focused on transitions for childrenKids Come FirstEast
## Summary
This descriptive analysis of OHTs provides an overview of the status, transitions of care models, and reporting from each OHT approved before September 2021. Of the approved OHTs, 38 had identified ten distinct priority populations, and 34 reported partnerships with local organizations. We also identified nine transitions of care programs across nine OHTs. The approved OHTs currently cover $86\%$ of Ontario’s population; however, not all OHTs are at the same stage of activity. Based on these findings, we have identified several areas for improvement, including public reporting, patient and public engagement, and accountability for meeting deliverables.
## Reporting
Initially when designing the study, we were interested in learning about the OHTs’ active projects from their public reporting but as we begun the data extraction process, we soon realized there was an absence of publicly available information. While there is a standardized process to approve OHTs, at the time of this study there was no standard for reporting outcome measures and evaluations. In November 2022, the Ontario Ministry of Health released new requirements for OHTs that included a section entitled “Cultivating Consistency in OHT-Led Public Communications” that lays out expectations and standards for how communications are delivered to the public [12, 20]. During the application process for approval, there are key steps and stages that must be followed and reporting requirements at certain time intervals. Additionally, there is clear maturity from the “in discovery” phase to “in development,” followed by “OHT candidate” stage and finally, “approved OHT” [21]. Moreover, all OHT candidates must submit the standardized OHT application outlining their plans and make it publicly available. However, once the OHT has been approved, there is no standard method of reporting to the public. This is of particular concern since a recent report by the National Academies of Sciences, Engineering, and Medicine on the realization of high-quality primary and community care asserted that processes for keeping parties accountable to meeting their goals were paramount to the success of a strong health system [22]. The lack of information relating to the evaluation and accountability of each OHT is notable as these data are essential to facilitate effective quality improvement and research at a systems-level; however, there is evidence of improvement here in the coming years in the Ontario organization, Health System Performance Network (HSPN). HSPN is conducting on-going OHT evaluations that encompass both the development of OHTs following approval and health and system improvement indicators of OHT attributable and priority populations [23].
## Population coverage at full approval
Currently, 42 OHTs are approved and cover $86\%$ of Ontario’s population. Once the OHTs in development reach approval, $99\%$ of Ontario’s population will be covered; however, it should be noted that the OHTs currently in development are not all at the same stage in the approval process and do not have the same level of activity [11].
One pitfall of innovation in this sector is that the model might not provide adequate or equitable coverage. For example, the Family Health Team (FHT) model has been cited as a solution to increase access to and quality of primary care [24, 25]. However, a recent study in Ontario found that the patient rosters of FHTs tend to be wealthier, healthier, located in rural regions and have low immigrant representation [26]. Once all OHTs are implemented, the model will offer greater population coverage relative to FHTs, which were only partially implemented in Ontario. To ensure equitable population coverage is offered, it must be measured and compared between teams. We recommend the development of a maturity index after OHT approval to monitor how each OHT progresses that includes the adoption of standardized equity outcome measures.
## Patient medical neighbourhood
OHTs are moving towards the PMN model by creating integrated care to serve communities based on their needs, supported by local partners and organizations with adequate financial and administrative resources. Although there is a high level of partner engagement with an OHT’s community, OHTs provide little information to engage physicians [27]. This is consistent with findings from other studies on OHT implementation. For example, in a qualitative study by Embuldeniya et al. [ 2020], physicians reported being unaware of how OHT implementation would affect them. A report by the National Academies of Sciences, Engineering, and Medicine highlighted the importance of physician and community engagement as an essential component of strong primary and community care [22]. Hence, there is a need for OHTs to strengthen public engagement and communication with those in the community to ensure that health organizations are included in the process. Some OHT websites had not been updated since their approval in 2019 and did not outline any information for community members looking to get involved. This indicates that patient engagement, community co-design, and patient-reported outcomes need to be further addressed in the OHT model.
## COVID-19
There are notable differences between the OHTs formed before and after the beginning of the COVID-19 pandemic in March 2020. Overall, it was noted that some OHTs approved before the pandemic were not as successful in meeting their targets for Year 1 priority populations. Instead, it seems as though they had to pivot very quickly to focus on demands associated with COVID-19 and were unable to continue with their planned work outlined in the application. In comparison, OHTs approved after the onset of the pandemic often had COVID-19 strategies built directly into their full applications. As a result, these groups moved forward with both their COVID-19 work and their Year 1 priority population focus.
## Strengths and limitations
To the best of our knowledge, our study is the first to examine the current status of each approved OHT since the initial 24 OHTs were approved and the onset of COVID-19 [28]. It offers a succinct description of progress made before August 2021 and identifies gaps that should be improved moving forward. Limitations of this study include its short timeframe and exclusion of OHTs approved after August 2021. Furthermore, this study relies on data derived from publicly available resources only; as a result, important information that is not reported to the public might not be included in this analysis. We also excluded articles that focused on COVID-19 activities that were not initially outlined in the full application. Regardless, this study provides an overview of the current state of OHT implementation and provides recommendations to enhance public engagement and to measure OHT maturity.
## Conclusion
This paper aimed to describe the current state of OHTs across the province. Currently, 42 OHTs have been approved, covering $86\%$ of the province’s population; in Ontario, this accounts for almost two million residents without coverage by an OHT. Once the OHTs in development are approved, $99\%$ of the population will be covered. Concerns, however, remain standards of reporting, accountability, and equitable coverage across the OHTs. This reflects one of the biggest challenges in implementing healthcare systems: ensuring sufficient population coverage and equitable access to care for all. It is recommended that more effort be made to measure OHTs’ progress and outcomes in a standardized manner.
## Supplementary Information
Additional file 1.
## References
1. 1.Ontario Medical Association. Ontario Health Teams. n.d. https://www.oma.org/advocacy/ontario-health-teams/. Accessed 28 October 2022.
2. Pilon M, Brouard F. **Description and observations of the transition to a model of Ontario health teams**. *SSRN Electron J* (2020.0). DOI: 10.2139/ssrn.3765535
3. 3.Ontario Health. Ontario Taking Next Steps to Integrate Health Care System. 2019. https://news.ontario.ca/en/release/54585/ontario-taking-next-steps-to-integrate-health-care-system. Accessed 3 May 2022.
4. 4.Statistics Canada. Population estimates, quarterly. 2021. https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1710000901. Accessed 26 October 2022.
5. 5.Grinspun D. Revisiting the past to improve the future. Registered Nurses Association of Ontario Registered Nurse Journal. 2019. https://rnj.rnao.ca/opinion/revisiting-past-improve-future. Accessed 28 October 2022.
6. 6.LaFleche G, Frketich J. Did Ontario’s Local Health Integration Networks fail? Ontario Health Coalition. 2019. https://www.ontariohealthcoalition.ca/index.php/did-ontarios-local-health-integration-networks-fail/. Accessed 28 October 2022.
7. 7.College of Family Physicians of Canada. Best Advice guide: Patient’s Medical Neighbourhood. Mississauga, ON: College of Family Physicians of Canada. 2020.
8. 8.Newbery SL, Malette J. Integration of Care in Northern Ontario: Patient Medical Homes, Rural Health Hubs and Evolving Ontario Health Teams. Northern Policy Institute. 2020. http://www.deslibris.ca/ID/10103953. Accessed 29 July 2021.
9. Spatz C, Gabbay R. **The patient-centered medical neighborhood and diabetes care**. *Diabetes Spectr* (2014.0) **27** 131-133. DOI: 10.2337/diaspect.27.2.131
10. 10.Drynan L, Bingley J, Hollington E. Lanark County Agenda Ontario Health Teams Working Group. 2019. https://vdocuments.mx/agenda-ontario-health-teams-working-group.html?page=1. Accessed 15 August 2021.
11. 11.Ontario Ministry of Health and Long-Term Care (MOHLTC). Ontario Health Teams: Data Supports Guidance Document. 2021. https://health.gov.on.ca/en/pro/programs/connectedcare/oht/docs/OHT_data_supports_guidance.pdf. Accessed 3 May 2022.
12. 12.Piatkowski A, Ryckman K. Understanding (and Using) Physician Networks for Ontario Health Teams. Health Commons Solutions Lab. n.d. https://www.healthcommons.ca/blog/understanding-using-physician-networks-for-oht. Accessed 8 June 2022.
13. 13.Ontario Ministry of Health and Long-Term Care (MOHLTC). Ontario Health Team: Full Application. 2020a. https://health.gov.on.ca/en/pro/programs/connectedcare/oht/docs/OHT_Full_Application_EN.pdf. Accessed 3 May 2022.
14. 14.Ontario Health. Ontario Health Regions. 2022. https://www.ontariohealth.ca/about-us/our-programs/ontario-health-regions. Accessed 3 November 2022.
15. 15.CADTH. Environmental Scan Process. 2015. https://www.cadth.ca/sites/default/files/pdf/ES%20External%20Audience%20Process%20Doc.pdf. Accessed 16 May 2021.
16. 16.Government of Ontario Ministry of Health and Long-Term Care (MOHLTC). Become an Ontario Health Team—Health Care Professionals—MOHLTC. n.d. https://health.gov.on.ca/en/pro/programs/connectedcare/oht/default.aspx?fbclid=IwAR15u5EcOFVf0IEvEyPFKt-0x11Iu2ByKx-eQxBRk_cBU-H4dbX04tEwlx8#meet. Accessed 15 March 2022.
17. Manderson L, Levine S. **Aging, care, and isolation in the time of COVID-19**. *Anthropology and Aging* (2020.0) **41** 132-140. DOI: 10.5195/aa.2020.314
18. 18.Statistics Canada. Survey on COVID-19 and Mental Health, February to May 2021. 2021. https://www150.statcan.gc.ca/n1/daily-quotidien/210927/dq210927a-eng.htm. Accessed 4 November 2022.
19. Baxter S, Johnson M, Chambers D, Sutton A, Goyder E, Booth A. **The effects of integrated care: a systematic review of UK and international evidence**. *BMC Health Serv Res* (2018.0) **18** 350. DOI: 10.1186/s12913-018-3161-3
20. 20.Ministry of Health. Ontario Health Teams: The Path Forward. November 2022. https://health.gov.on.ca/en/pro/programs/connectedcare/oht/docs/OHT_path_forward.pdf?utm_source=Connected+Care+Updates&utm_campaign=062dd012e1-EMAIL_CAMPAIGN_CC_15102019_EN_COPY_01&utm_medium=email&utm_term=0_bb924cd748-062dd012e1-35142197. Accessed 19 January 2023.
21. 21.Ontario Health Teams: Guidance for Health Care Providers and Organizations. 2020b. https://health.gov.on.ca/en/pro/programs/connectedcare/oht/docs/guidance_doc_en.pdf. Accessed 6 July 2021.
22. 22.McCauley L, Phillips Jr. RL, Meisnere M, Robinson SK. Implementing High-Quality Primary Care: Rebuilding the Foundation of Health Care. National Academies of Sciences, Engineering, and Medicine. Washington, DC: The National Academies Press. 2021. doi:10.17226/25983.
23. 23.Health System Performance Network. Ontario Health Teams Central Evaluation. n.d. https://hspn.ca/evaluation/oht/reports/. Accessed 21 November 2022.
24. Ulrich LR, Pham TNT, Gerlach FM, Erler A. **Family health teams in Ontario: ideas for Germany from a Canadian primary care model**. *Gesundheitswesen* (2019.0) **81** 492-497. PMID: 28697527
25. Somé NH, Devlin RA, Mehta N, Zaric GS, Sarma S. **Team-based primary care practice and physician’s services: evidence from family health teams in Ontario**. *Canada Soc Sci Med* (2020.0) **2020** 113310. DOI: 10.1016/j.socscimed.2020.113310
26. 26.Glazier RH, Hutchison BG, Kopp A. Comparison of family health teams to other primary care models, 2004/05 to 2011/12. Institute for Clinical Evaluative Sciences. 2016. https://www.deslibris.ca/ID/248808. Accessed 6 January 2022.
27. 27.Embuldeniya GE, Everall-Day A, Sibbald SL, Gutberg J, Nessa NS, Hall RE, et al. Ontario Health Teams Central Evaluation. Health System Performance Network. 2020. https://moht.ca/partners/documents/Insights_from_Case_Studies_of_the_Early_Experience_of_Developing_OHTs.pdf. Accessed 7 July 2021.
28. 28.Sibbald SL, Hall RE, Embuldeniya GE, Gutberg J, Everall-Day A, Abdelhalim R, Carbone S, Jopling S, Nessa NS, Thampinathan S, & Wodchis WP. Ontario Health Team Central Evaluation – Formative Evaluation: Document Analysis. Toronto, ON: Health System Performance Network. 2020.
|
---
title: Application of machine learning to identify risk factors of birth asphyxia
authors:
- Fatemeh Darsareh
- Amene Ranjbar
- Mohammadsadegh Vahidi Farashah
- Vahid Mehrnoush
- Mitra Shekari
- Malihe Shirzadfard Jahromi
journal: BMC Pregnancy and Childbirth
year: 2023
pmcid: PMC9993370
doi: 10.1186/s12884-023-05486-9
license: CC BY 4.0
---
# Application of machine learning to identify risk factors of birth asphyxia
## Abstract
### Background
Developing a prediction model that incorporates several risk factors and accurately calculates the overall risk of birth asphyxia is necessary. The present study used a machine learning model to predict birth asphyxia.
### Methods
Women who gave birth at a tertiary Hospital in Bandar Abbas, Iran, were retrospectively evaluated from January 2020 to January 2022. Data were extracted from the Iranian Maternal and Neonatal Network, a valid national system, by trained recorders using electronic medical records. Demographic factors, obstetric factors, and prenatal factors were obtained from patient records. Machine learning was used to identify the risk factors of birth asphyxia. Eight machine learning models were used in the study. To evaluate the diagnostic performance of each model, six metrics, including area under the receiver operating characteristic curve, accuracy, precision, sensitivity, specificity, and F1 score were measured in the test set.
### Results
Of 8888 deliveries, we identified 380 women with a recorded birth asphyxia, giving a frequency of $4.3\%$. Random Forest Classification was found to be the best model to predict birth asphyxia with an accuracy of 0.99. The analysis of the importance of the variables showed that maternal chronic hypertension, maternal anemia, diabetes, drug addiction, gestational age, newborn weight, newborn sex, preeclampsia, placenta abruption, parity, intrauterine growth retardation, meconium amniotic fluid, mal-presentation, and delivery method were considered to be the weighted factors.
### Conclusion
Birth asphyxia can be predicted using a machine learning model. Random Forest Classification was found to be an accurate algorithm to predict birth asphyxia. More research should be done to analyze appropriate variables and prepare big data to determine the best model.
## Background
Birth asphyxia (BA) is a serious clinical problem worldwide and is a major contributor to neonatal mortality and morbidity [1]. BA is defined as the inability of a newborn to initiate and maintain adequate respiration after birth [2]. According to the world health organization (WHO) Classification of Diseases ICD10, severe BA is present if the APGAR score is 0–3 after 1 min. Mild and moderate birth asphyxia is present when the APGAR score at 1 min is 4–7 [3]. In most developed countries, birth asphyxia accounts for less than $0.1\%$ of newborn deaths. However, it ranged from $\frac{4.6}{1000}$ to 7–$\frac{26}{1000}$ live births in developing countries [1]. BA May cause serious systemic and neurological sequelae due to decreased blood flow and/or oxygen supply to the fetus or infant during the peripartum period [4]. It is also responsible for about a quarter of all neonatal deaths worldwide [5]. BA is one of the top three causes of mortality in children under five ($11\%$), after premature birth ($17\%$), and pneumonia ($15\%$) [6]. According to WHO, 4 million deaths are attributable to BA each year, accounting for $38\%$ of all deaths in children under 5 years of age. In low-income countries, $23\%$ of all neonatal deaths are due to BA [7].
Efforts to improve child health indices have focused on identifying predictors of BA. Both traditional statistical analysis techniques and artificial intelligence (AI) approaches have been used to identify the risk factors of BA. The applications of AI in medicine have increased significantly in recent years. AI in the form of machine learning, natural language processing, expert systems, planning, and logistics methods, and image processing networks offers great analytical capabilities [8]. Machine learning (ML) is a branch of computer science and a branch of AI. These techniques make it possible to derive meaningful connections between data elements from different data sets that would otherwise be difficult to correlate. Due to the large amount and complexity of medical information, ML is considered a promising method to aid diagnosis or predict clinical outcomes [9]. ML can help professionals make decisions, reduce medical errors, improve accuracy in interpreting various diagnoses, and thereby reduce workloads [10]. According to some studies, the use of machine learning methods has been promising in predicting neonatal mortality. For example, Mboya et al. showed that the predictive ability of perinatal death in ML algorithms was significantly superior to the traditional logistic regression method [11]. Therefore, we aimed to use the ML approach to identify the risk factors for BA.
## Methods
This was a cross-sectional study to identify the risk factors of BA. Women who gave birth at Khaleej-e-Fars Hospital in Bandar Abbas, Iran, were retrospectively evaluated from January 2020 to January 2022. Khaleej-e-Fars *Hospital is* a tertiary hospital with a birth rate of 4000–5000 per year. Data were extracted from the Iranian Maternal and Neonatal Network (IMaN Net), a valid national system, by trained recorders using electronic medical records. Data of all women with singleton pregnancies delivered at the timeline of the study were included in the analysis. Those who gave birth to newborns with congenital anomalies were excluded.
Demographic factors include nationality, age, education level, place of residence, adequate prenatal care (more than six prenatal care visits), smoking status, maternal comorbidities such as anemia, cardiovascular disease, chronic hypertension, pyelonephritis, hepatitis, COVID-19, diabetes, and thyroid dysfunction, drug addiction, and obstetric factors such as gestational, parity, the onset of labor (spontaneous/induced labor/planned cesarean section), preeclampsia, abnormal placentation (placenta previa, placenta accrete), placental abruption, intrauterine growth retardation (IUGR), chorioamnionitis, meconium fluid, fetal presentation, delivery methods, newborn weight, newborn sex, congenital malformation were obtained from patient records.
The primary outcome was whether a machine learning algorithm showed better performance in predicting BA. BA was determined based on a clinical diagnosis from the women’s records using the WHO classification of diseases ICD10 [3].
The following eight machine learning models were used in the study: Logistic regression, Decision Tree Classifier, Random Forrest Classification, XGBoost Classification, Permutation Classification, Feed Forward Deep Learning, Light GBM (LGB), Feed Forward Deep Learning and Support Vector Machines (SVM).
To evaluate the diagnostic performance of each model, six metrics, including area under the receiver operating characteristic curve (AUROC), accuracy, precision, sensitivity, specificity, and F1 score, were measured in the test set. Because AUROC is a widely used index to describe a machine learning model’s ability to predict outcomes [12], we used it as the primary performance metric. The metrics ranged from 0 to 1, with values closer to 1 indicating a better model. The error rate of each model was also analyzed.
The methods for calculating accuracy, precision, recall, and classification error are shown in the equations. Accuracy = (TP)/(TP + FP). In this equation, true positive (TP) represents transactions that were positive and classified as positive. True negative (TN) represents the number of transactions that were negative and classified as positive. False positive (FP) also indicates the number of transactions that were positive and classified as negative. Finally, FN (False Negative) indicates the transactions that were negative and were classified as negative. The equation used to evaluate validity and recall is as follows: Recall = (TP)/(TP + FN) [13]. The F1 value is the harmonic mean of precision and recall. The highest possible value of an F score is 1.0, indicating perfect precision and recall, and the lowest possible value is zero when either precision or recall is zero.
## Results
We found 380 women with a recorded BA out of 8888 deliveries, for a frequency of $4.3\%$. BA was found in $3.4\%$ of the 5848 vaginally delivered newborns, $10.8\%$ of the 83 vacuum-assisted deliveries, and $5.8\%$ of the 2957 cesarean section newborns. In this study, we attempt to evaluate parameters and feature selection based on performance parameters using various machine learning algorithms. We oversample the dataset using the Adaptive Synthetic (ADASYN) algorithm, then run all of the algorithms in 30- and 70-percentage-point separations of the dataset, plot a ROC chart as shown in Fig. 1 and calculate AUROC as a plot that allows the user to visualize the tradeoff between the classifier’s sensitivity. The accuracy of each algorithm is shown in Table 1. Random Forest Classification, Decision Tree Classification, Permutation Feature Classification with KNN, and Deep Learning were among the most accurate algorithms with an accuracy of 0.98–0.99. Other performance parameters for each algorithm are shown in Table 2. The comparison of performance parameters of different machine algorithms showed that Random Forest *Classification is* the best model for BA prediction.
Fig. 1The ROC curves of machine learning models Table 1ROC_AUC of machine learning modelsAlgorithmsROC_AUCAccuracyLogistic Regression0.880.88Decision Tree Classification0.980.98Random Forest Classification0.990.99XGBoost Classification0.930.92Permutation Feature Classification with KNN0.980.98Light GBM0.930.93Deep Learning-Feed Forward1.00.98SVM0.880.88 Table 2The performance of machine learning modelsAlgorithmsTPTNFPFNAccuracyPrecisionRecallF_1 ScoreDecision Tree Classification$19811740511198\%$$97\%$$99\%$$98\%$Random Forest Classification$1984177714899\%$$99\%$$99.6\%$$99\%$Permutation Feature Classification with KNN$1983172962998\%$$96\%$$99\%$$98\%$Deep Learning$19511771412098\%$$99\%$$98\%$$98\%$TP: True Positive; TN: True Negative; FP: False Positive; FN: False Negative Figure 2 presents an analysis of the importance of variables in the Random Forest Classification algorithm. The importance of the variables revealed that gestational age, newborn weight, newborn sex, preeclampsia, placenta abruption, parity, anemia, and delivery method were considered to be weighted factors.
Fig. 2Random Forest Classification Feature Importance
## Discussion
Despite rapid technological advances, under-five deaths among children remain high. A significant proportion of these deaths worldwide are due to BA. Several studies have been conducted using traditional statistical analysis techniques to identify risk factors for BA. For example, a meta-analysis conducted by Desalew et al. found that maternal illiteracy, prepartum hemorrhage, cesarean section, instrumental delivery, duration of labor, pregnancy-related hypertension, induction of labor, parity, low birth weight, preterm birth, non-cephalic delivery, and meconium staining were significantly associated with BA [14]. Our study was conducted to identify the various factors leading to BA in neonates delivered in a hospital in Bandar Abbas, Iran. According to the findings using the ML approach, Random Forest Classification was found to be the best model to predict BA with an AUC and an accuracy of 0.99. The analysis of the importance of the variables showed that maternal chronic hypertension, maternal anemia, diabetes, drug addiction, gestational age, newborn weight, newborn sex, preeclampsia, placenta abruption, parity, IUGR, meconium amniotic fluid, mal-presentation, and delivery method were considered to be the weighted factors.
Sociodemographic factors were not associated with BA. Among maternal comorbidities, chronic hypertension and diabetes were found to be correlated with BA. Hypertension can lead to a reduction in blood flow and thus asphyxia [15], while diabetes causes intrapartum hypoxia by developing placenta insufficiency [16]. Anemia was also found to be a risk factor for BA, as also observed in previous studies [17, 18]. Maternal anemia is a common pregnancy problem that disrupts maternal and fetal oxygen transport. The disorder may cause fetal hypoxia inside the womb, resulting in BA [19].
Another factor linked to BA was drug addiction. Drug addiction was demonstrated in the current study by declarations from mothers themselves. The actual number of addicts is always several times greater than the number of those identified; however, detecting addicted women is further complicated by their proclivity to conceal and deny the problem. Infants born to addicted mothers are more likely to have prematurity, low birth weight, and IUGR, all of which can contribute to BA [20]. We have found a significant association between gestational age and risk for BA. Preterm birth was found to be one of the most important risk factors for BA, as reported in previous studies [21, 22]. This could be due to the fact that preterm infants face multiple morbidities, including organ system, immaturity, and especially lung immaturity, which leads to respiratory failure [23]. However, some studies have shown that BA increases with gestational age [24, 25]. According to our findings, newborn weight was associated with BA. Low birth weight newborns were at higher risk of developing BA. A potential confounding factor for this could be the fact that mothers of low birth weight babies are often associated with complications such as maternal hypertension and diabetes that occur before conception or before birth [26]. Indeed, many LBW neonates are more likely to be preterm, unable to produce sufficient surfactant, and prone to multiple morbidities, including organ system immaturity, inability to initiate breathing, challenges with cardiopulmonary transition, and eventually developing BA. Fetuses with IUGR who experience growth restriction inside the uterus do not reach their full growth potential for a given gestational age and are at an increased risk of perinatal mortality and morbidity. In IUGR, the reduced rate of fetal growth is essentially an adaptation to an unfavorable intrauterine environment, and it can result in long-term changes in metabolism, growth, and development [27]. Fetuses with IUGR who have intrauterine hypoxia are more vulnerable to asphyxia. BA was observed in $34.4\%$ of IUGR neonates in the clinical setting [28].
Our study showed that parity is associated with BA. The incidence of BA was higher in primiparous mothers. This is consistent with previous studies [29, 30]. Primiparous mothers are more likely to be younger, and they are more likely to have mal-presentations and prolonged obstructed labor. As a result, BA is expected to be more common in these women than in multipara women [31].
Preeclampsia is significantly associated with an increased risk of BA. The finding is in agreement with the evidence [32, 33]. This may be due to the reduction in blood, nutrient, and oxygen supply to the fetus, which may increase the risk of restriction of intrauterine development leading to BA [32].
Placenta abruption was also found to be associated with BA in our study, which was in contrast to previous studies [34]. The association of placenta abruption with BA can be explained by the fact that blood flows from the placenta to the fetus is restricted, leading to hypoxemia and thus asphyxia or stillbirth if maternal transfusion is delayed at the time of delivery [32].
It has long been known that non-cephalic fetuses are at greater risk during the birth process, including asphyxia, birth trauma, and death. This may be because non-cephalic fetuses are more likely to have other problems, such as cord prolapse and head entrapment, that predispose them to BA [35].
Newborns delivered via cesarean section and assisted vaginal birth had a higher rate of BA than those delivered via spontaneous vaginal delivery. This finding is consistent with previous research [36]. This is because either most mothers arrived late due to labor complications or the decision to have a cesarean section was delayed, increasing the burden of BA [37]. Another possibility is that the fetal chest is pressed as the newborns pass through the birth canal, causing secretion to be evacuated. This reduces the likelihood of developing BA, but this physiological benefit is not seen in cesarean section deliveries [38]. Furthermore, both vacuum and forceps extraction exert pressure on the newborn’s brain, which may cause the brain to bleed on the cranium, which contributes to intracranial hemorrhage and BA [39]. This finding suggests that interventions should be carefully evaluated and decided upon during intrapartum care to reduce unnecessary indications for an assisted vaginal birth and cesarean section to reduce the magnitude of BA.
In terms of newborn sex, our findings show that male infants are more likely to develop BA. This is consistent with previous research [40]. Our findings support previous findings of an association between congenital malformation and BA [41]. Although CNS anomalies might be expected to be associated with BA, the presence of other non-CNS birth defects raises important questions about the etiology of BA in these infants.
In line with several previous studies [41, 42], meconium-stained amniotic fluid was associated with BA. Grade III or IV meconium staining has been considered an indicator of a prolonged or severe episode of asphyxia [43]. One possible reason for this could be the inhalation of meconium-stained amniotic fluid, which causes irritation and inflammation of lung tissue or can obstruct the airways, leading to hypoxia and asphyxia at birth. In healthy, well-oxygenated fetuses, this diluted meconium is readily expelled from the lungs by normal physiologic mechanisms, but in a few cases, a meconium aspiration syndrome occurs [44].
The strength of our study is that we used a high-quality registration system in accordance with birth records. We studied both BA after vaginal birth and after cesarean section. We also examined a wide range of clinical factors associated with BA that may not be easily found in registries. Our study was retrospective, which is another limitation. The database did not allow us to determine the exact timing of the different events during pregnancy. For some variables, such as body mass index and weight gain during pregnancy, there was a lack of other data that might influence BA.
## Conclusion
BA can be predicted using a machine learning model. Random Forest Classification was found to be an accurate algorithm to predict BA. Maternal chronic hypertension, maternal anemia, diabetes, drug addiction, gestational age, newborn weight, newborn sex, IUGR, preeclampsia, placenta abruption, parity, meconium amniotic fluid, mal-presentation, and delivery method are risk factors of BA. More research should be done to analyze appropriate variables and prepare big data to determine the best model.
## References
1. Hakobyan M, Dijkman KP, Laroche S. **Outcome of infants with therapeutic hypothermia after Perinatal Asphyxia and Early-Onset Sepsis**. *Neonatology* (2019.0) **115** 127-33. DOI: 10.1159/000493358
2. Lawn JE, Cousens S, Zupan J. **4 million neonatal deaths: when? Where? Why?**. *Lancet* (2005.0) **365** 891-900. DOI: 10.1016/S0140-6736(05)71048-5
3. 3.International Statistical Classification of Diseases and Related Health Problems (ICD). ICD-10 Version. 2010. Available at [http://www.who.int/classifications/icd/icdonlineversions/en
4. 4.Gillam-Krakauer M, Gowen CW Jr, Birth Asphyxia. [Updated 2021 Aug 27]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK430782/
5. 5.Guidelines on Basic Newborn Resuscitation. Geneva:World Health Organization; 2012.
6. 6.WHO. Global health observatory data repository world health organization. 2015, August 2019, http://apps.who.int/ghodata/
7. Wardlaw T, You D, Hug L, Amouzou A, Newby H. **UNICEF Report: enormous progress in child survival but greater focus on newborns urgently needed**. *Reprod Health* (2014.0) **11** 82. DOI: 10.1186/1742-4755-11-82
8. Hadley TD, Pettit RW, Malik T, Khoei AA, Salihu HM. **Artificial Intelligence in Global Health -A Framework and Strategy for Adoption and sustainability**. *Int J MCH AIDS* (2020.0) **9** 121-7. DOI: 10.21106/ijma.296
9. 9.Sheikhtaheri A, Zarkesh MR, Moradi R, Kermani F. Prediction of neonatal deaths in NICUs: development and validation of machine learning models. BMC Med Inform Decis Mak. 2021;21(1):131. 10.1186/s12911-021-01497-8. Published 2021 Apr 19.
10. Ramakrishnan R, Rao S, He JR. **Perinatal health predictors using artificial intelligence: a review**. *Womens Health (Lond)* (2021.0) **17** 17455065211046132. DOI: 10.1177/17455065211046132
11. Mboya IB, Mahande MJ, Mohammed M, Obure J, Mwambi HG. **Prediction of perinatal death using machine learning models: a birth registry-based cohort study in northern Tanzania**. *BMJ Open* (2020.0) **10** e040132. DOI: 10.1136/bmjopen-2020-040132
12. Shin D, Lee KJ, Adeluwa T, Hur J. **Machine learning-based predictive modeling of Postpartum Depression**. *J Clin Med* (2020.0) **9** 2899. DOI: 10.3390/jcm9092899
13. Farashah M, Etebarian A, Azmi R, Dastjerdi R. **An analytics model for TelecoVAS customers’ basket clustering using ensemble learning approach**. *J Big Data* (2020.0) **8** 1-24
14. Desalew A, Semahgn A, Tesfaye G. **Determinants of birth asphyxia among newborns in Ethiopia: a systematic review and meta-analysis**. *Int J Health Sci (Qassim)* (2020.0) **14** 35-47. PMID: 32082102
15. 15.Magee LA, von Dadelszen P, Peter V, William S, Matthews M. he FIGO Textbook of Pregnancy Hypertension: An evidence-based guide to monitoring, prevention and management. The Global Library of Women’s Medicine, London. “Retrieved Auguest 19, 2022, from https://www.glowm.com/resource_type/resource/textbook/title/the-figo-textbook-of-pregnancy-hypertension/resource_doc/2768 “2016.
16. Mimouni F, Miodovnik M, Siddiqi TA, Khoury J, Tsang RC. **Perinatal asphyxia in infants of insulin-dependent diabetic mothers**. *J Pediatr* (1988.0) **113** 345-53. DOI: 10.1016/s0022-3476(88)80282-8
17. Majeed R, Memon Y, Majeed F, Shaikh NP, Rajar UD. **Risk factors of birth asphyxia**. *J Ayub Med Coll Abbottabad* (2007.0) **19** 67-71. PMID: 18444595
18. Rachatapantanakorn O, Tongkumchum P, Chaisuksant Y. **Factors associated with birth asphyxia in Pattani Hospital, Thailand**. *Songklanagarind Med J* (2010.0) **23** 17-27
19. Kalteren WS, Ter Horst HJ, den Heijer AE, de Vetten L, Kooi EMW, Bos AF. **Perinatal Anemia is Associated with neonatal and neurodevelopmental outcomes in infants with moderate to severe perinatal asphyxia**. *Neonatology* (2018.0) **114** 315-22. DOI: 10.1159/000490369
20. Vucinovic M, Roje D, Vucinovic Z, Capkun V, Bucat M, Banovic I. **Maternal and neonatal effects of substance abuse during pregnancy: our ten-year experience**. *Yonsei Med J* (2008.0) **49** 705-13. DOI: 10.3349/ymj.2008.49.5.705
21. Nayeri F, Shariat M, Dalili H, Bani Adam L, Zareh Mehrjerdi F, Shakeri A. **Perinatal risk factors for neonatal asphyxia in Vali-e-Asr hospital, Tehran-Iran**. *Iran J Reprod Med* (2012.0) **10** 137-40. PMID: 25242987
22. Aslam HM, Saleem S, Afzal R. **Risk factors of birth asphyxia**. *Ital J Pediatr* (2014.0) **40** 94. DOI: 10.1186/s13052-014-0094-2
23. Onyearugha CN, Ugboma HA. **Fetal outcome of antepartum and intrapartum eclampsia in Aba, southeastern Nigeria**. *Trop Doct* (2012.0) **42** 129-32. DOI: 10.1258/td.2012.110206
24. Berhe YZ, Kebedom AG, Gebregziabher L. **Risk factors of Birth Asphyxia among Neonates born in Public Hospitals of Tigray, Northern Ethiopia**. *Pediatr Health Med Ther* (2020.0) **11** 13-20. DOI: 10.2147/PHMT.S231290
25. Sadeghnia A, Mohammadpoor S. **The investigation of rate of Birth Asphyxia and its relationship with Delivery Mode at Shahid Beheshti Hospital of Isfahan during 2013, 2014, and 2015**. *Int J Prev Med* (2019.0) **10** 23. DOI: 10.4103/ijpvm.IJPVM_383_16
26. 26.Baker PN, Campbell S, Lees C. Obstetrics By Ten Teachers. 2006, Hodder Arnold Publishers, 338 Euston road, London, NW1 3Bh
27. Alexander BT. **Placental insufficiency leads to development of hypertension in growth-restricted offspring**. *Hypertension* (2003.0) **41** 457-62. DOI: 10.1161/01.HYP.0000053448.95913.3D
28. Longo S, Bollani L, Decembrino L, Di Comite A, Angelini M, Stronati M. **Short-term and long-term sequelae in intrauterine growth retardation (IUGR)**. *J Matern Fetal Neonatal Med* (2013.0) **26** 222-5. DOI: 10.3109/14767058.2012.715006
29. 29.Nadeem G, Rehman A, Bashir H. Risk Factors Associated With Birth Asphyxia in Term Newborns at a Tertiary Care Hospital of Multan, Pakistan. Cureus. 2021;13(10):e18759. 10.7759/cureus.18759. Published 2021 Oct 13.
30. Ellis M, Manandhar N, Manandhar DS, Costello AM. **Risk factors for neonatal encephalopathy in Kathmandu, Nepal, a developing country: unmatched case-control study**. *BMJ* (2000.0) **320** 1229-36. DOI: 10.1136/bmj.320.7244.1229
31. Woday A, Muluneh A, St Denis C. **Birth asphyxia and its associated factors among newborns in public hospital, northeast Amhara, Ethiopia**. *PLoS ONE* (2019.0) **14** e0226891. DOI: 10.1371/journal.pone.0226891
32. 32.Dassah ET, Odoi AT, Opoku BK. Stillbirths and very low Apgar scores among vaginal births in a tertiary hospital in Ghana: a retrospective cross-sectional analysis. BMC Pregnancy Childbirth. 2014;14:289. 10.1186/1471-2393-14-289. Published 2014 Aug 28.
33. Ibrahim MH, Asmaa MN. **Perinatal factors preceding neonatal hypoxic-ischemic encephalopathy in el-minia locality**. *Gynecol Obstet* (2016.0) **6** 403-90. DOI: 10.4172/2161-0932.1000403
34. 34.Pitsawong C, Panichkul P. Risk factors associated with birth asphyxia in Phramongkutklao Hospital. Thai J Obstet Gynaecol. 2012;19(4):165–71.
35. 35.Sunny AK, Paudel P, Tiwari J et al. A multicenter study of incidence, risk factors and outcomes of babies with birth asphyxia in Nepal. BMC Pediatr. 2021;21(1):394. Published 2021 Sep 10. doi:10.1186/s12887-021-02858-y
36. Bedie NA, Wodajo LT, Mengesha ST. **Magnitude and determinants of birth asphyxia: unmatched case control study Assela Referral Teaching Hospital, Arsi Zone, Ethiopia**. *Glob J Reprod Med* (2019.0) **7** 22-9
37. Harrison MS, Goldenberg RL. **Cesarean section in sub-saharan Africa**. *Matern Health Neonatol Perinatol* (2016.0) **2** 6. DOI: 10.1186/s40748-016-0033-x
38. Ferraz A, Nunes F, Resende C, Almeida MC, Taborda A. **Complicaciones neonatales a corto plazo de los partos por ventosa. Estudio caso-control [Short-term neonatal outcomes of vacuum-assisted delivery. A case-control study]**. *An Pediatr (Engl Ed)* (2019.0) **91** 378-85. DOI: 10.1016/j.anpedi.2018.11.016
39. Kune G, Oljira H, Wakgari N, Zerihun E, Aboma M. **Determinants of birth asphyxia among newborns delivered in public hospitals of West Shoa Zone, Central Ethiopia: a case-control study**. *PLoS ONE* (2021.0) **16** e0248504. DOI: 10.1371/journal.pone.0248504
40. Mohamed MA, Aly H. **Impact of race on male predisposition to birth asphyxia**. *J Perinatol* (2014.0) **34** 449-52. DOI: 10.1038/jp.2014.27
41. 41.Kruse AY, Ho BT, Phuong CN, Stensballe LG, Greisen G, Pedersen FK. Prematurity, asphyxia and congenital malformations underrepresented among neonates in a tertiary pediatric hospital in Vietnam. BMC Pediatr. 2012;12:199. 10.1186/1471-2431-12-199. Published 2012 Dec 29.
42. Chiabi A, Nguefack S, Mah E. **Risk factors for birth asphyxia in an urban health facility in cameroon**. *Iran J Child Neurol* (2013.0) **7** 46-54. PMID: 24665306
43. 43.Trimmer KJ, Gilstrap LC 3. Meconiumcrit” and birth asphyxia. Am J Obstet Gynecol. 1991;165(4 Pt 1):1010–3. 10.1016/0002-9378(91)90460-9
44. 44.Shekari M, Jahromi MS, Ranjbar A, Mehrnoush V, Darsareh F, Roozbeh N. The incidence and risk factors of meconium amniotic fluid in singleton pregnancies: an experience of a tertiary hospital in Iran. BMC Pregnancy Childbirth. 2022 Dec 12;22(1):930. 10.1186/s12884-022-05285-8
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---
title: 'Identifying positive and negative deviants and factors associated with healthy
dietary practices among young schoolchildren in Nepal: a mixed methods study'
authors:
- Prasant Vikram Shahi
- Rachana Manandhar Shrestha
- Pepijn Schreinemachers
- Akira Shibanuma
- Junko Kiriya
- Ken Ing Cherng Ong
- Masamine Jimba
journal: BMC Nutrition
year: 2023
pmcid: PMC9993389
doi: 10.1186/s40795-023-00700-5
license: CC BY 4.0
---
# Identifying positive and negative deviants and factors associated with healthy dietary practices among young schoolchildren in Nepal: a mixed methods study
## Abstract
### Background
School-based interventions have been implemented in resource-limited settings to promote healthy dietary habits, but their sustainability remains a challenge. This study identified positive deviants (PDs) and negative deviants (NDs) from the control and treatment groups in a nutrition-sensitive agricultural intervention in Nepal to identify factors associated with healthy dietary practices.
### Methods
This is an explanatory mixed methods study. Quantitative data come from the endline survey of a cluster randomized controlled trial of a school and home garden intervention in Nepal. Data were analyzed from 332 and 317 schoolchildren (grades 4 and 5) in the control and treatment group, respectively. From the control group, PDs were identified as schoolchildren with a minimum dietary diversity score (DDS) ≥ 4 and coming from low wealth index households. From the treatment group, NDs were identified as schoolchildren with a DDS < 4 and coming from high wealth index households. Logistic regression analyses were conducted to identify factors associated with PDs and NDs. Qualitative data were collected through in-depth phone interviews with nine pairs of parents and schoolchildren in each PD and ND group. Qualitative data were analyzed thematically and integrated with quantitative data in the analysis.
### Results
Twenty-three schoolchildren were identified as PDs, and 73 schoolchildren as NDs. Schoolchildren eating more frequently a day (AOR = 2.25; $95\%$ CI:1.07–5.68) and whose parents had a higher agricultural knowledge level (AOR = 1.62; $95\%$ CI:1.11–2.34) were more likely to be PDs. On the other hand, schoolchildren who consumed diverse types of vegetables (AOR = 0.56; $95\%$ CI: 0.38–0.81), whose parents had higher vegetable preference (AOR = 0.72; $95\%$ CI: 0.53–0.97) and bought food more often (AOR = 0.71; $95\%$ CI: 0.56–0.88) were less likely to be NDs. Yet, schoolchildren from households with a grandmother (AOR = 1.98; $95\%$ CI: 1.03–3.81) were more likely to be NDs. Integrated results identified four themes that influenced schoolchildren’s DDS: the availability of diverse food, the involvement of children in meal preparation, parental procedural knowledge, and the grandmother’s presence.
### Conclusion
Healthy dietary habit can be promoted among schoolchildren in Nepal by encouraging parents to involve their children in meal preparation and increasing the awareness of family members.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40795-023-00700-5.
## Background
A lack of diet diversity is a major cause of malnutrition [1] in children, thereby affecting their nutritional status [2]. Thus, consuming diverse foods ensures the attainment of balanced nutritional requirements [3, 4]. However, consuming less diverse foods may increase the probability of malnutrition, even leading to cognitive impairment [5].
Cultivating healthy dietary habits during early childhood is crucial [6, 7]. School-based interventions can have a lifelong impact on dietary behavior [8]. School gardens improve children’s knowledge of healthy eating, help identify healthy food, and improve their preference for fruits and vegetables [9–11]. Moreover, home gardens are associated with increased dietary diversity [12, 13].
In Nepal, the World Vegetable Center designed a project entitled, “Nudging children toward healthier food choices: An experiment combining school and home gardens” (hereafter “school and home garden project”). The project taught schoolchildren about gardening and nutrition through a school garden while their parents were trained in home vegetable garden and nutrition [14]. It encouraged healthy eating practices among schoolchildren, providing a positive environment at school and at home. An impact evaluation of the project, using a cluster randomized controlled trial design, showed that parents increased their food and nutrition knowledge, their liking for vegetables and their productivity of their home garden while schoolchildren improved their liking for vegetables and increased their vegetable consumption by 15–$26\%$ depending on the season [14].
The project focused on the frequency of vegetable consumption as the main outcome. Most of children already consumed vegetables, and dietary diversity score (DDS) data were therefore thought not to be sensitive enough to measure an increase in vegetable consumption [14]. The DDS of schoolchildren, defined as consuming different food groups [15], could be further improved. The intervention was implemented as a pilot and some schools may have discontinued the school garden after the project ended while others may have continued even without support [16]. Applying locally-available approaches can efficiently bring positive and sustainable changes to the community [17]. There might be positive deviants (PDs) with unusual behavior and strategies in any community, showing outstanding results relative to other community members sharing similar resources and constraints [17–19].
Deviants may also be negative. Negative deviants (NDs) do not imply reciprocal PDs. NDs (individuals or groups) cannot utilize or benefit from a specific program while others in the community do benefit. Factors associated with being NDs help identify non-programmatic components that hinder participants from achieving a positive outcome [20]. Identifying the factors associated with PDs and NDs may help develop an effective and more sustainable program, contributing to children’s nutritional status in resource-limited settings. Thus, this study aimed to (i) identify PDs among those households did not receive support and NDs among those who did receive support from the project, (ii) determine the factors associated with being PDs and NDs, and (iii) identify factors associated with healthy dietary practices.
## Study design and study population
This study employed an explanatory mixed methods design. Regarding the quantitative analysis, the study employed secondary data from the endline survey data of the project, collected in June 2019. However, some household and demographic data missing in the endline survey, such as household assets, education, and gender, were extracted from the baseline data (collected in June 2018). Additional file 1 presents the project outline [14].
The population for this study comprised grade 4 and 5 schoolchildren and their parents from Shindhupalchok, Nepal who were involved in the “school and home garden project”. The exclusion criteria for the analysis were schoolchildren who did not complete the endline food logbook, did not have household asset information, lived in an orphanage, and had no parent respondents. Moreover, three students above 14 years old were excluded from the control group to make groups more homogeneous. The flow diagram (Fig. 1. Participant selection flow diagram) shows the complete participant selection process. Finally, the sample size was 332 and 317 schoolchildren in the control and treatment group, respectively (Fig. 1).Fig. 1Participant selection flow diagram For the qualitative part, primary data were collected through in-depth phone interviews of nine pairs of parents and children in each PD and ND group in October 2020. The exclusion criteria were PD and ND who were ill, whose contact was changed, and refused to participate. Additional file 2 presents the in-depth-interview procedure. Semi-structured, open-ended in-depth interview guidelines were prepared (see additional file 3).
## Variables
Being PD or ND was the outcome variable in this study. This was identified based on two criteria: DDS and wealth index (Fig. 2. Analysis framework). DDS is a proxy for the nutritional adequacy of an individual’s diet [3]. The household wealth index was chosen because socioeconomic factors are positively associated with DDS [21]. PDs (from the control group) were schoolchildren belonging to household with a comparatively low wealth index but nevertheless had, at least, a minimum DDS (≥ 4 DDS). Other children from the same group were defined as non-PDs. However, NDs (from the treatment group) were schoolchildren with a comparatively high wealth index but had nevertheless a lower than the minimum DDS. Other children from the same group were defined as non-NDs (Fig. 2).Fig. 2Analysis framework Endline DDS was used for analyzing individual DDS. For reliability, we checked the correlation between endline DDS and mean 3-month DDS for each student. The endline DDS correlated strongly (ρ = 0.7) with the 3-month-logbook-mean-DDS. The dataset had 16 food group categories [15]. They were recategorized into seven categories. The minimum DDS was calculated based on the food category consumed by the schoolchildren. The minimum DDS for schoolchildren was the consumption of at least four of seven food categories in a 24-h dietary recall [22]. The seven food groups are (i) grains, roots, and tubers; (ii) legumes and nuts; (iii) dairy products (milk, yogurt, cheese); (iv) eggs; (v) vitamin-A-rich fruits and vegetables; (vi) other fruits and vegetables; and (vii) flesh foods (meat, fish, poultry and liver/organ meats) [22].
The wealth index was calculated using principal component analysis [23] on the household asset variables. The asset variables were recoded into binary variables. After running the analysis, the second factor was selected as it had a high eigenvalue, a minimal difference (0.3) from the first factor’s eigenvalue, and had positive factor loadings in all the asset variables. The score of the second factor was divided into wealth tercile, namely, low, medium, and high. [ 23].
The independent variables were child characteristics, parent characteristics, household characteristics, and community-level variables. Additional file 1 describes the questionnaire. The preference, knowledge, and practice variables were computed as composite variables from their respective different measures (additional file 1). These composite variables were centered and standardized before treatment in the logistic regression to obtain a standardized logistic regression coefficient [24]. Children’s ethnicity was categorized into three major ethnic groups: Brahaman/Chhetri, Dalits, and Adivasi/Janajatis [25].
## Data analysis
The data analysis comprised two parts: quantitative and qualitative analysis (Fig. 2. Analysis framework).
## Quantitative data
Descriptive statistics were used to summarize the characteristics of the schoolchildren, parents, and their households in the control and treatment group. Food consumption was compared between the PDs and non-PDs and NDs and non-NDs. The difference in proportions (food consumed) was measured using the chi-square (χ2) test. Simple and multiple (Firth’s) logistic regression analyses were performed to identify the association between dependent and independent variables. Firth’s (penalized) logistic regression was performed due to the small number of the events (PD and ND) [26–28]. Independent variables for the multiple logistic regression model were selected based on literature review [14, 29, 30]. First, we examined the correlation between continuous variables for multicollinearity. Variables were excluded if the correlation coefficient was higher than 0.7 [31]. The variance inflation factor was less than 2, suggesting that multicollinearity was not a problem [32]. Statistical significance was set at $p \leq 0.05.$ *Statistical analysis* was performed using RStudio 1.3.1093 [2020] [33] and Stata 13.1 (StataCorp, College Station, TX, United States). This study followed the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guideline.
## Qualitative data
The interview recordings were transcribed verbatim by the research assistants. All transcripts were read thoroughly and coded using deductive and inductive approaches. The researcher and the research assistant performed the coding and employed Microsoft Excel for data management, merging similar codes into themes. The consolidated criteria for reporting qualitative research (COREQ) were followed for this study.
Finally, quantitative and qualitative results of both PD and ND groups were compared and interpreted.
## Quantitative results
In the control group, the mean age of the schoolchildren was 10.4 (standard deviation [SD] 1.5) years, and the mean age of the respondent (parents) was 36.1 (SD 7.0) years. Of the respondents, $87\%$ were mothers. Of the 332 schoolchildren, approximately $23\%$ had the minimum DDS. Based on pre-determined criteria, 23 schoolchildren were PDs. The mean age of PDs and non-PDs were 10.4 (SD 1.5) years and 10.5 (SD 2.1) years, respectively.
The schoolchildren in the treatment group had an average age of 10.3 (SD 1.5) years, while parents’ mean age was 36.3 (SD 7.2) years. Most parents ($92\%$) were mothers. Among the 317 schoolchildren, approximately $26\%$ had a minimum DDS. After applying the criteria, 73 participants were identified as NDs. The mean age of NDs and non-NDs were 10.1 (SD 1.4) years and 10.4 (SD 1.5) years, respectively.
Table 1 shows the characteristics of schoolchildren, parents and household in the control group (stratified by PDs and non-PDs) and in the treatment group (stratified by NDs and non-NDs). For control group, girls made up $56\%$ of the sample schoolchildren. Moreover, approximately $55\%$ were of Adivasi/Janajatis ethnicity, followed by Brahmin/Chhetri ($35\%$) and Dalits. More than half of the parents ($56\%$) could not read or write; most had farming as their major occupation. Most households ($87\%$) produced vegetables in their home gardens with mothers being the primary caretaker of the home garden in $87\%$ of the cases. Table 1Characteristics of the schoolchildren, parents, and household, in the control group and treatment group (stratified by deviants)±VariablesCategoryControl group ($$n = 332$$)Treatment group ($$n = 317$$)Positive Deviant ($$n = 23$$) *Non-positive Deviant ($$n = 309$$)Total n (prop.)Negative deviants ($$n = 73$$)Non-negative Deviants ($$n = 244$$)Total n (prop.)SexBoy0.390.44146 (0.44)0.480.44143 (0.45)Girl0.610.56186 (0.56)0.520.56174 (0.55)EthnicityBrahaman/Chhetri0.170.36117 (0.35)0.290.2993 (0.29)Dalits0.130.1033 (0.10)0.040.0924 (0.08)Adivasi/Janajatis0.700.54182 (0.55)0.670.62200 (0.63)Parents’ literacyIlliterate0.520.56186 (0.56)0.490.61184 (0.58)Literate†0.480.44146 (0.44)0.510.39133 (0.42)Parents’ OccupationFarmer0.780.78259 (0.78)0.790.83261 (0.82)Not farmer0.220.2273 (0.22)0.210.1756 (0.18)Grandmother in householdYes0.260.2172 (0.22)0.360.2484 (0.27)Decision-maker—for what to cookMother0.830.87288 (0.87)0.890.91288 (0.91)Other family member0.170.1344 (0.13)0.110.0929 (0.09)Produced vegetables in home gardenYes0.780.88289 (0.87)0.990.97309 (0.97)Responsible for managing home garden‡Mother0.940.87252 (0.87)0.860.87269 (0.87)Other family member0.060.1337 (0.13)0.140.1340 (0.13)Father helping in home garden‡Yes0.670.54158 (0.55)0.500.46146 (0.47)Children assisting in home garden‡ Yes0.220.2162 (0.22)0.210.3189 (0.29)Money received for snacksYes0.350.34112 (0.34)0.270.2171 (0.22)prop Proportion±A base package of R-software was used to perform the chi-square test (Pearson’s). However, no statistical significance was found (for a comprehensive table, see supplementary table 1)†Literate: those who can read and write‡the total number of samples is 289 and 307 in the control and treatment groups, respectively*total number of PDs for variables (‡) is 18 For the treatment group, girls made up $55\%$ of the schoolchildren in the sample. More than half ($63\%$) were from Adivasi/Janajatis; approximately $8\%$ were from Dalits. About $58\%$ of parents could not read and write, and most were farmers ($82\%$). Vegetables were produced by $97\%$ of the households in home gardens, and mothers ($87\%$) were main person responsible for taking care of the home gardens.
Table 2 shows the frequency of the seven food groups consumed by PDs and non-PDs. The schoolchildren consumed food from “grains, roots, and tubers.” The least consumed food group was eggs, consumed by only $7.2\%$. The frequency of consuming “legumes and nuts,” dairy products, eggs, and flesh foods were different between PDs and non-PDs ($p \leq 0.05$). Compared with non-PDs, PDs were more likely to consume “legumes and nuts” (odds ratio [OR] = 8.62; $95\%$ confidence interval [CI]:1.14 – 64.95), dairy products (OR = 4.96; $95\%$ CI:1.94 to 12.66), eggs (OR = 5.71; $95\%$ CI:2.01 to 16.23), and flesh foods (OR = 3.02; $95\%$ CI:1.27 to 7.21).Table 2Food consumption (proportion of schoolchildren) by positive ($$n = 23$$) and non-positive deviants ($$n = 309$$) in the control group†Food groups Positive deviantsNon-positive deviantsOdds ratio$95\%$ CIGrains, roots, and tubers1.001.00-Legumes and nuts0.960.728.62*1.14, 64.95Dairy products0.350.104.96***1.94, 12.66Eggs0.260.065.71***2.01, 16.23Flesh foods0.610.343.02*1.27, 7.21Vitamin-A rich fruits and vegetables0.480.312.060.88, 4.85Other fruits and vegetables0.570.590.920.39, 2.16CI confidence interval†The epiR package [34] of R software was used to perform the chi-square test (Mantel–Haenszel) and odds ratio (Wald); significance* < 0.05** < 0.01*** < 0.001. For a comprehensive table, see supplementary table 2 Table 3 shows the seven food groups consumed by the NDs and non-NDs. In the treatment group, all schoolchildren consumed “grains, roots, and tubers.” Similar to the control group, eggs were the least consumed by the schoolchildren. The frequency of consuming “legumes and nuts” and “other fruits and vegetables” were significantly different between NDs and non-NDs. Relative to non-NDs, NDs were less likely to consume “legumes and nuts” (OR = 0.49; $95\%$ CI: 0.28—0.86) and dairy product (OR = 0.17; $95\%$ CI: 0.04 to 0.74). In contrast, NDs were more likely to consume “other fruits and vegetables” (OR = 1.87; $95\%$ CI:1.05 to 3.32).Table 3Food consumption (proportion of schoolchildren) by negative deviants ($$n = 73$$) and non-negative deviants ($$n = 244$$) in the treatment group†Food groups Negative deviantsNon-negative deviantsOdds ratio$95\%$ CIGrains, roots, and tubers1.001.00-Legumes and nuts0.600.750.49*0.28, 0.86Dairy products0.030.140.17*0.04, 0.74Eggs0.040.050.760.21, 2.75Flesh foods0.450.431.070.63, 1.82Vitamin-A rich fruits and vegetables0.300.370.740.42, 1.30Other fruits and vegetables0.730.591.87*1.05, 3.32CI confidence interval† The epiR package [34] of R software was used to perform (Mantel–Haenszel) chi-square test and (Wald) odds ratio; significance* < 0.05** < 0.01. For a comprehensive table, see supplementary table 3 Table 4 shows factors associated with being PDs (control group) and NDs (treatment group) as identified using simple and multiple (Firth’s) logistic regression. In simple logistic regression, number of vegetables and fruits grown was negatively, and parents’ agricultural knowledge was positively associated with being PDs. After controlling for other covariates, schoolchildren’s eating frequency was positively associated with being PDs. Those who ate more frequently in a day (adjusted odds ratio, AOR = 2.25; $95\%$ CI:1.07 to 5.68) and those whose parents had higher agricultural knowledge (AOR = 1.62; $95\%$ CI:1.11 to 2.34) were more likely to be PDs. Table 4Factors associated with being positive deviants (control group) and negative deviants (treatment group) among schoolchildrenExplanatory variablesControl group $$n = 332$$Treatment group $$n = 317$$OR$95\%$ CIAOR$95\%$CIOR$95\%$CIAOR$95\%$ CIChild characteristics Schoolgirl (Ref. schoolboy)1.240.53, 3.061.180.49, 2.950.860.51, 1.461.040.58, 1.87 Child < 10 years old (Ref. Child > 10 years old)0.860.30, 2.141.020.35, 2.751.170.67, 2.031.030.55, 1.92 Knowledge of food and nutrition0.910.60, 1.380.980.61, 1.561.010.77, 1.321.060.78, 1.43 Agricultural knowledge1.200.80, 1.751.150.76, 1.701.210.92, 1.581.240.93, 1.66 Vegetable preference0.940.62, 1.450.790.50, 1.260.870.66, 1.120.780.58, 1.04 Snack choice0.880.58,1.350.880.56, 1.360.840.65, 1.090.870.66, 1.15 Frequency of eating2.211.03, 5.772.25*1.07,5.680.980.72, 1.371.020.73, 1.46 Consuming diverse vegetables1.030.61, 1.720.990.56, 1.730.58**0.41, 0.810.56**0.38, 0.81Parent characteristics Age (years)1.030.97, 1.081.030.97, 1.090.990.96, 1.031.010.97, 1.05 Years of education0.910.79, 1.021.090.92, 1.271.060.25, 1.061.030.94, 1.12 Main occupation farming (Ref. Not farmer)1.020.39, 3.171.310.43, 4.470.840.44, 1.690.540.25, 1.19 Vegetable preference0.760.48, 1.180.690.41, 1.120.880.67, 1.140.72*0.53, 0.97 Food and nutrition Knowledge0.810.54, 1.220.740.48, 1.171.260.96, 1.671.350.99, 1.86 Agricultural Knowledge1.60*1.07, 2.251.62*1.11, 2.341.210.92, 1.581.110.83, 1.48Household characteristics Family members < 6 (Ref. family member > 5)0.740.31, 1.780.940.37, 2.480.810.48, 1.410.960.50, 1.85 Household having grandmother (Ref. No grandmother)1.300.45, 3.271.080.36, 2.971.77 *1.00, 3.101.98*1.03, 3.81 Food practices0.910.61, 1.401.280.79, 2.171.120.87, 1.471.080.79, 1.50 Number of vegetables and fruits produced0.80*0.65, 0.980.840.64, 1.070.930.81, 1.060.890.76, 1.05 Number of days vegetables bought per week1.180.98, 1.401.130.90, 1.420.840.70, 0.990.71**0.56, 0.88 Dalits (= 1) (Ref. Brahaman/Chhetri)2.830.53, 13.482.510.44, 13.590.490.11, 1.600.630.14, 2.19 Adivasi / Janajatis (= 1) (Ref. Brahaman/Chhetri)2.720.97,9.692.350.72, 9.291.110.63, 2.021.450.76, 2.83OR odds ratio, CI confidence interval, AOR adjusted odds ratio; The base package (glm) of Rsoftware was used to perform simple logistic regression, and the Logistf package [35] was used for firth logistic regression; significance* < 0.05** < 0.01 For the treatment group, the result of simple logistic regression shows that the consumption of diverse vegetables was negatively associated with being ND. However, the presence of grandmothers was positively associated with being ND. In the multiple (Firth’s) logistic regression analysis, after controlling for covariates, those schoolchildren who consumed diverse vegetables (AOR = 0.56; $95\%$ CI: 0.38 to 0.81), had higher vegetable preference among parents (AOR = 0.72; $95\%$ CI: 0.53 to 0.97), and had a higher number of days vegetables were bought per week (AOR = 0.71; $95\%$ CI: 0.56 to 0.88), were less likely to be ND. Schoolchildren with grandmothers at home were more likely to be ND (AOR = 1.98; $95\%$ CI: 1.03 to 3.81).
## Qualitative results
Among the parents interviewed, one parent in the PDs group and three in the NDs group were fathers, one person in the ND group was a grandmother, and all others were mothers. Further, four girls and five boys were interviewed from the PDs. Likewise, seven girls and two boys were interviewed among the NDs. All themes that emerged are compiled in additional file 4.
## Theme 1: Availability of diverse foods from home gardens and shops
Most interviewees (PDs and NDs parents) cultivated cereals and different kinds of vegetables. However, other food groups were either produced less or not produced at all. In addition, PDs can access food from markets, neighbors, or relatives. Some PD interviewees started a small shop, which increased the accessibility of diverse food groups:“Now I migrated down (near road) and had a small shop… I have kept all vegetables for selling… that is (for selling and consumption) why I also cultivate coriander, radish, carrot, garlic, potato…” — (PD8; mother, 31 years old) Willingness to buy or produce food also promotes food consumption. In addition to availability and accessibility, parents of PDs are willing to provide diverse foods for their children.
## Theme 2: Involving children in meal preparation
In rural areas in Nepal, the preparation of diverse food is time-consuming. ND interviewees expressed that the lack of adequate time was a barrier. Relative to parents of NDs, parents of PDs mentioned that their children contribute to the meal preparation, which may save parents’ time. Moreover, children could cook diverse foods:“Children cooked yesterday… they cooked green leafy and green beans, vegetables, rice, and porridge.” — ( PD4; mother, 36 years old) Further, parents of PDs also mentioned that, as their children are involved in the meal preparation, they have more liberty to choose their preferred vegetables and food and are therefore more likely to eat it.
## Theme 3: Decision-maker regarding the food to be cooked
In most communities, parents decide what food to prepare. In the interview, all NDs interviewees mentioned that parents or grandparents decide what to prepare. In contrast, PD interviewees indicated that children also take part in food decisions. “Sometimes, my mother [decides] what to cook… and, sometimes, we decide what to cook…” — (PDc7; girl, 14 years old, child)
## Theme 4: People’s knowledge of the benefits of diverse food
Most interviewees had some idea about the benefit of consuming diverse and healthy food, reflecting their declarative knowledge (knowledge of the content and things, for example, knowledge of green leafy vegetables helps to keep eyes healthy). However, an ND interviewee (grandmother) noted that she did not know about the benefits of consuming diverse foods:“I do not know what [comprises] healthy foods… and I have no idea about unhealthy foods… I heard that we must consume legumes and green leafy vegetables, but I do not know what it does…” — (ND9: grandmother, 70 years old).
## Theme 5: Child preference for various foods
Most ND interviewees indicated that children disliked some food groups. It was also mentioned that children were picky and asked for money to buy junk food. In contrast, PD schoolchildren were not as picky. Moreover, parents of PDs (contrast to NDs) mentioned that they employed different techniques to make their children healthy, which may reflect that parents of PDs have some procedural knowledge (knowledge of how to do things, for example, how to cook food or how to select healthier snacks):“He does not like beans; I advise him to eat beans… I must add potato with beans… he [does not] prefer beans… When I say it’s good to eat […]… he eats… he has some degree of preference only” — (PD2; mother, 33 years old) Moreover, PD interviewees consumed locally-available stinging nettles available throughout the year.
## Theme 6: Parents’ attitudes and behavior toward their children’s food habit
Parents of PDs indicated that they tried not to buy junk food for children (although they prefer it sometimes) or families. Moreover, parents tried to motivate children to buy healthy foods by inculcating healthy behavior;“We do not eat noodles… and we also never prepare noodles for them… I also do not eat noodles; […] I drink tea or milk… [not] noodles…” — (PD7; mother, 36 years old, mother) In contrast, parents of NDs did not show resistance to their children’s preference for junk food.
## Theme 7: Food and feeding culture in the community
People from a higher ethnic group, such as Brahmin/Chettri, do not consume pork or buffalo meat. However, a PD interviewee said that this culture was prevalent among the older generation. Moreover, interviewees mentioned that as the new generation learned about healthy diets, they became more caring for children than the older generation;“In the past, people did not care about children… they did not know what was good to feed their children… Now, even […] the village has changed; I think this [change] is because of the information they heard.” — ( PD2; mother, 33 years old)
## Integration of quantitative and qualitative results
The integration of quantitative and qualitative data generated four main findings. First, schoolchildren who consumed purchased foods in addition to homegrown foods had a higher DDS (Fig. 3. Availability of food groups improves dietary diversity). The quantitative findings showed that better parental agricultural knowledge and more frequent buying of vegetables were associated with a higher DDS in schoolchildren. The qualitative findings also confirmed that the PD interviewees can access food from their home garden as well as from the market. Fig. 3Availability of food groups improves dietary diversity Second, the involvement of children in meal preparation was associated with a higher DDS among schoolchildren (Fig. 4. Child involvement in meal preparation improves dietary diversity). The quantitative data also indicated that eating more frequently was associated with being PDs, and consuming diverse vegetables was negatively associated with being NDs. The qualitative data showed that lack of time for cooking was a major reason for not preparing diverse foods. Moreover, PD schoolchildren were more involved in meal preparation, which helped them prepare their preferred food or vegetables. Fig. 4Child involvement in meal preparation improves dietary diversity Third, more procedural knowledge about food and nutrition among parents was associated with a higher DDS among schoolchildren (Fig. 5. Parent’s procedural knowledge of food and nutrition, improves dietary diversity). The quantitative results showed that vegetable preference among parents was negatively associated with ND. The qualitative findings showed that parents of PDs had procedural knowledge. Moreover, interviewees mentioned that they used procedural knowledge, such as different child-counseling methods on eating home-based foods, and even introduced vegetables (stinging nettle) that are nutritious and freely available throughout the year. Fig. 5Parent’s procedural knowledge of food and nutrition, improves dietary diversity Fourth, the presence of a grandmother in the household is negatively associated with the consumption of diverse foods (Fig. 6. Influence of grandmother on schoolchildren’s dietary diversity). The quantitative results showed that schoolchildren with grandmothers at home were more likely to be NDs. The qualitative findings also showed that grandmothers might be comparatively poor in knowledge, decision-makers, and involved in the cooking. Furthermore, qualitative findings showed no better child feeding knowledge among the older generation than the present generation. Fig. 6Influence of grandmother on schoolchildren’s dietary diversity
## Discussion
This study identified 23 PD and 73 ND schoolchildren, and four major factors that impact the consumption of diverse food among the schoolchildren. Greater availability of food of multiple food groups, children’s participation in meal preparation, parents’ procedural knowledge of food and nutrition, and the presence of grandmother impacted the consumption of diverse food among the schoolchildren.
Less than $30\%$ of schoolchildren in this study had a minimum DDS in both the control and treatment group, which was less than the national data (above $40\%$) [36]. However, the national data show the lowest DDS in the mountain region [36], where our study site was situated. A majority of schoolchildren consumed starchy staple foods, which are similar throughout Nepal and countries such as Bangladesh, India, and Sri Lanka [21, 37, 38]. Moreover, NDs consumed more “other fruits and vegetables” than non-NDs, which might be due to the intervention though their overall DDS was still lower. From the food consumption patterns it shows that the schoolchildren consumed the basic diets consisting cereals and vegetables, which is similar to the consumption patterns among women in Tanzania [30].
The first integrated finding was that the increased availability of different food groups might be the reason for the improvement in DDS. This study showed that parental agricultural knowledge was associated with PD. Parents with good knowledge are likely to be involved in agriculture, thereby improving food security, increasing food diversity, and ultimately improving nutrition [29, 39]. A nationally representative study conducted in Nepal showed that agricultural diversity with different food groups was associated with children’s higher DDS among poorer households [40]. However, for a household with a small farm, self-produced food diversity cannot influence dietary diversity; accessibility to the market and purchasing or getting additional vegetables improve food diversity [30, 41]. Similar to the findings of Tanzania and Malawi [30, 41], this study found that schoolchildren whose households bought vegetables more frequently were more likely to have higher DDS. Moreover, the study has shown that parents of PDs were more willing to produce and buy vegetables.
The second integrated finding was the benefit of involving the child in meal preparation, which might improve DDS. A systematic review showed that children involved in meal preparation have more positive preferences, attitudes, and behavior toward healthy food [42]. Children involved in cooking have a higher preference for vegetables [43], leading to the consumption of diverse vegetables. In the Nepalese context, parents in a rural setting are usually busy with their day-to-day work. Therefore, in addition to improving preferences, attitudes, and behavior, children’s involvement in cooking could help prepare a higher frequency of meals. The increase in the number of meals can lead to a higher frequency of eating and improving schoolchildren’s dietary diversity, as found in the case of Ethiopia [44].
The procedural knowledge of parents was another important integrated finding that might increase schoolchildren’s DDS. The quantitative study showed that less vegetable preference among parents was associated with being NDs. The availability of different food groups might not be sufficient for the dietary consumption of diverse foods; accordingly, there should be a habit of consuming healthy foods. The literature has suggested that parents’ food habits majorly impact children’s food choices and eating behavior [45]. The indicator used (quantitative) to measure nutrition knowledge by the project were primarily comprised of declarative knowledge [14, 46]. The parents of PD interviewees mentioned that they used different techniques (considered as procedural knowledge), such as mixing food, not buying junk food for children, and teaching their children to consume nutritious food from an early age. The primary notion is that nutrition knowledge (declarative) is necessary but not sufficient to change nutritional behavior. More importantly, there should be additional procedural knowledge to change the behavior [46].
The final integrated finding was that grandmothers might influence the consumption of diverse foods. The quantitative data showed that schoolchildren with grandmothers at home were more likely to be NDs. The qualitative data also showed that even though grandmothers are not primary caregivers, they are involved in decision-making and food preparation. Moreover, they are more rigid with old cultures and customs. Children living with three-generational family members have a significant influence on their eating behavior [47]. Socio-cultural norms greatly impact grandparents’ feeding habits [48]. These norms were passed from grandparents to parents and from parents to children. A study conducted in Nepal on infant- and young-child-feeding found that households with grandmothers having correct nutrition knowledge were fed well relative to those with incorrect nutrition knowledge [49].
## Strength and limitations of the study
This study employed mixed methods and identified deviants and their behaviors, providing a better understanding of the dietary behaviors of the “school and home garden project” parents and schoolchildren. It can provide valuable information for future programs.
However, these results must be interpreted considering several limitations. Primarily, the study employed cross-sectional endline data, and causal inference cannot be interpreted between the outcome and determinants. The DDS was calculated based on self-reported 24-h recall consumption data; thus, the information may be subjected to day-to-day variability and social desirability bias. The project did not measure the food portion. Hence, to minimize the bias, we excluded food consumed in meager quantities, such as sesame seeds mixed in a pickle, milk tea, and ice cream (mostly made with powder in Nepal). Anthropometric and biomarker (for micronutrient) data of schoolchildren were not obtained, limiting the precise linkage of DDS and nutritional status of schoolchildren. Nonetheless, the study method to measure DDS has been widely used [50, 51].
Moreover, in Nepal, DDS varied among different age groups by different ecological regions and rurality [36]; this finding cannot be generalized to other areas. In the context of PD studies, generalizability could sometimes be a limitation; however, PD interventions are a problem-solving approach for a particular community [52].
## Conclusions
Schoolchildren’s involvement in meal preparation at home and raising awareness among family members is necessary to improve schoolchildren’s dietary diversity score (DDS). These findings could provide vital information for designing and implementing future research and programs, thus improving the DDS of schoolchildren in resource-limited settings. The findings were obtained by focusing on positive and negative deviants. Similar efforts may be applied to other health studies.
## Supplementary Information
Additional file 1. Additional file 2. Additional file 3. Additional file 4. Additional file 5:
## References
1. Zhao W, Yu K, Tan S, Zheng Y, Zhao A, Wang P, Zhang Y. **Dietary diversity scores: an indicator of micronutrient inadequacy instead of obesity for Chinese children**. *BMC Public Health* (2017.0) **17** 440. DOI: 10.1186/s12889-017-4381-x
2. Arimond M, Ruel MT. **Dietary diversity is associated with child nutritional status: evidence from 11 demographic and health surveys**. *J Nutr* (2004.0) **134** 2579-85. DOI: 10.1093/jn/134.10.2579
3. Steyn N, Nel J, Nantel G, Kennedy G, Labadarios D. **Food variety and dietary diversity scores in children: are they good indicators of dietary adequacy?**. *Public Health Nutr* (2006.0) **9** 644-650. DOI: 10.1079/phn2005912
4. Drescher LS, Thiele S, Mensink GBM. **A new index to measure healthy food diversity better reflects a healthy diet than traditional measures**. *J Nutr* (2007.0) **137** 647-651. DOI: 10.1093/jn/137.3.647
5. Black MM. **Micronutrient deficiencies and cognitive functioning**. *J Nutr* (2003.0) **133** 3927S-3931S. DOI: 10.1093/jn/133.11.3927S
6. Birch LL. **Development of food acceptance patterns in the first years of life**. *Proc Nutr Soc* (1998.0) **5** 617-624. DOI: 10.1079/pns19980090
7. Dittus KL, Hillers VN, Beerman KA. **Benefits and barriers to fruit and vegetable intake: relationship between attitudes and consumption**. *J Nutr Educ* (1995.0) **27** 120-126. DOI: 10.1016/S0022-3182(12)80802-8
8. 8.Isolade B. Improving health through schools: National and International strategies. Geneva: World Health Organization; 1999. Available from: https://www.who.int/publications/i/item/improving-health-through-schools-national-and-international-strategies
9. Berlin L, Norris K, Kolodinsky J, Nelson A. **The role of social cognitive theory in farm-to-school-related activities: Implications for child nutrition**. *J Sch Health* (2013.0) **83** 589-595. DOI: 10.1111/josh.12069
10. Parmer SM, Salisbury-Glennon J, Shannon D, Struempler B. **School gardens: an experiential learning approach for a nutrition education program to increase fruit and vegetable knowledge, preference, and consumption among second-grade students**. *J Nutr Educ Behav* (2009.0) **41** 212-7. DOI: 10.1016/j.jneb.2008.06.002
11. Schreinemachers P, Bhattarai DR, Subedi GD, Acharya TP, Chen HP, Yang RY, Kashichhawa NK, Dhungana U, Luther GC, Mecozzi M. **Impact of school gardens in Nepal: a cluster randomised controlled trial**. *J Dev Eff* (2017.0) **9** 329-43. DOI: 10.1080/19439342.2017.1311356
12. Rammohan A, Pritchard B, Dibley M. **Home gardens as a predictor of enhanced dietary diversity and food security in rural Myanmar**. *BMC Public Health* (2019.0) **19** 1145. DOI: 10.1186/s12889-019-7440-7
13. Cabalda AB, Rayco-Solon P, Solon JA, Solon FS. **Home gardening is associated with filipino preschool children’s dietary diversity**. *J Am Diet Assoc* (2011.0) **111** 711-715. DOI: 10.1016/j.jada.2011.02.005
14. 14.Schreinemachers P, Baliki G, Shrestha RM, Bhattarai DR, Gautam IP, Ghimire PL, Subedi BP, Brück T. Nudging children toward healthier food choices: an experiment combining school and home gardens. Glob Food Sec. 2020;26:100454.
15. 15.Kennedy G, Ballard T, Dop M. Guidelines for measuring household and individual dietary diversity. FAO; 2010. Available from: https://www.fao.org/3/i1983e/i1983e.pdf
16. Swerissen H, Crisp BR. **The sustainability of health promotion interventions for different levels of social organization**. *Health Promot Int* (2004.0) **19** 123-130. DOI: 10.1093/heapro/dah113
17. Marsh DR, Schroeder DG, Dearden KA, Sternin J, Sternin M. **The power of positive deviance**. *Br Med J* (2004.0) **329** 1177-1179. DOI: 10.1136/bmj.329.7475.1177
18. Marty L, Dubois C, Gaubard MS, Maidon A, Lesturgeon A, Gaigi H, Darmon N. **Higher nutritional quality at no additional cost among low-income households: Insights from food purchases of “positive deviants”**. *Am J Clin Nutr* (2015.0) **102** 190-198. DOI: 10.3945/ajcn.114.104380
19. Herington MJ, van de Fliert E. **Positive deviance in theory and practice: a conceptual review**. *Deviant Behav* (2018.0) **39** 664-678. DOI: 10.1080/01639625.2017.1286194
20. Shekar M, Habicht JP, Latham MC. **Use of positive-negative deviant analyses to improve programme targeting and services: example from the tamilnadu integrated nutrition project**. *Int J Epidemiol* (1992.0) **21** 707-713. DOI: 10.1093/ije/21.4.707
21. Ali NB, Tahsina T, Emdadul Hoque DM, Hasan MM, Iqbal A, Huda TM, El Arifeen S. **Association of food security and other socioeconomic factors with dietary diversity and nutritional statuses of children aged 6–59 months in rural Bangladesh**. *PLoS One* (2019.0) **14** 1-18. DOI: 10.1371/journal.pone.0221929
22. 22.World Health Organization. Indicators for assessing infant and young child feeding practices: Part 2 measurement. Geneva: World Health Organization;2010. Available from: https://www.who.int/publications/i/item/9789241599290.
23. 23.Hjelm L, Mathiassen A, Miller D, Wadhwa A. VAM Guidence Paper: Creation of a Wealth Index. World Food Program;2017. Available from: https://www.wfp.org/publications/creation-wealth-index-june-2017. Accessed 12 Nov 2019.
24. Menard S. **Six approaches to calculating standardized logistic regression coefficients**. *Am Stat* (2004.0) **58** 218-23. DOI: 10.1198/000313004X946
25. 25.Bennett L, Dahal DR, Govindasamy P. Caste, Ethnic and Regional Identity in Nepal: Further Analysis of the 2006 Nepal Demographic and Health Survey. Macro INternational Inc. Calverton, Maryland, USA. 2008. Available from: https://dhsprogram.com/publications/publication-fa58-further-analysis.cfm
26. Cole SR, Chu H, Greenland S. **Maximum likelihood, profile likelihood, and penalized likelihood: a primer**. *Am J Epidemiol* (2014.0) **17** 252-60. DOI: 10.1093/aje/kwt245
27. Wang X. **Firth logistic regression for rare variant association tests**. *Front Genet* (2014.0) **5** 187. DOI: 10.3389/fgene.2014.00187
28. Rahman MS, Sultana M. **Performance of Firth-and logF-type penalized methods in risk prediction for small or sparse binary data**. *BMC Med Res Methodol* (2017.0) **17** 33. DOI: 10.1186/s12874-017-0313-9
29. Fanzo J, Hunter D, Borelli T, Mattei F. **Diversifying food and diets: using agricultural biodiversity to improve nutrition and health**. *Diversifying Food and Diets: Using Agricultural Biodiversity to Improve Nutrition and Health* (2013.0). DOI: 10.4324/9780203127261
30. Keding GB, Msuya JM, Maass BL, Krawinkel MB. **Relating dietary diversity and food variety scores to vegetable production and socio-economic status of women in rural Tanzania**. *Food Secur* (2012.0) **4** 129-40. DOI: 10.1007/s12571-011-0163-y
31. Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G. **Collinearity: a review of methods to deal with it and a simulation study evaluating their performance**. *Ecography (Cop)* (2013.0) **36** 27-46. DOI: 10.1111/j.1600-0587.2012.07348.x
32. Akinwande MO, Dikko HG, Samson A. **Variance inflation factor: as a condition for the inclusion of suppressor variable(s) in regression analysis**. *Open J Stat* (2015.0) **05** 754-767. DOI: 10.4236/ojs.2015.57075
33. 33.RStudio Team. RStudio: Integrated Development Environment for R. Boston, MA; 2020.
34. 34.Stevenson M, Elmo N, Cord H, Jonathon M, Javier S, Ron T, Jeno R, Jim R-C, Paola S, Peter S, et al. epiR: Tools for the analysis of epidemiological data. 2020.
35. 35.Georg H, Meinhard P, Lena J. logistf: Firth’s Bias-Reduced Logistic Regression. 2020. Available from: https://cran.r-project.org/package=logistf
36. 36.Ministry of Health and Population, Nepal; New ERA; UNICEF, EU, USAID C. Nepal National Micronutrient Status Survey 2016. Kathmandu; 2018. Available from: https://www.unicef.org/nepal/sites/unicef.org.nepal/files/2018-08/NNMSS Report 2016.pdf.
37. Agrawal S, Kim R, Gausman J, Sharma S, Sankar R, Joe W, Subramanian SV. **Socio-economic patterning of food consumption and dietary diversity among Indian children: evidence from NFHS-4**. *Eur J Clin Nutr* (2019.0) **73** 1361-1372. DOI: 10.1038/s41430-019-0406-0
38. Sirasa F, Mitchell L, Harris N. **Dietary diversity and food intake of urban preschool children in North-Western Sri Lanka**. *Matern Child Nutr* (2020.0) **16** 13006. DOI: 10.1111/mcn.13006
39. 39.FAO, CFS, IFAD, WFP. Developing the Knowledge, Skills and Talent of Youth To Further Food Security and Nutrition. Rome; 2015. Available from: http://www.fao.org/3/a-i5024e.pdf
40. Mulmi P, Masters WA, Ghosh S, Namirembe G, Rajbhandary R, Manohar S, Shrestha B, West KP, Webb P. **Household food production is positively associated with dietary diversity and intake of nutrient-dense foods for older preschool children in poorer families: results from a nationally-representative survey in Nepal**. *PLoS ONE* (2017.0) **12** 1-24. DOI: 10.1371/journal.pone.0186765
41. Koppmair S, Kassie M, Qaim M. **Farm production, market access and dietary diversity in Malawi**. *Public Health Nutr* (2017.0) **20** 325-35. DOI: 10.1017/S1368980016002135
42. 42.Hersch D, Perdue L, Ambroz T, Boucher JL. The impact of cooking classes on food-related preferences, attitudes, and behaviors of school-aged children: a systematic review of the evidence, 2003–2014. 2014 p. 1–10.
43. Allirot X, da Quinta N, Chokupermal K, Urdaneta E. **Involving children in cooking activities: a potential strategy for directing food choices toward novel foods containing vegetables**. *Appetite* (2016.0) **103** 275-285. DOI: 10.1016/j.appet.2016.04.031
44. Mekuria G, Wubneh Y, Tewabe T. **Household dietary diversity and associated factors among residents of finote selam town, north west Ethiopia: a cross sectional study**. *BMC Nutr* (2017.0) **3** 3-8. DOI: 10.1186/s40795-017-0148-0
45. Scaglioni S, De Cosmi V, Ciappolino V, Parazzini F, Brambilla P, Agostoni C. **Factors influencing children’s eating behaviours**. *Nutrients* (2018.0) **1** 1-17. DOI: 10.3390/nu10060706
46. Worsley A. **Nutrition knowledge and food consumption: can nutrition knowledge change food behaviour?**. *Asia Pac J Clin Nutr* (2002.0) **11** 579-85. DOI: 10.1046/j.1440-6047.11.supp3.7.x
47. Jingxiong J, Rosenqvist U, Huishan W, Greiner T, Guangli L, Sarkadi A. **Influence of grandparents on eating behaviors of young children in Chinese three-generation families**. *Appetite* (2007.0) **48** 377-383. DOI: 10.1016/j.appet.2006.10.004
48. Tapera R, Harwood M, Anderson A. **A qualitative Kaupapa Ma-ori approach to understanding infant and young child feeding practices of Ma-ori and Pacific grandparents in Auckland**. *New Zealand Public Heal Nutr* (2017.0) **20** 1090-1098. DOI: 10.1017/S1368980016002950
49. Karmacharya C, Cunningham K, Choufani J, Kadiyala S. **Grandmothers’ knowledge positively influences maternal knowledge and infant and young child feeding practices**. *Public Health Nutr* (2017.0) **20** 2114-23. DOI: 10.1017/S1368980017000969
50. Shrestha RM, Schreinemachers P, Nyangmi MG, Sah M, Phuong J, Manandhar S, Yang RY. **Home-grown school feeding: assessment of a pilot program in Nepal**. *BMC Public Health* (2020.0) **20** 28. DOI: 10.1186/s12889-019-8143-9
51. Steyn NP, Nel J, Labadarios D, Maunder EMW, Kruger HS. **Which dietary diversity indicator is best to assess micronutrient adequacy in children 1 to 9 y?**. *Nutrition* (2014.0) **30** 55-60. DOI: 10.1016/j.nut.2013.06.002
52. Albanna B, Heeks R. **Positive deviance, big data, and development: a systematic literature review**. *Electron J Inf Syst Dev Ctries* (2019.0) **85** 12063. DOI: 10.1002/isd2.12063
|
---
title: Determination of Factors Influencing the Health Belief Model (HBM) and Adherence
to Intravitreal Anti-vascular Endothelial Growth Factor (VEGF) Among Patients With
Diabetic Macular Edema (DME)
journal: Cureus
year: 2023
pmcid: PMC9993440
doi: 10.7759/cureus.34669
license: CC BY 3.0
---
# Determination of Factors Influencing the Health Belief Model (HBM) and Adherence to Intravitreal Anti-vascular Endothelial Growth Factor (VEGF) Among Patients With Diabetic Macular Edema (DME)
## Abstract
Background Diabetic macular edema (DME) is becoming one of the leading causes of blindness worldwide with a significant impact on quality of life. The effectiveness of intravitreal (IVT) anti-vascular endothelial growth factor (VEGF) therapy has been established by clinical trials and has become the treatment of choice in the majority of DME patients in reducing macular edema and improving visual acuity. Frequent drop-out and discontinuation of treatment are major issues. Lack of compliance can lead to worsening outcomes and be a burden to patients and the healthcare system.
Purpose The purpose of this study is to assess multiple factors that affect adherence to IVT anti-VEGF treatment among patients with DME, including socioeconomic causes and the Health Belief Model (HBM) domains, in addition to exploring the relationship between them.
Methods This cross-sectional study was conducted among DME patients in Hospital Canselor Tuanku Muhriz, Kuala Lumpur, Malaysia, from December 2020 to June 2021. We identified eligible patients using a retrospective chart review of clinical findings and optical coherence tomography (OCT) findings. Included subjects were of Malaysian nationality, aged 18 years and above, who were initiated or re-initiated IVT anti-VEGF treatment regime and on follow-up for at least six months from initial injection from January 2019 onwards. A translated and validated self-administered questionnaire was given to the respondents. Data were analyzed using IBM SPSS Statistics for Windows, Version 26.0 (Released 2019; IBM Corp., Armonk, New York, United States). Demographics of the patient were summarized using descriptive statistics, independent sample t-test was used to compare the difference in components of the HBM questionnaire. Linear regression was further used to explore the relationship between patients’ demographics and the HBM component.
Results A total of 141 patients participated in this study, of whom $56.2\%$ patients were adherent to treatment. The majority were aged 60 years and above ($56.7\%$), male ($52.5\%$), Malay ($38.9\%$), and married ($71.6\%$). There was a significant statistical difference in patients who were adherent to treatment, in terms of life entourage ($$p \leq 0.004$$, Fisher Exact test). HBM domains that influenced adherence to treatment included perceived severity, perceived barriers, perceived benefits, cues to action, and self-efficacy ($p \leq 0.05$, independent sample t-test). Further, multiple logistic regression tests on sociodemographic factors and HBM domains after eliminating confounding factors narrowed down the significant variables to perceived susceptibility ($$p \leq 0.023$$), perceived benefits ($p \leq 0.001$), and self-efficacy ($p \leq 0.001$).
Conclusion Patients’ adherence to IVT anti-VEGF is influenced by perceived susceptibility to complications from DME, perceived benefits to the treatment, and self-efficacy.
## Introduction
Diabetes mellitus (DM) is a major public health concern in Malaysia, proven by the increasing prevalence of DM as seen from the National Health and Morbidity (NHMS) survey [1]. The number of diabetic retinopathy (DR) and vision-threatening diabetic macular edema (DME) cases is expected to reach 191.0 million and 56.3 million, respectively, by 2030 worldwide [2]. DR was the cause of blindness in $10.4\%$ of Malaysian elderly citizens, as seen in the National Eye Survey in Malaysia [3]. Furthermore, DME can occur at any stage of DR and is a common cause of vision loss [4]. A Malaysian study found that among diabetes patients who had follow-ups at ophthalmology clinics, $51.6\%$ had DR while $26.7\%$ had DME [4]. Globally, DME is also becoming one of the leading causes of blindness [5]. Increasing loss of visual acuity not only affects the quality of life but also affects work-related productivity and healthcare-related costs [5]. It is estimated that the total annual cost of diabetes in *Malaysia is* around 600 million USD [6].
The pathogenesis of DME is highly complex and not fully understood. DME is characterized by the accumulation of intraretinal fluid, primarily in the inner and outer plexiform layers and within the central portion of the retina [7]. It is caused by the breakdown of the blood-retinal barrier that results in the leakage of fluid and proteins into the macula, causing the macula to swell, which in turn affects visual function [7,8]. Meanwhile, ischemic drive can cause further upregulation and release of the vascular endothelial growth factor (VEGF) [9]. VEGF is a potent factor in increasing retinal vascular permeability besides promoting angiogenesis. VEGF is a critical player between angiogenesis and inflammation, therefore, reinforcing the use of anti-VEGF agents for the treatment of DME [9].
In the past decade, treatment for DME has evolved extensively. Focal and grid photocoagulation therapy used to be the only option but the introduction of intravitreal (IVT) anti-VEGF has proven to be effective and superior to laser treatment [10,11]. The efficacy and safety profile of anti-VEGF therapy has been established by several large clinical trials and has become the treatment of choice in the majority of DME patients [10,12,13]. Recent studies depicted significantly positive visual and anatomical results regarding the use of anti-VEGF for the treatment of DME [14-16]. Following the Malaysia Diabetic Macular Edema Consensus Guidelines 2021, initiation of anti-VEGF treatment requires a loading phase of three or more consecutive monthly injections [17], followed by a maintenance phase during which intervals between injections are titrated according to the patient's needs, based on clinical signs and optical coherence tomography (OCT) findings [13,18].
Despite knowing the benefits of the treatment and the importance of adherence to IVT anti-VEGF, frequent drop-out and discontinuation of treatment is a major issue globally [19]. Studies have proven that almost half the patients will drop out and a big proportion of patients do not receive the ideal monthly treatment either due to the accessibility of medication or monetary issues [20]. In addition, Habib et al. depicted that $21\%$ of patients with DME dropped injections during the first year of treatment [21]. Abu-Yaghi et al. illustrated high compliance of $85\%$ to injections during the first year of follow-up [22], while Angermann et al. reported a $51\%$ adherence rate in the first two years [23]. Lack of compliance has proven to lead to a worse outcome and could be a burden not only to patients but also to the healthcare and economic sector [20].
The Health Belief Model (HBM) was developed in the 1950s and is one of the most widely used models to understand and explain health behaviors including adherence to treatment by patients [24]. The underlying concept of the HBM is that health behavior is determined by personal beliefs or perceptions about a disease and is outlined by six domains, namely: perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy [24-26]. Recently, it has been applied to other health issues including understanding intentions to coronavirus disease 2019 (COVID-19) vaccinations [27] and compliance with medical treatment [26]. The HBM framework was utilized by Habib et al. to develop a structured questionnaire to determine factors affecting adherence to treatment among DME patients [21].
To the best of our knowledge, current literature and studies on patients’ adherence to treatment assess sociodemographic factors or HBM domains individually. There might be a gap in assessing both areas that may be interrelated where sociodemographics might influence HBM domains as well. Data for Malaysia remain scarce due to a lack of local studies and a more comprehensive approach is needed. Identification and understanding of these factors can guide future treatment strategies and policy setting. The result from this study can also be used for healthcare budget planning in view of the high burden cost of diabetes mellitus.
Thus, this study aimed to assess all related factors that may affect adherence to IVT treatment among patients with DME. These include socioeconomic factors and HBM domains in addition to exploring the relationship between them.
## Materials and methods
Study population and methodology This was a cross-sectional study, conducted at the ophthalmology clinic in Hospital Canselor Tuanku Muhriz (HCTM), Kuala Lumpur, Malaysia, from December 2020 to June 2021. All the patients fulfilling the inclusion and exclusion criteria were invited to join the study and recruited via convenient sampling.
The targeted subjects were DME patients of Malaysian nationality, 18 years old and above, who were initiated or re-initiated IVT anti-VEGF treatment regime and on follow-up for at least six months from the initial injection, from January 2019 onwards. Exclusion criteria included patients who were unable to understand English or Malay language, with medical conditions that may impair their ability to communicate or respond logically, with incomplete or untraceable medical records, and whose follow-up and appointment were postponed during the COVID-19 Movement Control Order.
All procedures in this study adhered to the Declaration of Helsinki and Malaysian Guidelines for Good Clinical Practice (GCP). The study was approved by the Universiti Kebangsaan Malaysia (UKM) Research and Ethics Committee (Approval number: JEP-2020-683). Patients were fully informed and written informed consent was obtained. They were counseled on the research topic and its benefits and risks involved in the research and confidentiality of data. They were informed that their participation is voluntary, that they can withdraw anytime, and that there is no compensation from this research.
Questionnaire The questionnaire used was from the study by Habib et al. to identify factors affecting compliance with follow-up and IVT anti-VEGF injection [18]. Permission was obtained from the author for translation and use in this study. The questionnaire has 26 items and was constructed to assess patients’ perception towards anti-VEGF treatment in DME based on six domains; perceived susceptibility (three items), perceived severity (three items), perceived benefits (four items), perceived barriers (nine items), cues to action (five items) and self-efficacy (two items). For perceived severity, susceptibility, benefits, and barriers, responses were recorded on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). For cues to action, responses were in the form of dichotomous questions, Yes and No, with a score of 1 for Yes and 0 for No. For self-efficacy, a five-point Likert scale was used ranging from 1 (strongly impossible) to 5 (very possible). The total sum of scores for each domain was calculated for mean and standard deviation.
The original validated English questionnaire was translated into Malay using international guidelines for cross-cultural adaptation to ensure the quality of the translated version and its consistency of meaning to the original version [28]. The content validity index (CVI) and face validity index (FVI) was 0.91 and 0.89, respectively, for all the domains after improvements were made based on the experts’ suggestions. Reliability testing was done on 30 respondents. Cronbach’s alpha value range between 0.84-0.93 was obtained for each of the domains and considered adequate for validation of the questionnaire to be used in this study [28].
A total of 180 eligible patients were identified but only 141 responded and participated in this study. Patients were identified using a retrospective chart review of all patients diagnosed with DME based on clinical and OCT findings. Information was obtained from the treatment logbook. The investigator was masked about the adherence of the patient to the IVT anti-VEGF treatment. In a clinical setting, each respondent was given a validated self-administered questionnaire. Respondents could choose to answer the English or Bahasa Melayu version of the questionnaire. They were given 25 minutes to answer the questionnaire.
Demographic data including age, gender, ethnicity, marital status, education level, occupation, life entourage, and financial source for IVT anti-VEGF were recorded. Information on adherence to treatment and follow-up was obtained from the medical record and treatment log sheet. Patients were subsequently divided into two categories, namely adherent and non-adherent. Patients were considered adherent if compliant to IVT anti-VEGF six monthly loading doses and maintained follow-up for a minimum of six months including injections during the period if indicated. Meanwhile, patients who failed the above condition are considered non-adherent.
Statistical analysis Data were cleaned and analyzed using IBM SPSS Statistics for Windows, Version 26.0 (Released 2019; IBM Corp., Armonk, New York, United States). The distribution of numerical data was assessed using skewness, kurtosis, and histogram. Continuous variables were presented using mean and standard deviation if the data were normally distributed, otherwise median and interquartile range (25th percentile, 75th percentile). Categorical variables were presented as frequency and percentage.
Demographics of the patients were summarized using descriptive statistics, and differences in demographics between adherent vs non-adherent patients were compared using the Mann-Whitney U test, Pearson chi-squared test, and Fisher exact test. An Independent sample t-test was used to compare the difference in components of the HBM questionnaire between adherent vs non-adherent patients as well.
The components of the HBM questionnaire associated with adherence were further tested using logistic regression. Simple logistic regression was used as a univariable analysis to explore the relationships, and variables with a p-value less than 0.200 in univariable analysis were included in the variable selection process in a multivariable model. Variable selection forward logistic regression method was used. Multicollinearity and interaction terms were checked for the final model, while the model fit was assessed using the Hosmer Lemenshow goodness of fit test, classification table, and area under the receiver operating characteristic (ROC) curve.
The relationship between patients’ demographics and with HBM component was explored using linear regression. Simple linear regression was used as a univariable analysis to first explore the relationships, and variables with a p-value less than 0.200 in the univariables analysis were included in the variable selection process with a stepwise method. Multicollinearity, interaction, and heterocedasticity of the multivariable model were checked.
## Results
A total of 141 patients participated in this study. Table 1 depicted the sociodemographic characteristics of the patients. The majority of them were aged 60 years and above ($56.7\%$), male ($52.5\%$), Malay ($38.9\%$), and married ($71.6\%$); $26.9\%$ of the patients live with their spouse while another $26.9\%$ live with their children. With regard to education, $51.7\%$ of them had reached the secondary level. The majority of them used their personal savings ($45.5\%$) as the financial source; $30.5\%$ were pensioners followed by $20.6\%$ who are unemployed
**Table 1**
| Sociodemographic characteristics | n (%) |
| --- | --- |
| Age (years) | |
| ≤ 30 | 1 (0.7) |
| 31- 40 | 4 (2.8) |
| 41 -50 | 18 (12.8) |
| 51-60 | 38 (27.0) |
| >61 | 80 (56.7) |
| Gender | |
| Female | 67 (47.5) |
| Male | 74 (52.5) |
| Ethnicity | |
| Malay | 69 (38.9) |
| Chinese | 43 (30.5) |
| Indian | 29 (20.6) |
| Others | 0 |
| Marital Status | |
| Single | 21 (14.9) |
| Married | 101 (71.6) |
| Divorced | 2 (1.4) |
| Widowed | 17 (12.1) |
| Education level | |
| No formal education | 6 (4.3) |
| Primary | 30 (21.3) |
| Secondary | 73 (51.7) |
| Tertiary | 32 (22.7) |
| Occupation | |
| Pensioner | 43 (30.5) |
| Self-employed | 21 (14.9) |
| Government | 22 (15.6) |
| Private | 26 (18.4) |
| Unemployed | 29 (20.6) |
| Others | 0 |
| Life entourage | |
| With spouse | 38 (26.9) |
| With children | 38 (26.9) |
| Spouse and children | 44 (31.2) |
| Alone | 18 (12.8) |
| Others | 3 (2.2) |
| Financial Source | |
| Personal | 64 (45.4) |
| Government | 50 (35.5) |
| Insurance | 7 (5) |
| Others | 20 (14.1) |
Our study showed that $56.2\%$ of the patients were adherent to treatment as opposed to $43.8\%$ who were non-adherent and there was a significant association between life entourage and adherence ($$p \leq 0.004$$) (Table 2).
**Table 2**
| Sociodemographic characteristics | Adherence | Adherence.1 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 |
| --- | --- | --- | --- | --- | --- |
| Sociodemographic characteristics | Adherence | Adherence | Adherent | Non-adherent | p-value |
| Sociodemographic characteristics | n= 68 (56.2%) | n = 53 (43.8%) | | | |
| Sociodemographic characteristics | Age (years) | | | | |
| ≤ 30 | 0 | 1 | | | |
| 31- 40 | 2 | 2 | 0.888b | | |
| 41 -50 | 11 | 7 | | | |
| 51-60 | 20 | 18 | | | |
| >61 | 46 | 34 | | | |
| Gender | | | | | |
| Female | 45 | 29 | 0.240a | | |
| Male | 34 | 33 | | | |
| Ethnicity | | | | | |
| Malay | 43 | 26 | | | |
| Chinese | 22 | 21 | 0.328a | | |
| Indian | 14 | 15 | | | |
| Others | 0 | 0 | | | |
| Marital Status | | | | | |
| Single | 9 | 12 | | | |
| Married | 60 | 41 | 0.270b | | |
| Divorced | 2 | 0 | | | |
| Widowed | 8 | 9 | | | |
| Education level | | | | | |
| No formal education | 1 | 5 | | | |
| Primary | 13 | 16 | 0.125b | | |
| Secondary | 45 | 28 | | | |
| Tertiary | 20 | 13 | | | |
| Occupation | | | | | |
| Pensioner | 24 | 19 | | | |
| Self-employed | 13 | 9 | | | |
| Government | 13 | 9 | 0.891a | | |
| Private | 16 | 10 | | | |
| Unemployed | 13 | 15 | | | |
| Others | 0 | 0 | | | |
| Life entourage | | | | | |
| With spouse | 17 | 21 | | | |
| With children | 25 | 13 | 0.004b | | |
| Spouse and children | 30 | 15 | | | |
| Alone | 4 | 13 | | | |
| Others | 3 | 0 | | | |
| Financial Source | | | | | |
| Personal | 31 | 33 | | | |
| Government | 29 | 21 | 0.113b | | |
| Insurance | 4 | 3 | | | |
| Others | 15 | 5 | | | |
Table 3 demonstrates the relationship between various HBM domains and treatment adherence, Perceived barriers were significantly lower in adherent patients compared to non-adherent patients. Adherent patients were observed to have higher perceived severity, perceived susceptibility, perceived benefits, cues to action, and self-efficacy.
**Table 3**
| HBM domain | Adherent (mean ± SD) | Non-adherent (mean ± SD) | p-value* |
| --- | --- | --- | --- |
| Perceived severity | 11.51 ± 1.75 | 10.52 ± 2.62 | 0.012 |
| Perceived susceptibility | 11.57 ± 2.34 | 10.74 ± 2.83 | 0.059 |
| Perceived benefits | 17.70 ± 2.02 | 13.34 ± 2.57 | <0.001 |
| Perceived barriers | 15.33 ± 3.65 | 20.66 ± 5.76 | <0.001 |
| Cues to action | 1.61 ± 1.21 | 0.90 ± 0.76 | <0.001 |
| Self-efficacy | 8.94 ± 1.29 | 6.42 ± 1.24 | <0.001 |
Simple and multiple logistic regression was subsequently conducted and summed up in Table 4. The variables which were significant in the univariable analysis included perceived severity ($$p \leq 0.010$$), perceived benefits ($p \leq 0.001$), perceived barriers ($p \leq 0.001$), cues to action ($p \leq 0.001$), and self-efficacy ($p \leq 0.001$), and were similar to Table 3 results. Nonetheless, the variables which were significant in the final model after eliminating the confounding effects included perceived susceptibility ($$p \leq 0.023$$), perceived benefits ($p \leq 0.001$), and self-efficacy ($p \leq 0.001$).
**Table 4**
| HBM domain | Simple logistic regression | Simple logistic regression.1 | Simple logistic regression.2 | Multiple logistic regression | Multiple logistic regression.1 | Multiple logistic regression.2 |
| --- | --- | --- | --- | --- | --- | --- |
| | OR | 95% CI | p-value | Adjusted OR | 95% CI | p-value |
| Perceived severity | 1.24 | 1.05, 1.46 | 0.01 | | | |
| Perceived susceptibility | 1.14 | 0.99, 1.30 | 0.062 | 0.75 | 0.58, 0.96 | 0.023 |
| Perceived benefits | 2.19 | 1.70, 2.81 | <0.001 | 1.91 | 1.41, 2.59 | <0.001 |
| Perceived barriers | 0.78 | 0.71, 0.86 | <0.001 | | | |
| Cues to action | 1.99 | 1.37, 2.90 | <0.001 | | | |
| Self- efficacy | 4.06 | 2.58, 6.39 | <0.001 | 3.03 | 1.85, 4.98 | <0.001 |
We used linear regression test to explore the relationship between each HBM domain and sociodemographic factors. Table 5 summarizes and demonstrates that education, life entourage, and financial source have effects on perceived benefits, life entourage on perceived barriers, and cues to action, respectively. Meanwhile, occupation, life entourage, and financial source affect self-efficacy.
**Table 5**
| HBM domain | Demographics |
| --- | --- |
| Perceived severity | |
| Perceived susceptibility | |
| Perceived benefits | Education |
| Perceived benefits | Life entourage |
| Perceived benefits | Financial source |
| Perceived barriers | Life entourage |
| Cues to action | Life entourage |
| Self- efficacy | Occupation |
| Self- efficacy | Life entourage |
| Self- efficacy | Financial source |
Patients who completed secondary school (coef ($95\%$CI): 1.78 (0.61, 2.95); $$p \leq 0.003$$) and tertiary education (coef ($95\%$CI): 1.95 (0.48, 3.42); $$p \leq 0.010$$) had higher perceived benefits compared to patients with no formal education. Apart from that, patients who live with children [coef ($95\%$ CI): 1.24 (0.02, 2.46); $$p \leq 0.047$$], spouse and children (coef ($95\%$CI): 1.50 (0.29, 2.71); $$p \leq 0.015$$) and others (coef ($95\%$CI): 4.55 (1.17, 7.92); $$p \leq 0.009$$) were observed to have higher perceived benefits than those who live alone. Patients whose financial source came from the government (coef ($95\%$CI): 1.23 (0.15, 2.32); $$p \leq 0.026$$) and others (coef ($95\%$CI): 1.90 (0.38, 3.42); $$p \leq 0.015$$) has higher perceived benefits as well (Table 6).
**Table 6**
| Unnamed: 0 | Simple linear regression | Simple linear regression.1 | Simple linear regression.2 | Multiple linear regression | Multiple linear regression.1 | Multiple linear regression.2 |
| --- | --- | --- | --- | --- | --- | --- |
| | Coef | 95% CI | P-value | Coef | 95% CI | P-value |
| Education | | | | | | |
| No formal education | Ref | | | Ref | | |
| Primary | 1.5 | -1.20, 4.20 | 0.274 | - | - | - |
| Secondary | 3.05 | 0.49, 5.62 | 0.02 | 1.78 | 0.61, 2.95 | 0.003 |
| Tertiary | 3.15 | 0.46, 5.83 | 0.022 | 1.95 | 0.48, 3.42 | 0.01 |
| Life entourage | | | | | | |
| With spouse | Ref | | | Ref | | |
| With children | 1.65 | 0.23, 0.98 | 0.022 | 1.24 | 0.02, 2.46 | 0.047 |
| Spouse &children | 1.8 | 0.48, 3.13 | 0.008 | 1.5 | 0.29, 2.71 | 0.015 |
| Alone | -0.04 | -1.79, 1.71 | 0.967 | - | - | - |
| Others | 4.32 | 0.72, 7.91 | 0.019 | 4.55 | 1.17, 7.92 | 0.009 |
| Financial source | | | | | | |
| Personal | Ref | | | Ref | | |
| Government | 1.41 | 0.26, 2.56 | 0.017 | 1.23 | 0.15, 2.32 | 0.026 |
| Insurance | 0.11 | -2.31, 2.54 | 0.928 | - | - | - |
| Others | 1.72 | 0.16, 3.28 | 0.031 | 1.9 | 0.38, 3.42 | 0.015 |
Patients who lived alone were found to have higher perceived barriers compared to patients who live with spouses (coef ($95\%$CI): 4.45 (1.79, 7.11); $$p \leq 0.001$$) (Table 7).
**Table 7**
| Unnamed: 0 | Simple linear regression | Simple linear regression.1 | Simple linear regression.2 | Multiple linear regression | Multiple linear regression.1 | Multiple linear regression.2 |
| --- | --- | --- | --- | --- | --- | --- |
| | coef | 95% CI | P-value | coef | 95% CI | P-value |
| Life entourage | | | | | | |
| With spouse | Ref | | | Ref | | |
| With children | -1.34 | -3.70, 1.02 | 0.263 | - | - | - |
| Spouse &children | -1.42 | -3.69, 0.84 | 0.216 | - | - | - |
| Alone | 3.43 | 0.43, 6.43 | 0.025 | 4.45 | 1.79, 7.11 | 0.001 |
| Others | -3.82 | -9.99, 2.34 | 0.222 | - | - | - |
Patients who live with others such as in nursing homes or with relatives had higher cues to action compared to those who live with spouses only (coef ($95\%$ CI): 1.40 (0.15, 2.64); $$p \leq 0.028$$) (Table 8).
**Table 8**
| Unnamed: 0 | Simple linear regression | Simple linear regression.1 | Simple linear regression.2 | Multiple linear regression | Multiple linear regression.1 | Multiple linear regression.2 |
| --- | --- | --- | --- | --- | --- | --- |
| | coef | 95% CI | P value | coef | 95% CI | P value |
| Life entourage | | | | | | |
| With spouse | Ref | | | Ref | | |
| With children | 0.42 | -0.07, 0.91 | 0.091 | - | - | - |
| Spouse &children | 0.21 | -0.26, 0.68 | 0.378 | - | - | - |
| Alone | 0.04 | -0.58, 0.66 | 0.902 | - | - | - |
| Others | 1.59 | 0.31, 2.87 | 0.015 | 1.4 | 0.15, 2.64 | 0.028 |
We used logistic regression to explore the factors associated with adherence. The variables significant in the univariable analysis simple logistic regression include perceived severity ($$p \leq 0.010$$), perceived benefits ($p \leq 0.001$), barriers ($p \leq 0.001$), cue to action ($p \leq 0.001$), and self-efficacy ($p \leq 0.001$). The variables with $p \leq 0.200$ were included in the variable selection process, and the variables significant in the final model were perceived susceptivity ($$p \leq 0.023$$), perceived benefits ($p \leq 0.001$), and self-efficacy ($p \leq 0.001$).
It was found that a unit increase in perceived susceptibility will decrease the odds for adherence by $25\%$ (adjusted OR (aOR) ($95\%$ CI): 0.75 (0.58, 0.96); $$p \leq 0.023$$). On the other hand, a unit increase in perceived benefits was observed to increase the odds for adherence by $91\%$ (aOR ($95\%$ CI): 1.91 (1.41, 2.59); $p \leq 0.001$). Moreover, it was observed that a unit increase in the self-efficacy score will increase the odds for adherence by 3.03 times (aOR ($95\%$ CI): 3.03 (1.85, 4.98); $p \leq 0.001$). The Nagelkerke's R squared of the final model was 0.759, indicating that $75.9\%$ of the variation in adherence was explained by the three factors in the final model (Table 9).
**Table 9**
| Unnamed: 0 | Unnamed: 1 | Simple Logistic Regression | Simple Logistic Regression.1 | Simple Logistic Regression.2 | Unnamed: 5 | Multiple Logistic Regression | Multiple Logistic Regression.1 | Multiple Logistic Regression.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | coef | OR | 95% CI | P-value | Adjusted coef | Adjusted OR | 95% CI | P-value |
| Age | 0.01 | 1.01 | 0.98, 1.04 | 0.585 | | | | |
| Gender | | | | | | | | |
| Male | Ref | | | | | | | |
| Female | -0.41 | 0.66 | 0.34, 1.30 | 0.23 | | | | |
| Ethnicity | | | | | | | | |
| Malay | Ref | | | | | | | |
| Chinese | -0.46 | 0.63 | 0.29, 1.37 | 0.246 | | | | |
| Indian | -0.57 | 0.56 | 0.24, 1.36 | 0.201 | | | | |
| Marital status | | | | | | | | |
| Single | Ref | | | | | | | |
| Married | 0.67 | 1.95 | 0.75, 5.05 | 0.168 | | | | |
| Divorced | 21.49 | 2.15x109 | 0.00, ∞ | >0.950 | | | | |
| Widowed | 0.17 | 1.19 | 0.33, 4.29 | 0.796 | | | | |
| Education | | | | | | | | |
| No formal education | Ref | | | | | | | |
| Primary | 1.47 | 4.38 | 0.46, 42.08 | 0.201 | | | | |
| Secondary | 2.08 | 8.04 | 0.89, 72.40 | 0.063 | | | | |
| Tertiary | 1.99 | 7.31 | 0.76, 70.03 | 0.085 | | | | |
| Occupation | | | | | | | | |
| Pensioner | Ref | | | | | | | |
| Self-employed | 0.05 | 1.06 | 0.37, 3.03 | 0.92 | | | | |
| Government | 0.13 | 1.14 | 0.40, 3.24 | 0.801 | | | | |
| Private | 0.24 | 1.27 | 0.47, 3.42 | 0.641 | | | | |
| Unemployed | -0.3 | 0.74 | 0.29, 1.90 | 0.53 | | | | |
| Life entourage | | | | | | | | |
| With spouse | Ref | | | | | | | |
| With children | 0.87 | 2.38 | 0.94, 6.00 | 0.067 | | | | |
| Spouse and children | 0.9 | 2.47 | 1.01, 6.02 | 0.047 | | | | |
| Alone | -0.97 | 0.38 | 0.11, 1.38 | 0.142 | | | | |
| Others | 21.41 | 2.00x109 | 0.00, ∞ | >0.950 | | | | |
| Income source | | | | | | | | |
| Personal | Ref | Ref | | 0.230 | | | | |
| Government | 0.39 | 1.47 | 0.70, 3.10 | 0.311 | | | | |
| Insurance | 0.35 | 1.42 | 0.29, 6.86 | 0.663 | | | | |
| Others | 1.16 | 3.19 | 1.04, 9.83 | 0.043 | | | | |
| Perceived severity | 0.21 | 1.24 | 1.05, 1.46 | 0.01 | | | | |
| Perceived susceptibility | 0.13 | 1.14 | 0.99, 1.30 | 0.062 | -0.29 | 0.75 | 0.58, 0.96 | 0.023 |
| Perceived benefits | 0.78 | 2.19 | 1.70, 2.81 | <0.001 | 0.65 | 1.91 | 1.41, 2.59 | <0.001 |
| Perceived barriers | -0.25 | 0.78 | 0.71, 0.86 | <0.001 | | | | |
| Cues to action | 0.69 | 1.99 | 1.37, 2.90 | <0.001 | | | | |
| Self-efficacy | 1.4 | 4.06 | 2.58, 6.39 | <0.001 | 1.11 | 3.03 | 1.85, 4.98 | <0.001 |
## Discussion
To the best of our knowledge, this study was the first to explore the relationship between sociodemographic factors and HBM domains to adherence to IVT anti-VEGF among those with DME. In 2021, Wong et al. reported a non-compliance rate of $35.1\%$ in a small study cohort and assessed the socio-economic factors affecting non-adherence to treatment in a private center in Melaka [29]. Meanwhile, our study depicted a higher non-adherence rate of $43.8\%$.
Adherent patients were observed to have higher perceived severity, perceived susceptibility, perceived benefits, cues to action, and self-efficacy. These observations are quite similar to the findings by Habib et al. [ 21].
Patients with higher perceived susceptibility are more concerned about the loss of vision secondary to DME. When combined with a higher perception of disease severity, they have a stronger effect on the intention to treatment [27]. Zampetakis et al. also demonstrated this synergistic effect in their study using HBM to predict vaccination intention [27]. This highlights the importance to increase the risk perception and severity among the patients, especially among those who perceive the disease as being non-sight-threatening.
Perceived benefits were also affected by education level, life entourage, and financial sources. Both Abu-Yaghi et al. [ 22] and Habib et al. [ 21] showed contrasting results that lack of education was not found to be significantly affecting compliance with treatment. In Malaysia. Wong et al. reported that there is considerable evidence to suggest that education level is an important social determinant to support and access health services and information [30]. We postulated that the possible reason is that most health education including pamphlets and social media videos regarding IVT anti-VEGF are mostly in English. Thus, patients who do not receive formal education and cannot understand English have less access to treatment information.
Regarding cues to action and self-efficacy, the findings of the current study are quite similar to the ones reported by Reiter et al. [ 31] suggesting providers’ recommendation is key in promoting health behavior. Otherwise, the most prevalent cues that especially drove patients’ compliance were when friends and relatives shared their positive experiences [32]. Another important point to highlight is the effect of financial source on self-efficacy. Wong et al. demonstrated that financial constraint was the commonest reason for non-compliance to IVT anti-VEGF [29]. Meanwhile, Muller et al. also identified financial cost as a challenge for DME patients in Germany [33]. In our study, treatment cost has clearly been shown to influence patients’ confidence in maintaining regular IVT treatment and follow-up. Our patients were mainly self-paying or subsidized groups under Jabatan Perkhidmatan Awam (JPA). Where there are out-of-pocket expenses, high drug cost leads to poor treatment adherence among our patients [34].
Furthermore, other domains also play a minimal influential role in affecting adherence to treatment. When perceived barriers to getting IVT anti-VEGF were low, it had a positive effect on adherence to treatment. This finding is comparatively similar to the study by Habib et al. [ 21]. Examples of barriers assessed in this study include the experience of injection, disbelief in injection, time, travel and financial cost, difficulties in remembering appointments, and also the psychological burden of the accompanying person. Wong et al. depicted that financial constraints and logistic difficulties contributed $38\%$ and $19\%$, respectively, to the dropout rate from the IVT treatment regime in Melaka [29]. Other studies have also identified similar barriers to treatment adherence, including time and financial burden and disbelief in treatment [35]. Otherwise, the discomfort-associated experience of pain and fear are also commonly reported, similar to findings by Habib et al. [ 21].
The World Health Organization (WHO) has outlined non-adherence as a major issue in the care of chronic illness [36]. Thus, it is pertinent to identify the contributing factors and outline suggestions to improve the adherence rate. From the results of this study, we would like to suggest ways to improve adherence via increasing perceived susceptibility, perceived benefits, and self-efficacy.
Effective counseling towards patients and their families to improve understanding of the disease itself, the benefits of treatment, and the consequences of non-adherence to treatment. Counseling should be done not only in the ophthalmology clinics but also can be reinforced in a multidisciplinary manner by including other healthcare counterparts involved in the care of diabetic patients. Recommendations and assurance provided by doctors and peers do influence patients’ behavior [34].
Information should ideally be available in English, Malay, Chinese, and Tamil in easy-to-understand language. It should be widely available on various media platforms for easy access. Otherwise, common barriers include discomfort during injection and anxiety, which can be addressed via assurance before and during the procedure. Moreover, financial cost has always been an issue for those of lower income. Hence, policies can be looked into to lower the cost of IVT anti-VEGF.
In addition, we also see the opportunity for the use of this validated questionnaire in future studies in other eye diseases including adherence to IVT anti-VEGF in age-related macular degeneration.
Nevertheless, there are a few limitations to this study. First, the developer of the original questionnaire was not involved in the translation and validation process. The translated version of the questionnaire may not be a perfect reflection of the original English version and could introduce bias in the results. Second, this is a single-center study and the result may not be representative of the whole nation. Otherwise, the convenience sampling method used in this study may not provide a representative result. Third, when answering the questionnaire, some patients were assisted by family members in recalling information, which can result in recall bias. In addition, the self-administered format of the questionnaire may introduce bias as participants may have different levels of comfort with answering questions and may have varying literacy levels. The 25-minute time given may be too short for some participants to reflect on their responses, leading to potentially biased answers.
## Conclusions
There are multiple factors that influence adherence to IVT anti-VEGF treatment among DME patients. These factors include sociodemographic factors and HBM. However, the most significant factors are HBM domains; perceived susceptibility to complications from DME, perceived benefits to IVT anti-VEGF treatment, and self-efficacy, which is confidence to adhere to treatment. Identifying these factors allow better understanding and outline suggestions to improve adherence. Life entourage is also an important factor that affects the significant HBM domains. This study can be a guide to future strategies to improve treatment adherence. It will also be useful in setting new treatment policies, contributing to better treatment outcomes.
## References
1. 1National Health and Morbidity Survey 2019 Technical Report - Volume I: NCDs—Non-Communicable Diseases: Risk Factors and other Health Problems. Shah Alam, Selangor Darul EhsanInstitute for Public Health (IPH), National Institutes of Health, Ministry of Health Malaysia2020https://iku.moh.gov.my/images/IKU/Document/REPORT/NHMS2019/Report_NHMS2019-NCD_v2.pdf. *National Health and Morbidity Survey 2019 Technical Report - Volume I: NCDs—Non-Communicable Diseases: Risk Factors and other Health Problems.* (2020)
2. Ting DS, Cheung GC, Wong TY. **Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review**. *Clin Exp Ophthalmol* (2016) **44** 260-277. PMID: 26716602
3. Chew FL, Salowi MA, Mustari Z. **Estimates of visual impairment and its causes from the National Eye Survey in Malaysia (NESII)**. *PLoS One* (2018) **13** 0
4. Tajunisah I, Wong P, Tan L, Rokiah P, Reddy S. **Awareness of eye complications and prevalence of retinopathy in the first visit to eye clinic among type 2 diabetic patients**. *Int J Ophthalmol* (2011) **4** 519-524. PMID: 22553714
5. Shea AM, Curtis LH, Hammill BG. **Resource use and costs associated with diabetic macular edema in elderly persons**. *Arch Ophthalmol* (2008) **126** 1748-1754. PMID: 19064859
6. Ganasegeran K, Hor CP, Jamil MF. **A systematic review of the economic burden of type 2 diabetes in Malaysia**. *Int J Environ Res Public Health* (2020) **17** 5723. PMID: 32784771
7. Musat O, Cernat C, Labib M, Gheorghe A, Toma O, Zamfir M, Boureanu AM. **Diabetic macular edema**. *Rom J Ophthalmol* (2015) **59** 133-136. PMID: 26978879
8. Ciulla TA, Amador AG, Zinman B. **Diabetic retinopathy and diabetic macular edema: pathophysiology, screening, and novel therapies**. *Diabetes Care* (2003) **26** 2653-2664. PMID: 12941734
9. Fogli S, Mogavero S, Egan CG, Del Re M, Danesi R. **Pathophysiology and pharmacological targets of VEGF in diabetic macular edema**. *Pharmacol Res* (2016) **103** 149-157. PMID: 26607863
10. Nguyen QD, Brown DM, Marcus DM. **Ranibizumab for diabetic macular edema: results from 2 phase III randomized trials: RISE and RIDE**. *Ophthalmology* (2012) **119** 789-801. PMID: 22330964
11. Korobelnik JF, Do DV, Schmidt-Erfurth U. **Intravitreal aflibercept for diabetic macular edema**. *Ophthalmology* (2014) **121** 2247-2254. PMID: 25012934
12. Wenick AS, Bressler NM. **Diabetic macular edema: current and emerging therapies**. *Middle East Afr J Ophthalmol* (2012) **19** 4-12. PMID: 22346109
13. Lanzetta P, Loewenstein A. **Fundamental principles of an anti-VEGF treatment regimen: optimal application of intravitreal anti-vascular endothelial growth factor therapy of macular diseases**. *Graefes Arch Clin Exp Ophthalmol* (2017) **255** 1259-1273. PMID: 28527040
14. Blinder KJ, Dugel PU, Chen S, Jumper JM, Walt JG, Hollander DA, Scott LC. **Anti-VEGF treatment of diabetic macular edema in clinical practice: effectiveness and patterns of use (ECHO Study Report 1)**. *Clin Ophthalmol* (2017) **11** 393-401. PMID: 28260851
15. Sun JK, Jampol LM. **The Diabetic Retinopathy Clinical Research Network (DRCR.net) and its contributions to the treatment of diabetic retinopathy**. *Ophthalmic Res* (2019) **62** 225-230. PMID: 31554001
16. Do DV, Nguyen QD, Boyer D. **One-year outcomes of the da Vinci Study of VEGF Trap-Eye in eyes with diabetic macular edema**. *Ophthalmology* (2012) **119** 1658-1665. PMID: 22537617
17. **Diabetic Macula Edema Consensus Guidelines**. (2022)
18. Schmidt-Erfurth U, Garcia-Arumi J, Bandello F. **Guidelines for the management of diabetic macular edema by the European Society of Retina Specialists (EURETINA)**. *Ophthalmologica* (2017) **237** 185-222. PMID: 28423385
19. VanderBeek BL, Shah N, Parikh PC, Ma L. **Trends in the care of diabetic macular edema: analysis of a national cohort**. *PLoS One* (2016) **11** 0
20. Best AL, Fajnkuchen F, Nghiem-Buffet S. **Treatment efficacy and compliance in patients with diabetic macular edema treated with ranibizumab in a real-life setting**. *J Ophthalmol* (2018) **2018** 4610129. PMID: 29850205
21. Habib AE, Abdel-Kader AA, Eissa IM, Awadein A. **Adherence to intravitreal anti-vascular endothelial growth factor (anti-VEGF) drugs in diabetic macular edema in an Egyptian population: a health belief model**. *Curr Eye Res* (2019) **44** 303-310. PMID: 30383436
22. Abu-Yaghi NE, Abed AM, Khlaifat DF. **Factors affecting compliance to anti-vascular endothelial growth factor treatment of diabetic macular edema in a cohort of Jordanian patients**. *Clin Ophthalmol* (2020) **14** 921-929. PMID: 32273676
23. Angermann R, Hofer M, Huber AL. **The impact of compliance among patients with diabetic macular oedema treated with intravitreal aflibercept: a 48-month follow-up study**. *Acta Ophthalmol* (2022) **100** 0-52
24. Grosser LR. **Health belief model aids understanding of patient behavior**. *AORN J* (1982) **35** 1056-1059. PMID: 6921013
25. Rosenstock IM. **Historical origins of the health belief model**. *Health Educ Monogr* (1974) **2** 328-335
26. Janz NK, Becker MH. **The health belief model: a decade later**. *Health Educ Q* (1984) **11** 1-47. PMID: 6392204
27. Zampetakis LA, Melas C. **The health belief model predicts vaccination intentions against COVID-19: a survey experiment approach**. *Appl Psychol Health Well Being* (2021) **13** 469-484. PMID: 33634930
28. Ling JT, Zain AM, Halim WH. **Translation and validation of the Malay version health belief model questionnaire (Abstract)**. *Med & Health* (2022) **17** 60
29. Wong YM, Chong EL, Yeo VYL. **Non-compliance among diabetic macular oedema patients on antivascular endothelial growth factor therapy in Malaysia**. *Malays J Ophthal* (2021) **21** 91-100
30. Wong ST, Saddki N, Tin-Oo MM. **Readability and suitability of oral health education pamphlets produced by the Ministry of Health Malaysia**. *Med J Malaysia* (2019) **74** 312-319. PMID: 31424039
31. Reiter PL, Pennell ML, Katz ML. **Acceptability of a COVID-19 vaccine among adults in the United States: how many people would get vaccinated?**. *Vaccine* (2020) **38** 6500-6507. PMID: 32863069
32. Limbu YB, Gautam RK, Pham L. **The health belief model applied to COVID-19 vaccine hesitancy: a systematic review**. *Vaccines (Basel)* (2022) **10** 973. PMID: 35746581
33. Müller S, Junker S, Wilke T. **Questionnaire for the assessment of adherence barriers of intravitreal therapy: the ABQ-IVT**. *Int J Retina Vitreous* (2021) **7** 43. PMID: 34078475
34. Viswanathan M, Golin CE, Jones CD. **Interventions to improve adherence to self-administered medications for chronic diseases in the United States: a systematic review**. *Ann Intern Med* (2012) **157** 785-795. PMID: 22964778
35. Weiss M, Sim DA, Herold T. **Compliance and adherence of patients with diabetic macular edema to intravitreal anti-vascular endothelial growth factor therapy in daily practice**. *Retina* (2018) **38** 2293-2300. PMID: 29068914
36. Brown MT, Bussell JK. **Medication adherence: WHO cares?**. *Mayo Clin Proc* (2011) **86** 304-314. PMID: 21389250
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---
title: A novel selective spleen tyrosine kinase inhibitor SKI-O-703 (cevidoplenib)
ameliorates lupus nephritis and serum-induced arthritis in murine models
authors:
- Somi Cho
- Eunkyeong Jang
- Taeyoung Yoon
- Haejun Hwang
- Jeehee Youn
journal: Clinical and Experimental Immunology
year: 2022
pmcid: PMC9993459
doi: 10.1093/cei/uxac096
license: CC BY 4.0
---
# A novel selective spleen tyrosine kinase inhibitor SKI-O-703 (cevidoplenib) ameliorates lupus nephritis and serum-induced arthritis in murine models
## Abstract
Spleen tyrosine kinase (Syk) plays a pivotal role in the activation of B cells and innate inflammatory cells by transducing immune receptor-triggered signals. Dysregulated activity of *Syk is* implicated in the development of antibody-mediated autoimmune diseases including systemic lupus erythematosus (SLE) and rheumatoid arthritis, but the effect of Syk inhibition on such diseases remains to be fully evaluated. We have developed a novel selective Syk inhibitor, SKI-O-592, and its orally bioavailable salt form, SKI-O-703 (cevidoplenib). To examine the efficacy of SKI-O-703 on the progression of SLE, New Zealand black/white mice at the autoimmunity-established phase were administrated orally with SKI-O-703 for 16 weeks. Levels of IgG autoantibody, proteinuria, and glomerulonephritis fell significantly, and this was associated with hypoactivation of follicular B cells via the germinal center. In a model of serum-transferred arthritis, SKI-O-703 significantly ameliorated synovitis, with fewer neutrophils and macrophages infiltrated into the synovial tissue. This effect was recapitulated when mice otherwise refractory to anti-TNF therapy were treated by TNF blockade combined with a suboptimal dose of SKI-O-703. These results demonstrate that the novel selective Syk inhibitor SKI-O-703 attenuates the progression of autoantibody-mediated autoimmune diseases by inhibiting both autoantibody-producing and autoantibody-sensing cells.
Orally administered SKI-O-703, a new Syk inhibitor, reduces lupus nephritis-like manifestations in NZB/W mice.
## Graphical Abstract
Graphical Abstract
## Introduction
Systemic autoimmune diseases, such as systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), are characterized by activation of autoreactive T and B cells [1]. This activation represents a functional loss of self-tolerance and leads to autoantibody (autoAb) production. Hallmark autoAbs abundant in patients with SLE include antibodies (Abs) that recognize nuclear components such as histone, DNA, and ribonucleoproteins [2]. High titers of anti-double-stranded DNA (dsDNA) IgG Abs are specific for SLE and correlate with disease severity [3, 4]. Patients with RA also exhibit elevated levels of autoAbs, such as rheumatoid factors and anti-cyclic citrullinated peptide IgG Abs, although their pathogenicity is still a subject of debate [2]. Excessive amounts of such Abs are deposited into tissues as immune complexes and are sensed by innate inflammatory cells including macrophages and neutrophils via receptors recognizing their IgG Fc portions and complement. The inflammatory responses of these cells promote tissue destruction, further loss of self-tolerance, and, ultimately, the symptoms of autoimmune diseases [5]. Therefore, persistent production of pathogenic autoAbs and their sensing at the distal effector phase are the major disease-precipitating elements of the chronic systemic autoimmune diseases.
The pathogenic autoAbs enriched in SLE and RA are largely produced in reactions in germinal centers (GCs), in which antigen-primed follicular B cells undergo *Ig* gene class-switch recombination and somatic hypermutation during intensive proliferation, with the help with CD4+ follicular helper T (Tfh) cells [6–8]. Finally, a fraction of GC B cells gives rise to plasma cells secreting Ig class-switched and affinity-matured Abs. All these step-wise events are instructed by signals initiated by B cell receptor (BCR) cross-linking, which requires the activity of spleen tyrosine kinase (Syk).
Syk is a non-receptor protein tyrosine kinase acting as a key signal transducer downstream of BCRs [9]. BCR engagement with cognate antigens results in phosphorylation of tyrosine residues in the immunoreceptor tyrosine-based activation motifs (ITAMs) of Igα and Igβ chains within the BCR complex, which then recruit *Syk via* their SH2 domains. This event induces autophosphorylation and activation of Syk, leading to phosphorylation of direct downstream targets, such as SLP-65 and PLCγ1, and subsequent activation of diverse signaling cascades. Syk is required not only for the early development of B cells but also for the survival, proliferation, and differentiation of mature follicular B cells into GC B cells and Ab-secreting plasma cells [10–14]. For example, selective genetic ablation of Syk in primary B cells impairs T-dependent and -independent Ab responses in vivo, due to a severe reduction in GC B cells and plasma cells [12]. Thus, Syk appears to play an essential role in preserving Ab-dependent humoral immunity.
Apart from B cells, neutrophils and macrophages utilize Syk to transduce FcγR-triggered signals into the cell interior [15, 16]. FcγR cross-linking by IgG Ab on the surface of these cells causes phosphorylation of ITAM motifs of FcγR, which lead Syk to be recruited and activated. This signaling event favors the development of these cells to be phagocytic and pro-inflammatory.
There is evidence that Syk dysregulation is implicated in the pathogenesis of SLE and RA. Patients with lupus nephritis have infiltrates of Syk-expressing cells in their glomeruli [17]. The peripheral B cells of SLE patients with active disease exhibit increased levels of Syk phosphorylation [18, 19]. Conversely, genetic ablation of Syk in hematopoietic cells completely protected Syk−/− bone marrow (BM) chimeric mice from K/BxN serum-transferred arthritis (KSTA), an experimental model of RA [20]. A cell-autonomous function of Syk in neutrophils may be involved in the development of KSTA since neutrophil-specific deletion of Syk protected the mice from this disease [21]. These investigations together have suggested that *Syk is* a potential target for treating Ab-mediated autoimmune diseases.
Compound R406 was initially reported to be a Syk inhibitor and its orally bioavailable prodrug, R788 (fostamatinib), was shown to ameliorate lupus-like disease in New Zealand black/white (NZB/W), MRL-lpr, and FcγRIIb-deficient C57BL/6 models of SLE, as well as serum-transferred and collagen-induced models of RA [22–26]. However, it turned out later that the inhibitor was non-selective, so it is unclear whether its activity was solely attributable to the inhibition of Syk activity [27]. Although fostamatinib is beneficial in suppressing autoimmune indications in clinical settings, its toxic effects appear to limit its usefulness [28, 29]. This has prompted us to seek a more selective Syk inhibitor to be able to draw unequivocal conclusions about the effect of Syk inhibition on such diseases.
We have synthesized a new Syk inhibitor named SKI-O-592 and its orally bioavailable salt form, SKI-O-703 (cevidoplenib). In the present study, we provide biochemical and in vitro evidence that this inhibitor is highly selective for Syk, targeting specifically BCR- and FcγR-mediated signaling events in B cells and innate inflammatory cells, respectively. Furthermore, oral administration of SKI-O-703 to NZB/W mice dose-dependently attenuated autoAb production and lupus nephritis-like manifestations. Optimal dose SKI-O-703 alone, or suboptimal dose SKI-O-703 combined with anti-TNF Ab, protected mice from KSTA by inhibiting Ab-mediated inflammatory responses. Thus, we demonstrate here that SKI-O-703 is a novel selective Syk inhibitor that targets two processes, namely B cell activation and innate inflammatory cell functioning, and consequently inhibits autoAb-mediated manifestations such as SLE and RA.
## Production of SKI-O-592 and -703
SKI-O-592, a novel pyrazolylpyrimidine compound, was synthesized and characterized by Oscotec Inc. (Seongnam-si, Korea), following a structure-based drug design approach. SKI-O-703 is a mesylate salt form of SKI-O-592 to be used orally in in vivo studies.
## Kinase assays
Kinase assays were conducted in 384-well plates using LANCE ultra 66 time-resolved fluorescence resonance energy transfer (TR-FRET) methods, according to the manufacturer’s instructions (Perkin-Elmer). In brief, each kinase was pre-incubated with a serially diluted compound for 30 min, incubated with ULight poly-GT peptide substrate and ATP for up to 1 h. Thirty minutes after adding Eu-labeled anti-phosphopeptides Ab diluted in LANCE Detection Buffer, LANCE signals were measured with an EnVision multilabel reader (Perkin-Elmer).
## Cell culture
THP-1 and Ramos cell lines and human CD14+ monocytes were purchased from the American Type Culture Collection (ATCC) and Lonza, respectively. After treatment with Syk inhibitors for 1 h, THP-1 cells and monocytes were stimulated for 15 min with 100 μg/ml human IgG (Invitrogen), and Ramos cells were stimulated with 2 μg/ml anti-human IgM monoclonal Ab (mAb) (Bathyl). Cells were lysed in RIPA buffer containing $1\%$ NP-40, $1\%$ sodium deoxycholate, $0.1\%$ SDS, and a mixture of protease and phosphatase inhibitors (Thermo Scientific). The lysates were assayed by standard immunoblotting methods and ELISA. Primary Abs used for immunoblotting were anti-phospho-Syk (Y$\frac{526}{526}$), anti-phospho-Vav (Y174), anti-phospho-PLCγ1 (Y1217) and anti-phospho-BLNK (Y96) Abs (all from Cell Signaling). ELISAs to detect phospho-Syk (Y$\frac{525}{526}$) were performed according to the manufacturer’s protocol (Cell Signaling).
Human CD19+ B cells (Lonza) were pre-treated with inhibitors for 1 h and stimulated for 24 h with 50 nM CpG ODN 2006 plus 10 ng/ml IL-2 or 1 μM CpG ODN 2006 plus 20 ng/ml IFN-α. Culture supernatants were assayed by ELISA to detect secreted IgG, IgM, and IL-6, according to the manufacturer’s instructions.
Splenic CD19+ B cells from C57BL/6 mice were sorted by MACS (Milteny Biotec), stained with 3 μM cell proliferation dye eFluor 670 (CP670; eBioscience), and cultured for 72 h in the presence of either 10 μg/ml LPS (Sigma Aldrich) or 5 μg/ml anti-mouse IgM (Jackson Immuno Research Laboratories), 10 μg/ml anti-CD40 mAb (Biolegend) and 10 ng/ml IL-4 (Peprotech), and assayed by FACS. To detect apoptotic cells, unstimulated cells were cultured for 24 h, stained with Annexin V and 7-aminoactinomycin D (7-AAD), and assayed by FACS.
## Mice
Female NZB/W F1 mice were purchased from the Jackson Laboratory. C57BL/6 and BALB/c mice were purchased from Orient-Bio (Gyeonggi-do, Korea). K/BxN mice were bred in our facility by crossing KRN transgenic C57BL/6 mice with NOD mice [30]. All mice were maintained in a specific pathogen-free barrier facility at Hanyang University. The study was approved by the Institutional Animal Care and Use Committee (Approval numbers: 2020-0194, 2020-0184).
NZB/W female mice aged 18 weeks were assigned to four groups by distributing mice by body weight and anti-dsDNA IgG titer equivalently across the groups. Each group of mice was administered orally with 42 mg per kg body weight (mpk) of SKI-O-703 dissolved in 50 mM citric acid, 84 mpk of SKI-O-703, 30 mpk of tofacitinib (Selleckchem) dissolved in $0.5\%$ carboxymethyl cellulose (Sigma Aldrich) containing $0.025\%$ Tween 20, or a mixture of the two vehicles. The drugs were given once daily up to 34 weeks of age. Body weight was measured every 2 weeks and serum was collected every 4 weeks. At the termination of the experiments (34 weeks of age), serum, urine, spleen, BM, and kidneys were removed post mortem and assayed by histopathological and biochemical methods as below. Levels of blood urea nitrogen (BUN) and creatinine were measured by Knotus (Incheon, Korea).
Arthritogenic serum was collected from 8-to-15-week-old K/BxN mice. Eight-week-old male BALB/c mice were injected intraperitoneally (ip) with 100 μl of K/BxN serum to induce arthritis. The mice were treated orally twice daily with 42 or 84 mpk of SKI-O-703 from that day for 9 days. In some experiments, mice were injected ip every 2 days with 400 μg/mouse of the TNF blocker, etanercept (Amgen). Ankle thickness was measured, and arthritic index was determined daily, as described previously [31]. On day 9 post-induction, spleens and draining (inguinal and axillary) lymph nodes (LNs) were removed post mortem and assayed by histopathological and immunologic methods.
## Histopathologic examination
Kidneys removed from the NZB/W mice were fixed in $4\%$ paraformaldehyde, embedded in paraffin, sectioned at 3 μm thickness, and stained with periodic acid-Schiff (PAS) and hematoxylin (Sigma Aldrich). Numbers of intra-glomerular cells per glomerular cross-section were counted, graded 0–3 (0 = 35–40 cells, 1 = 41–50 cells, 2 = 51–60 cells, 3 = > 60 cells), and displayed as glomerular hypercellularity [32]. Glomerular size was measured using Image J software. To assess the glomerular size and hypercellularity of each mouse, more than 20 glomeruli per mouse were individually examined and averaged.
Hind paws were removed from KSTA mice, fixed, and decalcified in $5.5\%$ EDTA in phosphate-buffered formalin. The paws were embedded in paraffin, sectioned, and stained with hematoxylin and eosin (H&E). Arthritic changes in the ankle and foot were scored (0–4 scale) and expressed as a histopathologic index, as described previously [33]. Safranine O-stained sections were scored (0–4 scale) and expressed as the score for cartilage erosion, as described [34].
## Fluorescence microscopy
Kidneys and spleens of NZB/W mice were embedded in OCT compound (Sakura Finetek, Torrance, CA, USA) and snap-frozen in liquid nitrogen. Frozen sections were fixed in acetone and blocked with $10\%$ normal donkey serum (Sigma Aldrich). Kidney sections were stained with a 1:200 dilution of anti-IgG-biotin (Sigma Aldrich), anti-IgM-biotin (Southern Biotech) and anti-C3-biotin (Bioss), followed by reaction with 1:200-dilution of streptavidin-cy3 (Invitrogen). Spleen sections were stained with anti-GL7-FITC, anti-CD4-APC, and anti-IgD-PE (all from BD Biosciences or eBioscience). Fluorescence images were acquired using a TCS SP5 confocal microscope (Leica). The area of GCs composed with GL7+ cells per image was calculated using ImageJ software (NIH, Bethesda, MD, USA).
## ELISA
Titers of anti-dsDNA IgG Ab were measured by ELISA, as described previously [35]. In brief, sera were diluted $\frac{1}{10}$ 000 to $\frac{1}{50}$ 000 in PBS and applied to immunosorbent plates (Nunc) precoated with 5 μg/ml poly-L-lysine and 5 μg/ml thymic DNA (Sigma Aldrich). The serum containing the highest titer of anti-dsDNA IgG was serially diluted and used as standard. The plates were incubated with anti-mouse IgG-biotin (Sigma Aldrich) and streptavidin-HRP (BD Biosciences). The concentration of albumin in urine was measured by quantitative ELISA using a mouse albumin ELISA kit (Bethyl), according to the manufacturer’s instructions. The concentration of serum B-cell activating factor of the TNF family (BAFF) was quantitated using a BAFF/BLys/TNFSF13B ELISA Kit (R&D Systems).
## FACS
Single-cell suspensions of spleen and LN cells were prepared as previously described [35]. Joint tissues from hind paws of KSTA mice were dissected free of soft tissue and bones, digested with 100 μg/ml Liberase (Roche) for 45 min at 37oC, and filtered through a 70-μm-pore cell strainer (SPL Life Sciences) to prepare single cell suspensions of synovial infiltrates. The single cell suspensions were stained with an appropriate combination of mAbs and analyzed by FACS. The mAbs used were: CD138-PE, B220-PerCP or -APC-cy7, FAS-PE, CD19-PerCP or -APC, CD21-FITC or -APC, CD43-APC, CD23-PE-cy7, CD8a-PE, CD25-APC-cy7, CXCR5-biotin, GL7-FITC, CD45.2-FITC, CD27-FITC, Ki-67-FITC, Gr-1-FITC, CD4-FITC or -APC-cy7, phospho-Syk-PE, CD11b-PE or -PerCP, PD-1-APC, CD44-APC-cy7, and F$\frac{4}{80}$-PerCP mAbs (all from BD Biosciences, eBioscience or Biolegend). Streptavidin-PerCP was purchased from BD Biosciences.
## Reverse transcription (RT) and quantitative PCR
Splenic CD19+ B cells from NZB/W mice were sorted by MACS, and lysed with Trizol reagent (Invitrogen) followed by standard methods of total RNA purification. The RNA was assayed by RT and quantitative PCR as described [36]. Levels of BAFFR transcripts were normalized to the level of β-actin transcripts. PCR primers used were 5ʹ-TCGACCCTCTGGTGAGAAAC-3ʹ and 5ʹ-CACGCTGCTTGTATGTCCAG-3ʹ for BAFFR and 5ʹ-GACGGCCAGGTCATCACTATTG-3ʹ and 5ʹ-AGGAAGGCTGGAAAAGAGCC-3ʹ for β-actin.
## SKI-O-592 is a potent and selective Syk kinase inhibitor
We have developed a novel pyrazolylpyrimidine compound named SKI-O-592 to selectively inhibit Syk. We also converted SKI-O-592 to SKI-O-703, a mesylate salt form with suitable physicochemical properties and ADME profile. SKI-O-703 is soluble in aqueous solution and can be used orally in vivo. The IUPAC name of SKI-O-703 is (S)-cyclopropyl(5-((4-(4-((4-hydroxyisoxazolidin-2-yl)methyl)-3-methyl-1H-pyrazol-1-yl)pyrimidin-2-yl)amino)-1-methyl-1H-indol-3-yl)methanone dimesylate, and its structure is shown in Supplementary Fig. S1A.
We first carried out kinase assays to determine how efficiently and selectively SKI-O-592 inhibits the kinase activity of Syk in comparison with R406, a well-known Syk inhibitor. SKI-O-592 and R406 inhibited the kinase activity of Syk with IC50 values of 6.2 nM and 56.5 nM, respectively, indicating that SKI-O-592 is approximately 9-fold more potent than R406 as a Syk kinase inhibitor (Table 1). SKI-O-592 was highly specific for Syk, since it had IC50 values against all other kinases we tested from 67- to 2753-fold higher than against Syk. Unlike SKI-O-592, R406 inhibited all these kinases except FGFR1 and AuroraB more efficiently than Syk. Thus SKI-O-592 is much superior to R406 as a Syk kinase inhibitor in terms of both potency and selectivity.
**Table 1:**
| Kinases | SKl-O-592 | SKl-O-592.1 | R406 | R406.1 |
| --- | --- | --- | --- | --- |
| Kinases | IC50 (nM) | Ratio to Syk | IC50 (nM) | Ratio to Syk |
| Syk | 6.2 | 1.0 | 56.5 | 1.0 |
| Jak2 | 1859 | 302 | 1.3 | <0.1 |
| Jak3 | 5807 | 943 | 16.3 | 0.3 |
| RET | 412 | 67 | 10.7 | 0.2 |
| KOR | 687 | 111 | 18.8 | 0.3 |
| FLT3 | 1783 | 289 | 0.5 | <0.1 |
| FGFR1 | 16 960 | 2753 | 88.9 | 1.6 |
| FGFR2 | >10 000 | >1623 | 22.4 | 0.4 |
| FGFR3 | 5662 | 919 | 32.2 | 0.6 |
| Pyk2 | 709 | 115 | 24.3 | 0.4 |
| AuroraB | >10 000 | >1623 | 164.7 | 2.9 |
Phosphorylation of tyrosine residues in *Syk is* required for its activity. To determine whether SKI-O-592 inhibits the phosphorylation of Syk that occurs upon crosslinking BCR and FcγR, we cultured Ramos human B cells and human monocytes (primary CD14+ monocytes and THP-1 cells) with anti-IgM and IgG, respectively, and measured phosphorylated Syk by immunoblotting. SKI-O-592 treatment inhibited phosphorylation of tyrosine residues 525 and 526 of Syk, and its downstream molecules (BLNK, PLCγ1, and Vav1), in all three cell lines in a dose-dependent manner, and had a more potent effect than R406 (Fig. 1A–C). According to measurements of phosphorylated Syk by ELISA, the IC50 value of SKI-O-592 was 3.7- to 5.8-times lower than that of R406 in all three cell lines (Fig. 1D). Thus, these results confirm that SKI-O-592 inhibits the phosphorylation and subsequent kinase activity of Syk in B cells and monocytes more efficiently than R406. Consistent with this, treatment with SKI-O-703 (a mesylate salt of SKI-O-592) reduced the phosphorylation of Syk in response to BCR and CD40 stimulation in mouse primary B cells (Fig. 1E).
**Figure 1::** *in vitro inhibitory effects of SKI-O-592 and -703 on Syk in B cells and monocytes. Ramos cells (A), human primary monocytes (B), and THP cells (C) were pretreated with inhibitors at the indicated concentrations and stimulated with anti-human IgM (A) and human IgG (B and C). The cells were then lysed and assayed by immunoblotting methods. The relative intensities of protein phosphorylation levels were quantitated with reference to each lane of β-actin control bands. (D) Ramos cells, human primary monocytes, and THP cells were treated as in A–C for 24 h and levels of phosphorylated Syk (p-Syk) were measured by ELISA. (E) Mouse primary B cells were stimulated with anti-IgM mAb for 60 min in the presence or absence of SKI-O-703 and assayed for p-Syk by FACS. A representative FACS profile and data on the dose-response of mean fluorescence intensity (MFI) are shown. The data are representative of two independent experiments.*
Zap70 kinase transduces T cell receptor (TCR)-mediated signals in T cells in a manner similar to that of Syk in B cells. We therefore tested whether SKI-O-592 inhibited Zap70 in T cells. SKI-O-592 at 0.3–10 μM failed to affect the phosphorylation of Zap70 and its downstream target PLCγ1 in TCR-stimulated T cells, whereas these phosphorylation events were inhibited by ≥ 1.1 μM R406 (Supplementary Fig. S1B). Thus, SKI-O-592 appears to be ineffective in inhibiting TCR-mediated signaling in T cells.
## SKI-O-592 and -703 selectively inhibit BCR-mediated survival, proliferation, and differentiation of B cells
Given that Syk-mediated signaling is critical for the survival, proliferation, and differentiation of B cells, we asked whether SKI-O-703 interfered with these processes in primary B cells. We stimulated mouse primary B cells with either anti-IgM mAb or LPS in the presence or the absence of SKI-O-703 and examined these phosphorylation events. In the CP670-dilution assay to detect dividing cells, 0.1–5 μM SKI-O-703 inhibited BCR crosslinking-induced proliferation in a concentration-dependent manner (Fig. 2A). LPS-induced proliferation was less susceptible to SKI-O-703, since at least 5 μM SKI-O-703 was required to significantly inhibit proliferation (Fig. 2B).
**Figure 2::** *in vitro effects of SKI-O -703 on the proliferation, survival, and differentiation of B cells. Mouse primary B cells were labeled with CP670, and stimulated with either anti-IgM mAb, CD40L and IL-4 (A) or LPS (B) for 72 h in the presence or absence of SKI-O-703 and tofacitinib at the indicated concentrations; they were then assayed by FACS. (C and D) Mouse primary B cells were cultured as in A and B for 24 h, stained with Annexin V and 7-AAD, and assayed by FACS. Representative FACS profiles and graphs displaying mean Annexin V-positivity are shown. (E) Human primary B cells were stimulated with CpG ODN plus IL-2 (to detect IgM and IgG) or CpG ODN 2006 plus IFN-α (to detect IL-6) in the presence or absence of inhibitors. The culture supernatants were assayed by ELISA. Graphs are expressed as % positivity relative to the controls (without inhibitor). The data are representative of 2–3 independent experiments. *P < 0.05, **P < 0.01, and ***P < 0.001, compared with vehicle control by two-tailed unpaired Students t-tests.*
When early apoptotic (AnnexinV+7-AAD−) and late apoptotic (AnnexinV+7-AAD+) cells were detected by FACS, SKI-O-703 treatment increased the proportions of early and late apoptotic cells and this cytotoxic effect was more potent when the B cells were activated by anti-IgM mAb than by LPS (Fig. 2C and D). This observation demonstrates that SKI-O-703 selectively inhibits survival and proliferation in response to the BCR-Syk signaling axis and supports the idea that Syk acts proximal to BCR rather than prior to TLR4. To confirm that this cytotoxicity is associated with selective inhibition of Syk activity, we tested the effect of SKI-O-703 on the survival of TCR-engaged primary T cells and found that up to 5 μM SKI-O-703 failed to increase the proportions of early and late apoptotic cells (Supplementary Fig. S1C).
We next tested whether SKI-O-592 was able to inhibit the differentiation of B cells into Ab- and IL-6-secreting cells. For this purpose, we cultured human primary B cells stimulated with a TLR9 agonist plus cytokines (IFN-α or IL-2) in the presence or absence of inhibitors and performed ELISAs on culture supernatants. SKI-O-592 inhibited the production of IgM, IgG, and IL-6, with an efficacy comparable to that of R406 (Fig. 2E). SKI-O-592 also inhibited IL-2 production by activated T cells, but with potency about 10-fold lower than that of R406 (Supplementary Fig. S1D). These results demonstrate that SKI-O-592 efficiently blocks the differentiation of B cells into Ab- and proinflammatory cytokine-secreting cells.
We also tested the effect of the Jak-1 and Jak-3 inhibitor, tofacitinib (CP-690550), and found that tofacitinib up to 1 μM did not significantly inhibit the proliferation and death of primary B and T cells (Fig. 2A–D, Supplementary Fig. S1C). It had a minimal effect on Ab production and even increased IL-6 production (Fig. 2E). This result suggests that SKI-O-703 might be more effective than tofacitinib in reducing B cell activation-mediated pathology.
## Orally administered SKI-O-703 reduces lupus nephritis-like manifestations in NZB/W mice
NZB/W female mice have been used as models of the multifactorial complexity of SLE. Disease development in this model is largely dependent on GC-driven Ab responses and subsequent type III hypersensitivity, which is most prominent in kidney tissues [37]. We used the NZB/W model to see whether SKI-O-703 inhibited the onset and progression of lupus nephritis by suppressing the activation of autoreactive B cells, and autoAb production. NZB/W female mice at 18 weeks of age contained about 27- to 81-fold higher titers of anti-dsDNA IgG Ab than normal C57BL/6 mice, without any overt pathologic manifestations (data not shown). Female NZB/W mice at this age, namely at the autoimmunity-established preclinical phase, were assigned to four groups, and the groups were administered orally with 42 mpk of SKI-O-703, 84 mpk of SKI-O-703, a control drug, or vehicle, respectively. Thrity mpk of tofacitinib was used as a control drug, according to a previous study [38]. The drugs and vehicle were given once daily up to 34 weeks of age.
Splenomegaly is a hallmark of SLE. SKI-O-703 treatment at high dose (84 mpk), but not low dose (42 mpk), significantly reduced spleen weights without significantly affecting body weights, indicating that it reduced splenomegaly without causing wasting (Fig. 3A and B). By contrast, exposure to tofacitinib reduced both body weights and spleen weights.
**Figure 3::** *in vivo effect of oral SKI-O-703 on lupus-like signs in NZB/W mice. Female NZB/W F1 mice were administrated orally with 42 mpk of SKI-O-703 (SKI 42), 84 mpk of SKI-O-703 (SKI 84), or tofacitinib (Tofa) from 18 to 34 weeks of age. (A) Body weights were measured every 2 weeks and are displayed as mean percent change of body weight, with SEM. (B) Spleen weights measured at the end of the experiment. (C) Serum was collected every 4 weeks and assayed by ELISA to measure titers of anti-dsDNA IgG. Mean titers are shown. AU, arbitrary unit. (D) Urine collected at 34 weeks of age and urinary albumin was assayed by ELISA. (E) Concentrations of blood urine nitrogen (BUN) and creatinine at 34 weeks of age. Graphs display means ± SEMs, and symbols represent values of individual mice (B, D, and E). *P < 0.05, **P < 0.01, and ***P < 0.001, compared with the vehicle control group by two-way ANOVA (A and C) and two-tailed unpaired Students t-test (B, D, and E).*
During the period of treatment (18–34 weeks of age), the serum level of anti-dsDNA IgG Ab gradually rose in the vehicle control group, and this effect was dramatically attenuated by 84 mpk of SKI-O-703 ($P \leq 0.01$ by two-way ANOVA test) (Fig. 3C). Treatment with 42 mpk of SKI-O-703 did not have the same effect, and even temporarily elevated the Ab titer. There was a trend towards reduced Ab titer at 26 and 30 weeks of age in the tofacitinib-treated group, but it did not reach statistical significance.
Levels of urinary protein, blood urea nitrogen (BUN), and blood creatinine are indicators of lupus nephritis and renal dysfunction. We measured these markers at 34 weeks of age. The mean concentration of urinary albumin was 742.9 mg/dl in the vehicle control group, and this was markedly reduced in the group dosed with 84 mpk SKI-O-703, but not in the 42 mpk group (Fig. 3D). The concentrations of BUN and creatinine were significantly lowered in both the high-dose and low-dose-treatment groups. Taken together these findings demonstrate that SKI-O-703 can inhibit splenomegaly, autoantibody production, and renal dysfunction in NZB/W mice.
## Oral administration of NZB/W mice with SKI-O-703 attenuates the histopathological manifestations of glomerulonephritis
The histopathologic manifestations of lupus nephritis include glomerular hypercellularity, glomerular enlargement, and the appearance of eosinophilic protein casts and crescents. All these manifestations were clearly evident in the vehicle control group and significantly improved in all three drug-treated groups, as eosinophilic protein casts and crescents were absent and glomerular hypercellularity and size were significantly reduced (Fig. 4A).
**Figure 4::** *histopathological alteration of kidney tissues by SKI-O-703 in NZB/W mice. Female NZB/W F1 mice were administrated orally with 42 mpk SKI-O-703 (SKI 42), 84 mpk SKI-O-703 (SKI 84), or tofacitinib (Tofa) from 18 to 34 weeks of age, and kidneys were examined at 34 weeks by histopathological methods. (A) Paraffin sections were stained with PAS and hematoxylin. An eosinophilic protein cast and crescent are indicated by the arrowhead and arrow, respectively. The images are representative of each group. Graphs show means ± SEMs with symbols representing values of individual mice. (B) Cryosections were stained with anti-IgG, anti-IgM, and anti-C3 Abs and observed by fluorescence confocal microscopy. Boxes in the left images are magnified in the right images. Representative images with mean fluorescence intensities are shown. Bar scale, 512 μm. *P < 0.05 and **P < 0.01 by two-tailed unpaired Students t-test.*
Since immune complexes along with complement factors are deposited into glomeruli and then induce inflammatory responses, we examined whether SKI-O-703 treatment blocked these pathologic processes. Once again, whereas the glomeruli of the kidneys from the vehicle control mice were highly stained with fluorescence-labeled mAbs to IgG, IgM, and C3, the intensities of IgM and C3 staining were significantly reduced in the high dose, but not low dose, SKI-O-703 group (Fig. 4B).
## Oral administration of SKI-O-703 to NZB/W mice reduces the GC- and BAFF-signaling involved in humoral immune responses
To address the cellular and molecular mechanisms by which SKI-O-703 attenuates lupus nephritis in NZB/W mice, we collected spleens, BM, and sera at 34 weeks of age, and examined them by FACS and biochemical methods. Consistent with the reduced splenomegaly, total spleen cells were significantly less numerous in the high dose SKI-O-703 group (Fig. 5A). This was mainly due to a reduction in the B cell population, because the number of T cells was not significantly altered (Fig. 5B and D). All subsets of B cells, follicular, GC, and plasma cells, but not marginal zone cells, were significantly less numerous than those of vehicle controls (Fig. 5C and Supplementary Fig. S2). In the plasma cells, the ratio of Ki-67+ dividing cells to Ki-67- non-dividing cells was reduced, indicative of selective deletion of short-lived plasma cells (Supplementary Fig. S3). Despite the unaltered number of CD4+ T cells, CXCR5+PD-1+ Tfh cells were reduced in number (Fig. 5D). Moreover, fluorescence microscopy revealed fewer GL7+ GC B cells and IgD+ follicular B cells in the spleens of SKI-O-703-treated mice than in the vehicle controls (Fig. 5E).
**Figure 5::** *reduced numbers of spleen cells in SKI-O-703-treated NZB/W mice. Female NZB/W F1 mice were administrated orally with 42 mpk SKI-O-703 (SKI 42), 84 mpk SKI-O-703 (SKI 84), or tofacitinib (Tofa) from 18 to 34 weeks of age. Spleens and sera were collected at 34 weeks of age. (A–D) Spleens were assayed by FACS. The gating strategy is displayed in Supplementary Fig. S2. (E) Spleens were cryosectioned, stained with Abs to GL-7, IgD, and CD4, and analyzed by confocal microscopy. Two representative images per group are shown. Bar scale, 80 μm. Percentage of GC area (area of GL7+/area of total area) from 3 to 4 images/group are displayed as mean ± SD. (F) Splenic B cells were assayed by quantitative RT-PCR. Levels of BAFFR mRNA were normalized to the level of β-actin mRNA. (G) Concentrations of BAFF measured by ELISA in sera. The data are representative of at least two independent experiments. Graphs show means + SEMs, and symbols represent values of individual mice. *P < 0.05 and **P < 0.01 by two-tailed unpaired Students t-test.*
To see whether long-term treatment with SKI-O-703 affected BM hematopoiesis, we determined the cellular composition of BM cells and found that the proportions and numbers of the various BM cell populations did not differ between the groups. In particular, all B lineage-committed cells, such as prepro-, pro-, pre- and immature B cells, normally populated in the BM of SKI-O-703-treated mice (Supplementary Fig. S4). Therefore, SKI-O-703 appeared not to interfere with BM hematopoiesis, and the reduction in B cells did not result from defective B lymphopoiesis in the BM.
The cytokine BAFF is critical for the survival and differentiation of B cells and plays a role in the development of SLE [39]. We found, interestingly, that B cells exposed to high dose SKI-O-703 expressed the BAFF receptor at a reduced level (Fig. 5F), and the level of serum BAFF was also reduced (Fig. 5G). Therefore, the effect of SKI-O-703 on the suppression of lupus nephritis was mirrored by the reduced BAFF-BAFF receptor system.
## SKI-O-703 inhibits KSTA by reducing recruitment of neutrophils and macrophages into synovial tissue
AutoAb-mediated inflammatory responses are evident in human RA as well as SLE, and this effect is well illustrated in the KSTA mouse model. To distinguish the effect of SKI-O-703 on innate inflammatory cells from that on B cells, we orally dosed normal BALB/c mice with SKI-O-703 during the progression of KSTA. We found that both ankle thickness and arthritic index were dramatically reduced to the basal level in 84 mpk SKI-O-703-treated mice and the reduction was less pronounced at the lower dose (Fig. 6A). Both histopathologic index and cartilage erosion score were significantly reduced only in the high dose mice (Fig. 6B).
**Figure 6::** *SKI-O-703 attenuates KSTA. BALB/c mice were infused with K/BxN serum and orally dosed with 42 mpk SKI-O-703 (SKI 42) or 84 mpk SKI-O-703 (SKI 84) twice a day for 9 days. (A) Ankle thickness and arthritic indexes are displayed as means + SEMs. (B) On Day 9, hind paws were examined by histopathologic methods after staining with H&E and Safranine O. Representative images and graphs displaying means ± SEMs with symbols representing each individual are shown. (C) Spleen cells and joint-draining LN cells were assayed by FACS. Graphs display means ± SEMs, with symbols representing values of individual mice. The data are representative of four independent experiments. *P < 0.05, **P < 0.01, and ***P < 0.001, compared with the vehicle control group by two-way ANOVA (A) and two-tailed unpaired Student’s t-test (B-D).*
AutoAbs deposited in the synovial tissue can elicit innate inflammatory responses by recruiting and activating neutrophils and macrophages. Indeed, these cells are the main agents of KSTA [21, 40]. As expected, SKI-O-703 treatment at 84 mpk, but not 42 mpk, significantly reduced numbers of whole immune cells (CD45+) infiltrated into synovial tissue. Neutrophils and macrophages were the main cell types whose numbers were strongly affected (Fig. 6C). Minor cell types, such as B cells, CD4+ T cells, and plasma cells, were also significantly less numerous in the high dose mice (data not shown). Thus, these results demonstrate that SKI-O-703 can suppress macrophage- and neutrophil-mediated inflammatory activation at sites of autoAb deposition.
Interestingly, despite the acute synovitis induced by arthritogenic serum transfer, the joint-draining LNs from the mice receiving K/BxN serum exhibited lymphadenopathy on Day 9 post-infusion. This pathologic manifestation disappeared upon high dose SKI-O-703 treatment, which also resulted in significantly reduced numbers of total cells, B cells and GC B cells (Fig. 6D), indicating that SKI-O-703 is effective in preventing episodes of innate inflammation in local tissue from developing into adaptive immune activation in draining LNs.
## Combined treatment with TNF blockade and SKI-O-703 suppresses KSTA
TNF is a proinflammatory cytokine that plays a critical role in the progression of RA and KSTA. Because of this, anti-TNF treatments have been used in many inflammatory diseases. Nevertheless, a substantial proportion of RA patients are refractory to anti-TNF therapy [41] and the effects of single treatments of TNF blockade in animal models of RA are subtle or uncertain [42, 43]. Consistent with this, we found that treatment of mice with up to 400 μg/mouse of the TNF blocker eternacept (a soluble TNF receptor II-human IgG1 Fc fusion protein) did not significantly inhibit progression of KSTA, as was true for suboptimal doses (42 mpk) of SKI-O-703. However, when we combined the two treatments they had a synergistic effect and significantly suppressed disease progression, as judged by the reductions in ankle thickness, arthritic index, and infiltration of innate inflammatory cell into joints and draining LNs (Fig. 7). Thus, SKI-O-703 at a suboptimal dose is a candidate drug for use in combination with anti-TNF treatments.
**Figure 7::** *effects of SKI-O-703 combined with TNF blockade on KSTA. BALB/c mice were infused with K/BxN serum and treated with 42 mpk SKI-O-703 (SKI 42) alone, etanercept (a-TNF) alone, or both, for 9 days. (A) Ankle thickness and arthritic indexes on a given day are shown as means + SEM. (B and C) Spleen and dLN cells were analyzed by FACS. The data are representative of four independent experiments. *P < 0.05, **P < 0.01, and ***P < 0.001, compared with the vehicle control group by two-way ANOVA (A) and two-tailed unpaired Student’s t-test (B and C).*
## Discussion
In the present study, we have presented in vitro and in vivo evidence that a novel selective Syk inhibitor, SKI-O-703, that we have developed can reduce SLE- and RA-like symptoms in animal disease models. The underlying mechanism involves blocking BCR- and FcR-proximal signals that are essential for activation of B cells and innate inflammatory cells, respectively. Therefore, SKI-O-703 appears to be a promising drug candidate for treatment of Ab-mediated inflammatory diseases, targeting both Ab-producing and Ab-sensing cells.
IgG class-switched affinity-matured autoAbs are the cause of lupus and are mainly produced as a result of GC reactions. The coordinated events taking place in the GC are tightly controlled by a variety of mechanisms including Syk signals. For example, the Syk-Mcl1 pathway enables GC-like B cells to survive, and inhibition of that pathway induces apoptosis of those cells [14]. A cell-autonomous function of Syk in GC B cells is required for their differentiation into plasma cells, and Syk degradation interferes with plasma cell formation in GCs [13]. We found above that the reduction of plasma cell numbers due to SKI-O-703 was associated with a reduction in GC B cells. This suggests that Syk inhibition by SKI-O-703 suppresses the differentiation of follicular B cells into plasma cells via the GC pathway, thereby leading to reduced production of IgG autoAb. The idea that the “follicular B-GC B-plasma cell” axis is the target of SKI-O-703 is supported by our observation that SKI-O-703-treated mice had reduced numbers of Tfh cells, a population responsible for positive selection of GC B cells in the GC.
Although the impact of SKI-O-703 on follicular B cells was obvious, we found, interestingly, that it did not alter the size of the marginal zone B cell population. Marginal zone B cells constitute a subset of mature B cells specifically dwelling in marginal zones of the spleen. In addition to phenotypic differences, they have functional features distinct from follicular B cells, as they readily mount rapid Ab responses to blood-borne antigens via a pathway that does not require the GC reaction [44]. They also maintain their numbers in a manner more dependent on TLR signals than is the case for follicular B cells [45, 46]. These characteristics may explain why marginal zone B cells were resistant to SKI-O-703 treatment. This interpretation is also consistent with our finding that SKI-O-703 did not significantly alter TLR4-induced proliferation of B cells. Maintaining the marginal zone B cell population upon SKI-O-703 treatment while suppressing the immune competence of follicular B cells might be beneficial in protecting the host from blood-borne infectious agents. Given that many immunosuppressive drugs have unwanted complications, such as enhancing susceptibility to infectious agents [47], our data suggest that SKI-O-703 may provide a superior therapeutic strategy by selectively targeting the follicular to GC B cell pathway without reducing marginal zone B cell function.
Importantly, we have demonstrated that SKI-O-703 does not alter the early development of B-lineage cells or of other hematopoietic cells, as judged by the normal proportions of all early progenitors, myeloid- and B lymphoid-lineage cells in the BM of mice treated with SKI-O-703 for a long period (16 weeks). This finding provides robust evidence that SKI-O-703 does not perturb hematopoiesis, as long-term treatment with immunosuppressants frequently does [48]. Furthermore, given that lymphopenia is prone to precipitate homeostatic proliferation-driven autoimmune states [49, 50], SKI-O-703 may not amplify autoimmunity via such a lymphopenia-associated compensatory mechanism.
It is noteworthy that, although the number of plasma cells was decreased upon SKI-O-703 treatment, the ratio of non-dividing to dividing plasma cells increased. This result implies that non-dividing long-lived plasma cells, unlike dividing short-lived plasma cells, are refractory to the cytotoxic effect of SKI-O-703. This result is not surprising, since a hallmark of long-lived plasma cells is resistance to most cytostatic drugs (e.g. cyclophosphamide) [51, 52]. Because one aspect of the action of vaccines is the preservation of long-lived plasma cells targeting pathogenic microbes, this lack of effect of SKI-O-703 on long-lived plasma cells would be beneficial in maintaining immune competence against microbial infection.
Since *Syk is* known to be relatively inert in T cells, we did not expect SKI-O-703 to inhibit T cell activation. Indeed, we found that T cells were refractory to the cytotoxic effects of SKI-O-703, unlike B cells. Moreover, T cells were less susceptible to SKI-O-703 than to R406 and tofacitinib in terms of inhibition of IL-2 production. Given that the regulatory T cells that are crucial for maintaining self-tolerance require IL-2 for their survival and function [53, 54], this property of SKI-O-703 should contribute to the maintenance of regulatory T cell-mediated self-tolerance.
Because of the complexity of inflammatory processes, optimal therapeutic efficacy would be achieved by inhibiting more than one mechanism. Indeed, we found that concurrent treatment by TNF blockade and SKI-O-703 at a suboptimal dose was more effective than the separate treatments in inhibiting the synovitis of KSTA. This finding suggests that use of SKI-O-703 in combination with TNF blockades could be attractive for treating RA. Such a combined strategy might help cope with various anti-TNF therapy-associated complications, such as the refractoriness of certain groups of patients, elevated infection rates, and high treatment costs.
In summary, our data provide evidence that SKI-O-703 is a highly selective and potent Syk inhibitor in vitro and suppresses Ab-mediated inflammatory diseases in vivo. It also acts synergistically with anti-TNF therapy. Thus, we propose that it is a promising candidate for the treatment of Ab-mediated inflammatory diseases.
## Ethics Approval
This study was carried out in accordance with the guidelines for animal care and use approved by the Institutional Animal Care and Use Committee at Hanyang University.
## Conflict of Interest
The authors declare no conflict of interest.
## Funding
Not Applicable.
## Data availability
The data that support the findings of this study are available from the corresponding author, [J.Y], upon reasonable request.
## Author contributions
S.C., T.Y., H.H., and E.J. performed experiments and prepared figures. E.J., H.H., and J.Y. designed experiments and interpreted data. E.J., H.H., and J.Y. wrote the manuscript. J.Y. supervised the study. All authors have read and agreed published version of the manuscript.
## Permission to reproduce
Not Applicable. All content is the original work of the authors.
## Clinical trial registration
Not Applicable.
## References
1. Theofilopoulos AN, Kono DH, Baccala R.. **The multiple pathways to autoimmunity**. *Nat Immunol* (2017) **18** 716-24. DOI: 10.1038/ni.3731
2. Suurmond J, Diamond B.. **Autoantibodies in systemic autoimmune diseases: specificity and pathogenicity**. *J Clin Invest* (2015) **125** 2194-202. DOI: 10.1172/JCI78084
3. Isenberg DA, Manson JJ, Ehrenstein MR, Rahman A.. **Fifty years of anti-ds DNA antibodies: are we approaching journey’s end?**. *Rheumatology (Oxford)* (2007) **46** 1052-6. DOI: 10.1093/rheumatology/kem112
4. Rahman A, Isenberg DA.. **Systemic lupus erythematosus**. *N Engl J Med* (2008) **358** 929-39. DOI: 10.1056/NEJMra071297
5. Tsokos GC.. **Autoimmunity and organ damage in systemic lupus erythematosus**. *Nat Immunol* (2020) **21** 605-14. DOI: 10.1038/s41590-020-0677-6
6. Dudley DD, Chaudhuri J, Bassing CH, Alt FW.. **Mechanism and control of V(D)J recombination versus class switch recombination: similarities and differences**. *Adv Immunol* (2005) **86** 43-112. DOI: 10.1016/S0065-2776(04)86002-4
7. Tiller T, Kofer J, Kreschel C, Busse CE, Riebel S, Wickert S. **Development of self-reactive germinal center B cells and plasma cells in autoimmune Fc gammaRIIB-deficient mice**. *J Exp Med* (2010) **207** 2767-78. DOI: 10.1084/jem.20100171
8. Vinuesa CG, Sanz I, Cook MC.. **Dysregulation of germinal centres in autoimmune disease**. *Nat Rev Immunol* (2009) **9** 845-57. DOI: 10.1038/nri2637
9. Mocsai A, Ruland J, Tybulewicz VL.. **The SYK tyrosine kinase: a crucial player in diverse biological functions**. *Nat Rev Immunol* (2010) **10** 387-402. DOI: 10.1038/nri2765
10. Cheng AM, Rowley B, Pao W, Hayday A, Bolen JB, Pawson T.. **Syk tyrosine kinase required for mouse viability and B-cell development**. *Nature* (1995) **378** 303-6. DOI: 10.1038/378303a0
11. Turner M, Mee PJ, Costello PS, Williams O, Price AA, Duddy LP. **Perinatal lethality and blocked B-cell development in mice lacking the tyrosine kinase Syk**. *Nature* (1995) **378** 298-302. DOI: 10.1038/378298a0
12. Ackermann JA, Nys J, Schweighoffer E, McCleary S, Smithers N, Tybulewicz VLJ.. **Syk Tyrosine kinase is critical for B cell antibody responses and memory B cell survival**. *J Immunol* (2015) **194** 4650-6. DOI: 10.4049/jimmunol.1500461
13. Davidzohn N, Biram A, Stoler-Barak L, Grenov A, Dassa B, Shulman Z.. **Syk degradation restrains plasma cell formation and promotes zonal transitions in germinal centers**. *J Exp Med* (2020) **217**. DOI: 10.1084/jem.20191043
14. Roders N, Herr F, Ambroise G, Thaunat O, Portier A, Vazquez A. **SYK inhibition induces apoptosis in germinal center-like b cells by modulating the antiapoptotic protein myeloid cell leukemia-1, affecting B-cell activation and antibody production**. *Front Immunol* (2018) **9** 787. DOI: 10.3389/fimmu.2018.00787
15. Berton G, Mocsai A, Lowell CA.. **Src and Syk kinases: key regulators of phagocytic cell activation**. *Trends Immunol* (2005) **26** 208-14. DOI: 10.1016/j.it.2005.02.002
16. Underhill DM, Goodridge HS.. **The many faces of ITAMs**. *Trends Immunol* (2007) **28** 66-73. DOI: 10.1016/j.it.2006.12.004
17. McAdoo SP, Bhangal G, Page T, Cook HT, Pusey CD, Tam FW.. **Correlation of disease activity in proliferative glomerulonephritis with glomerular spleen tyrosine kinase expression**. *Kidney Int* (2015) **88** 52-60. DOI: 10.1038/ki.2015.29
18. Iwata S, Yamaoka K, Niiro H, Jabbarzadeh-Tabrizi S, Wang SP, Kondo M. **Increased Syk phosphorylation leads to overexpression of TRAF6 in peripheral B cells of patients with systemic lupus erythematosus**. *Lupus* (2015) **24** 695-704. DOI: 10.1177/0961203314560424
19. Chang NH, Li TT, Kim JJ, Landolt-Marticorena C, Fortin PR, Gladman DD. **Interferon-alpha induces altered transitional B cell signaling and function in systemic lupus erythematosus**. *J Autoimmun* (2015) **58** 100-10. DOI: 10.1016/j.jaut.2015.01.009
20. Jakus Z, Simon E, Balazs B, Mocsai A.. **Genetic deficiency of Syk protects mice from autoantibody-induced arthritis.**. *Arthritis Rheum-Us* (2010) **62** 1899-910
21. Elliott ER, Van Ziffle JA, Scapini P, Sullivan BM, Locksley RM, Lowell CA.. **Deletion of Syk in neutrophils prevents immune complex arthritis**. *J Immunol* (2011) **187** 4319-30. DOI: 10.4049/jimmunol.1100341
22. Bahjat FR, Pine PR, Reitsma A, Cassafer G, Baluom M, Grillo S. **An orally bioavailable spleen tyrosine kinase inhibitor delays disease progression and prolongs survival in murine lupus**. *Arthritis Rheum* (2008) **58** 1433-44. DOI: 10.1002/art.23428
23. Deng GM, Liu L, Bahjat FR, Pine PR, Tsokos GC.. **Suppression of skin and kidney disease by inhibition of spleen tyrosine kinase in lupus-prone mice**. *Arthritis Rheum* (2010) **62** 2086-92. DOI: 10.1002/art.27452
24. Issara-Amphorn J, Somboonna N, Pisitkun P, Hirankarn N, Leelahavanichkul A.. **Syk inhibitor attenuates inflammation in lupus mice from FcgRIIb deficiency but not in pristane induction: the influence of lupus pathogenesis on the therapeutic effect**. *Lupus* (2020) **29** 1248-1262. DOI: 10.1177/0961203320941106
25. Braselmann S, Taylor V, Zhao H, Wang S, Sylvain C, Baluom M. **R406, an orally available spleen tyrosine kinase inhibitor blocks fc receptor signaling and reduces immune complex-mediated inflammation**. *J Pharmacol Exp Ther* (2006) **319** 998-1008. DOI: 10.1124/jpet.106.109058
26. Pine PR, Chang B, Schoettler N, Banquerigo ML, Wang S, Lau A. **Inflammation and bone erosion are suppressed in models of rheumatoid arthritis following treatment with a novel Syk inhibitor**. *Clin Immunol* (2007) **124** 244-57. DOI: 10.1016/j.clim.2007.03.543
27. Currie KS, Kropf JE, Lee T, Blomgren P, Xu J, Zhao Z. **Discovery of GS-9973, a selective and orally efficacious inhibitor of spleen tyrosine kinase**. *J Med Chem* (2014) **57** 3856-73. DOI: 10.1021/jm500228a
28. Weinblatt ME, Genovese MC, Ho M, Hollis S, Rosiak-Jedrychowicz K, Kavanaugh A. **Effects of fostamatinib, an oral spleen tyrosine kinase inhibitor, in rheumatoid arthritis patients with an inadequate response to methotrexate: results from a phase III, multicenter, randomized, double-blind, placebo-controlled, parallel-group study**. *Arthritis Rheum* (2014) **66** 3255-64
29. Taylor PC, Genovese MC, Greenwood M, Ho M, Nasonov E, Oemar B. **OSKIRA-4: a phase IIb randomised, placebo-controlled study of the efficacy and safety of fostamatinib monotherapy**. *Ann Rheum Dis* (2015) **74** 2123-9. DOI: 10.1136/annrheumdis-2014-205361
30. Kouskoff V, Korganow AS, Duchatelle V, Degott C, Benoist C, Mathis D.. **Organ-specific disease provoked by systemic autoimmunity**. *Cell* (1996) **87** 811-22. DOI: 10.1016/s0092-8674(00)81989-3
31. Jang E, Cho SH, Park H, Paik DJ, Kim JM, Youn J.. **A positive feedback loop of IL-21 signaling provoked by homeostatic CD4+CD25− T cell expansion is essential for the development of arthritis in autoimmune K/BxN mice**. *J Immunol* (2009) **182** 4649-56. DOI: 10.4049/jimmunol.0804350
32. Chang JW, Hung SP, Wu HH, Wu WM, Yang AH, Tsai HL. **Therapeutic effects of umbilical cord blood-derived mesenchymal stem cell transplantation in experimental lupus nephritis**. *Cell Transplant* (2011) **20** 245-57. DOI: 10.3727/096368910X520056
33. Jang E, Kim HR, Cho SH, Paik DJ, Kim JM, Lee SK. **Prevention of spontaneous arthritis by inhibiting homeostatic expansion of autoreactive CD4+ T cells in the K/BxN mouse model**. *Arthritis Rheum* (2006) **54** 492-8. DOI: 10.1002/art.21567
34. Camps M, Ruckle T, Ji H, Ardissone V, Rintelen F, Shaw J. **Blockade of PI3Kgamma suppresses joint inflammation and damage in mouse models of rheumatoid arthritis**. *Nat Med* (2005) **11** 936-43. DOI: 10.1038/nm1284
35. Jang E, Jeong M, Kim S, Jang K, Kang BK, Lee DY. **Infusion of human bone marrow-derived mesenchymal stem cells alleviates autoimmune nephritis in a lupus model by suppressing follicular helper T-cell development**. *Cell Transplant* (2016) **25** 1-15. DOI: 10.3727/096368915X688173
36. Jang E, Kim UK, Jang K, Song YS, Cha JY, Yi H. **Bach2 deficiency leads autoreactive B cells to produce IgG autoantibodies and induce lupus through a T cell-dependent extrafollicular pathway**. *Exp Mol Med* (2019) **51** 1-13. DOI: 10.1038/s12276-019-0352-x
37. Sang A, Yin Y, Zheng YY, Morel L.. **Animal models of molecular pathology systemic lupus erythematosus**. *Prog Mol Biol Transl Sci* (2012) **105** 321-70. DOI: 10.1016/B978-0-12-394596-9.00010-X
38. Ikeda K, Hayakawa K, Fujishiro M, Kawasaki M, Hirai T. **JAK inhibitor has the amelioration effect in lupus-prone mice: the involvement of IFN signature gene downregulation**. *BMC Immunol* (2017) **18** 41. DOI: 10.1186/s12865-017-0225-9
39. Davidson A.. **The rationale for BAFF inhibition in systemic lupus erythematosus**. *Curr Rheumatol Rep* (2012) **14** 295-302. DOI: 10.1007/s11926-012-0258-2
40. Yarilina A, Xu K, Chan C, Ivashkiv LB.. **Regulation of inflammatory responses in tumor necrosis factor-activated and rheumatoid arthritis synovial macrophages by JAK inhibitors**. *Arthritis Rheum* (2012) **64** 3856-66. DOI: 10.1002/art.37691
41. Solehan HM, Said MS, Shaharir S, Rajalingham S.. **Rheumatoid arthritis: refractory to infliximab, a tumor necrosis factor inhibitor**. *J Clin Med Res* (2011) **3** 270-4. DOI: 10.4021/jocmr630w
42. Llop-Guevara A, Porras M, Cendon C, Di Ceglie I, Siracusa F, Madarena F. **Simultaneous inhibition of JAK and SYK kinases ameliorates chronic and destructive arthritis in mice**. *Arthritis Res Ther* (2015) **17** 356. DOI: 10.1186/s13075-015-0866-0
43. Yi H, Kim J, Jung H, Rim YA, Kim Y, Jung SM. **Induced production of anti-etanercept antibody in collagen-induced arthritis**. *Mol Med Rep* (2014) **9** 2301-8. DOI: 10.3892/mmr.2014.2127
44. Cerutti A, Cols M, Puga I.. **Marginal zone B cells: virtues of innate-like antibody-producing lymphocytes**. *Nat Rev Immunol* (2013) **13** 118-32. DOI: 10.1038/nri3383
45. Snapper CM, Yamada H, Smoot D, Sneed R, Lees A, Mond JJ.. **Comparative in vitro analysis of proliferation, Ig secretion, and Ig class switching by murine marginal zone and follicular B cells**. *J Immunol* (1993) **150** 2737-45. PMID: 7681079
46. Oliver AM, Martin F, Kearney JF.. **IgM**. *J Immunol* (1999) **162** 7198-207. PMID: 10358166
47. Schmaldienst S, Horl WH.. **Bacterial infections during immunosuppression-immunosuppressive agents interfere not only with immune response, but also with polymorphonuclear cell function.**. *Nephrol Dial Transpl* (1996) **11** 1243-5
48. Gluck T, Kiefmann B, Grohmann M, Falk W, Straub RH, Scholmerich J.. **Immune status and risk for infection in patients receiving chronic immunosuppressive therapy**. *J Rheumatol* (2005) **32** 1473-80. PMID: 16078322
49. Monti P, Scirpoli M, Maffi P, Ghidoli N, De Taddeo F, Bertuzzi F. **Islet transplantation in patients with autoimmune diabetes induces homeostatic cytokines that expand autoreactive memory T cells**. *J Clin Invest* (2008) **118** 1806-14. DOI: 10.1172/JCI35197
50. Sheu TT, Chiang BL.. **Lymphopenia, lymphopenia-induced proliferation, and autoimmunity**. *Int J Mol Sci* (2021) **22**. DOI: 10.3390/ijms22084152
51. Miller JJ, Cole LJ.. **Resistance of long-lived lymphocytes and plasma cells in rat lymph nodes to treatment with prednisone, cyclophosphamide, 6-mercaptopurine, and actinomycin D**. *J Exp Med* (1967) **126** 109-25. DOI: 10.1084/jem.126.1.109
52. Silverman GJ, Weisman S.. **Rituximab therapy and autoimmune disorders: prospects for anti-B cell therapy**. *Arthritis Rheum* (2003) **48** 1484-92. DOI: 10.1002/art.10947
53. Dominguez-Villar M, Hafler DA.. **Regulatory T cells in autoimmune disease**. *Nat Immunol* (2018) **19** 665-73. DOI: 10.1038/s41590-018-0120-4
54. Nelson BH.. **IL-2, regulatory T cells, and tolerance**. *J Immunol* (2004) **172** 3983-8. DOI: 10.4049/jimmunol.172.7.3983
|
---
title: 'Worldwide trends in prediabetes from 1985 to 2022: A bibliometric analysis
using bibliometrix R-tool'
authors:
- JingYi Zhao
- Min Li
journal: Frontiers in Public Health
year: 2023
pmcid: PMC9993478
doi: 10.3389/fpubh.2023.1072521
license: CC BY 4.0
---
# Worldwide trends in prediabetes from 1985 to 2022: A bibliometric analysis using bibliometrix R-tool
## Abstract
### Background
Prediabetes is a widespread condition that represents the state between normal serum glucose and diabetes. Older individuals and individuals with obesity experience a higher rate of prediabetes. Prediabetes is not only a risk factor for type 2 diabetes mellitus (t2dm) but is also closely related to microvascular and macrovascular complications. Despite its importance, a bibliometric analysis of prediabetes is missing. The purpose of this study is to provide a comprehensive and visually appealing overview of prediabetes research.
### Methods
First, the Web of Science (WOS) database was searched to collect all articles related to prediabetes that were published from 1985 to 2022. Second, R language was used to analyze the year of publication, author, country/region, institution, keywords, and citations. Finally, network analysis was conducted using the R package bibliometrix to evaluate the hotspots and development trends of prediabetes.
### Results
A total of 9,714 research articles published from 1985 to 2022 were retrieved from WOS. The number of articles showed sustained growth. Rathmann W was the most prolific author with 71 articles. Diabetes Care was the journal that published the highest number of articles on prediabetes (234 articles), and Harvard University (290 articles) was the most active institution in this field. The United States contributed the most articles (2,962 articles), followed by China (893 articles). The top five clusters of the keyword co-appearance network were “prediabetes”, “diabetes mellitus”, “glucose”, “insulin exercise”, and “oxidative stress”. The top three clusters of the reference co-citation network were “Knowler. WC 2002”, “Tabak AG 2012”, and “Matthews DR1985”.
### Conclusions
The combined use of WOS and the R package bibliometrix enabled a robust bibliometric analysis of prediabetes papers, including evaluation of emerging trends, hotspots, and collaboration. This study also allowed us to validate our methodology, which can be used to better understand the field of prediabetes and promote international collaboration.
## 1. Introduction
Prediabetes is a major worldwide public health issue. Individuals with prediabetes have a high risk of progression to diabetes and elevated risks of kidney disease, cardiovascular disease, and death [1]. The concept of prediabetes emerged in the late 1970s to better understand the process of diabetes [2, 3]. However, it is unclear whether prediabetes should be classified as a unique pathogenic state because it is a status that lies between healthy glucose homeostasis and the pathological condition of diabetes [4]. Prediabetes is a degree of impairment between euglycemia and the hyperglycemia of type 2 diabetes [5]. Professional societies such as the American Diabetes Association (ADA), the World Health Organization (WHO), and the International Expert Committee (IEC) have issued definitions of prediabetes. These definitions are based on a variety of hyperglycemia-related parameters such as FBG, 2hBG, and HbA1C [6, 7].
Nevertheless, there is still no consistent definition of prediabetes, and different definitions correspond to different groups of individuals in epidemiologic studies [8]. For example, large surveys of Chinese adults using all three glycemic tests (HbA1C, FBG, or 2hBG) revealed the prevalence of prediabetes, ranging from $36\%$ in one study to as high as $50.1\%$ in another [9]. Previous literature also suggested that, for individuals over 40 years of age or with a higher risk of diabetes, FBG and/or HbA1C were more effective [10]. For individuals with prediabetes, pharmacological and lifestyle changes could reduce cardiovascular risk and cost-effectively prevent diabetes [11], and restoring normoglycemia can produce long-lasting remission [10]. Hence, the National Institute for Health and Care Excellence (NICE) suggested that individuals with prediabetes should initially undergo lifestyle intervention in the form of intensive group education programs [12]. However, the effectiveness of these interventions relies on a consistent and accurate definition of prediabetes.
Insulin resistance, B-cell dysfunction, increased lipolysis, inflammation, poor incretin response, and hepatic glucose overproduction are all pathophysiologic abnormalities that underlie prediabetes [13]. Obesity-related metabolic abnormalities increase the risk of macrovascular and microvascular problems by impairing endothelial vasodilators and fibrinolytic activity. Additionally, prediabetes has been linked to an increased risk of cancer and dementia [14, 15].
Bibliometric analysis has evolved into the most effective tool for investigating detailed research trends in a research field over time. It objectively presents research contributions related to particular scientific fields from different countries, institutions, journals, and authors through statistical analysis and forecasts future directions or hotspots [16]. It is important to note that hotspots flag emerging problems in a specific field that have not been resolved and are of great concern to global academics, and future research directions forecast research that must be undertaken urgently and that will have a significant impact in the future. Furthermore, bibliometric analysis has played a significant role in the development of policy and clinical guidelines for a variety of diseases. However, to date, no bibliometric analysis of prediabetes has been conducted, and even less attention has been given to the prediction of research hotspots.
In this research, we retrieved prediabetes-related articles from the Web of Science (WOS) database and used bibliometric analysis tools to examine the literature characteristics and research hotspots. The Web of Science (WOS) is the most comprehensive and authoritative citation database in which peer review is a requirement in the journal evaluation process for inclusion. Therefore, we chose WOS in this study. The goal of this study is to provide a comprehensive and visually appealing overview of prediabetes studies and to lay a robust foundation for future research.
## 2. Methods
The Core Collection of WOS was searched to obtain relevant literature. The search strategy was as follows: TS = Prediabet* AND PY = (1985–2022). The search was performed on 17 August 2022. Only articles and reviews were included in the analysis. Two researchers independently retrieved and downloaded the literature. After data confirmation and standardization, the online literature was exported to plain text format, including full documents and cited references. The data were then imported into R for analysis.
We used the R package bibliometrix to clean, analyze, and visualize the literature data. Bibliometrix was created by Massimo Aria and Corrado Cuccurullo and built in R, a programming language for statistical computing and graphics [17]. It contains all the necessary instruments to pursue a complete bibliometric analysis, following the Science Mapping Workflow. It is a powerful tool because it makes bibliometric analysis more sophisticated and replicable.
## 3. Results
We used the R package bibliometrix to analyze the quantity of prediabetes literature, and publications of different journals, authors, countries, and institutions. We used keyword analysis, themes, and theme evolution to understand the main research areas of prediabetes articles. We also used citation analysis to explore the logical relationships between the literature and a collaboration network to show the collaboration between countries, institutions, and authors in this field.
## 3.1. Annual literature quantity and growth forecast
A total of 9,714 papers were collected from the WOS (see the workflow in Figure 1). We excluded 161 non-English papers and other 1,917 papers, including early access publications, book chapters, retracted publications, proceedings papers, and editorial materials. Thereafter, 7,636 publications remained for analysis. The first article in the field of prediabetes was written by A R Dian and published in the journal “Diabetologia” [18]. As shown in Figure 2, the number of articles each year has exhibited a sustained growth trend since 2005 and reached 876 in 2021. Furthermore, we ran a polynomial regression model to predict how many articles will be published in 2022. The predicted number of articles in 2022 was 906 with a $95\%$ confidence interval of 876 to 935.
**Figure 1:** *Bibliometric analysis of prediabetes presented in the workflow.* **Figure 2:** *Growth trend and prediction of prediabetes.*
The above pattern suggests that prediabetes is an emerging field. As shown in Figure 3, the number of articles by country also demonstrated an increasing growth trend. The United States published the most articles, followed by China, Germany, Canada, and South Korea. However, while studies on prediabetes have increased significantly over the past few decades, it is still a relatively new and promising area of research. China, India, Pakistan, and the United States (US) are the countries with the largest numbers of patients with diabetes aged 20–79 years in 2021. The US and China have the highest interest in the area of prediabetes because of the high prevalence of diabetes and the high economic level in these countries. India and Pakistan ranked only 10th and 44th in the prediabetes field in terms of the number of publications, which may be related to the investment in research and the emphasis on diabetes prevention.
**Figure 3:** *Number of publications in different countries and their growth trends.*
## 3.2. Distribution of literature
We then analyzed the distribution of authors, journals, and institutions of the literature. More than 34,914 authors contributed to the 7,636 prediabetes-related studies published in the WOS. Among the 20 most-productive authors, Rathmann W had the most publications (71 articles), followed by Peters A (58 articles). Haring HU and Meisinger C were tied for third place (47 articles each) (Supplementary material 1).
The articles on prediabetes were published in more than 1,549 journals. Diabetes Care published 234 articles, which accounted for $3.02\%$ of all articles, followed by “PLoS ONE” (193 articles), “Diabetes Research and Clinical Practice” (187 articles), “Diabetologia” (186 articles), and “Diabetes” (167 articles) (Supplementary material 2). The impact factor (IF) is a widely used indicator measuring the academic impact of a journal and the quality of its publications. Among the top five journals, Diabetes Care had the highest IF, reaching 17.152 in 2022; the IF of the other four journals, i.e., Diabetologia, Diabetes, Diabetes Research, Clinical Practice, and PLoS ONE were 10.12, 9.46, 5.60, and 3.75, respectively. A majority of the prediabetes-related articles published in these journals were of high quality and worth further analysis.
According to the retrieval results of the WOS database, the authors were affiliated with 139 countries/regions. The United States was the country with the highest number of publications (2,962 articles), followed by China (893 articles), Germany (471 articles), England (446 articles), and Canada (398 articles) (Supplementary material 3). Notably, the literature on prediabetes in China has shown rapid growth in the last decade.
In terms of affiliations, there were 7,834 institutes involved in the field of prediabetes. Harvard University ranked first with 290 articles on prediabetes, followed by the University of California (269 articles), the U.S. Department of Veterans Affairs (194 articles), Veterans Health Administration (192 articles), and the University of Texas system (191 articles). Eight of the top 10 institutions were located in the US (Supplementary material 4). There is no doubt that the US has maintained its lead in the field of prediabetes. Shanghai Jiao Tong University was the top Chinese institution in terms of the number of articles on prediabetes and ranked 22nd overall.
## 3.3. Keywords analysis
Keywords are brief phrases used in indexing or classifying to describe the topic of an article accurately and concisely. Through keyword analysis, we can gain a general understanding of the themes and features of publications [19]. Co-occurrence analysis assumes that keywords in the same documents are strongly related to the conceptual space of the research area. Clustering the keyword co-occurrence network provides a method to identify the subfields of a research area [20]. We used author keywords in the following analysis and built a network with 8,960 nodes and 51,101 links.
Research frontiers can be identified by examining the frequency and centrality of keywords [21]. The top 20 most common keywords are shown in Table 1. “ Prediabetes” was the most commonly used keyword in the literature, followed by “diabetes”, “type 2 diabetes”, “diabetes mellitus”, “insulin resistance”, “obesity”, “metabolic syndrome”, “HBA1C”, “IGT”, “IFG”, and “Insulin”.
**Table 1**
| Ranking | Counts | Centrality | Keywords |
| --- | --- | --- | --- |
| 1 | 2284 | 0.387 | Prediabetes |
| 2 | 1497 | 0.247 | Diabetes |
| 3 | 903 | 0.118 | Type 2 diabetes |
| 4 | 526 | 0.048 | Insulin resistance |
| 5 | 478 | 0.043 | Obesity |
| 6 | 256 | 0.013 | Metabolic syndrome |
| 7 | 246 | 0.011 | HBA1C |
| 8 | 226 | 0.012 | IGT |
| 9 | 179 | 0.007 | IFG |
| 10 | 151 | 0.015 | Insulin |
| 11 | 150 | 0.018 | Type 1 diabetes |
| 12 | 143 | 0.008 | Inflammation |
| 13 | 137 | 0.005 | Cardiovascular disease |
| 14 | 135 | 0.007 | Prevention |
| 15 | 134 | 0.004 | Risk factors |
| 16 | 127 | 0.005 | Hyperglycemia |
| 17 | 106 | 0.004 | Hypertension |
| 18 | 106 | 0.005 | Secretion of insulin |
| 19 | 106 | 0.007 | Metformin |
| 20 | 104 | 0.005 | Epidemiology |
The top 100 keywords can be classified into five groups: prediabetes-related diseases, diagnostic criteria, risk factors, intervention modalities, and pathological mechanisms (Supplementary material 5). Diseases frequently addressed in the literature on prediabetes are “obesity”, “metabolic syndrome”, and “cardiovascular disease”. Among them, “type 2 diabetes” appeared more frequently as a keyword than “type 1 diabetes”. “ Insulin resistance”, “inflammation”, and “sensitivity to insulin” were popular pathological mechanisms in prediabetes. The discussions of the diagnostic criteria in order of frequency were “HBA1C”, “OGTT”, and “FPG”. Physiological indicators such as “BMI”, “blood pressure”, and “waist circumference” caused relatively high concern; “metformin”, “exercise”, and “physical activity” were the most frequently studied interventions in the field of prediabetes.
## 3.4. Cluster analysis of keywords: Cooccurrence
In Figure 4, we demonstrate the co-occurrence network of the top 400 most frequent keywords. They are clustered into five categories: “prediabetes”, “type 2 diabetes”, “insulin resistance”, “exercise”, and “insulin”.
**Figure 4:** *Co-occurrence network of the top 400 keywords.*
Keywords in the same cluster were presented by the same color, and they were clustered together because they often appeared together in the same article. The purple cluster contained four major keywords: type 1 diabetes, nod, glucose, and insulin. “ Nod” is commonly used for modeling “type 1 diabetes”, and loss of “insulin” secretion is a key mechanism for the progression of prediabetes to “type 1 diabetes”. The red cluster gathered the most articles. It represented some basic questions about prediabetes such as prevalence, risk factors, and screening. The green cluster was related to the study of prediabetes mechanisms, such as insulin resistance, inflammation, and oxidative stress, and the clustering of prediabetes-related diseases, such as hypertension, metabolic syndrome, and atherosclerosis. The brown cluster contained some common measures of prediabetes, such as IFG, IGT, OGTT, insulin secretion, and insulin sensitivity. The blue cluster contained common types of prediabetes, such as IFG and IGT, and combined the most directly related diseases together, such as type 2 diabetes and cardiovascular disease. The orange cluster mainly reflected lifestyle interventions for prediabetes such as diet, exercise, and weight loss.
When taking the time dimension into the analysis, Figures 5, 6 show that prediabetes, diabetes, and type 2 diabetes were the top three keywords in almost all periods, demonstrating the dominance of these three keywords. Between 2005 and 2007, obesity ranked in the top three one time, and then, it was surpassed by other keywords. Another interesting pattern is that the use of insulin resistance as a keyword increased very quickly since 2010 and ranked fourth in 2020, which may suggest that it is an emerging research direction.
**Figure 5:** *Growth trend of the top 10 keywords.* **Figure 6:** *Production of the top 20 keywords over time.*
## 3.5. Themes and thematic evolution
The themes included the title, abstract and keywords, and features by conceptualization and normalization. To investigate the dynamic pattern of the research theme over time, we mapped all clusters into a strategic diagram using two metrics: centrality and density; the degree of interaction between clusters is referred to as centrality, and the degree of internal cohesion is referred to as density [22]. The strategic diagram has four quadrants (Figure 7) and the themes can be categorized into four groups: (a) motor themes in the upper-right quadrant which are well-developed and relevant for the research field; (b) basic and transversal themes in the lower-right quadrant which are considered relevant for the research field, but not fully developed; (c) emerging or declining themes in the lower left quadrant which are poorly or marginally developed, and (d) highly developed and isolated themes in the upper-left quadrant which are well-developed but not relevant for the research area. The size of a given cluster is dependent on the number of keywords it contains, and the label cluster conforms to the cluster's most frequently used word. The Walktrap algorithm was used to cluster the data in this study [23].
**Figure 7:** *Strategic diagram for four periods.*
As shown in Figure 7, the total period was split into four sub-periods: 1990–2005, 2006–2013, 2014–2018, and 2019–2022. The reason for keeping the last period so short, at only 4 years, was to gain a better understanding of current trends.
In the first period (1990–2005), the fully developed themes were related to “type 1 diabetes”, “prediabetes”, “hyperglycemia”, and “nitric oxide”. At that time, scientists believed that the early prediabetic process may be a suitable target for immunomodulation aimed at delaying or preventing progression to type 1 diabetes. The niche themes included Chinese hamster and glucose tolerance, which were not developed into moto themes in the following period. Diabetes was among the basic themes.
In the second period (2006–2013), “type 1 diabetes” remained a fully developed theme. Taken together, the period from 1990 to 2013 had much research focused on type 1 diabetes. However, after 2014, research on “type 2 diabetes” emerged and finally became the motor theme in the last 4 years [2019-2022]. Notably, “obesity” and “diabetes” were the other two developed themes in the second period. “ Prediabetes” was still a basic theme, despite its larger density. There were also several new niche themes, such as gene expression, palatability, and tissue Doppler imaging.
From 2014 to 2018, the theme of “type 1 diabetes” decreased while the density and centrality of “type 2 diabetes” increased. “ Prediabetes” became a new motor theme, together with “insulin resistance”. Moreover, “meta-analysis” emerged as a new theme with moderate centrality and density. After a period of development, scholars reviewed and examined the existing findings of prediabetes.
In the most recent period from 2019 to 2022, “prediabetes” remained a motor theme, and “type 2 diabetes” finally joined the motor quadrant. The research on “insulin” merged into a single cluster in this period. The basic theme quadrant included two new clusters: “metformin” and “hyperglycemia”. Moreover, “hyperglycemia” was a motor theme during the first period.
In summary, the most solid theme identified in the thematic evolution was “prediabetes”, which is also the most frequent keyword over time. We also found that the research interest shifted from “type 1 diabetes” to “type 2 diabetes”. “ Obesity” and “insulin” topics were also relatively solid. However, the identified niche themes were basically different for different periods. This may suggest that the research interests changed rapidly over time.
## 3.6. References analysis
Table 2 presents the top 20 most highly cited references. Eleven of these articles were written in the United States, followed by China (three articles). The epidemiology of prediabetes was the subject of one-fourth of the 20 most frequently cited articles. The most frequently cited article “Prevalence of diabetes among men and women in China” [24], was published by Yang WY in the New England Journal of Medicine in 2010.
**Table 2**
| Rank | Citations | Citations/year | Centrality | Year | First author | Journal |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | 2315 | 178.1 | 0.00501 | 2010 | Yang WY | New England Journal of Medicine |
| 2 | 2042 | 204.2 | 0.00318 | 2013 | Xu Y | JAMA |
| 3 | 1653 | 71.9 | 0.000806 | 2000 | Salomon B | Immunity |
| 4 | 1329 | 120.8 | 0.051 | 2012 | Tabak AG | Lancet |
| 5 | 1288 | 92.0 | 0.000114 | 2009 | Scheer FAJL | Proceedings of the National Academy of Sciences of United States of America |
| 6 | 1276 | 116.0 | 0.0101 | 2012 | Chen L | Nature Reviews Endocrinology |
| 7 | 1151 | 143.9 | 0.00438 | 2015 | Menke A | JAMA |
| 8 | 1127 | 140.9 | 0.000302 | 2015 | Zeevi D | Cell |
| 9 | 1097 | 99.7 | 3.74e-06 | 2012 | Booth FW | Comprehensive Physiology |
| 10 | 1037 | 207.4 | 0.000272 | 2018 | Saklayen MG | Current Hypertension Reports |
| 11 | 981 | 163.5 | 0.00308 | 2017 | Wang LM | JAMA |
| 12 | 947 | 30.5 | 0.00201 | 1992 | Martin BC | Lancet |
| 13 | 944 | 37.8 | 0.000311 | 1998 | Shimabukuro M | Proceedings of the National Academy of Sciences of United States of America |
| 14 | 917 | 34.0 | 1.48e-05 | 1996 | Yamagata K | Nature |
| 15 | 868 | 31.0 | 0.000833 | 1995 | Unger RH | Diabetes |
| 16 | 863 | 107.9 | 0.000679 | 2015 | Pi-Sunyer X | New England Journal of Medicine |
| 17 | 856 | 25.9 | 0.00214 | 1990 | Haffner SM | JAMA |
| 18 | 854 | 47.4 | 5.46e-06 | 2005 | Krentz AJ | Drugs |
| 19 | 689 | 28.7 | 2.86e-05 | 1999 | Perseghin G | Diabetes |
| 20 | 687 | 49.1 | 0.000475 | 2009 | Eizirik DL | Nature Reviews Endocrinology |
The literature type was assessed by reading the title and abstract of the top 100 articles. Table 3 shows the literature types of the 100 most frequently cited articles in the last 3 years. Cardio-cerebrovascular complications and gut microbiota-related studies are the two research directions that have been highly cited in the past 3 years, accounting for $20\%$ of the 100 most frequently cited articles. Clinical trials and randomized controlled trials are the most common types of literature in the field of prediabetes, accounting for $20\%$ of the 100 most frequently cited articles.
**Table 3**
| Type of research | Number | Percentage |
| --- | --- | --- |
| Cardio-cerebrovascular complications | 13 | 13% |
| Clinical Trial | 12 | 12% |
| Randomized controlled trial | 11 | 11% |
| Epidemiology | 10 | 10% |
| Review | 10 | 10% |
| Experimental Research | 8 | 8% |
| Gut Microbiota-related studies | 7 | 7% |
| Meta-analysis | 6 | 6% |
| Pathophysiology | 5 | 5% |
| Diagnostic techniques | 4 | 4% |
| Cohort study | 4 | 4% |
| Medical care | 4 | 4% |
| Guidelines | 3 | 3% |
| Lifestyle intervention | 2 | 2% |
## 3.7. Cluster analysis of references: Co-citations
To better understand the relationship among the references, we clustered them based on the co-citation network using bibliometrix. As shown in Figure 8, three groups were obtained: “Knowler. Wc 2002”, “Tabak ag 2012”, and “Matthews Dr1985-1”. The most highly cited article in the red cluster, “Reduction in the Incidence of Type 2 Diabetes with Lifestyle Intervention or Metformin” [25], was published in 2002 in the New England Journal of Medicine. The most highly cited article in the green cluster, “Prediabetes: a high-risk state for diabetes development” [26], was published in 2012 in Lancet. The most highly cited article in the blue cluster, “Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man” [27], was published in 1985 in Diabetologia.
**Figure 8:** *Co-citation network.*
Citations featured in the red cluster had the highest number of total citations, and their prediabetes related articles were especially significant in the first period. Most of them were published in high-impact journals such as the Lancet and the New England Journal of Medicine. The majority of the studies were long-term follow-up studies to investigate the prevalence of diabetes and related diseases and the impact of lifestyle interventions. Citations featured in the blue cluster were also less consistent in terms of their topics. They covered the longest time span (1972–2009). Most of the cited literature focuses on the detection, evaluation, and treatment of blood glucose, blood pressure, and blood lipids. Most were published in Diabetes Care. The green cluster cited many important prediabetes guidelines and expert consensus. These studies were relatively new, concentrated in the third period, and were mostly published in Diabetes Care.
## 3.8. Collaboration network
We clustered the countries and authors based on their collaboration network using bibliometrix. The nodes in the collaboration network were authors or countries, and the links represented co-authorship.
The collaboration network between countries can be seen in Figure 9. The US was the country with the most international collaboration in the field, followed by the United Kingdom, Germany, Denmark, and Australia. It is worth noting that, while China was the second most active country in terms of the number of articles, it ranked sixth in international collaboration. In terms of the frequency of collaboration, the top five country pairs were all between the United States, China, the UK, Italy, Canada, and Germany.
**Figure 9:** *Country collaboration network.*
The authors' collaboration network (Figure 10) was mapped into four clusters. Each color in this network represents a single cluster or a group of collaborating authors. Figure 10 shows that the collaborators were mostly from the same country or region. Most of the collaborative studies were large clinical trials, cohort studies, or randomized controlled studies of diabetes, prediabetes, and related diseases. These studies require collaboration between research institutions. Authors clustered in the red and orange groups were from Germany. However, the authors of the orange cluster were all from the University of Tübingen. The authors clustered in the blue and purple clusters were from China; the authors of the blue cluster were all from Shanghai Jiao Tong University.
**Figure 10:** *Author collaboration network.*
There were four clusters in the institution collaboration network (Figure 11). In the purple cluster, all but Imperial College London were Finnish universities. In the green cluster, all institutions were Chinese universities and hospitals, except for Tulane University and Johns Hopkins University. The US and Canadian universities comprised the red cluster. Mahidol University in Thailand also belonged to the red cluster. Finally, two institutions from Spain formed the blue cluster.
**Figure 11:** *Institution collaboration network.*
## 4. Discussion
*The* general term “prediabetes” refers to the stage between normal glucose tolerance and T2DM. It is generally recognized that individuals with prediabetes are at a high risk of developing T2DM [28]. The bibliometric analysis is a useful tool for mining information about a research field. Through a bibliometric analysis, researchers can quickly capture the characteristics and hot spots of the literature in a specific field [19]. Therefore, a comprehensive understanding of prediabetes could be obtained by using the bibliometric analysis method, which contributes to subsequent research and clinical treatment.
However, there are differing viewpoints regarding the necessity of and criteria for the diagnosis and intervention of prediabetes. Institutions such as the WHO, the National Institute for Health and Care Excellence (NICE), the European Association of the Study of Diabetes (EASD), and the International Diabetes Federation (IDF) do not use or emphasize the term prediabetes, and they normally advise treatment only when blood sugar levels approach those of diabetes. The ADA and the Centers for Disease Control and Prevention (CDC) fund much of the nation's research and programs on diabetes prevention. The ADA criteria and the WHO criteria are currently the two most commonly used criteria for prediabetes. Different from the WHO standard, in the ADA standard, the lower FPG cutoff point value of IFG was reduced to 5.6 mmol/L and included glycosylated hemoglobin (HbA1C) from 5.7 to $6.4\%$ as one of the diagnostic criteria for prediabetes. The lower cutoff value defined by the ADA guidelines led to much higher prevalence rates compared with those defined by the WHO guidelines. In a cohort of 1,547 American adults without diabetes, changing the lower IFG threshold from 110 to 100 mg/dL resulted in an increase in prediabetes prevalence from 19.8 to $34.6\%$ [29].
Currently, the frequency and rate of prediabetes progression to diabetes are unclear. Whether prediabetes itself causes harm is not clear, particularly when a person's average glucose levels are at the low end of the test result spectrum. The CDC data show a progression from prediabetes to diabetes at a rate of <$2\%$ per year or <$10\%$ in 5 years. The Cochrane Library in London showed that up to $59\%$ of prediabetes patients returned to normal glycemic values over 1–11 years with no treatment whatsoever. Therefore, the diagnosis and treatment of prediabetes is not only a medical problem but also a social and economic problem. More research is still needed to determine a suitable definition and other tipping points in identifying the risk of progression to diabetes and other complications1 This study examined the progression of prediabetes-related research during the last 37 years. Since 2005, the number of articles on prediabetes has been increasing steadily. With the improvement of living standards and unhealthy behaviors such as physical inactivity, the incidence of prediabetes has increased significantly [30]. The booming literature on prediabetes reflects the growing awareness of the importance of detecting and treating prediabetes. Over $98\%$ of the articles were written in English. The majority of the articles were published by corresponding authors from the United States, China, Germany, Canada, and South Korea. These countries face a high incidence of diabetes and emphasize disease prevention [31]. The majority of prediabetes relationships are similarly based in the United States, which is consistent with the country's substantial contribution to this academic subject, indicating that collaborations with other countries/territories should be strengthened. As a country with the second-highest number of prediabetes articles, China only ranks sixth in international collaboration. Therefore, as the country with the highest incidence of diabetes, China should strengthen international collaboration in the future to improve the ability to diagnose, prevent, and treat prediabetes.
In terms of authorship, the 20 most prolific authors have written 786 articles, accounting for $10.3\%$ of all papers. They have made significant contributions to the development and progression of prediabetes research. Rathmann W was the most prolific author (71 articles) followed by Peters A (58 articles). The journal Diabetes Care published most of the literature relevant to prediabetes among the top 20 medical journals. It also had the highest IF, which reached 17.152 in 2021, demonstrating its superiority in quantity and quality. Furthermore, eight of the top 20 journals were American journals, reflecting the US's considerable interest and leadership in this field. Eight of the top 10 institutions were from the US, and Harvard University ranked first. The collaborators tended to come from the same country or region. China and Germany were the two countries with the highest concentration of collaboration networks, especially Shanghai Jiao Tong University in China and the University of Tubingen in Germany.
According to the 2021 worldwide diabetes atlas issued by the International Diabetes Federation (IDF), China, India, and Pakistan had the highest number of people with diabetes among the 20- to 79-year-old population in 2021[31]. The highest diabetes-related health expenditure was observed in the United States (USD $379.5 billion), followed by China and Brazil (USD $165.3 billion and USD $42.9 billion, respectively). Both China and the United States attach great importance to diabetes prevention. The idea of “preventive treatment of disease” has existed in China since ancient times and a series of guidelines, such as the “Guideline for the prevention and treatment of type 2 diabetes mellitus in China (2020 edition)”[32], which has been published. The Diabetes Prevention Program (MDPP) had already been launched in 25 centers in the United States [33]. As shown in this study, the United States and China have published the most articles in the prediabetes field. Low- and middle-income countries have higher numbers of people with diabetes and higher growth rates of diabetes prevalence. However, we found that their attention to prediabetes is low. In terms of economic development, although large-scale screening and education for prediabetes also require high financial investment, it may reduce the incidence of diabetes and the economic burden of diabetes and diabetes complications in the long run. India and Pakistan rank third and fourth in the number of patients with diabetes, respectively, but rank 10th and 44th in the area of prediabetes publications. India's research and development (R&D) intensity was only $0.66\%$ in 2018 [34], much lower than that of $2.14\%$ in China2. The disparity is a result of less research investment, lower diabetes-related health expenditures, and insufficient attention to diabetes prevention [35]. On the one hand, the national annual cost associated with the diagnosis of diabetes is USD $327.2 billion and that for prediabetes is $43.4 billion. The economic burden of diagnosed diabetes may be reduced by intervening in prediabetes [36]. On the other hand, screening and education for prediabetes may also pose a financial burden. The ADA, CDC, and other organizations have already spent billions on research, education, and health improvement programs. To date, no studies have been undertaken to calculate whether the investment in diagnosing and treating prediabetes can reduce the cost of diabetes treatment due to failure to intervene early.
A detailed reading of the literature in the field of prediabetes over the past 3 years revealed that $13\%$ of the articles were related to cardiovascular risk [37]. “ Insulin resistance”, “inflammation”, and “sensitivity to insulin” are common mechanisms in the field of prediabetes (38–40). Research related to gut microbes is an emerging hot topic in the field of prediabetes over the past 3 years [41]. Epidemiological studies accounted for $10\%$ of prediabetes studies. Much attention has been given to the prevalence of prediabetes. However, there is no consensus on the definition of prediabetes. The complexity of defining prediabetes makes it challenging to obtain profiles of relative prediabetes prevalence from the literature [42]. At least five different definitions have been endorsed by different clinical organizations and guidelines. Comparisons of incidence rates between countries will be meaningful only if diagnostic criteria are standardized.
The classification of prediabetes is mainly based on plasma glucose, which is divided into impaired fasting glucose (IFG) with elevated FPG and normal OGTT and into impaired glucose tolerance (IGT) with elevated OGTT and normal FPG. In addition, there are classification of IFG + IGT as well as classification with elevated glycated hemoglobin (HbA1C) [43]. In the ranking of keywords in articles related to prediabetes, IGT appeared as a keyword in 205 prediabetes articles and ranked 9th. IFG appeared as a keyword in 165 articles and ranked 11th. HBA1c appeared as a keyword in 135 articles and ranked 14th. It can be seen that the type of prediabetes has received much attention in the field of prediabetes. We are not sure about the effectiveness of different types of prediabetes for the assessment and prevention of diabetes conversion. Further research is needed to explore blood glucose (FPG, OGTT) and HbA1C in identifying the risk of progression to diabetes and whether there are other tipping points. Further research is needed to determine which of the current definitions of prediabetes has the highest ability to discriminate between individuals who transition to diabetes and those that do not and to see how their performance varies with age, sex, and geographic location.
Clinical trials and randomized controlled trials accounted for $23\%$ of the prediabetes literature in the last 3 years. This shows the strong need to develop an appropriate prediabetes intervention. To date, no drugs have been approved specifically for prediabetes, meaning that doctors are limited to prescribing diabetes drugs or other medications “off label” to treat the condition. Metformin is the most commonly used drug [44]. However, metformin is not always prescribed for prediabetes, even if a patient meets the prediabetes criteria. Only people who are at a higher risk for developing type 2 diabetes or who have more risk factors may benefit from metformin therapy. Risk factors include having a higher body mass index (BMI) and prior gestational diabetes [45]. Exercise, physical activity, and diet are common lifestyle interventions (46–48). With early detection and simple lifestyle changes (such as diet and exercise), prediabetes is often reversible (49–51). However, $38\%$ of the lifestyle treatment group failed to maintain the strict regimen after only 6 months. More studies are needed to determine the best method and timing for intervention in prediabetes.
This study explores research trends and hotspots of prediabetes, which is useful to many researchers. On the one hand, researchers can use the research trend to prevent certain obsolete research on specific themes, reduce the repetitive effort in research initiatives, and reduce project funding waste. On the other hand, depending on research hotspots, researchers can optimize and improve their study design, making prediabetes research more novel and realistic. This study also presents a timeline of the changes in prediabetes research. It lays the groundwork for precise prediabetes prevention and treatment and provides a necessary reference value for the formulation of prediabetes guidelines and the adjustment of medical insurance policies. Ultimately, more individuals will benefit from lessening the medical load as well as the economic costs associated with prediabetes prevention and treatment around the world.
However, the limitations of this study must be mentioned. First, this study only examined publications in English, which might have led to bias in the study outcomes. Second, we only retrieved data from the WOS database and did not search additional databases or preprint articles for information, resulting in inadequate literature collection. Finally, while bibliometric analysis is a strong tool for revealing precise study trends, it provides little information about research content, such as methods or results. More review studies are needed to go deeper into the research content to enhance prediabetes research.
## 5. Conclusion
The current study examined the research hotspots, frontiers, and development patterns in the field of prediabetes, with a focus on global research outcomes. The number of articles on prediabetes has increased over the last few decades, indicating that this new topic is gaining traction. Our findings provide an overview of the current status of diabetes research and have significant implications for future research directions.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
## Author contributions
JZ wrote the first draft of the editorial. ML reviewed and provided feedback on the manuscript. Both authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1072521/full#supplementary-material
## References
1. Echouffo-Tcheugui JB, Selvin E. **Prediabetes and what it means: the epidemiological evidence**. *Annu Rev Public Health* (2021) **42** 59-77. DOI: 10.1146/annurev-publhealth-090419-102644
2. Fajans SSI. **Identification of chemical diabetes. The definition of chemical diabetes**. *Metabolism.* (1973) **22** 211-7. DOI: 10.1016/0026-0495(73)90163-7
3. Forouhi NG, Luan J, Hennings S. **Incidence of Type 2 diabetes in England and its association with baseline impaired fasting glucose: the Ely study 1990-2000**. *Diabet Med.* (2007) **24** 200-7. DOI: 10.1111/j.1464-5491.2007.02068.x
4. Rett K, Gottwald-Hostalek U. **Understanding prediabetes: definition, prevalence, burden and treatment options for an emerging disease**. *Curr Med Res Opin.* (2019) **35** 1529-34. DOI: 10.1080/03007995.2019.1601455
5. Braga T, Kraemer-Aguiar LG, Docherty NG. **Treating prediabetes: why and how should we do it?**. *Minerva Med.* (2019) **110** 52-61. DOI: 10.23736/S0026-4806.18.05897-4
6. **Standards of medical care in diabetes−2010**. *Diabetes Care.* (2010) **33** S11-61. DOI: 10.2337/dc10-S011
7. Gillett MJ. **International Expert Committee report on the role of the A1c assay in the diagnosis of diabetes: diabetes care (2009) 32:1327–34**. *Clin Biochem Rev.* (2009) **30** 197-200. DOI: 10.2337/dc09-9033
8. Barbu E, Popescu MR, Popescu AC, Balanescu SM. **Phenotyping the prediabetic population-a closer look at intermediate glucose status and cardiovascular disease**. *Int J Mol Sci.* (2021) **22** 6864. DOI: 10.3390/ijms22136864
9. Wang L, Gao P, Zhang M. **Prevalence and ethnic pattern of diabetes and prediabetes in China in (2013)**. *JAMA.* (2017) **317** 2515-23. DOI: 10.1001/jama.2017.7596
10. Khetan AK, Rajagopalan S. **Prediabetes**. *Can J Cardiol.* (2018) **34** 615-23. DOI: 10.1016/j.cjca.2017.12.030
11. Fritsche A, Wagner R, Heni M. **Different effects of lifestyle intervention in high- and low-risk prediabetes: results of the randomized controlled prediabetes lifestyle intervention study (PLIS)**. *Diabetes.* (2021) **70** 2785-95. DOI: 10.2337/db21-0526
12. Mahat RK, Singh N, Arora M, Rathore V. **Health risks and interventions in prediabetes: a review**. *Diabetes Metab Syndr* (2019) **13** 2803-11. DOI: 10.1016/j.dsx.2019.07.041
13. Lawal Y, Bello F, Kaoje YS. **Prediabetes deserves more attention: a review**. *Clin Diabetes.* (2020) **38** 328-38. DOI: 10.2337/cd19-0101
14. Cefalu WT. **“Prediabetes”: are there problems with this label?**. (2016) **39** 1472-7. DOI: 10.2337/dc16-1143
15. Brannick B, Wynn A, Dagogo-Jack S. **Prediabetes as a toxic environment for the initiation of microvascular and macrovascular complications**. *Exp Biol Med (Maywood).* (2016) **241** 1323-31. DOI: 10.1177/1535370216654227
16. Rodríguez-Soler R, Uribe-Toril J, Valenciano J D P. **Worldwide trends in the scientific production on rural depopulation, a bibliometric analysis using bibliometrix R-tool**. *Land Use Policy.* (2020) **97** 104787. DOI: 10.1016/j.landusepol.2020.104787
17. Aria M, Cuccurullo C. **bibliometrix: an R-tool for comprehensive science mapping analysis**. *J Informetr.* (2017) **11** 959-75. DOI: 10.1016/j.joi.2017.08.007
18. Diani AR, Peterson T, Sawada GA. **Elevated levels of vasoactive intestinal peptide in the eye and urinary bladder of diabetic and prediabetic Chinese hamsters**. *Diabetologia.* (1985) **28** 302-7. DOI: 10.1007/BF00271690
19. Di Cosmo A, Pinelli C, Scandurra A. **Research trends in octopus biological studies**. *Animals (Basel).* (2021) **11** 1808. DOI: 10.3390/ani11061808
20. Zhou X, Zhou M, Huang D, Cui L. **A probabilistic model for co-occurrence analysis in bibliometrics**. *J Biomed Inform.* (2022) **128** 104047. DOI: 10.1016/j.jbi.2022.104047
21. Cobo MJ, López-Herrera AG, Herrera-Viedma E, Herrera F. **An approach for detecting, quantifying, and visualizing the evolution of a research field: a practical application to the Fuzzy Sets Theory field**. *J Informetr.* (2011) **5** 146-66. DOI: 10.1016/j.joi.2010.10.002
22. Callon M, Courtial J P, Laville F. **Co-word analysis as a tool for describing the network of interactions between basic and technological research: the case of polymer chemsitry**. *Scientometrics* (1991) **22** 155-205. DOI: 10.1007/BF02019280
23. Pons P, Latapy M. **Computing communities in large networks using random walks**. *International Symposium on Computer and Information Sciences* (2005) p. 284-293. DOI: 10.1007/11569596_31
24. Yang W, Lu J, Weng J. **China national diabetes and metabolic disorders study group. Prevalence of diabetes among men and women in China**. *N Engl J Med.* (2010) **362** 1090-101. DOI: 10.1056/NEJMc1004671
25. Knowler WC, Barrett-Connor E, Fowler SE. **Diabetes prevention program research group. reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin**. *N Engl J Med.* (2002) **346** 393-403. DOI: 10.1056/NEJMoa012512
26. Tabák AG, Herder C. **Prediabetes: a high-risk state for diabetes development**. *Lancet.* (2012) **379** 2279-90. DOI: 10.1016/S0140-6736(12)60283-9
27. Matthews DR, Hosker JP, Rudenski AS. **Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man**. *Diabetologia.* (1985) **28** 412-9. DOI: 10.1007/BF00280883
28. Beulens J, Rutters F, Rydén L. **Risk and management of pre-diabetes**. *Eur J Prev Cardiol* (2019) **26** 47-54. DOI: 10.1177/2047487319880041
29. Karve A, Hayward RA. **Prevalence, diagnosis, and treatment of impaired fasting glucose and impaired glucose tolerance in nondiabetic US adults**. *Diabetes Care.* (2010) **33** 2355-9. DOI: 10.2337/dc09-1957
30. Weisman A, Fazli GS, Johns A. **Evolving trends in the epidemiology, risk factors, and prevention of type 2 diabetes: a review**. *Can J Cardiol.* (2018) **34** 552-64. DOI: 10.1016/j.cjca.2018.03.002
31. 31.IDF. Diabetes Atlas. 10th ed.
Brussels: International Diabetes Federation. (2021).. *Diabetes Atlas. 10th ed.* (2021)
32. **Clinical guidelines for prevention and treatment of type 2 diabetes mellitus in the elderly in China (2022 edition)**. *Zhonghua Nei Ke Za Zhi.* (2022) **61** 12-50. DOI: 10.3760/cma.j.cn112138-20211027-00751
33. O'Connell JM, Manson SM. **Understanding the economic costs of diabetes and prediabetes and what we may learn about reducing the health and economic burden of these conditions**. *Diabetes Care.* (2019) **42** 1609-11. DOI: 10.2337/dci19-0017
34. Li Y, Teng D, Shi X, Qin G. **Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study**. *BMJ.* (2020) **369** m997. DOI: 10.1136/bmj.m997
35. Burt T, Sharma P, Dhillon S, Manchanda M, Mittal S, Trehan N. **Clinical research environment in india: challenges and proposed solutions**. *J Clin Res Bioeth* (2014) **5** 1-8. DOI: 10.4172/2155-9627.1000201
36. Joan M, O'Connell M. **Manson; understanding the economic costs of diabetes and prediabetes and what we may learn about reducing the health and economic burden of these conditions diabetes care 1 September**. *Diabetes Care* (2019) 1609-11. PMID: 31431494
37. Cai X, Zhang Y, Li M. **Association between prediabetes and risk of all-cause mortality and cardiovascular disease: updated meta-analysis**. *BMJ.* (2020) **370** m2297. DOI: 10.1136/bmj.m2297
38. Wen J, Cai X, Zhang J. **Relation of adipose tissue insulin resistance to prediabetes**. *Endocrine.* (2020) **68** 93-102. DOI: 10.1007/s12020-020-02186-8
39. Luc K, Schramm-Luc A, Guzik TJ. **Oxidative stress and inflammatory markers in prediabetes and diabetes**. *J Physiol Pharmacol* (2019) **70** 6. DOI: 10.26402/jpp.2019.6.01
40. Ramírez-Rodríguez AM, González-Ortiz M, Martínez-Abundis E. **Effect of dapagliflozin on insulin secretion and insulin sensitivity in patients with prediabetes**. *Exp Clin Endocrinol Diabetes.* (2020) **128** 506-11. DOI: 10.1055/a-0664-7583
41. Allin KH, Tremaroli V, Caesar R, Hansen T, Bäckhed F, Pedersen O. **IMI-DIRECT consortium aberrant intestinal microbiota in individuals with prediabetes**. *Diabetologia.* (2018) **61** 810-20. DOI: 10.1007/s00125-018-4550-1
42. Hostalek U. **Global epidemiology of prediabetes–present and future perspectives**. *Clin Diabetes Endocrinol.* (2019) **5** 5. DOI: 10.1186/s40842-019-0080-0
43. Davidson MB. **Historical review of the diagnosis of prediabetes/intermediate hyperglycemia: case for the international criteria**. *Diabetes Res Clin Pract.* (2022) **185** 109219. DOI: 10.1016/j.diabres.2022.109219
44. Zhuo C, Xu Y, Wang H. **Clozapine induces metformin-resistant prediabetes/diabetes that is associated with poor clinical efficacy in patients with early treatment-resistant schizophrenia**. *J Affect Disord.* (2021) **295** 163-72. DOI: 10.1016/j.jad.2021.08.023
45. Davidson MB. **Metformin should not be used to treat prediabetes**. *Diabetes Care.* (2020) **43** 1983-7. DOI: 10.2337/dc19-2221
46. Wang S, Li J, Zhang C. **Effects of aerobic exercise on the expressions and activities of nitric oxide synthases in the blood vessel endothelium in prediabetes mellitus**. *Exp Ther Med.* (2019) **17** 4205-12. DOI: 10.3892/etm.2019.7437
47. Wang J, Wu Y, Ning F. **The association between leisure-time physical activity and risk of undetected prediabetes**. *J Diabetes Res.* (2017) **2017** 4845108. DOI: 10.1155/2017/4845108
48. Costello E, Goodrich J, Patterson WB. **Diet quality is associated with glucose regulation in a cohort of young adults**. *Nutrients.* (2022) **14** 3734. DOI: 10.3390/nu14183734
49. Davidson KW, Barry MJ, Mangione CM. **Screening for prediabetes and type 2 diabetes: US preventive services task force recommendation statement**. *JAMA* (2021) **326** 736-43. DOI: 10.1001/jama.2021.12531
50. Basit A, Fawwad A, Abdul Basit K. **Glycated hemoglobin (HbA1c) as diagnostic criteria for diabetes: the optimal cut-off points values for the Pakistani population; a study from second National Diabetes Survey of Pakistan (NDSP) 2016-2017**. *BMJ Open Diabetes Res Care.* (2020) **8** e001058. DOI: 10.1136/bmjdrc-2019-001058
51. Zhang P, Zhu X, Du Y. **Screening and functional studies of long noncoding RNA in subjects with prediabetes**. *Endocrine.* (2020) **68** 296-305. DOI: 10.1007/s12020-020-02226-3
|
---
title: Comparison of mothers’ perceptions of hunger cues in 3-month-old infant under
different feeding methods
authors:
- Fenghua Zhao
- Yijie Sun
- Yue Zhang
- Tao Xu
- Nianrong Wang
- Shuangqin Yan
- Ting Zeng
- Fenghua Zhang
- Jie Gao
- Qing Yue
- Scott Rozelle
journal: BMC Public Health
year: 2023
pmcid: PMC9993530
doi: 10.1186/s12889-023-15325-3
license: CC BY 4.0
---
# Comparison of mothers’ perceptions of hunger cues in 3-month-old infant under different feeding methods
## Abstract
### Background
Mothers’ perception of infant hunger cues is a critical content of responsive feeding, which is central to the promotion of early childhood development. However, only a few studies have examined responsive feeding in China, especially lacking the studies on perceptions of infant hunger cues. Consider the cultural differences, the aim of this study was to describe the perceptions of infant hunger cues of Chinese mothers for infants aged 3 months, and explore the relationship between maternal perceptions of infant hunger cues and different feeding methods.
### Methods
A cross-sectional study was conducted with a sample of 326 mothers of healthy 3-month-old infants, including 188 exclusive breastfeeding (EBF) mothers and 138 formula feeding (FF) mothers. It was implemented in four provincial and municipal maternal and child health hospitals. The mothers’ perceptions of infant hunger cues were surveyed by self-reporting questionnaires. Chi-square tests and logistic analysis were applied to analyze the differences in maternal perceptions of infant hunger cues, including the number of hunger cues and the specific cues, between EBF group and FF group by controlling sociodemographic variables and the daily nursing indicators.
### Results
We found that a higher proportion of EBF mothers could perceive multiple hunger cues (≥ 2) than FF mothers ($66.5\%$ vs$.55.1\%$). For specific cues, the EBF mothers had higher perceptions of infant’s “hand sucking” ($67.6\%$ vs. $53.6\%$) and “moving head frantically from side to side” ($34.6\%$ vs. $23.9\%$), all $p \leq 0.05.$ *Regression analysis* revealed that EBF might support mothers to perceive infant hunger cues than FF mothers, with the number of infant hunger cues (OR = 1.70, $95\%$ CI: 1.01–2.85), “hand sucking” (OR = 1.72, $95\%$ CI: 1.04–2.87), “moving head frantically from side to side” (OR = 2.07, $95\%$ CI: 1.19–3.62). The number of infant hunger cues perceived by mothers was also associated with their educational level and family structure.
### Conclusion
EBF mothers of 3-month-old infants may be more likely to perceive infant hunger cues than FF mothers in China. It is necessary to increase the health education about infant hunger and satiety cues to caregivers in China, especially among mothers with lower education levels, mothers living in nuclear families, and FF mothers.
## Introduction
In recent years, responsive feeding has attracted considerable attention due to its significance in promoting infants health [1– 6]. Responsive feeding is defined as “the interaction between the child and the caregiver” in a feeding context [1], and is related to on-demand feeding and baby-led feeding [7]. Its definition is similar to Mary Ainsworth’s definition of parental sensitivity [8, 9] as a parent’s ability to [1] notice child signals, [2] interpret these signals correctly, and [3] respond to these signals promptly and appropriately. Both responsive feeding and parental sensitivity involve the accurate perception of infant cues.
Empirical research has confirmed the importance of maternal perceptions of infant hunger cues for promoting healthy child outcomes. Several studies have found that mothers who promptly perceive and respond appropriately to their infant hunger cues reduce the risk of overfeeding, rapid weight gain or malnutrition, and even stunting [4, 10–13]. Maternal perceptions of hunger cues in infancy also may influence whether the infant becomes over- or underweight during infancy and toddlerhood [14]. In addition, raising awareness of these cues with mothers may encourage more responsive and positive mealtime interactions [15, 16]. This facilitates emotional linkage and good attachment relationships between parents and infants, thereby promoting cognitive ability and psycho-behavioral development in infants [3]. Therefore, it is necessary to understand the parents’ perception of infant hunger and satiety cues to lay the foundation for achieving responsive feeding.
Mothers’ perception of infant hunger cues is an essential aspect of achieving responsive feeding [7, 15]. Previous studies have shown that infants can self-regulate their energy intake based on their needs [17–20] and that this ability to self-regulate is best promoted by feeding practices in which the caregiver responds to infant hunger or satiety cues [1, 4, 21]. Infant hunger cues include putting their hand in their mouth, increasing physical activity, mouth opening or closing, moving their head frantically from side to side, crying and so on [10, 22–25]. Infants signal hunger through their body movements, facial expressions, and vocalizations [26]. These hunger cues are typically subtle (e.g., putting hand in mouth) in the early stages of hunger and gradually escalate until the cues are perceived and responded to by the mother [25, 27]. Research suggests parental responsiveness to children’s hunger and satiety cues is critical for developing healthy eating habits [4, 14]. However, most studies focus on infants after 6 months [15, 28–30], and there are few literature studies on infants aged 3 months [8]. Exploring mothers’ perception of early infant hunger cues may provide insight into responsive feeding practices during lactation.
The literature has identified various factors that may influence infant hunger cues and parental perception of these hunger cues, including social demography, feeding methods and parenting behavior. Some studies have pointed out maternal perceptions associated with income [31], maternal education level, mother’s country of origin, maternal BMI [23], infant age, infant gender [32] and minority ethnicity [33, 34], history of breastfeeding [23], breast milk intake, sleep patterns and feeding methods [23, 35, 36]. Parenting experience is associated with maternal age and the number of children [37, 38]. The family structure [39] and daily nursing may also influence parenting practices. Therefore, these factors may be related to the mothers’ perception of these hunger cues. Shloim et al. [ 2015] has shown that breastfeeding mothers of the 2 to 6-month-old infants, compared to FF mothers, can better understand infant hunger cues and provide a more positive feeding experience [36]. Cultural contexts have been shown to have important effects on family care [40], which may also be an effect on mothers’ perception of infant hunger cues [41]. Unfortunately, there is still a lack of research in the current Chinese literature on maternal perception of infant hunger cues. Few studies has examined mothers’ perception of infant hunger cues in China. It is necessary to investigate the Chinese mothers’ perception of infant hunger cues, which can encourage mothers to better understand how to perceive infant hunger cues and respond promptly, and may have reference value for other Southeast Asian countries.
This study is part of the study of mother-infant interaction under different feeding methods [42, 43], which videotaped the feeding progress of mother-infant dyads. However, we focused on analyzing perceptions of infant hunger cues by mothers of 3-month-old infants in this study. The main goals were as follows: [1] to understand Chinese maternal perceptions of hunger cues of 3-month-old infants. [ 2] to evaluate the association between feeding methods and maternal perceptions of infant hunger cues. Therefore, we hypothesized that feeding methods and other factors may be related to maternal perceptions of infant hunger cues. This is helpful for child health providers to guide the development of responsive feeding for parents.
## Design and sampling
A cross-sectional survey approach was used. The survey recruited participants at Maternal and Child Health Hospitals (MCHs: abbreviation of Maternal and Child Health Care Institutions) in 4 cities located in 4 provinces in China from January to December 2019, including Ma’anshan in central China, Liuzhou in southern China, Chongqing in western China, and Qingdao in eastern China. These cities are geographically widespread. The sample size for this study was computed as 270, according to the calculation based on the $66\%$ of mothers could perceive hand sucking as hunger cue [23] (lack of reports of Chinese population), α = 0.05, permissible error = 0.08, and stratified by feeding methods. Each group(EBF group and FF group)was determined to contain at least 135 mother-child dyads.
To recruit study participants, the research team connected with the MCHs who participated in the study. The child healthcare doctors of these hospitals, trained by the project team, approached and interviewed mothers whose healthy infants were undergoing routine health examinations at the hospital at the time of the survey to determine if they met the inclusion criteria and whether they would like to participate. The inclusion criteria were as follows: (a) the family had a local household registration or had resided in the city for more than six months; (b) the infant was full term (≥ 37 weeks); (c) the infant’s birth weight was ≥ 2500 g; (d) the infant had been fed only fed by breastmilk for the last 24 h and did not supplement with any food except drugs and vitamins (for infants in the EBF group),or had not been breastfed for the last 24 h (for infants in the FF group). All infants were required to a single child with either EBF or FF was required to account for more than $90\%$ of total intake for the first three months after birth. The above information was obtained through interview, and mothers were given ample time to recall and consider their responses. The following were excluded: (a) infants with health issues that might affect feeding abilities (e.g. swallowing); (b) mothers or infants with serious diseases, including chronic health problems that would affect the growth and/or development of the infant or complications during pregnancy and/or childbirth of the mother; (c) mothers who did not have normal reading and writing skills.
## Measurements
Variables assessed in this study were obtained from self-report questionnaires designed by the study team, including general sociodemographic data, nursing-related variables, and variables of maternal perceptions of infant hunger cues.
Sociodemographic variables included infant gender, ethnicity, maternal age, maternal education level, family structure, and one-child status. Nursing-related variables included “whether the mother is the primary caregiver”, “whether the baby sleeps with mother”, feeding interval, and feeding duration.
## Measures the mother’s perceptions of infant hunger cues
Maternal perceptions of infant hunger cues were investigated by a self-administered questionnaire. A multiple choice question was used to ask “What are signs of your infant’s desire to feed?” The options provided were based on the literature and expert recommendations and included hand sucking, drooling, mouth opening, crying, and moving head frantically from side to side, other behaviors, and not clear. The mother chose one or more options according to the actual situation and could provide a specific text description of what is not covered in the options in “Other”. According to the previous literature [15, 24, 25], mouth opening and drooling act as early hunger cues, hand sucking is an active hunger cue, moving head frantically from side to side and crying are late hunger cues.
## Statistical analysis
Sociodemographic characteristics, daily nursing variables, and maternal perceptions of infant hunger cues in EBF and FF groups were described by frequencies and percentages. In univariate analysis, bivariate associations between the feeding methods and the maternal perceptions of infant hunger cues, including maternal perceptions of the number of infant hunger cues, and early, active, late hunger cues, were evaluated using the chi-square test.
Logistic regression was used to examine the association between maternal perceptions to infant hunger cues and the sociodemographic variables, daily nursing variables, feeding methods measures in multivariate analysis. This regression employed four models, in which the dependent variables included the number of perceived hunger cues (Model A), hand sucking (Model B), moving head frantically from side to side (Model C), and crying (Model D), respectively. And nine multi-categorical variables, including infant’s birth weight, maternal age, father’s age, maternal education level, father’s education level, family structure, location, feeding interval, and feeding duration, were transformed into dummy variables in logistic analysis. Odds ratios (ORs) were presented as results for both bivariate associations and logistic regression model. All of the data preparation and statistical analyses were performed using the SPSS for Windows software program (version 25.0).
## Demographic characteristics & daily nursing related to infant hunger cues perceptions
The sample included a total of 326 mother-infant dyads, including 188 dyads in EBF group and 138 dyads in FF group. About the number of hunger cues, 125 mothers ($38.34\%$) perceived 1 hunger cue, 103 mothers ($31.60\%$) perceived 2 hunger cues, 65 mothers ($19.94\%$) perceived 3 hunger cues, 30 mothers ($9.20\%$) perceived 4 hunger cues, and 3 mothers ($0.92\%$) perceived 5 hunger cues. Three participants marked the “other” option ($0.92\%$, including “Waving hands, reaching tongue, not sleeping”), and zero chose the “not clear” option ($0.00\%$). However, the mothers who selected “other” also all perceived at least one hunger cues that this study involved. The demographic characteristics of the participants were shown in Table 1. The analysis indicated that there were no statistically significant differences in gender, ethnicity, mother’s age, mother’s education level, family structure, and one-child status between EBF group and FF group (all $p \leq 0.05$). When the demographic variables of the two groups were compared as stratified by region, only the study subjects in Qingdao had statistical differences in infant’s gender and infant’s ethnicity.
Table 1Demographic Characteristics and daily nursing variables of the ParticipantsVariableExclusive Breastfeeding (EBF) N (%)Formula Feeding (FF)N (%) χ 2 p Demographic Characteristics Infant’s Gender0.510.48Boy96(51.1)76(55.1)Girl92(48.9)62(44.9)Infant’s Ethnicity\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<$$\end{document}0.0010.99Han Nationality150(79.8)110(79.7)Minority Nationality38(20.2)28(20.3)Mother’s Age1.820.40≤ 2415(8.0)14(10.1)25–35152(80.9)103(74.6)≥3621(11.2)21(15.2)Mother’s Education Level4.900.18Completed Junior High School or Less21(11.2)26(18.8)Completed Senior High School or Technical Certificate32(17.0)26(18.8)Associate Degree55(29.3)39(28.3)Bachelor Degree or Above80(42.6)47(34.1)Family Structure0.590.74Nuclear Family73(38.8)50(36.2)Linear Family103(54.8)81(58.7)Composite/Single-parent/Reconstituted12(6.4)7(5.1)One-child Status0.030.87Yes121(65.1)91(65.9)No65(34.9)47(34.1) Daily Nursing Mother Is the Primary Caregiver12.57\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<$$\end{document}0.01Yes182(96.8)119(86.2)No6(3.2)19(13.8)Sleeping with Mother15.15\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<$$\end{document}0.01Yes182(96.8)117(84.8)No6(3.2)21(15.2)Feeding Interval (hour)17.64< 0.01≤ 273(38.8)24(17.4)393(49.5)90(65.2)≥ 422(11.7)24(17.4)Feeding Durations (min)1.650.44≤ 1073(38.8)56(40.6)11–2076(40.4)61(44.2)> 2039(20.7)21(15.2) Table 1 also compared the differences in daily parenting factors that may be related to perceptions of infant hunger cues in the two groups. The proportions of EBF mothers who were primary caregivers, who slept with their infants, and who fed their infants at intervals of no more than 2 h ($96.8\%$, $96.8\%$ & $38.8\%$, respectively) were higher than that in FF group ($86.2\%$, $84.8\%$ & $17.4\%$, respectively), and these differences were statistically significant ($p \leq 0.05$).
## Comparison of self-reported maternal perceptions of infant hunger cues between the two feeding method groups
Overall, mothers in this study chose at least one infant hunger cue, and $61.7\%$ chose two or more infant hunger cues. The percentage of mothers who perceived two or more infant hunger cues in EBF group ($66.5\%$) was higher than in FF group ($55.1\%$), with statistically significant ($p \leq 0.05$).
Figure 1 showed the percentages of mothers who self-reported perceiving five infant hunger cues in the early, active, and late cues in EBF and FF groups. The percentages of mothers who perceived infant hand sucking and moving their head frantically from side to side as hunger cues were higher in the EBF group ($67.6\%$ & $34.6\%$, respectively) than that in FF group ($53.6\%$ & $23.9\%$%, respectively). However, the percentage of mothers in EBF group who perceived infants’ crying as a hunger cue ($67.6\%$), was lower than that in FF group ($79.0\%$). The above three differences were statistically significant ($p \leq 0.05$).
Fig. 1The percentage of early, active and late infant hunger cue perceived by the two groups of mothers’ self-report (*$P \leq 0.05$) In terms of perceiving hunger cues in different periods, the percentage of mothers in EBF group who could perceive the active hunger cue ($67.6\%$) was higher than in FF group ($53.6\%$; $p \leq 0.05$). However, the differences in the percentage of EBF and FF mothers who could perceive early hunger cues ($37.2\%$ vs. $35.5\%$) and late hunger cues ($79.3\%$ vs. $82.6\%$) were not statistically significant ($p \leq 0.05$).
## Multivariate analysis of maternal perceptions of infant hunger cues
Before multivariate analysis, correlation analysis was performed on daily care variables. The Kaiser-Meyer-Olkin (KMO) value was 0.502 and Bartlett ‘s Sphericity Test χ2 was 94.48 ($p \leq 0.01$), showing that there was a correlation between variables and suitability for factor analysis. As shown in Tables 2, there was a significant correlation between “Mother Is the Primary Caregiver” and “Sleeping with Mother” ($p \leq 0.01$), which were selected as the interaction items for the following binary logistic regression analysis.
Table 2Correlation Matrix of Daily Nursing VariablesVariableMother Is the Primary CaregiverSleeping with MotherFeeding IntervalFeeding DurationMother Is the Primary CaregiverPearson Correlation10.500.020.04Sig. ( 2-tailed)<0.01*0.770.52Sleeping with MotherPearson Correlation0.501-0.010.07Sig. ( 2-tailed)<0.01*0.810.20Feeding IntervalPearson Correlation0.02-0.011-0.02Sig. ( 2-tailed)0.770.810.74Feeding DurationPearson Correlation0.040.07-0.021Sig. ( 2-tailed)0.520.200.74*. Correlation is significant at the 0.01 level (2-tailed) Binary logistic regression analysis (independent variable entry level = 0.05, elimination level = 0.10) was conducted, with the sociodemographic, feeding method, and daily nursing variables as the independent variables and infant hunger cues as the dependent variables. Only hand sucking, moving head, and crying were considered in this analysis, as in the univariate analysis there was no difference between the EBF group and FF groups in mouth opening and drooling). This regression employed four models, in which the dependent variables included the number of perceived hunger cues (Model A), hand sucking (Model B), moving head frantically from side to side (Model C), and crying (Model D), respectively.
As shown in Table 3, all four models showed that mothers’ perception of infant hunger cues was related to feeding methods ($p \leq 0.05$).
Table 3Analysis of Multi-factors Affecting Maternal perceptions of Infant Hunger Cuesthe Independent Variable the Dependent VariablesModel A: based on the Number of Hungry Cues perceived by MothersModel B: Based on Hand-suckingModel C: Based on Moving Head Frantically from Side to SideModel D: Based on CryingOR ($95\%$ CI)OR ($95\%$ CI)OR ($95\%$ CI)OR ($95\%$ CI)Infant’s Ethnicity (base: Han Nationality)1.63(0.63–2.15)0.77(0.42–1.40)1.43(0.77–2.66)1.25(0.64–2.41)Infant’s Gender (base: boy)0.68(0.42–1.10)1.11(0.69–1.80)0.91(0.55–1.51)0.60(0.36–1.01)Mother’s Age (base:≤24y)25-35y0.75(0.30–1.85)0.81(0.33–1.99)1.42(0.54–3.75)0.55(0.19–1.59)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>$$\end{document}35y0.87(0.28–2.73)0.55(0.18–1.72)2.00(0.59–6.79)0.61(0.17–2.24)Mother’s Education Level (base: Completed Junior High School or Less)Completed Senior High School or Technical Certificate1.60(0.68–3.75)2.76(1.17–6.50) *0.74(0.28–1.92)1.54(0.62–3.82)Associate Degree3.20(1.65–7.90) *4.20(1.85–9.55) *0.78(0.32–1.90)1.35(0.59–3.12)Bachelor Degree or Above3.01(1.34–6.76) *2.73(1.22–6.12) *0.88(0.37–2.15)1.71(0.73-4.00)Family Structure (base: Nuclear Family)Linear Family1.83(1.10–3.07) *0.83(0.50–1.40)2.68(1.50–4.78) *1.47(0.86–2.52)Composite/Single-parent/Reconstituted family0.98(0.34–2.88)0.50(0.17–1.45)2.82(0.93–8.57)1.31(0.41–4.17)One-child Status (base: No)0.76(0.44–1.31)0.96(0.55–1.67)0.87(0.49–1.57)0.85(0.47–1.51) Feeding Patterns (base: FF group) EBF group 1.70(1.01–2.85) * 1.72(1.04–2.87) * 2.07(1.19–3.62) * 0.53(0.30–0.93) * Feeding Interval(hours) (base: ≤2 h)3 h1.23(0.70–2.20)0.84(0.47–1.51)1.79(0.95–3.36)0.84(0.45–1.56)≥4 h1.54(0.70–3.44)0.83(0.38–1.85)1.77(0.75–4.16)0.76(0.33–1.75)Feeding Duration(minutes) (base: ≤10 min)11-20 min1.34(0.79–2.29)0.94(0.55–1.60)1.37(0.79–2.39)0.92(0.53–1.60)≥21 min1.23(0.62–2.43)0.53(0.27–1.04)1.19(0.57–2.48)1.47(0.69–3.17)Mother Is the Primary Caregiver*Sleeping with Mother (base: Yes)0.96(0.65–1.40)1.07(0.73–1.55)0.99(0.66–1.49)1.23(0.73–2.05)The dependent variable were identified hunger cues (0="yes”, 1="no”) and the number of identified infant hunger cues, including overall(1="1”, 2="≥2”), Hand-sucking, Moving Head Frantically from Side to Side and Crying,respectively (1="yes”, 0="no”). Independent variables included the gender (1="boy”, 2="girl”), infants’ ethnicity (1="Han Nationality”, 2="Minority Nationality”), mothers’ age (1="≤24”, 2="25–35”, 3="≥36”), mother’s education level (1="junior high school and below”, 2="senior high school or technical certificate”, 3="associate degree”, 4="bachelor degree or above”), family structure (1="nuclear family”, 2="linear family”, 3="composite/single-parent/reconstituted family”), one-child status(0="no”, 1="yes”), mother as the main caregiver (1="yes”, 2="no”)*infant sleeping with mother(1="yes”, 2="no”), feeding interval (1="≤2 hours”, 2="3 hours”), 3="≥4 hours”), feeding duration (1="≤10 minutes”, 2="11–20 minutes”, 3="≥21 minutes”), and feeding pattern (0="formula feeding”, 1="exclusive breastfeeding”)*$p \leq 0.05$a: Model goodness of fit: -2 Likelihood = 394.93, H-L test: χ2 = 4.55, $$p \leq 0.81$$b: Model goodness of fit: -2 Likelihood = 402.03, H-L test: χ2 = 5.38, $$p \leq 0.72$$c: Model goodness of fit: -2 Likelihood = 353.58, H-L test: χ2 = 11.28, $$p \leq 0.19$$d: Model goodness of fit: -2 Likelihood = 371.00, H-L test: χ2 = 8.18, $$p \leq 0.42$$ The number of infant hunger cues perceived by the mother in Model A was related to the feeding method, mother’s educational level and family structure. EBF mothers were 1.70 times more likely than FF mothers to perceive multiple infant hunger cues ($95\%$ CI: 1.01–2.85). Mothers with an associate’s degree and a bachelor’s degree or above were 3.20 times more likely ($95\%$ CI: 1.65–7.90) and 3.01 times more likely ($95\%$ CI: 1.34–6.76), respectively, to perceive multiple infant hunger cues than those with a junior high school education or less. Mothers who were living in a linear family were 1.83 times more likely than those in a nuclear family to perceive multiple infant hunger cues ($95\%$ CI: 1.10–3.07).
In Model B, the feeding method and mother’s educational level were associated with the perception of “infant hand sucking”. EBF mothers were 1.72 times more likely to perceive infant hand sucking ($95\%$ CI: 1.04–2.87) than FF mothers. Mothers with a senior high school or technical certificate were 2.76($95\%$ CI: 1.17–6.50) times more likely to perceive “hand sucking” as a hunger cue than those with a junior high school education or less. Similar results were shown in mothers with an associate’s degree (OR = 4.20,$95\%$ CI: 1.85–9.55), and a bachelor degree or above (OR = 2.73,$95\%$ CI: 1.22–6.12).
In Model C, the feeding method and family structure were associated with a mother’s perception of “infant moving his head frantically from side to side”. EBF mothers were 2.07 times more likely to perceive the cue of “infant moving his head frantically from side to side” than FF mothers ($95\%$ CI: 1.19–3.62). Compared to mothers who were living in nuclear families, mothers living in linear families were 2.68 times more likely to perceive this cue ($95\%$ CI: 1.50–4.78).
In Model D, only feeding method was associated with mothers’ perception of infant crying. EBF mothers were less likely to perceive infant crying as a hunger cue than FF mothers (OR = 0.53, $95\%$ CI: 0.30–0.93).
## Discussion
This study mainly explored the differences in perception of infant hunger cues in Chinese mothers with 3-month-old infants under different feeding methods. The results of this study provided preliminary evidence for understanding mothers’ perceptions of infant hunger cues in the Chinese population, which will provide a support to promoting early childhood responsive feeding.
We found that the breastfeeding might support mothers to perceive infant hunger cues. This result is consisted with the previous researches [16, 36].
This study further explored the maternal perceptions of infant hunger cues under different feeding methods. As presented in the results, mothers in EBF group and mothers in FF group differed in their perceptions of the number of hunger cues, infant “sucking hands”, and “moving their heads frantically from side to side”, which supports the notion that mothers’ perception of infant hunger cues differs across feeding methods. Mothers in the EBF group perceived more infant hunger cues than mothers in the FF group, proved that breastfeeding may establish mothers’ higher levels of sensitivity to infants’ needs, especially in the first three months of life [44, 45]. Breastfeeding mothers cannot always assess the amount of breast milk consumed by their infants and, thus, must pay more attention to the hunger cues sent by the infants to determine when to feed. FF mothers, in contrast, have easier access to milk intake information and may disregard infant hunger cues, especially when hunger cues are inconsistent or unclear [46, 47]. It is also possible that breastfed infants had a higher level of engagement with hunger cues [16], and a more positive mealtime experience than formula-fed infants [36], and produced more frequent hunger cues [15]. In addition, infant-led feeding approaches (associated with breastfeeding) were related to higher awareness of infant hunger cues [48].
Interestingly, mothers in the breastfeeding group had lower perceptions of infant crying than mothers in FF group. This may be due to crying is a late hunger cue, and breastfeeding mothers were more in tune with their baby’s cues during feeding [36]. It also might be the differences in the locus of control in the feeding [49], that satiating FF is due to volume compared to the frequency of breastfeeding. Mothers who breastfed have less control over feeding behaviors [1]. Breastfed infants may be more satisfied and they may not cry as often because they are in charge of when the meal is initiated [16]. It should be noted that crying is not a specific feeding cue [25], when the infant is abnormally crying, non-starvation reasons should be considered and actively seeking medical treatment.
This study also explored maternal perceptions of infant hunger cues for different stages of feeding. There were high or low perceptions of different hunger cues, and there was still room for improvement in the mothers’ perceptions of infant early hunger cues. Hodges et al.[2016] found that, from 3 to 18 months, mouth opening was frequent and predominated at each time point [25]. However, the results of this study showed that the perceptions of infant hunger cues were mainly concentrated in the active and late stages in mothers in the EBF and FF groups, and the perceptions of early hunger cues (“mouth opening” and “drooling”) were lower in both groups. It may be because early hunger cues are less intense than in the active and late stages [27], early hunger cues were relatively rare [25], or because the mother did not realize that these cues represent hunger in real feeding situations [16]. The current lack of guidance on infant early hunger cues in health care services in China may be the most important influencing factor. In addition, consideration must be given to the fact that mother’s failure to use early cues may not reflect a mistake on her part. It could be a result of a faulty regulatory mechanism in the baby; no appropriate cues were available as feedback [24]. Or because mothers need a certain amount of time to perceive infant hunger cues, and tend to ignore the early hunger cues. Further exploration is necessary in this regard.
Regarding the multifactor analysis of maternal perceptions of infant hunger cues, we found that maternal education level and family structure were associated with the mother’s perception of infant hunger cues, a point that deserves further attention. Gross et al.[2010] similarly found that maternal education level and feeding methods were associated with infant hunger cues perceptions [23]. In our study, mothers with higher education levels and those in multigenerational families were more likely to perceive infant hunger cues, likely because more educated mothers tend to have higher cognitive and health management abilities. They also enjoy more socio-economic advantages, which can help them to obtain health knowledge and services [50–53], and better mastery of the child’s health knowledge and health awareness [54]. For these reasons, maternal education level may be an especially important factor impacting feeding methods, particularly since most primary caregivers of infants in this study were mothers ($92.33\%$). The direct and indirect roles played by maternal education level should be further explored in future studies. In terms of the impact of family structure (i.e., with grandparents, parents, and children living together) can allow parents to receive childrearing help from grandparents, thereby reducing the stress they face [42]. Parents living in linear family structures may also be able to benefit from the parenting advice and lived experiences of grandparents. For these reasons, linear family structures can allow mothers to focus more time and energy on their children. These factors suggest that child healthcare providers need to provide more targeted and specific guidance to mothers with lower education level, living in nuclear families, and FF to improve their perceptions of infant hunger cues and to promote responsive feeding.
## Limitations
This study is the first to explore maternal perceptions of infant hunger cues under different feeding methods in a Chinese population, however, there are still some potential limitations. First, this study used self-reported data reflecting maternal perceptions of infant hunger cues, which may be affected by selective reporting or recall bias. The research group also collected videos to objectively observe the mother-infant feeding process so that they can be analyzed in subsequent studies. Additionally, mothers perceiving hunger cues does not necessarily imply that they promptly and adequately respond to these cues, which is an important additional component of responsive feeding. The potential disconnect between perception of hunger cues and correct response to these cues requires further study and attention. Second, this study only evaluated the common indicators of infant hunger cues, without incorporating infant satiety cues. But tried to use 5 indicators at different levels of early, active and late to reflect the mother’s perception, and will further enrich the content of satiety cues in future studies. Third, the population corresponding to the sample size calculation was not entirely consistent with this study population, as the perceived rate of 3-month-old infant hunger cues by mothers was not found in previous reports of Chinese population (This study used the report of Latina mothers participating in New York City WIC programs with a singleton infant aged < 5 months). This study obtained maternal perceptions of some hunger cues of Chinese population, which provided a basis for future research. And last, some results, while technically statistically significant, had a $95\%$ CI close to 1. We will try to use the observation data to make our conclusions stronger in subsequent studies.
## Conclusion
Mothers who use different feeding methods in China also display different levels of perceptiveness toward infant hunger cues. Exclusively breastfeeding mothers of 3-month-old infants are more likely to perceive infant hunger cues than FF mothers in China, and impacts from the mother’s educational level and family structure are also observed. It is necessary to increase the health education of infant hunger and satiety cues to caregivers in Chinese population, especially among less educated mothers, mothers living in nuclear families, and FF mothers. Targeted education interventions can improve the perception of infant hunger cues by these caregivers and may contribute to the promotion of responsive feeding practices.
## References
1. Black MM, Aboud FE. **Responsive feeding is embedded in a theoretical Framework of responsive parenting**. *J Nutr* (2011.0) **141** 490-4. DOI: 10.3945/jn.110.129973
2. Fang R, Xiaoyang S. **Feeding patterns of infants during the complementary feeding period**. *Chin J Pediatr* (2015.0) **53** 153-5
3. Meiling T. **Responsive feeding of premature infants and infants**. *Chin J Practical Pediatr Clin Pract* (2017.0) **32** 1763-5
4. DiSantis KI, Hodges EA, Johnson SL, Fisher JO. **The role of responsive feeding in overweight during infancy and toddlerhood: a systematic review**. *Int J Obes* (2011.0) **35** 480-92. DOI: 10.1038/ijo.2011.3
5. 5.Institute of Medicine (U.S.), Birch LL, Burns AC, Parker L, Institute of Medicine (U.S.), editors. Early childhood obesity prevention policies. Washington, D.C: National Academies Press; 2011.
6. Thompson AL, Adair LS, Bentley ME. **Pressuring and restrictive feeding styles influence infant feeding and size among a low-income african-american sample: feeding Styles, Infant Diet, and size**. *Obesity* (2013.0) **21** 562-71. DOI: 10.1002/oby.20091
7. 7.World Health Organization, United Nations Children’s Fund (UNICEF)Protecting, promoting and supporting breastfeeding: the baby-friendly hospital initiative for small, sick and preterm newborns2020GenevaWorld Health Organization. *Protecting, promoting and supporting breastfeeding: the baby-friendly hospital initiative for small, sick and preterm newborns* (2020.0)
8. Fuglestad AJ, Demerath EW, Finsaas MC, Moore CJ, Georgieff MK, Carlson SM. **Maternal executive function, infant feeding responsiveness and infant growth during the first 3 months: infant feeding**. *Pediatr Obes* (2017.0) **12** 102-10. DOI: 10.1111/ijpo.12226
9. Mesman J, Emmen RAG. **Mary Ainsworth’s legacy: a systematic review of observational instruments measuring parental sensitivity**. *Attach Hum Dev* (2013.0) **15** 485-506. DOI: 10.1080/14616734.2013.820900
10. Hodges EA, Hughes SO, Hopkinson J, Fisher JO. **Maternal decisions about the initiation and termination of infant feeding**. *Appetite* (2008.0) **50** 333-9. DOI: 10.1016/j.appet.2007.08.010
11. Ventura AK, Inamdar LB, Mennella JA. **Consistency in infants’ behavioural signalling of satiation during bottle-feeding**. *Pediatr Obes* (2015.0) **10** 180-7. DOI: 10.1111/ijpo.250
12. Bentley ME, Wasser HM, Creed-Kanashiro HM. **Responsive feeding and child undernutrition in low- and middle-income countries**. *J Nutr* (2011.0) **141** 502-7. DOI: 10.3945/jn.110.130005
13. Mentro AM, Steward DK, Garvin BJ. **Infant feeding responsiveness: a conceptual analysis**. *J Adv Nurs* (2002.0) **37** 208-16. DOI: 10.1046/j.1365-2648.2002.02068.x
14. McNally J, Hugh-Jones S, Caton S, Vereijken C, Weenen H, Hetherington M. **Communicating hunger and satiation in the first 2 years of life: a systematic review**. *Matern Child Nutr* (2016.0) **12** 205-28. DOI: 10.1111/mcn.12230
15. Shloim N, Shafiq I, Blundell-Birtill P, Hetherington MM. **Infant hunger and satiety cues during the first two years of life: developmental changes of within meal signalling**. *Appetite* (2018.0) **128** 303-10. DOI: 10.1016/j.appet.2018.05.144
16. Shloim N, Vereijken CMJL, Blundell P, Hetherington MM. **Looking for cues - infant communication of hunger and satiation during milk feeding**. *Appetite* (2017.0) **108** 74-82. DOI: 10.1016/j.appet.2016.09.020
17. Fomon SJ, Filmer LJ, Thomas LN, Anderson TA, Nelson SE. **Influence of formula concentration on caloric intake and growth of normal infants**. *Acta Paediatr Scand* (1975.0) **64** 172-81. DOI: 10.1111/j.1651-2227.1975.tb03818.x
18. Fomon SJ, Filer LJ, Thomas LN, Rogers RR, Proksch AM. **Relationship between formula concentration and rate of growth of normal infants**. *J Nutr* (1969.0) **98** 241-54. DOI: 10.1093/jn/98.2.241
19. Ounsted M, Sleigh G. *The Lancet* (1975.0) **305** 1393-7. DOI: 10.1016/S0140-6736(75)92605-7
20. Dewey KG, Lönnerdal B. **Infant self-regulation of breast milk intake**. *Acta Paediatr Scand* (1986.0) **75** 893-8. DOI: 10.1111/j.1651-2227.1986.tb10313.x
21. Daniels LA, Mallan KM, Battistutta D, Nicholson JM, Meedeniya JE, Bayer JK. **Child eating behavior outcomes of an early feeding intervention to reduce risk indicators for child obesity: the NOURISH RCT: child eating behavior outcomes of the NOURISH RCT**. *Obesity* (2014.0) **22** E104-11. DOI: 10.1002/oby.20693
22. Black MM, Hurley KM. **Responsive feeding: strategies to promote healthy mealtime interactions**. *Nestle Nutr Inst Workshop Ser* (2017.0) **87** 153-65. DOI: 10.1159/000448965
23. Gross RS, Fierman AH, Mendelsohn AL, Chiasson MA, Rosenberg TJ, Scheinmann R. **Maternal perceptions of infant hunger, satiety, and pressuring feeding styles in an urban Latina WIC population**. *Acad Pediatr* (2010.0) **10** 29-35. DOI: 10.1016/j.acap.2009.08.001
24. Crow RA. **An ethological study of the development of infant feeding**. *J Adv Nurs* (1977.0) **2** 99-109. DOI: 10.1111/j.1365-2648.1977.tb00183.x
25. Hodges EA, Wasser HM, Colgan BK, Bentley ME. **Development of feeding Cues during Infancy and Toddlerhood**. *MCN Am J Matern Child Nurs* (2016.0) **41** 244-51. DOI: 10.1097/NMC.0000000000000251
26. Hetherington MM. **Understanding infant eating behaviour - Lessons learned from observation**. *Physiol Behav* (2017.0) **176** 117-24. DOI: 10.1016/j.physbeh.2017.01.022
27. Beebe B, Stern DN, Freedman N, Grand S. **Engagement-Disengagement and early object experiences**. *Communicative structures and psychic structures* (1977.0) 35-55
28. de Lauzon-Guillain B, Oliveira A, Charles MA, Grammatikaki E, Jones L, Rigal N. **A review of methods to assess parental feeding Practices and Preschool Children’s eating behavior: the need for further development of tools**. *J Acad Nutr Dietetics* (2012.0) **112** 1578-1602e8. DOI: 10.1016/j.jand.2012.06.356
29. DiSantis K, Hodges EA, Fisher J. **The association of breastfeeding duration with later maternal feeding styles in infancy and toddlerhood: a cross-sectional analysis**. *Int J Behav Nutr Phys Act* (2013.0) **10** 53. DOI: 10.1186/1479-5868-10-53
30. Taveras EM, Scanlon KS, Birch L, Rifas-Shiman SL, Rich-Edwards JW, Gillman MW. **Association of Breastfeeding with maternal control of infant feeding at Age 1 year**. *Pediatrics* (2004.0) **114** e577-83. DOI: 10.1542/peds.2004-0801
31. Baughcum AE, Powers SW, Johnson SB, Chamberlin LA, Deeks CM, Jain A. **Maternal feeding Practices and Beliefs and their Relationships to overweight in early childhood**. *J Dev Behav Pediatr* (2001.0) **22** 391-408. DOI: 10.1097/00004703-200112000-00007
32. Birch LL, Davison KK. *Pediatr Clin North Am* (2001.0) **48** 893-907. DOI: 10.1016/S0031-3955(05)70347-3
33. Sherry B, McDivitt J, Birch LL, Cook FH, Sanders S, Prish JL. **Attitudes, practices, and concerns about child feeding and child weight status among socioeconomically diverse white, hispanic, and african-american mothers**. *J Am Diet Assoc* (2004.0) **104** 215-21. DOI: 10.1016/j.jada.2003.11.012
34. Hughes SO, Power TG, Orlet Fisher J, Mueller S, Nicklas TA. **Revisiting a neglected construct: parenting styles in a child-feeding context**. *Appetite* (2005.0) **44** 83-92. DOI: 10.1016/j.appet.2004.08.007
35. Anderson AS, Guthrie CA, Alder EM, Forsyth S, Howie PW, Williams FL. **Rattling the plate–reasons and rationales for early weaning**. *Health Educ Res* (2001.0) **16** 471-9. DOI: 10.1093/her/16.4.471
36. Shloim N, Rudolf M, Feltbower RG, Mohebati L, Hetherington M. **Breast is best: positive mealtime interactions in breastfeeding mothers from Israel and the United Kingdom**. *Health Psychol Open* (2015.0) **2** 2055102915579605. DOI: 10.1177/2055102915579605
37. Ursache A, Noble KG. **The Pediatric Imaging, Neurocognition and Genetics Study. Socioeconomic status, white matter, and executive function in children**. *Brain Behav* (2016.0) **6** e00531. DOI: 10.1002/brb3.531
38. Yichen L, Xiaozheng CH, Xiaorui SH, Hui H. **Analysis of developmental screening and influencing factors of 2 980 infants under three months in Beijing**. *Beijing Med* (2022.0) **44** 513-7
39. 39.Morawska A, Sanders MR, editors., editors. Handbook of Parenting and Child Development Across the Lifespan. 1st ed. 2018. Cham: Springer International Publishing: Imprint: Springer; 2018.
40. Cartagena DC, Ameringer SW, McGrath J, Jallo N, Masho SW, Myers BJ. **Factors contributing to infant overfeeding with hispanic mothers**. *J Obstet Gynecol Neonatal Nurs* (2014.0) **43** 139-59. DOI: 10.1111/1552-6909.12279
41. Shloim N, Rudolf M, Feltbower R, Hetherington M. **Adjusting to motherhood. The importance of BMI in predicting maternal well-being, eating behaviour and feeding practice within a cross cultural setting**. *Appetite* (2014.0) **81** 261-8. DOI: 10.1016/j.appet.2014.06.011
42. Jinliuxing Y, Yue Z, Weiwei F, He T, Jie SH, Nianrong W. **Practice of parenting and related factorson children aged 0–5 in the urban areas of China**. *Chin J Epidemiol* (2019.0) **40** 422-6
43. Zhang Y, Sanders M, Feng W, Tang H, Wang H, Jin X. **Using epidemiological data to identify needs for child-rearing support among chinese parents: a cross-sectional survey of parents of children aged 6 to 35 months in 15 chinese cities**. *BMC Public Health* (2019.0) **19** 1470. DOI: 10.1186/s12889-019-7635-y
44. 44.Britton C, McCormick FM, Renfrew MJ, Wade A, King SE. Support for breastfeeding mothers.Cochrane Database Syst Rev. 2007;:CD001141.
45. Kuzela AL, Stifter CA, Worobey J. **Breastfeeding and mother-infant interactions**. *J Reproductive Infant Psychol* (1990.0) **8** 185-94. DOI: 10.1080/02646839008403623
46. Ventura AK, Pollack Golen R. **A pilot study comparing opaque, weighted bottles with conventional, clear bottles for infant feeding**. *Appetite* (2015.0) **85** 178-84. DOI: 10.1016/j.appet.2014.11.028
47. Ventura AK, Hernandez A. **Effects of opaque, weighted bottles on maternal sensitivity and infant intake**. *Matern Child Nutr* (2019.0) **15** e12737. DOI: 10.1111/mcn.12737
48. Brown A, Arnott B. **Breastfeeding duration and early parenting behaviour: the importance of an infant-led, responsive style**. *PLoS ONE* (2014.0) **9** e83893. DOI: 10.1371/journal.pone.0083893
49. Singhal A, Kennedy K, Lanigan J, Fewtrell M, Cole TJ, Stephenson T. **Nutrition in infancy and long-term risk of obesity: evidence from 2 randomized controlled trials**. *Am J Clin Nutr* (2010.0) **92** 1133-44. DOI: 10.3945/ajcn.2010.29302
50. Augustine JM. **Mothers’ out-of-sequence Postsecondary Education and their Health and Health Behaviors**. *J Health Soc Behav* (2021.0) **62** 2-18. DOI: 10.1177/0022146520979664
51. Zajacova A, Lawrence EM. **The relationship between Education and Health: reducing disparities through a Contextual Approach**. *Annu Rev Public Health* (2018.0) **39** 273-89. DOI: 10.1146/annurev-publhealth-031816-044628
52. Mirowsky J, Ross CE. *Education, social status, and health* (2003.0)
53. Mirowsky J, Ross CE. **Education, Health, and the default american lifestyle**. *J Health Soc Behav* (2015.0) **56** 297-306. DOI: 10.1177/0022146515594814
54. Yan L, Xia L, Shousen H, Li W. **Analysis of mother’s parenting knowledge level and health education needs of 6 ~ 3 years old children in urban communities of Zibo and Zibo, Jinan**. *Maternal and Child Health Care in China* (2003.0) **18** 526-7
|
---
title: 'Myocardial fat accumulation is associated with cardiac dysfunction in patients
with type 2 diabetes, especially in elderly or female patients: a retrospective
observational study'
authors:
- Risa Kashiwagi-Takayama
- Junji Kozawa
- Yoshiya Hosokawa
- Sarasa Kato
- Satoshi Kawata
- Harutoshi Ozawa
- Ryohei Mineo
- Chisaki Ishibashi
- Megu Y. Baden
- Ryuya Iwamoto
- Kenji Saisho
- Yukari Fujita
- Sachiko Tamba
- Takuya Sugiyama
- Hitoshi Nishizawa
- Norikazu Maeda
- Koji Yamamoto
- Masahiro Higashi
- Yuya Yamada
- Yasushi Sakata
- Yuji Matsuzawa
- Iichiro Shimomura
journal: Cardiovascular Diabetology
year: 2023
pmcid: PMC9993532
doi: 10.1186/s12933-023-01782-y
license: CC BY 4.0
---
# Myocardial fat accumulation is associated with cardiac dysfunction in patients with type 2 diabetes, especially in elderly or female patients: a retrospective observational study
## Abstract
### Background
Ectopic fat is fat that accumulates in or around specific organs or compartments of the body including myocardium. The clinical features of type 2 diabetes patients with high fat accumulation in the myocardium remain unknown. Moreover, little is known about the influence of myocardial fat accumulation in type 2 diabetes on coronary artery disease and cardiac dysfunction. We aimed to clarify the clinical features, including cardiac functions, of type 2 diabetes patients with myocardial fat accumulation.
### Methods
We retrospectively enrolled type 2 diabetes patients who underwent ECG-gated coronary computed tomography angiography (CCTA) and abdominal computed tomography (CT) scan examinations within 1 year of CCTA from January 2000 to March 2021. High fat accumulation in the myocardium was defined as the low mean myocardial CT value of three regions of interest, and the associations between CT values and clinical characteristics or cardiac functions were assessed.
### Results
In total, 124 patients were enrolled (72 males and 52 females). The mean age was 66.6 years, the mean BMI was 26.2 kg/m2, the mean ejection fraction (EF) was $67.6\%$, and the mean myocardial CT value was 47.7 Hounsfield unit. A significant positive correlation was found between myocardial CT value and EF ($r = 0.3644$, $$p \leq 0.0004$$). The multiple regression analyses also showed that myocardial CT value was independently associated with EF (estimate, 0.304; $95\%$ confidence interval (CI) 0.092 to 0.517; $$p \leq 0.0056$$). Myocardial CT value showed significant negative correlations with BMI, visceral fat area and subcutaneous fat area (r = − 0.1923, − 0.2654, and -0.3569, respectively, $p \leq 0.05$). In patients who were ≥ 65 years or female, myocardial CT value showed significant positive correlations with not only EF ($r = 0.3542$ and 0.4085, respectively, $p \leq 0.01$) but also early lateral annular tissue Doppler velocity (Lat e’) ($r = 0.5148$ and 0.5361, respectively, $p \leq 0.05$). The multiple regression analyses showed that myocardial CT value was independently associated with EF and Lat e’ in these subgroups ($p \leq 0.05$).
### Conclusions
Patients with type 2 diabetes, especially in elderly or female patients, who had more myocardial fat had more severe left ventricular systolic and diastolic dysfunctions. Reducing myocardial fat accumulation may be a therapeutic target for type 2 diabetes patients.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12933-023-01782-y.
## Introduction
Ectopic fat is fat that accumulates in or around specific organs or compartments of the body [1]. According to recent studies, fat accumulations are observed in organs such as liver, skeletal muscle, kidney, heart, and pancreas [2–5]. In the heart, epicardial fat is another type of ectopic fat. Epicardial fat is increased in patients with type 2 diabetes [6], and its accumulation is associated with the presence of coronary artery disease and cardiac arrhythmias [7–9]. Moreover, the accumulation of triglycerides (TGs) in coronary arteries has been recently reported [10, 11]. Ectopic fat is also observed in the myocardium. Myocardial fat accumulation has been assessed using proton magnetic resonance (MR) spectroscopy, and myocardial TG content in obesity, metabolic syndrome or type 2 diabetes patients was increased compared with that in control subjects [12, 13]. Myocardial fat accumulation has been reported to be associated with higher left ventricular mass in healthy subjects [14] as well as in heart failure patients [15], and, among Blacks with diabetes, high left ventricular mass is associated with worse outcomes, indicating subclinical myocardial dysfunction [16]. However, the clinical features of type 2 diabetes patients with high fat accumulation in the myocardium remain unknown. Moreover, little is known about the influence of myocardial fat accumulation in type 2 diabetes on coronary artery disease and myocardial systolic or diastolic dysfunction.
Not only MR spectroscopy but also noncontrasted computed tomography (CT) attenuation values (Hounsfield units) are used to assess the fat accumulation in the liver, pancreas, and skeletal muscle [17, 18]. It has been reported that CT values of the myocardium were apparently low in two patients with myocardial TG deposition compared to control subjects [19], indicating that this modality could be applicable to the myocardium. The present study was designed to clarify the clinical features of type 2 diabetes patients with myocardial fat accumulation assessed by CT values. This study also investigated the associations between myocardial fat accumulation and coronary artery disease or myocardial function in type 2 diabetes.
## Patients
We searched the database of patients with type 2 diabetes who were referred for ECG-gated coronary computed tomography angiography (CCTA) examinations (Toshiba Aquilion CT scanner, Toshiba Medical, Tochigi, Japan; SOMATOM Definition or Force, Siemens Healthineers, Forchheim, Germany) for the first time and underwent abdominal CT scans (Toshiba Aquilion CT scanner, Toshiba Medical, Tochigi, Japan; Discovery CT750 HD, General Electric Healthcare, Chicago, IL, USA; SOMATOM Definition, Siemens Healthineers, Forchheim, Germany; GE Optima CT660, General Electric Healthcare, Chicago, IL, USA) within 1 year of CCTA at Osaka University Hospital or Sumitomo Hospital between January 2000 and March 2021. A total of 411 patients met these criteria. Among these patients, we excluded patients to avoid the influence of cardiac function or the myocardial CT value of pathological conditions other than myocardial fat accumulation. The excluded patients included those with heart failure with reduced ejection fraction (≤ $40\%$), those with valvular heart disease and those who had received past percutaneous coronary intervention (PCI) for coronary artery disease. Moreover, we excluded patients who had liver cirrhosis, renal failure (estimated glomerular filtration rate of < 30 mL/min/1.73 m2), malignant diseases and diseases requiring glucocorticoids for the treatment of other diseases. Furthermore, it is known that changes in X-ray tube voltage affect CT attenuation values [20]. Therefore, patients who underwent CCTA or abdominal CT examinations that were not performed at 120 kV were excluded. Using these criteria, 124 patients were finally included in our analyses. The flowchart for the recruitment of the patients is shown in Additional file 1: Fig S1. Among these 124 patients, the medications for diabetes at the time of CCTA were as follows: insulin for 42 patients, glucagon-like peptide-1 (GLP-1) receptor agonists for 9 patients, sulfonylureas for 31 patients, biguanides for 43 patients, dipeptidyl peptidase-4 inhibitors for 40 patients, α-glucosidase inhibitors for 26 patients, thiazolidinediones for 13 patients, glinides for 12 patients and sodium–glucose cotransporter 2 (SGLT2) inhibitors for 3 patients. The medications for dyslipidemia at the time of CCTA were as follows: statins for 69 patients, fibrates for 9 patients, ezetimibe for 5 patients, omega-3 fatty acids for 8 patients and probucol for 1 patient.
This study was approved by the Institutional Ethics Review Boards of Osaka University Hospital and Sumitomo Hospital and was carried out in accordance with the principles of the Declaration of Helsinki. The study was announced to the public on the websites of our department at Osaka University Hospital and Sumitomo Hospital, and all patients were allowed to participate or refuse to participate in the study.
## Clinical parameters
We obtained the following data at the time of the first CCTA examinations from medical records: age, sex, body mass index (BMI), previous highest BMI, waist circumference, systolic and diastolic blood pressure, levels of hemoglobin A1c (HbA1c), fasting plasma glucose (FPG), C-peptide index (CPI), homeostasis model assessment of β-cell function (HOMA-β), homeostasis model assessment of insulin resistance (HOMA-IR), total cholesterol (T-Chol), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), uric acid (UA), aspartate transaminase (AST), alanine transaminase (ALT), γ-glutamyltranspeptidase (γGTP), estimated glomerular filtration rate (eGFR), brain natriuretic peptide (BNP), and serum total adiponectin levels. CPI was defined as F-CPR (nmol/L) × 100/FPG (mmol/L), HOMA-β as F-IRI (µIU/mL) × 20/(FPG [mmol/L]—3.5), HOMA-IR as F-IRI (μU/mL) × FPG (mg/dL)/405. Serum total adiponectin levels were measured by the latex particle-enhanced turbidimetric immunoassay (human adiponectin latex kit; Otsuka Pharmaceutical Co., Ltd., Tokyo, Japan).
We also obtained the following echocardiographic data at the time of the first CCTA examinations from medical records: ejection fraction (EF), maximum inferior vena cava diameters (IVCmax), early lateral annular tissue Doppler velocity (Lat e’), septum mitral early diastolic velocity/early lateral annular tissue Doppler velocity (sep E/e’), mean mitral early diastolic velocity/early lateral annular tissue Doppler velocity (mean E/e’), and wall motion abnormalities. Echocardiographic studies were performed with commercially available equipment.
CCTA images were visually interpreted by radiologists. We obtained the data of the presence of stenosis of ≥ $50\%$ in at least one coronary arterial segment. Finally, we obtained the clinical history of percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) surgery after the first CCTA examinations until April 2021.
## Measurement of myocardial, pancreatic, liver and iliopsoas muscle fat
It has been shown that unenhanced CT values are well correlated with the degree of fat content in the pancreas, liver and iliopsoas muscle [17, 21, 22]. The myocardial CT value (H) was defined as the mean CT value of three regions of interest with areas of 10 mm2 in two different parts of the left ventricular free wall and one part of the myocardial septum. To confirm the intramyocardial measurement areas excluding blood pool and epicardial fat, we compared contrasted and noncontrasted images side-by-side, as shown in Additional file 1: Figure S2. High fat accumulation in the myocardium was defined as a low CT value (low H). The first reader performed CT measurements of all cases. To confirm the inter- and intra-observer variabilities, the second investigator who was blinded to clinical data analyzed CT values of all cases independently. Inter-observer and intra-observer variabilities were examined with the intraclass correlation coefficient (ICC) [2,1] and [1,1], respectively. The measurement of the myocardial CT value showed good inter- and intra-observer variabilities {ICC [2,1] = 0.92 and ICC [1,1] = 0.90}.
The pancreatic CT value (P) was defined as the mean CT value of three regions of interest with areas of 1 cm2 in three different pancreatic parts, the head, body and tail, as previously shown [25]. We also defined a liver CT value (L) as the mean CT value of three regions of interest with areas of 1 cm2 in three different segments of liver: anterior, posterior and lateral [25]. The CT value of the iliopsoas muscle (M) was defined as the mean CT value of the regions of interest with areas of 1 cm2 in iliopsoas muscles on both the left and right sides at the umbilical level. Similar methods were previously used in the study of paraspinal muscle density [23, 24]. We defined a splenic CT value (S) as the mean of three regions of interest with areas of 1 cm2 in three different splenic levels: upper, middle and lower [25]. Both pancreatic and hepatic attenuation measurements were analyzed with normalization with the spleen [17, 25, 26]. As previously shown, indices of pancreatic and hepatic fat content were defined as the differences between the pancreatic and splenic CT values (P-S) and the liver and splenic CT values (L-S), respectively [25]. High fat accumulations in the pancreas and liver were defined as low CT values (low P-S and low L-S, respectively). The first reader performed CT measurements of all cases, and the second investigator who was blinded to clinical data analyzed CT values of L-S, P-S, M and S of all cases independently. The measurements of L-S, P-S, M and S showed good inter- and intra-observer variabilities {L-S, ICC [2,1] = 0.94 and ICC [1,1] = 0.98; P-S, ICC [2,1] = 0.87 and ICC [1,1] = 0.97; M, ICC [2,1] = 0.94 and ICC [1,1] = 0.96; S, ICC [2,1] = 0.95 and ICC [1,1] = 0.96}.
The images were analyzed using the software program Synapse viewer (Fujifilm Inc., Tokyo, Japan). The visceral fat area (VFA) and subcutaneous fat area (SFA) were computed or measured manually using commercial software for CT scans taken at the umbilical level in a supine position, based on Japanese guidelines for obesity treatment (Japan Society for the Study of Obesity, in Japanese) [27].
## Statistical analysis
Data are presented as the mean ± standard deviation or number of patients and compared by the Student’s t-test or Pearson’s chi-square test. The relationships between the myocardial CT value and clinical parameters or CT values of each organ were assessed using Pearson’s correlation coefficient analyses. The factors that contributed to EF or Lat e’ were assessed using multiple regression analyses. We did not impute the missing data, and we performed complete case analyses. The association between coronary arteries with ≥ $50\%$ stenosis in CCTA images and the myocardial CT value was evaluated using multiple logistic regression analyses. We also performed multiple logistic regression analyses to evaluate the relationship between the clinical history of PCI or CABG surgery after the first CCTA examinations until April 2021 and the myocardial CT value. All statistical analyses were performed using JMP Pro 14 software (SAS Institute Inc., Cary, NC, USA). P values < 0.05 were considered statistically significant.
## Correlation analyses between clinical parameters and myocardial fat content
Table 1 summarizes the baseline clinical characteristics of the patients at the first CCTA examination. The results of the correlation analyses between myocardial fat content represented by H and clinical parameters are shown in Fig. 1. In analyses of all patients, H showed significant negative correlations with BMI, VFA and SFA. In other words, patients with higher levels of obesity had more myocardial fat. With regard to the echocardiographic data, a significant positive correlation was found between H and EF. That is, patients with more myocardial fat had more severe left ventricular systolic dysfunction. There was no significant correlation between H and echocardiographic parameters of diastolic dysfunction (Lat e’). H also showed a significant positive correlation with L-S and M. No significant correlation was found between H and P-S. The multiple regression analyses showed that H was independently associated with EF (estimate, 0.304; $95\%$ confidence interval (CI) 0.092 to 0.517; $p \leq 0.05$) (Table 2). There was no significant correlation between H and glycemic parameters. No significant correlation was found between H and TGs, and the result was consistent after a log transformation of TGs. Table 1Clinical characteristics of patientsNAge (years)66.6 ± 10.1124Sex (male/female)$\frac{72}{52124}$BMI (kg/m2)26.2 ± 4.4124Waist circumference (cm)94.8 ± 10.361Previous highest BMI (kg/m2)29.4 ± 4.491Systolic blood pressure (mmHg)141 ± 21111Diastolic blood pressure (mmHg)79 ± 15111FPG (mg/dl)148 ± 45120HOMA-β42.3 ± 45.369CPR index1.28 ± 0.9583HOMA-IR2.6 ± 1.769HbA1c (%; mmol/mol)8.0 ± 1.8; 67 ± 19.7124Adiponectin (μg/ml)8.5 ± 5.636T-chol (mg/dl)199 ± 36121HDL-chol (mg/dl)55 ± 17124LDL-chol (mg/dl)118 ± 32120TGs (mg/dl)153 ± 122124UA (mg/dl)5.5 ± 1.3123AST (U/L)27 ± 19124ALT (U/L)27 ± 20124γGTP (U/L)53 ± 70123eGFR (ml/min/1.73 m2)73.3 ± 19.4120BNP (pg/ml)35.8 ± 42.569EF (%)67.6 ± 6.292IVCmax11.9 ± 3.486Lat e’ (cm/sec)7.4 ± 1.733Sep E/e’12.0 ± 4.557Mean E/e’11.0 ± 3.736Wall motion abnormalities (Yes/No)$\frac{7}{8592}$Myocardial CT value (HU)47.7 ± 6.9124Liver CT value (HU)53.2 ± 10.7124Liver CT value minus splenic CT value (HU)5.2 ± 10.4124Pancreatic CT value (HU)35.8 ± 9.4122Pancreatic CT value minus splenic CT value (HU)− 12.2 ± 10.2122Iliopsoas muscle CT value (HU)50.5 ± 6.3122Splenic CT value (HU)47.9 ± 4.7124VFA (cm2)131.9 ± 68.0104SFA (cm2)176.0 ± 92.393Coronary arteries with ≥ $50\%$ stenosis in CCTA images (Yes/No)$\frac{75}{49124}$Clinical history of PCI or CABG surgery after the first CCTA (Yes/No)$\frac{41}{83124}$Data are presented as the mean ± standard deviation or number of participants (N)BMI body mass index, HbA1c the levels of hemoglobin A1c, FPG fasting plasma glucose, CPI C-peptide index, HOMA-β homeostasis model assessment of β-cell function, HOMA-IR homeostasis model assessment of insulin resistance, T-Chol total cholesterol, TGs triglycerides, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, UA uric acid, AST aspartate transaminase, ALT alanine transaminase, γGTP γ-glutamyltranspeptidase, eGFR estimated glomerular filtration rate, BNP brain natriuretic peptide, EF ejection fraction, IVCmax maximum inferior vena cava diameters, Lat e’ early lateral annular tissue Doppler velocity, Sep E/e’ septum mitral early diastolic velocity/early lateral annular tissue Doppler velocity, Mean E/e’ mean mitral early diastolic velocity/early lateral annular tissue Doppler velocity, VFA visceral fat area, SFA subcutaneous fat area, CCTA coronary computed tomography angiographyFig. 1Correlation analysis of myocardial CT values (an index of myocardial fat content) and clinical parameters. The myocardial CT value was associated with EF. Furthermore, the myocardial CT value was associated with BMI, VFA, SFA, L-S and M. BMI body mass index, VFA visceral fat area, SFA subcutaneous fat area, TGs, triglycerides, EF ejection fraction, Lat e’ early lateral annular tissue Doppler velocity, S splenic CT value, L liver CT value, P pancreatic CT value, M iliopsoas muscle CT valueTable 2Multiple regression analysis (total)VariablesEstimateStandard Errort-valueP-value$95\%$ Confidence IntervalLowerUpperMultiple regression analysis for ejection fraction (EF) of subjects with complete data ($$n = 78$$) Myocardial CT value0.3040.1072.850.00560.0920.517 VFA− 0.0050.010− 0.490.6242− 0.0250.015 Liver CT value minus splenic CT value− 0.0410.076− 0.540.5934− 0.1930.111N, the number of patients; VFA, visceral fat area
## Correlation analyses between clinical parameters and myocardial fat content based on age
A previous study reported that there are age-related changes in the echocardiographic parameters of diastolic function [28]. Therefore, we divided the patients into two subgroups: the ≥ 65 years (the older subgroup) and < 65 years (the younger subgroup) subgroups. The clinical characteristics of each subgroup are shown in Additional file 1: Table S1. Figure 2 shows the results of the correlation analyses between myocardial fat content represented by H and clinical parameters of the older (≥ 65 years) and younger (< 65 years) subgroups. H showed significant negative correlations with BMI, VFA and SFA in the older subgroup. H showed a significant negative correlation with SFA and had a tendency of negative correlation with VFA in the younger subgroup. With regard to the echocardiographic data, significant positive correlations were found between H and EF in both subgroups. In addition, in the older subgroup, there was a significant positive correlation between H and Lat e', and the multiple regression analyses showed that H was independently associated with EF and Lat e’ (estimate, 0.312 and 0.184, respectively; $95\%$ CI 0.058 to 0.565 and 0.036 to 0.332, respectively; $p \leq 0.05$) (Table 3). In other words, the older subgroup with more myocardial fat had more severe left ventricular systolic and diastolic dysfunctions. In the younger subgroup, there was no significant correlation between H and Lat e’, and the multiple regression analysis showed that H was independently associated with EF (estimate, 0.421; $95\%$ CI 0.003 to 0.838; $p \leq 0.05$) (Table 3). H also showed a significant positive correlation with L-S and M in the older subgroup, and there was a significant correlation between H and M in the younger subgroup. No significant correlation was found between H and P-S in either subgroup. There was no significant correlation between H and glycemic parameters in either subgroup. No significant correlation was found between H and TGs, and the result was consistent after a log transformation of TGs in either subgroup. Fig. 2Correlation analysis between myocardial CT values (an index of myocardial fat content) and clinical parameters of the ≥ 65 years (the older subgroup) (A) and < 65 years (the younger subgroup) (B) subgroups. In the older subgroup, the myocardial CT value was associated with EF and Lat e’. Furthermore, the myocardial CT value was associated with BMI, VFA, SFA, L-S and M. In the younger subgroup, the myocardial CT value was associated with EF. Furthermore, the myocardial CT value was associated with SFA and M. BMI body mass index, VFA visceral fat areas, SFA subcutaneous fat areas, TGs triglycerides, EF ejection fraction, Lat e’ early lateral annular tissue Doppler velocity, S splenic CT value, L liver CT value, P pancreatic CT value, M iliopsoas muscle CT valueTable 3Multiple regression analyses (older, younger)VariablesEstimateStandard Errort-valueP-value$95\%$ Confidence IntervalLowerUpperMultiple regression analysis for ejection fraction (EF) in the older subgroup with complete data ($$n = 52$$) Myocardial CT value0.3120.1262.470.01710.0580.565 VFA− 0.0040.012− 0.390.6965− 0.0290.020 Liver CT value minus splenic CT value− 0.0480.101− 0.470.6372− 0.2500.154Multiple regression analysis for early lateral annular tissue Doppler velocity (Lat e') in the older subgroup with complete data ($$n = 22$$) Myocardial CT value0.1840.0712.610.01780.0360.332 VFA0.0050.0070.670.5134− 0.0100.020 Liver CT value minus splenic CT value− 0.0270.046− 0.590.5628− 0.1250.070Multiple regression analysis for ejection fraction (EF) in the younger subgroup with complete data ($$n = 26$$) Myocardial CT value0.4210.2011.090.04830.0030.838 VFA− 0.0070.019− 0.350.7275− 0.0470.033 Liver CT value minus splenic CT value− 0.1590.120− 1.330.1979− 0.4080.090N the number of patients, VFA visceral fat area
## Correlation analyses between clinical parameters and myocardial fat content based on sex
We divided patients into sex (female and male) subgroups and analyzed the correlation between H and clinical parameters. The clinical characteristics of each subgroup are shown in Additional file 1: Table S2. The results of the correlation analyses between the myocardial fat content represented by H and clinical parameters of the female and male subgroups are shown in Fig. 3. In the female subgroup, H showed significant negative correlations with VFA and SFA, while H showed significant negative correlations with BMI and VFA in the male subgroup. With regard to the echocardiographic data, a significant positive correlation was found between H and EF in both subgroups. In addition, in the female subgroup, there was a significant positive correlation between H and Lat e’, and the result of the significant association of myocardial fat and Lat e’ in female subgroup was consistent after we excluded two women of premenopausal age (40 and 48 years) ($r = 0.5451$, $$p \leq 0.0290$$). The multiple regression analyses showed that H was independently associated with EF and Lat e’ (estimate, 0.346 and 0.196, respectively; $95\%$ CI 0.070 to 0.622 and 0.013 to 0.379, respectively; $p \leq 0.05$) (Table 4). That is, the female subgroup with more myocardial fat had more severe left ventricular systolic and diastolic dysfunction. In the male subgroup, there was no significant association between H and Lat e’, and the multiple regression analysis showed that H was not independently associated with EF (Table 4). H also showed significant positive correlations with M in the female subgroup and with L-S and M in the male subgroup. Furthermore, no significant correlation was found between H and P-S in either subgroup. There was no significant correlation between H and glycemic parameters in either subgroup. No significant correlation was found between H and TGs, and the result was consistent after a log transformation of TGs in either subgroup. Fig. 3Correlation analysis between myocardial CT values (an index of myocardial fat content) and clinical parameters of the female subgroup (A) and the male subgroup (B). In the female subgroup, the myocardial CT value was associated with EF and Lat e’. Furthermore, the myocardial CT value was associated with VFA, SFA and M. In the male subgroup, the myocardial CT value was associated with EF. Furthermore, the myocardial CT value was associated with BMI, VFA, L-S and M. BMI body mass index, VFA visceral fat area, SFA subcutaneous fat area, TGs triglycerides, EF ejection fraction, Lat e’ early lateral annular tissue Doppler velocity, S splenic CT value, L liver CT value, P pancreatic CT value, M iliopsoas muscle CT valueTable 4Multiple regression analyses (female, male)VariablesEstimateStandard Errort-valueP-value$95\%$ Confidence IntervalLowerUpperMultiple regression analysis for ejection fraction (EF) in the female subgroup with complete data ($$n = 36$$) Myocardial CT value0.3460.1342.560.01560.0700.622 VFA− 0.0150.019− 0.750.4585− 0.0540.025 Liver CT value minus splenic CT value− 0.1730.084− 2.050.0488− 0.345− 0.001Multiple regression analysis for early lateral annular tissue Doppler velocity (Lat e') in the female subgroup with complete data ($$n = 17$$) Myocardial CT value0.1960.0852.310.03770.0130.379 VFA0.0090.0110.780.4487− 0.0150.032 Liver CT value minus splenic CT value0.0270.0470.560.5847− 0.0760.129Multiple regression analyses for EF in the male subgroup with complete data ($$n = 42$$) Myocardial CT value0.2670.1671.600.1180− 0.0710.605 VFA0.0120.0121.040.3070− 0.0120.038 Liver CT value minus splenic CT value0.1640.1321.240.2214− 0.1030.432N the number of patients, VFA visceral fat area
## Association between coronary atherosclerosis or clinical history of PCI or CABG surgery and myocardial fat content
The multiple logistic regression analyses of the relationship between the myocardial fat content represented by H and coronary arteries with ≥ $50\%$ stenosis in CCTA images showed no significant correlation (Additional file 1: Fig S3). Likewise, there was no significant association between H and clinical history of PCI or CABG surgery after the first CCTA examinations (Additional file 1: Fig S3).
## Discussion
The present study showed that myocardial fat in type 2 diabetes patients was strongly associated with left ventricular systolic dysfunction. Furthermore, this study demonstrated that myocardial fat in type 2 diabetes patients who were ≥ 65 years or female had a strong association with left ventricular diastolic dysfunction, evaluated by Lat e’ as was previously reported [29]. In addition, we showed that myocardial fat in type 2 diabetes patients was strongly associated with obesity and hepatic and iliopsoas muscle fat.
## Relationships between myocardial fat and left ventricular dysfunctions
To our knowledge, this is the first study that demonstrated that myocardial fat of type 2 diabetes patients had strong associations with not only left ventricular diastolic dysfunction but also left ventricular systolic dysfunction. It has been reported that the accumulation of myocardial fat is associated with left ventricular diastolic dysfunction by using 1H- MR spectroscopy; however, no association was found between myocardial fat accumulation and left ventricular ejection fraction [30]. This inconsistency might be derived from the relatively large number of enrolled patients restricted to type 2 diabetes patients in our study, while the relatively small number of patients, including not only type 2 diabetes patients but also healthy volunteers, were included in that study [30].
Mitochondrial dysfunction in type 2 diabetes mellitus has been observed throughout various organ systems [31]. Sixty to ninety percent of cardiac energy is generated by the β-oxidation of fatty acids [32]. Therefore, myocardial mitochondrial dysfunction may lead to the accumulation of myocardial fat. Moreover, the adipose triglyceride lipase (ATGL) activity in the peripheral leucocytes of type 2 diabetes patients has been reported to be lower than that of the healthy group [33]. If ATGL activity in the cardiomyocytes of type 2 diabetes patients is also reduced, this may be related to the accumulation of myocardial fat. These possible mechanisms may lead to a decrease in energy production and an increase in fat accumulation in the myocardium, resulting in left ventricular systolic dysfunction in type 2 diabetes patients. In this study, no significant relationship was found between the use of SGLT2 inhibitors or GLP-1 receptor agonists and myocardial CT value (data not shown), though these medications have been reported to improve cardiac functions in human diabetic hearts [34] and in a mouse model of heart failure with preserved ejection fraction [35]. Because this study is cross-sectional and the limited number of the patients with these medications were included, further studies are needed to investigate the influence of these medications on myocardial fat accumulation.
We also found that myocardial fat in type 2 diabetes patients who were ≥ 65 years or female had a significant association with left ventricular diastolic dysfunction represented by the decrease in Lat e’. Heart failure with preserved ejection fraction (HFpEF) is more prevalent in elderly individuals and women [36, 37], but there were no statistically significant differences in Lat e’ in the age and sex subgroups compared by analysis of variance ($$p \leq 0.2003$$ and 0.9921, respectively). The higher prevalence of HFpEF in elderly patients implies the effects of aging on myocardial structure (the high prevalence of cardiac hypertrophy), endothelial inflammation and vascular inflammation [38, 39]. In addition, the importance of 17β-estradiol (E2) is considered to be critical in the onset of HFpEF in women after menopause [36]. Although the mechanism remains unclear, the present study might imply the contribution of myocardial fat to left ventricular diastolic dysfunction in elderly and female patients with type 2 diabetes.
## Relationships between myocardial fat and obesity related factors
Moreover, we showed that myocardial fat was strongly associated with abdominal obesity and hepatic fat in patients with type 2 diabetes. Obesity is associated with pericardial or epicardial adipose tissue, which is associated with subclinical left ventricular functional deterioration or HFpEF [40, 41]. In this study, the younger subgroup had higher BMI than the older subgroup, which might have led to higher insulin resistance evaluated by HOMA-IR. The results might be reflected by higher triglyceride level as well as elevated level of liver enzymes and lower liver CT value (Additional file 1: Table S1). Cardiac insulin resistance is considered to have a predominant role in determining alterations of left ventricular mechano-energetic performance [42]. However, no significant correlation was found between HOMA-IR and myocardial CT value or cardiac function in the younger subgroup. Further studies are needed to investigate the influence of insulin resistance on myocardial fat accumulation as well as cardiac function.
In addition, another study reported that obesity- or metabolic syndrome-related alterations in lipid metabolism increase myocardial fat content, epicardial fat thickness, inflammation and oxidative stress which ultimately leads to cardiac lipotoxicity and diastolic dysfunction [43]. Lipotoxicity derived from myocardial lipid accumulation may partially contribute to cardiac dysfunction [44], as shown in this study. Moreover, it has been reported that targeting cholesteryl ester accumulation in the heart improves cardiac insulin response [45]. Thus, fat accumulation including TGs as well as cholesteryl ester accumulation in myocardium may be a therapeutic target for type 2 diabetes patients with heart failure.
## Relationship between myocardial fat and coronary atherosclerosis
The current study showed no significant association between myocardial fat accumulation and coronary arteries with ≥ $50\%$ stenosis in CCTA images. Moreover, no significant association was found between myocardial fat and clinical history of PCI or CABG surgery after the first CCTA examinations. It has recently been proposed that coronary microvascular dysfunction participates in HFpEF development [46]. We only evaluated coronary macrovascular lesions by CCTA images and the history of PCI or CABG surgery, and therefore, we cannot deny the possibility that myocardial fat accumulation has some impact on microvascular dysfunction. Further studies are needed to investigate the influence of myocardial fat on coronary atherosclerosis.
## Limitations
First, the sample size of the study was small, especially when we divided patients into age or sex subgroups. Second, the inclusion criteria of type 2 diabetes patients who underwent CCTA may imply selection bias toward patients with suspected heart diseases. Therefore, the results of our study may not be applicable to all patients with type 2 diabetes. Third, only Lat e’ was used to evaluate left ventricular diastolic function, and other echocardiographic data were not used in this study. However, we do think that our method using Lat e’ is appropriate to investigate the relationship between myocardial fat content and left ventricular diastolic function because the other parameters, such as the E/A ratio, do not change in a linear manner according to the severity of diastolic dysfunction [47]. Finally, we could not evaluate the effect of hypertension or dyslipidemia and the medications for them on the cardiac parameters in the analyses.
## Conclusions
Our study is the first to show that myocardial fat in type 2 diabetes patients has a strong association with left ventricular systolic dysfunction. Moreover, myocardial fat is associated with left ventricular diastolic dysfunction in ≥ 65-year-old or female patients with type 2 diabetes. Reducing myocardial fat accumulation may be a therapeutic target for type 2 diabetes patients with heart failure.
## Supplementary Information
Additional file 1: Table S1. Clinical characteristics of older and younger subgroups. Table S2. Clinical characteristics of female and male subgroups. Table S3. Clinical characteristics of patients with complete cases for multiple regression analysis (total). Table S4. Clinical characteristics of older and younger subgroups with complete cases for multiple regression analysis. Table S5. Clinical characteristics of female and male subgroups with complete cases for multiple regression analysis. Figure S1 Flowchart for the recruitment of the patients. Figure S2. Method for measuring myocardial CT values on non-contrasted CT images. Figure S3. Multiple logistic regression analyses (total).
## References
1. Kozawa J, Shimomura I. **Ectopic fat accumulation in pancreas and heart**. *J Clin Med* (2021) **10** 1326. DOI: 10.3390/jcm10061326
2. Després JP, Lemieux I. **Abdominal obesity and metabolic syndrome**. *Nature* (2006) **444** 881-887. DOI: 10.1038/nature05488
3. Miranda PJ, DeFronzo RA, Califf RM, Guyton JR. **Metabolic syndrome: definition, pathophysiology, and mechanisms**. *Am Heart J* (2005) **149** 33-45. DOI: 10.1016/j.ahj.2004.07.013
4. Taylor R. **Type 2 diabetes: etiology and reversibility**. *Diabetes Care* (2013) **36** 1047-1055. DOI: 10.2337/dc12-1805
5. Lim S, Meigs JB. **Links between ectopic fat and vascular disease in humans**. *Arterioscler Thromb Vasc Biol* (2014) **34** 1820-1826. DOI: 10.1161/ATVBAHA.114.303035
6. Iacobellis G. **Local and systemic effects of the multifaceted epicardial adipose tissue depot**. *Nat Rev Endocrinol* (2015) **11** 363-371. DOI: 10.1038/nrendo.2015.58
7. Milanese G, Silva M, Ledda RE, Goldoni M, Nayak S, Bruno L, Rossi E, Maffei E, Cademartiri F, Sverzellati N. **Validity of epicardial fat volume as biomarker of coronary artery disease in symptomatic individuals: results from the ALTER-BIO registry**. *Int J Cardiol* (2020) **314** 20-24. DOI: 10.1016/j.ijcard.2020.04.031
8. Braescu L, Gaspar M, Buriman D, Aburel OM, Merce AP, Bratosin F, Aleksandrovich KS, Alambaram S, Mornos C. **The role and implications of epicardial fat in coronary atherosclerotic disease**. *J Clin Med* (2022) **11** 4718. DOI: 10.3390/jcm11164718
9. Patel KHK, Hwang T, Se Liebers C, Ng FS. **Epicardial adipose tissue as a mediator of cardiac arrhythmias**. *Am J Physiol Heart Circ Physiol* (2022) **322** H129-H144. DOI: 10.1152/ajpheart.00565.2021
10. Ikeda Y, Zaima N, Hirano K, Mano M, Kobayashi K, Yamada S, Yamaguchi S, Suzuki A, Kanzaki H, Hamasaki T, Kotani J, Kato S, Nagasaka H, Setou M, Ishibashi-Ueda H. **Coronary triglyceride deposition in contemporary advanced diabetics**. *Pathol Int* (2014) **64** 325-335. DOI: 10.1111/pin.12177
11. Kozawa J, Higashi M, Shimomura I, Hirano KI. **Intractable coronary artery disease in a patient with type 2 diabetes presenting with triglyceride deposit cardiomyovasculopathy**. *Diabetes Care* (2019) **42** 983-986. DOI: 10.2337/dc18-2365
12. Gaborit B, Kober F, Jacquier A, Moro PJ, Cuisset T, Boullu S, Dadoun F, Alessi MC, Morange P, Clément K. **Assessment of epicardial fat volume and myocardial triglyceride content in severely obese subjects: relationship to metabolic profile, cardiac function and visceral fat**. *Int J Obes (Lond)* (2012) **36** 422-430. DOI: 10.1038/ijo.2011.117
13. McGavock JM, Lingvay I, Zib I, Tillery T, Salas N, Unger R, Levine BD, Raskin P, Victor RG, Szczepaniak LS. **Cardiac steatosis in diabetes mellitus: a 1H-magnetic resonance spectroscopy study**. *Circulation* (2007) **116** 1170-1175. DOI: 10.1161/CIRCULATIONAHA.106.645614
14. Szczepaniak LS, Dobbins RL, Metzger GJ, Sartoni-D’Ambrosia G, Arbique D, Vongpatanasin W, Unger R, Victor RG. **Myocardial triglycerides and systolic function in humans: in vivo evaluation by localized proton spectroscopy and cardiac imaging**. *Magn Reson Med* (2003) **49** 417-423. DOI: 10.1002/mrm.10372
15. Liao PA, Lin G, Tsai SY, Wang CH, Juan YH, Lin YC, Wu MT, Yang LY, Liu MH, Chang TC. **Myocardial triglyceride content at 3 T cardiovascular magnetic resonance and left ventricular systolic function: a cross-sectional study in patients hospitalized with acute heart failure**. *J Cardiovasc Magn Reason* (2016) **18** 9. DOI: 10.1186/s12968-016-0228-3
16. Echouffo-Tcheugui JB, Musani SK, Bertoni AG, Correa A, Fox ER, Mentz RJ. **Patients phenotypes and cardiovascular risk in type 2 diabetes: the Jackson heart Study**. *Cardiovasc Diabetol* (2022) **21** 89. DOI: 10.1186/s12933-022-01501-z
17. Kim SY, Kim H, Cho JY, Lim S, Cha K, Lee KH, Kim YH, Kim JH, Yoon YS, Han HS. **Quantitative assessment of pancreatic fat by using unenhanced CT: pathologic correlation and clinical implications**. *Radiology* (2014) **271** 104-112. DOI: 10.1148/radiol.13122883
18. Taira S, Shimabukuro M, Higa M, Yabiku K, Kozuka C, Ueda R, Sunagawa S, Ohshiro Y, Doi M, Nanba T. **Lipid deposition in various sites of the skeletal muscles and liver exhibits a positive correlation with visceral fat accumulation in middle-aged Japanese men with metabolic syndrome**. *Intern Med* (2013) **52** 1561-71. DOI: 10.2169/internalmedicine.52.0521
19. Higashi M, Hirano K, Kobayashi K, Ikeda Y, Issiki A, Otsuka T, Suzuki A, Yamaguchi S, Zaima N, Hamada S. **Distinct cardiac phenotype between two homozygotes born in a village with accumulation of a genetic deficiency of adipose triglyceride lipase**. *Int J Cardiol* (2015) **192** 30-32. DOI: 10.1016/j.ijcard.2015.05.004
20. Rhee DJ, Kim S, Jeong DH, Moon YM, Kim JK. **Effects of the difference in tube voltage of the CT scanner on dose calculation**. *J Korean Phys Soc* (2015) **67** 123-128. DOI: 10.3938/jkps.67.123
21. Park SH, Kim PN, Kim KW, Lee SW, Yoon SE, Park SW, Ha HK, Lee MG, Hwang S, Lee SG. **Macrovesicular hepatic steatosis in living liver donors: use of CT for quantitative and qualitative assessment**. *Radiology* (2006) **239** 105-112. DOI: 10.1148/radiol.2391050361
22. Goodpaster BH, Thaete FL, Kelley DE. **Composition of skeletal muscle evaluated with computed tomography**. *Ann N Y Acad Sci* (2000) **904** 18-24. DOI: 10.1111/j.1749-6632.2000.tb06416.x
23. Kalichman L, Hodges P, Li L, Guermazi A, Hunter DJ. **Changes in paraspinal muscles and their association with low back pain and spinal degeneration: CT study**. *Eur Spine J* (2010) **19** 1136-1144. DOI: 10.1007/s00586-009-1257-5
24. Kitajima Y, Eguchi Y, Ishibashi E, Nakashita S, Aoki S, Toda S, Mizuta T, Ozaki I, Ono N, Eguchi T. **Age-related fat deposition in multifidus muscle could be a marker for nonalcoholic fatty liver disease**. *J Gastroenterol* (2010) **45** 218-224. DOI: 10.1007/s00535-009-0147-2
25. Yamazaki H, Tsuboya T, Katanuma A, Kodama Y, Tauchi S, Dohke M, Maguchi H. **Lack of independent association between fatty pancreas and incidence of type 2 diabetes: 5-Year Japanese cohort study**. *Diabetes Care* (2016) **39** 1677-1683. DOI: 10.2337/dc16-0074
26. Lee SW, Park SH, Kim KW, Choi EK, Shin YM, Kim PN, Lee KH, Yu ES, Hwang S, Lee SG. **Unenhanced CT for assessment of macrovesicular hepatic steatosis in living liver donors: comparison of visual grading with liver attenuation index**. *Radiology* (2007) **244** 479-485. DOI: 10.1148/radiol.2442061177
27. Yoshizumi T, Nakamura T, Yamane M, Islam AH, Menju M, Yamasaki K, Arai T, Kotani K, Funahashi T, Yamashita S. **Abdominal fat: standardized technique for measurement at CT**. *Radiology* (1999) **211** 283-286. DOI: 10.1148/radiology.211.1.r99ap15283
28. Tighe DA, Vinch CS, Hill JC, Meyer TE, Goldberg RJ, Aurigemma GP. **Influence of age on assessment of diastolic function by Doppler tissue imaging**. *Am J Cardiol* (2003) **91** 254-257. DOI: 10.1016/S0002-9149(02)03122-3
29. Simpson TF, Tyler JM, Thomas RC, Fang Q, Bibby D, Schiller NB. **Mitral annular peak systolic and diastolic velocities are characteristic of healthy hearts: a Doppler tissue imaging study**. *Echocardiography* (2019) **36** 433-8. DOI: 10.1111/echo.14247
30. Rijzewijk LJ, van der Meer RW, Smit JW, Diamant M, Bax JJ, Hammer S, Romijn JA, de Roos A, Lamb HJ. **Myocardial steatosis is an independent predictor of diastolic dysfunction in type 2 diabetes mellitus**. *J Am Coll Cardiol* (2008) **52** 1793-1799. DOI: 10.1016/j.jacc.2008.07.062
31. Pinti MV, Fink GK, Hathaway QA, Durr AJ, Kunovac A, Hollander JM. **Mitochondrial dysfunction in type 2 diabetes mellitus: an organ-based analysis**. *Am J Physiol Endocrinol Metab* (2019) **316** E268-E285. DOI: 10.1152/ajpendo.00314.2018
32. Palm CL, Nijholt KT, Bakker BM, Westenbrink BD. **Short-chain fatty acids in the metabolism of heart failure—rethinking the fat stigma**. *Front Cardiovasc Med* (2022) **9** 915102. DOI: 10.3389/fcvm.2022.915102
33. Takagi A, Ikeda Y, Kobayashi K, Kobayashi K, Ikeda Y, Kozawa J, Miyauchi H, Li M, Hashimoto C, Hara Y. **Newly developed selective immunoinactivation assay revealed reduction in adipose triglyceride lipase activity in peripheral leucocytes from patients with idiopathic triglyceride deposit cardiomyovasculopathy**. *Biochem Biophys Res Commun* (2018) **495** 646-651. DOI: 10.1016/j.bbrc.2017.11.070
34. Marfella R, D'Onofrio N, Trotta MC, Sardu C, Scisciola L, Amarelli C, Balestrieri ML, Grimaldi V, Mansueto G, Esposito S. **Sodium/glucose cotransporter 2 (SGLT2) inhibitors improve cardiac function by reducing JunD expression in human diabetic hearts**. *Metabolism* (2022) **127** 154936. DOI: 10.1016/j.metabol.2021.154936
35. Withaar C, Meems LMG, Markousis-Mavrogenis G, Boogerd CJ, Silljé HHW, Schouten EM, Dokter MM, Voors AA, Westenbrink BD, Lam CSP. **The effects of liraglutide and dapagliflozin on cardiac function and structure in a multi-hit mouse model of heart failure with preserved ejection fraction**. *Cardiovasc Res* (2021) **117** 2108-2124. DOI: 10.1093/cvr/cvaa256
36. Sabbatini AR, Kararigas G. **Menopause-related estrogen decrease and the pathogenesis of HfpEF: JACC review topic of the week**. *J Am Coll Cardiol* (2020) **75** 1074-1082. DOI: 10.1016/j.jacc.2019.12.049
37. Kobak KA, Zarzycka W, Chiao YA. **Age and sex differences in heart failure with preserved ejection fraction**. *Front Aging* (2022) **3** 811436. DOI: 10.3389/fragi.2022.811436
38. Wei JY. **Age and the cardiovascular system**. *N Engl J Med* (1992) **327** 1735-1739. DOI: 10.1056/NEJM199212103272408
39. Gevaert AB, Shakeri H, Leloup AJ, Van Hove CE, De Meyer GRY, Vrints CJ, Lemmens K, Van Craenenbroeck EM. **Endothelial senescence contributes to heart failure with preserved ejection fraction in an aging mouse Model**. *Circ Heart Fail* (2017) **10** e003806. DOI: 10.1161/CIRCHEARTFAILURE.116.003806
40. Kim JS, Kim SW, Lee JS, Lee SK, Abbott R, Lee KY, Lim HE, Sung KC, Cho GY, Koh KK. **Association of pericardial adipose tissue with left ventricular structure and function: a region-specific effect?**. *Cardiovasc Diabetol* (2021) **20** 26. DOI: 10.1186/s12933-021-01219-4
41. Salvatore T, Galiero R, Caturano A, Vetrano E, Rinaldi L, Coviello F, Di Martino A, Albanese G, Colantuoni S, Medicamento G. **Dysregulated epicardial adipose tissue as a risk factor and potential therapeutic target of heart failure with preserved ejection fraction in diabetes**. *Biomolecules* (2022) **12** 176. DOI: 10.3390/biom12020176
42. Succurro E, Cicone F, Papa A, Miceli S, Vizza P, Fiorentino TV, Perticone M, Sciacqua A, Guzzi PH, Veltri P. **Impaired insulin-stimulated myocardial glucose metabolic rate is associated with reduced estimated myocardial energetic efficiency in subjects with different degrees of glucose tolerance**. *Cardiovasc Diabetol* (2023) **22** 4. DOI: 10.1186/s12933-022-01733-z
43. Daneii P, Neshat S, Mirnasiry MS, Moghimi Z, Dehghan Niri F, Farid A, Shekarchizadeh M, Heshmat-Ghahdarijani K. **Lipids and diastolic dysfunction: Recent evidence and findings**. *Nutr Metab Cardiovasc Dis* (2022) **32** 1343-1352. DOI: 10.1016/j.numecd.2022.03.003
44. Leggat J, Bidault G, Vidal-Puig A. **Lipotoxicity: a driver of heart failure with preserved ejection fraction?**. *Clin Sci (Lond)* (2021) **135** 2265-2283. DOI: 10.1042/CS20210127
45. Actis Dato V, Benitez-Amaro A, Garcia E, Claudi L, Lhoëst MTL, Iborra A, Escola-Gil JC, Guerra JM, Samouillan V, Enrich C, Chiabrando G. **Targeting cholesteryl ester accumulation in the heart improves cardiac insulin response**. *Biomed Pharmacother* (2022) **152** 113270. DOI: 10.1016/j.biopha.2022.113270
46. Cuijpers I, Simmonds SJ, van Bilsen M, Czarnowska E, González Miqueo A, Heymans S, Kuhn AR, Mulder P, Ratajska A, Jones EAV. **Microvascular and lymphatic dysfunction in HFpEF and its associated comorbidities**. *Basic Res Cardiol* (2020) **115** 39. DOI: 10.1007/s00395-020-0798-y
47. Mitter SS, Shah SJ, Thomas JD. **A test in context: E/A and E/e’ to assess diastolic dysfunction and LV filling pressure**. *J Am Coll Cardiol* (2017) **69** 1451-1464. DOI: 10.1016/j.jacc.2016.12.037
|
---
title: Effects of an 8-week multimodal exercise program on ground reaction forces
and plantar pressure during walking in boys with autism spectrum disorder
authors:
- Mahrokh Dehghani
- Amir Ali Jafarnezhadgero
- Mohamad Abdollahpour Darvishani
- Shirin Aali
- Urs Granacher
journal: Trials
year: 2023
pmcid: PMC9993582
doi: 10.1186/s13063-023-07158-7
license: CC BY 4.0
---
# Effects of an 8-week multimodal exercise program on ground reaction forces and plantar pressure during walking in boys with autism spectrum disorder
## Abstract
### Background
Autism spectrum disorder is a developmental disability with first signs appearing in children aged 3 years and younger. Given that autism spectrum disorder is accompanied by a broad range of symptoms such as impaired sensory, neurological, and neuromotor functions, it appears plausible to argue that an intervention program focusing on multimodal exercise rather than single-mode exercise might be more effective in treating this wide variety of symptoms.
### Objective
The aim of this study was to evaluate the effects of a multimodal exercise program entitled Sports, Play, and Active Recreation for Kids on variables of ground reaction forces and plantar pressure during walking in boys with autism spectrum disorder.
### Methods
Twenty-four autism spectrum disorder boys aged 7–11 years were recruited and randomly allocated into an intervention or a waiting control group. Sports, Play, and Active Recreation for Kids was conducted over a period of 8 weeks with three weekly sessions. This training protocol includes aerobic dance and jump rope exercises as well as running games. Pre- and post-training, ground reaction forces and plantar pressure variables were recorded while walking at a constant walking speed of 0.9 m/s using a foot scan embedded in a 15-m walkway.
### Results
Significant group-by-time interactions were found for the first peak of vertical ground reaction force, loading rate, and peak pressure at the medial heel region (all $$p \leq 0.001$$–0.49, $d = 0.89$–1.40). Post-hoc analyses showed significant pre-post decreases for the first peak of vertical ground reaction force ($$p \leq 0.001$$, $d = 1.27$), loading rate ($$p \leq 0.009$$, $d = 1.11$), and peak pressure at the medial heel region ($$p \leq 0.021$$, $d = 1.01$).
### Conclusions
Our results suggest that a joyful and multimodal exercise program has positive effects on kinetic walking characteristics of autism spectrum disorder boys. Accordingly, we recommend to implement this type of exercise in prepubertal autism spectrum disorder boys to improve gait kinetics.
### Trial registration
Iranian Registry of Clinical Trials IRCT20170806035517N4. Registered on November 8, 2021. This study was approved by the Ethical Committee of the University of Mohaghegh Ardabili, Ardabil, Iran (IR.UMA.REC.1400.019). The study was conducted in accordance with the latest version of the Declaration of Helsinki.
## Introduction
Autism spectrum disorder (ASD) is a developmental disability with first signs appearing in children aged 3 years and younger [1]. Autism spectrum disorder is a neurodevelopmental disorder estimated to affect 1 in 59 children in the USA [2]. Prevalence rates appear to be higher in boys compared with girls [3]. In fact, three out of four ASD children are boys [3]. ASD is characterized by a variety of physiological and/or behavioral symptoms including impaired sensory, neurological, and neuromotor functions resulting in muscle rigidity, akinesia, and bradykinesia [4].
While the majority of research has focused on social and neurological characteristics associated with ASD [5], motor deficits are also prominent but have received little attention in research so far. Examples of typical motor deficits observed in ASD individuals include altered walking patterns and reduced gait speed (~ $20\%$), particularly under more challenging conditions [6, 7]. Abnormal walking patterns may cause pain, fatigue, and increased joint stress that may lead to a decline in quality of life [8, 9]. In ASD children, there is evidence for decreased pressure under each region of the foot (especially in the hindfoot) by ~ $25\%$ along with lower walking speed [10, 11]. Children with ASD demonstrated higher maximum braking forces (~ $30\%$) and lower relative times to reach the maximum braking force by ~ $22\%$ [12]. In addition, ASD children compared with healthy controls showed a lower second peak of the vertical ground reaction force (~ $5\%$) during the terminal stance phase of walking [12]. These prominent differences indicate that ASD children have difficulties in supporting their body mass during the terminal stance phase which could negatively affect gait stability [13]. Taken together, ASD-related symptoms and side effects often result in low physical activity behavior in ASD children ultimately leading to adverse health effects [14]. Consequently, intervention programs should be designed and implemented with the goal to reduce ASD symptoms [15] and comorbidities [16].
Today, systematic reviews [17] and meta-analyses [18] have strengthened the available evidence on the positive effects of physical exercise on both, ASD-related symptoms (e.g., impaired neuromotor function) and comorbidities (e.g., obesity). Of note, the regular performance of exercise in the form of a graded treadmill training protocol was particularly effective in improving ASD symptoms such as walking speed with little effect on the overall health (e.g., musculoskeletal fitness) of the ASD individual [14]. Moreover, Pan [19] reported that aquatic exercise induced improvements in static balance (e.g., the center of pressure displacements during quiet standing) [20] and muscle strength (e.g., hand grip strength) in ASD children.
Given that ASD is accompanied by a broad range of symptoms such as impaired sensory, neurological, and neuromotor functions, it appears plausible to argue that an intervention program focusing on multimodal exercise rather than single-mode exercise might be more effective in treating this wide variety of symptoms. Previously, the Sports, Play, and Active Recreation for Kids (SPARK) multimodal exercise program has been introduced to promote physical activity of children aged 5–12 years [20]. There is evidence that 12 weeks of SPARK improved the gait pattern (e.g., increased step length) and gait speed in mentally retarded boys [21]. Therefore, it seems that SPARK could be a good candidate to be implemented as a treatment form in ASD children. To the best of our knowledge, there is no study available that examined the effects of a multimodal exercise program such as SPARK on walking kinetics in children with ASD. Therefore, the purpose of this study was to investigate the effects of an 8-week multimodal exercise program (SPARK) on variables of ground reaction forces and plantar pressure during walking in prepubertal boys with ASD. With reference to the relevant literature [20], we hypothesized that children with ASD who regularly participate in the SPARK program reduce their peak ground reaction force amplitudes, loading rates, and peak pressure variables during walking to a larger extent compared with ASD children enrolled in a waiting control group.
## Participants
We utilized the freeware tool GPower (http://www.gpower.hhu.de/) to calculate a one-sided a priori power analysis. The a priori power analysis was calculated using the F-test family (i.e., ANOVA repeated measures within-between interaction), and a related study that examined the effects of training on horizontal ground reaction force in ASD Children [22]. The included program variables were an assumed type I error of 0.05, a type II error rate of 0.20 ($80\%$ statistical power), and an effect size of 0.70 for horizontal ground reaction force taken from the reference study [22]. The analysis revealed that at least 12 participants would be needed per group to achieve medium- to large-sized interaction effects for the parameter horizontal ground reaction force. Accordingly, a total of twenty-four prepubertal ASD boys aged 7–11 years were recruited from a group of children who participated in an adapted physical activity program that was delivered in a local community center. The scores of the “Gilliam Autism Rating Scale-2” [23] for the participating ASD children were between 62 and 123 which shows that the participating children were diagnosed as ASD children. The enrolled boys were age-matched and randomly allocated to an experimental ($$n = 12$$) or a waiting control group ($$n = 12$$) (Fig. 1). Participants’ anthropometric and demographic data are presented in Table 1. The following information was gathered from all participating children: date of birth, intensity, and date of autism anamnesis. Children with Asperger’s or pervasive developmental disorder were excluded from this study. A medical doctor examined all participants prior to the start of the study and excluded those with neuromotor or orthopedic disorders or medication that could affect the central nervous system. None of the participants reported any secondary neurological or orthopedic conditions including lower limb injuries during the 12 months prior to data collection. ASD disorder status and the presence or absence of learning disabilities were assessed using an Iranian translation of the Social Communication Questionnaire [24, 25] and the Persian translation of the Autism Diagnostic Interview [26, 27] by the medical doctor. We obtained children’s oral consent and parents’ or legal representatives’ written consent before the start of the study. The block randomization method (block size = 4) was used to allocate study participants into the experimental groups [28]. A naïve examiner realized the block randomization process. During the randomization procedure, a set of sealed, opaque envelopes was used to ensure the concealment of the allocation. Each envelope contained a card stipulating to which group the participant would be allocated to. Of note, participants were blinded to the group allocation. One examiner determined whether a participant was eligible for inclusion, while the other carried out gait analyses of the eligible participants. Both examiners were unaware of the group allocation. Another naïve examiner (i.e., physiotherapist with 10 years of professional experience) controlled the allocation of each participant and was responsible for delivering the treatment to both groups. This study was approved by the Ethical Committee of the University of Mohaghegh Ardabili, Ardabil, Iran (IR.UMA.REC.1400.019) and registered at the Iranian Registry of Clinical Trials (IRCT20170806035517N4). The study was conducted in accordance with the latest version of the Declaration of Helsinki. The data were collected at sport biomechanics laboratory of University of Mohaghegh Ardabili, Ardabil, Iran. Fig. 1CONSORT flow diagram of the present studyTable 1Group-specific baseline characteristics of the study participantsIntervention ($$n = 12$$)Waiting control ($$n = 12$$)Significance levelAge (years)9.2 ± 0.69.4 ± 0.50.904Body mass (kg)36.70 ± 2.4736.70 ± 3.381.000Body height (cm)128.45 ± 4.84130.00 ± 4.810.443BMI (kg/m2)22.29 ± 1.8521.80 ± 2.610.599Values are means ± standard deviations n number of participants, BMI body mass index, NA not applicable
## Test procedures
During pre- and post-tests, children were instructed to walk barefoot along a 15-m walkway at a constant speed of ⁓ 0.9 m/s. In this study, walking speed was monitored and controlled using two sets of infrared photocells (Swift Performance Equipment, New South Wales, Australia). A plantar pressure plate (RsScan International, Belgium, 0.5 m × 0.5× 0.02 m, 4363 sensors) was embedded in the middle of the walkway. Formal data collection started after six familiarization trials. Thereafter, three test trials were recorded. The starting position was adjusted for each participant to make it more likely that the pressure plate was hit and that two consecutive footprints were recorded during one test trial. If the participant did not hit the pressure plate or lost his balance during the walking trials, the trial was repeated. Prior to the walking tests, a standing test was performed on the pressure-sensitive plate to record body mass together with foot length and to calibrate the system. Thereafter, the three walking test trials were performed. For data analysis, the foot was automatically divided into the following ten anatomical areas by the customized software (Footscan1 software 9 Gait 2nd Generation, Rs Scan International): medial heel (HM), lateral heel (HL), midfoot, metatarsal first to fifth (M1-5), and the hallux (T1) and other toes (T2–5). The mean of three trials was used for statistical analyses.
## Data analysis
The following dependent variables were extracted from GRF data recorded through the plantar pressure plate [29]: peak vertical GRF, their time to peak, vertical loading rate, and peak pressure (N/cm2) of 10 distinct regions of the foot. Of note, vertical GRF shows a bimodal curve during walking which is indicated through two peaks including the first peak during heel contact (FzHC) and the second peak during the push-off phase (FzPO). There is also a downfall between the two peaks (FzMS) during mid-stance. To calculate the vertical loading rate during walking, the slope of the connecting line was calculated from the moment of the heel contact to the initial peak of the curve of the vertical GRF [29]. A cutoff frequency equal to 20 Hz was used to filter GRF data during walking. Ground reaction force amplitudes were normalized to body mass (BM) and reported as %BM. The gait cycle was divided into the loading (0–$20\%$), mid-stance (20–$47\%$), and push-off ($4770\%$) phases. The maximum pressure (peak force/area of related region) was calculated for all ten anatomical zones before and after the 8 weeks training program. Walking data were recorded during pre and post-tests.
Intraclass correlation coefficients (ICC) were calculated for all analyzed variables using pre- and post-data from the control group. In accordance with Koo and Li, test-retest reliability in the form of ICCs was computed using two-way mixed models [30]. ICC values less than 0.5 are indicative of poor reliability, values between 0.5 and 0.75 indicate moderate reliability, values between 0.75 and 0.9 indicate good reliability, and values greater than 0.90 indicate excellent reliability [30]. Table 2 provides ICC values for all assessed parameters. Of note, sufficient test-retest reliability is important as a marker of the measurement error (noise). This is particularly important in intervention studies with pre-post design. Table 2Intraclass correlation coefficients (ICC) for all analyzed variables using pre- and post-data from the waiting control groupVariableComponentICCsVertical ground reaction forceFzHC 0.90FzMS 0.91FzPO 0.88Time to peak forceFzHC 0.92FzMS 0.90FzPO 0.88Loading rateVertical0.91Walking stance time (ms)Walking stance time (ms)0.86Peak pressureToe 10.84Toes 2–50.79Metatarsal 10.85Metatarsal 20.77Metatarsal 30.77Metatarsal 40.74Metatarsal 50.75Midfoot0.79Medial heel0.81Lateral heel0.74 Fz HC peak vertical ground reaction force at heel contact, FzMS vertical ground reaction force during mid-stance, FzPO peak vertical ground reaction force during the push-off phase
## The multimodal exercise program
Training was conducted over a period of 8 weeks with three weekly sessions, each lasting 45 min. The exercise program resembled the SPARK intervention program that involves exercise and free play. Each exercise session lasted 45 min and was divided into four parts. During the first 10 min, a warm-up program was conducted consisting of stretching, walking, and jogging exercises. Thereafter, children played and exercised for 25 min to specifically promote their fundamental movement skills (e.g., jumping) through the SPARK intervention. During the main part of the SPARK session, health- and skill-related physical fitness were promoted through exercise and free play [31]. Health-related fitness exercises comprised 13 activities that included aerobic dance, running games, and jump rope exercises [20]. Accordingly, the main focus was to develop cardiovascular endurance. This was realized through the systematic programming of intensity, duration, and complexity of the respective activities [20]. Sports, Play, and Active Recreation for Kids tries to promote skill-related fitness by focusing on different sports such as soccer, basketball, and Frisbee [31]. Finally, a 10-min cool-down program consisting of dynamic stretching was realized. Over the course of the intervention period, the waiting control group performed their regular physical activity program including walking and free play. All sessions of the intervention and the waiting control group were supervised by physiotherapist who had at least 10 years of professional experience in delivering physical education to children with developmental disorders (i.e., ASD children). Overall, the intervention program included 24 SPARK sessions.
## Statistical analyses
The normal distribution of data was assessed and confirmed using the Shapiro-Wilk test. The baseline between-group differences was computed using the independent samples t-test. To elucidate the effects of the intervention versus the waiting control group over time, a 2 (group: exercise vs control) × 2 (time: pretest vs posttest) analysis of variance (ANOVA) with repeated measures was computed. In case statistically significant group-by-time interaction effects were established, Bonferroni-adjusted post hoc analyses (paired sample t-tests) were calculated. Additionally, effect sizes were determined by converting partial eta-squared (η2p) from ANOVA output to Cohen’s d. Within-group effect sizes were calculated using the following equation: mean difference of pre and post-tests/pooled standard deviation. According to Cohen, d < 0.50 indicates small effects, 0.50 ≤ d < 0.80 indicates medium effects, and d ≥ 0.80 indicates large effects [32]. The significance level was set at $p \leq 0.05.$ The statistical analyses were computed using SPSS (version 24, SPSS Inc., 8 Chicago, IL).
## Results
All participants received treatment as allocated. The adherence rate for SPARK and the regular physical activity program was $100\%$ for the intervention (SPARK) and the waiting control group (regular program). No training or test-related injuries were reported over the course of the study.
There were no significant between-group baseline differences for demographic and anthropometric data (Table 1).
Table 2 shows the test-retest reliability for all analyzed variables using ICCs. ICCs ranged from 0.74 to 0.92 indicating good-to-excellent reliability.
Table 3 indicates no statistically significant between-group baseline differences for measures of ground reaction force, loading rate, and peak pressure ($p \leq 0.05$).Table 3Group-specific baseline data for vertical ground reaction force (% of body mass), time to peak force (ms), loading rate (N/kg/s), and peak pressure (kPa)VariableComponentIntervention, mean ± SDWaiting control, mean ± SD$95\%$ CI p-valueVertical ground reaction forceFzHC 1014.34 ± 168.70947.11 ± 221.76− 12.44, 10.860.412FzMS 736.03 ± 91.85743.92 ± 167.90− 20.49, 20.010.888FzPO 969.86 ± 221.30947.27 ± 197.53− 15.51, 20.030.794Time to peak forcesFzHC 159.83 ± 32.04163.19 ± 50.43− 30.37, 23.650.799FzMS 296.16 ± 50.43283.05 ± 49.12− 29.41, 18.720.526FzPO 438.75 ± 60.94411.66 ± 70.10− 29.03, 55.250.323Loading rateVertical6.53 ± 1.515.89 ± 1.31− 0.55, 1.840.279Walking stance time (ms)Walking stance time (ms)612.19 ± 82.68600.27 ± 68.49− 52.36, 30.990.704Peak pressureToe 188.43 ± 21.7077.80 ± 22.29− 8.00, 29.250.249Toe 2-535.87 ± 18.0838.95 ± 19.06− 18.81, 12.660.688Metatarsal 167.44 ± 14.7863.34 ± 9.10− 6.28, 14.500.423Metatarsal 298.09 ± 29.2187.50 ± 28.63− 13.90, 35.070.380Metatarsal 378.96 ± 19.7675.63 ± 12.16− 10.56, 17.220.624Metatarsal 464.90 ± 24.5261.04 ± 14.97− 13.34, 21.060.647Metatarsal 546.38 ± 15.8552.29 ± 22.62− 22.45, 10.260.466Midfoot36.29 ± 12.3341.19 ± 8.75− 13.95, 4.150.274Medial heel95.50 ± 28.4978.43 ± 29.28− 14.68, 29.600.162Lateral heel83.70 ± 22.6776.24 ± 29.22− 14.75, 29.680.492 SD standard deviation, FzHC peak vertical ground reaction force during heel contact, FzMS vertical ground reaction force during mid-stance, FzPO peak vertical ground reaction force during the push-off phase
## Ground reaction forces during walking
The statistical analysis did not demonstrate any significant main effect of “time” ($p \leq 0.05$, $d = 0.06$–0.81) for vertical ground reaction forces during walking at a constant speed, their time-to-peak, loading rate, and walking stance time (Table 4). We observed significant group-by-time interactions for the first peak of the vertical ground reaction force, time-to-peak until the second peak of the vertical ground reaction force, and loading rate ($p \leq 0.049$, $d = 0.89$–0.91) (Table 4). Pair-wise analyses demonstrated that the intervention but not the control group showed significant decreases for the first peak of vertical ground reaction force ($$p \leq 0.001$$, $d = 1.27$), time-to-peak until the second peak of vertical ground reaction force ($$p \leq 0.012$$, $d = 1.28$), and loading rate ($$p \leq 0.009$$, $d = 1.11$).Table 4Group-specific mean values and standard deviations for vertical ground reaction force (% of body mass), time to peak force (ms), and loading rate (N/kg/s) during walking at constant speed in ASD boysVariableComponentInterventionWaiting controlMain effect of time (p-value, Cohen’s d)Main effect of group (p-value, Cohen’s d)Group × time interaction (p-value, Cohen’s d)PrePostΔ %$95\%$ CIPrePostΔ %$95\%$ CIVertical ground reaction forceFzHC 1014.34 ± 168.70 811.83 ± 147.89 − 19.96106.71–298.29947.11 ± 221.76959.56±138.391.31− 210.78–185.890.071 (0.810)0.424 (0.346) 0.043* (0.915) FzMS 736.03 ± 91.85672.06 ± 162.05− 8.69− 71.50–199.44743.92 ± 167.90671.38±176.23− 9.75− 11.35–156.420.073 (0.804)0.944 (0.000)0.907 (0.063)FzPO 969.86 ± 221.30856.46 ± 219.21− 11.69− 97.34–324.14947.27 ± 197.53923.34±213.48− 2.52− 166.66–214.510.299 (0.454)0.708 (0.168)0.496 (0.293)Time to peak forceFzHC 159.83 ± 32.04156.74 ± 16.21− 1.93− 19.94–26.11163.19 ± 50.43159.41±38.40− 2.31− 31.71–39.270.725 (0.155)0.712 (0.155)0.971 (0.000)FzMS 296.16 ± 50.43290.66 ± 48.68− 1.85− 32.97–43.97283.05 ± 49.12284.77±37.820.60− 41.89–38.440.883 (0.063)0.514 (0.286)0.778 (0.127)FzPO 438.75 ± 60.94 373.74 ± 27.75 − 14.8117.63–112.37411.66 ± 70.10417.79±37.761.48− 63.53–51.270.096 (0.742)0.516 (0.278) 0.047* (0.896) Loading rateVertical 6.53 ± 1.51 5.18 ± 0.82 − 20.670.42–2.285.89 ± 1.316.32±1.667.30− 2.06–1.190.293 (0.459)0.493 (0.300) 0.048* (0.896) Walking stance time (ms)612.19±82.68596.86 ± 65.06− 2.50− 47.76–78.43600.27 ± 68.49599.05 ± 59.52− 0.20− 49.64–52.090.657 (0.191)0.824 (0.090)0.705 (0.168) FzHC peak vertical ground reaction force at heel contact, FzMS vertical ground reaction force during mid-stance, FzPO peak vertical ground reaction force during the push-off phase, CI confidence interval, d Cohen’s d *Significant level $P \leq 0.05$ *In* general, there is evidence that ASD children aged 4–12 years compared with healthy controls show higher maximal braking forces and lower relative times to reach the maximum braking force when walking at the preferred speed [12]. A greater maximal braking force in ASD children during walking may reflect a high demand in terms of weight-bearing stability and shock absorption during the first part of the stance phase [12]. Throughout the early stance phase, ASD children may encounter problems with the alignment of the lower limbs due to the rapid transfer of body mass to the limb that has just accomplished the swing phase [33]. It has previously been reported that increased loading rates and impact shocks (i.e., the first peak of the vertical ground reaction force) during walking may represent biomechanical factors associated with an increased risk of sustaining orthopedic injuries such as knee degenerative joint disease or stress fractures [34]. The SPARK exercise program induced a decrease in the first peak of vertical ground reaction force and vertical loading rates during walking in ASD boys which might contribute to lowering the injury risk in ASD children.
## Peak pressure during walking
The statistical analysis did not demonstrate any significant main effect of “time” for peak pressure ($p \leq 0.05$, $d = 0.00$–0.51) during walking at a constant speed (Table 5). We observed significant group-by-time interactions for peak pressure at the medial heel region ($$p \leq 0.003$$, $d = 1.40$) (Table 5). Only the intervention group showed significant decreases from pre-to-post for peak pressure at the medial heel region ($$p \leq 0.021$$, $d = 1.01$).Table 5Group-specific mean values and standard deviations for peak pressure (kPa) during walking at constant speed in ASD boysVariableInterventionWaiting controlMain effect of time (p-value, Cohen’s d)Main effect of group (p-value, Cohen’s d)Group × time interaction (p-value, Cohen’s d)PrePostΔ %$95\%$ CIPrePostΔ %$95\%$ CIToe 188.43 ± 21.7069.98 ± 19.27− 20.86− 2.34–39.2477.80 ± 22.2982.62 ± 19.956.19− 18.77–9.140.244 (0.510)0.875 (0.063)0.053 (0.873)Toes 2–535.87 ± 18.0833.16 ± 11.27− 7.55− 8.42–13.8338.95 ± 19.0634.70 ± 13.11− 10.91− 8.96–17.470.385 (0.375)0.654 (0.191)0.845 (0.090)Metatarsal 167.44 ± 14.7868.19 ± 19.591.11− 18.07–16.5863.34 ± 9.1068.43 ± 9.668.03− 15.02–4.840.527 (0.271)0.580 (0.238)0.636 (0.201)Metatarsal 298.09 ± 29.21102.90 ± 36.914.90− 33.27–23.6487.50 ± 28.6397.67 ± 24.5911.62− 37.56–17.230.413 (0.358)0.359 (0.397)0.768 (0.127)Metatarsal 378.96 ± 19.7685.33 ± 25.958.06− 28.92–16.1875.63 ± 12.1678.79 ± 18.624.17− 14.59–8.280.416 (0.352)0.392 (0.369)0.782 (0.127)Metatarsal 4 64.90 ± 24.52 75.45 ± 28.24 16.25− 35.44–14.3261.04 ± 14.9751.71 ± 13.48− 15.28− 2.67–21.320.923 (0.000) 0.031 (0.984) 0.127 (0.674)Metatarsal 546.38 ± 15.8555.26 ± 21.3519.14− 24.79–7.0352.29 ± 22.6251.46 ± 18.51− 1.58− 20.46–22.120.512 (0.286)0.845 (0.090)0.430 (0.346)Midfoot36.29 ± 12.3342.88 ± 16.1918.15− 18.15–4.9841.19 ± 8.7538.96 ± 11.31− 5.41− 4.85–9.300.487 (0.300)0.904 (0.063)0.167 (0.612)Medial heel 95.50 ± 28.49 73.01 ± 13.25 − 23.544.17–40.7978.43 ± 29.2893.74 ± 28.8219.52− 29.45–12.330.540 (0.263)0.839 (0.090) 0.003 (1.400) Lateral heel83.70 ± 22.6788.19 ± 21.895.36− 29.95–20.9776.24 ± 29.2284.80 ± 29.8811.22− 32.84–2.220.393 (0.369)0.484 (0.300)0.788 (0.110) CI confidence interval, d Cohen’s d The gait pattern of ASD children is characterized by slow walking speed and toe walking [35, 36]. More specifically, there is evidence from previous research that ASD children walk with lower pressure in the heel region accompanied by reduced walking speed [10]. Previous studies have shown that pressure distribution is affected by walking speed [10, 37, 38]. At slow walking speeds, pressure is low underneath the heel region [10, 37, 38]. Besides slow walking speed, ASD-related toe walking might also be responsible for low pressure in the heel region [35, 36]. The SPARK exercise program induced a decrease in peak pressure of the medial heel region during walking at constant speed in ASD boys. This finding implies that SPARK may have caused a shift of the plantar pressure component towards the lateral column of the foot. This again may allow greater ankle stability [39]. This is in line with another study showing that SPARK has the potential to improve the static and dynamic balance of ASD children aged 5–12 years [20]. Overall, our findings suggest that SPARK has the potential to alter the plantar pressure distribution during walking through a correction of toe walking in ASD children. Cause-effect relations have to be established in future studies.
This study has a few limitations that should be discussed. First, the number of study participants was relatively small. However, we conducted an a priori power analysis, and the findings supported our initial cohort size. Second, we did not record kinematic data as well as muscular activity in this study. These additional biomechanical analyses should be realized in future research to deduce the underlying neuromuscular mechanisms of training-induced adaptations. Another limitation of this study is that we did not apply any cardiorespiratory fitness tests. Future studies should therefore examine the effects of physical exercise on biomechanic walking characteristics and cardiorespiratory fitness in boys with autism spectrum disorder.
## Discussion
The main findings of this study were that compared with a waiting control group, the intervention group showed (i) declines in peak vertical GRFs during heel contact, (ii) a decrease in loading rate during the loading phase of walking, and (iii) a decrease in peak pressure of the medial heel region during walking at a constant speed. Taken together, the observed findings are in accordance with our study hypotheses.
## Conclusions
The results of this study suggest that a multimodal exercise program (SPARK) has positive effects on the loading rate during walking at constant speed in ASD boys. Moreover, we were able to show that SPARK induced a shift of the plantar pressure component towards the lateral column of the foot which may enhance ankle stability. Accordingly, we recommend to implement SPARK because it is a safe, joyful, and effective treatment form for prepubertal ASD boys. Eight weeks of SPARK training with three weekly sessions, each lasting 45 min constitutes a sufficient exercise stimulus to improve the walking pattern of 9-year-old boys with autism spectrum disorder.
## References
1. Karimi P, Kamali E, Mousavi SM, Karahmadi M. **Environmental factors influencing the risk of autism**. *J Res Med Sci.* (2017.0) **22** 27. DOI: 10.4103/1735-1995.200272
2. 2.Zablotsky B, Black LI, Maenner MJ, Schieve LA, Blumberg SJ. Estimated prevalence of autism and other developmental disabilities following questionnaire changes in the 2014 National Health Interview Survey. Natl Health Stat Report. 2015;(87):1–20.
3. Sigman M, Arbelle S, Dissanayake C. **Current research findings on childhood autism**. *Can J Psychiatry* (1995.0) **40** 289-294. DOI: 10.1177/070674379504000603
4. 4.Association AP, Association APDiagnostic and statistical manual of mental disorders: DSM-52013Washington, DCAmerican psychiatric association. *Diagnostic and statistical manual of mental disorders: DSM-5* (2013.0)
5. Bhat AN, Landa RJ, Galloway JC. **Current perspectives on motor functioning in infants, children, and adults with autism spectrum disorders**. *Phys Ther* (2011.0) **91** 1116-1129. DOI: 10.2522/ptj.20100294
6. Morrison S, Armitano CN, Raffaele CT, Deutsch SI, Neumann SA, Caracci H. **Neuromotor and cognitive responses of adults with autism spectrum disorder compared to neurotypical adults**. *Exp Brain Res* (2018.0) **236** 2321-2332. DOI: 10.1007/s00221-018-5300-9
7. 7.Kindregan D, Gallagher L, Gormley J. Gait deviations in children with autism spectrum disorders: a review. Autism Res Treat. 2015:741480, 8 pages. 10.1155/2015/741480.
8. Shetreat-Klein M, Shinnar S, Rapin I. **Abnormalities of joint mobility and gait in children with autism spectrum disorders**. *Brain Dev* (2014.0) **36** 91-96. DOI: 10.1016/j.braindev.2012.02.005
9. Calhoun M, Longworth M, Chester VL. **Gait patterns in children with autism**. *Clin Biomech* (2011.0) **26** 200-206. DOI: 10.1016/j.clinbiomech.2010.09.013
10. Lim B-O, O’Sullivan D, Choi B-G, Kim M-Y. **Comparative gait analysis between children with autism and age-matched controls: analysis with temporal-spatial and foot pressure variables**. *J Phys Ther Sci* (2016.0) **28** 286-292. DOI: 10.1589/jpts.28.286
11. Yang C-S, Lee G-S, Choi B-K, O’Sullivan D, Kwon Y-H, Lim B-O. **Gait analysis in children with autism using temporal-spatial and foot pressure variables**. *ISBS-Conf Proc Arch* (2012.0) **14** 307-310
12. Hasan CZC, Jailani R, Tahir NM, Ilias S. **The analysis of three-dimensional ground reaction forces during gait in children with autism spectrum disorders**. *Res Dev Disabil* (2017.0) **66** 55-63. DOI: 10.1016/j.ridd.2017.02.015
13. Hasan C, Jailani R, Tahir N, Desa H. **Vertical ground reaction force gait patterns during walking in children with autism spectrum disorders**. *Int J Eng* (2018.0) **31** 705-711
14. Srinivasan SM, Pescatello LS, Bhat AN. **Current perspectives on physical activity and exercise recommendations for children and adolescents with autism spectrum disorders**. *Phys Ther* (2014.0) **94** 875-889. DOI: 10.2522/ptj.20130157
15. Baghdadli A, Pry R, Michelon C, Rattaz C. **Impact of autism in adolescents on parental quality of life**. *Qual Life Res* (2014.0) **23** 1859-1868. DOI: 10.1007/s11136-014-0635-6
16. Broder-Fingert S, Brazauskas K, Lindgren K, Iannuzzi D, Van Cleave J. **Prevalence of overweight and obesity in a large clinical sample of children with autism**. *Acad Pediatr* (2014.0) **14** 408-414. DOI: 10.1016/j.acap.2014.04.004
17. Lang R, Koegel LK, Ashbaugh K, Regester A, Ence W, Smith W. **Physical exercise and individuals with autism spectrum disorders: a systematic review**. *Res Autism Spectr Disord* (2010.0) **4** 565-576. DOI: 10.1016/j.rasd.2010.01.006
18. Tan BW, Pooley JA, Speelman CP. **A meta-analytic review of the efficacy of physical exercise interventions on cognition in individuals with autism spectrum disorder and ADHD**. *J Autism Dev Disord* (2016.0) **46** 3126-3143. DOI: 10.1007/s10803-016-2854-x
19. Pan C-Y. **The efficacy of an aquatic program on physical fitness and aquatic skills in children with and without autism spectrum disorders**. *Res Autism Spectr Disord* (2011.0) **5** 657-665. DOI: 10.1016/j.rasd.2010.08.001
20. Najafabadi MG, Sheikh M, Hemayattalab R, Memari A-H, Aderyani MR, Hafizi S. **The effect of SPARK on social and motor skills of children with autism**. *Pediatr Neonatol* (2018.0) **59** 481-487. DOI: 10.1016/j.pedneo.2017.12.005
21. Rostamzadeh Z. *Effect of a spark training course on walking parameters and pulmonary capacity in educable mentally retarded boys* (2019.0)
22. Gürol B. **Analysis of gait patterns in individuals with autism spectrum disorder after recreational therapy program at Eskisehir Technical University**. *Int Educ Stud* (2019.0) **12** 105-110. DOI: 10.5539/ies.v12n11p105
23. Diken IH, Diken O, Gilliam JE, Ardic A, Sweeney D. **Validity and reliability of Turkish version of Gilliam Autism Rating Scale-2: results of preliminary study**. *Int J Spec Educ* (2012.0) **27** 207-215
24. Rutter M, Bailey A, Lord C, Cianchetti C, Fancello GS. *SCQ: Social Communication Questionnaire: Manuale* (2007.0)
25. Sasanfar R, Ghadami M. *Standardising and normalizing the Social Communication Questionnaire* (2006.0)
26. Rutter M, Le Couteur A, Lord C. *Autism diagnostic interview-revised* (2003.0) 30
27. 27.Sasanfar R, Toloie A. Standardising and normalizing the autism diagnostic interview-revised on Iranian population. In: The Iranian special education organisation Tehran: The Iranian Special Education Organisation Publication. 2006;25(4):22–30.
28. Lachin JM, Matts JP, Wei L. **Randomization in clinical trials: conclusions and recommendations**. *Control Clin Trials* (1988.0) **9** 365-374. DOI: 10.1016/0197-2456(88)90049-9
29. Farahpour N, Jafarnezhad A, Damavandi M, Bakhtiari A, Allard P. **Gait ground reaction force characteristics of low back pain patients with pronated foot and able-bodied individuals with and without foot pronation**. *J Biomech* (2016.0) **49** 1705-1710. DOI: 10.1016/j.jbiomech.2016.03.056
30. Koo TK, Li MY. **A guideline of selecting and reporting intraclass correlation coefficients for reliability research**. *J Chiropr Med* (2016.0) **15** 155-163. DOI: 10.1016/j.jcm.2016.02.012
31. Mostafavi R, Ziaee V, Akbari H, Haji-Hosseini S. **The effects of spark physical education program on fundamental motor skills in 4-6 year-old children**. *Iran J Pediatr* (2013.0) **23** 216. PMID: 23724186
32. 32.Cohen J. Statistical power analysis for the behavioral sciences. New York University: Routledge; 2013.
33. Perry J, Davids JR. **Gait analysis: normal and pathological function**. *J Pediatr Orthop* (1992.0) **12** 815. DOI: 10.1097/01241398-199211000-00023
34. Watemberg N, Waiserberg N, Zuk L, Lerman-Sagie T. **Developmental coordination disorder in children with attention-deficit–hyperactivity disorder and physical therapy intervention**. *Dev Med Child Neurol* (2007.0) **49** 920-925. DOI: 10.1111/j.1469-8749.2007.00920.x
35. Uljarević M, Hedley D, Alvares GA, Varcin KJ, Whitehouse AJ. **Relationship between early motor milestones and severity of restricted and repetitive behaviors in children and adolescents with autism spectrum disorder**. *Autism Res* (2017.0) **10** 1163-1168. DOI: 10.1002/aur.1763
36. Valagussa G, Trentin L, Signori A, Grossi E. **Toe walking assessment in autism spectrum disorder subjects: a systematic review**. *Autism Res* (2018.0) **11** 1404-1415. DOI: 10.1002/aur.2009
37. Vilensky JA, Damasio AR, Maurer RG. **Gait disturbances in patients with autistic behavior: a preliminary study**. *Arch Neurol* (1981.0) **38** 646-649. DOI: 10.1001/archneur.1981.00510100074013
38. Zhu H, Wertsch JJ, Harris GF, Alba HM. **Walking cadence effect on plantar pressures**. *Arch Phys Med Rehabil* (1995.0) **76** 1000-1005. DOI: 10.1016/S0003-9993(95)81037-4
39. Huang PY, Lin CF, Kuo LC, Liao JC. **Foot pressure and center of pressure in athletes with ankle instability during lateral shuffling and running gait**. *Scand J Med Sci Sports* (2011.0) **21** e461-e4e7. DOI: 10.1111/j.1600-0838.2011.01367.x
|
---
title: 'Circulating levels of micronutrients and risk of infections: a Mendelian randomization
study'
authors:
- Helene M. Flatby
- Anuradha Ravi
- Jan K. Damås
- Erik Solligård
- Tormod Rogne
journal: BMC Medicine
year: 2023
pmcid: PMC9993583
doi: 10.1186/s12916-023-02780-3
license: CC BY 4.0
---
# Circulating levels of micronutrients and risk of infections: a Mendelian randomization study
## Abstract
### Background
Micronutrients play an essential role at every stage of the immune response, and deficiencies can therefore lead to increased susceptibility to infections. Previous observational studies and randomized controlled trials of micronutrients and infections are limited. We performed Mendelian randomization (MR) analyses to evaluate the effect of blood levels of eight micronutrients (copper, iron, selenium, zinc, beta-carotene, vitamin B12, vitamin C, and vitamin D) on the risk of three infections (gastrointestinal infections, pneumonia, and urinary tract infections).
### Methods
Two-sample MR was conducted using publicly available summary statistics from independent cohorts of European ancestry. For the three infections, we used data from UK Biobank and FinnGen. Inverse variance-weighted MR analyses were performed, together with a range of sensitivity analyses. The threshold for statistical significance was set at $P \leq 2.08$E−03.
### Results
We found a significant association between circulating levels of copper and risk of gastrointestinal infections, where a one standard deviation increase in blood levels of copper was associated with an odds ratio of gastrointestinal infections of 0.91 ($95\%$ confidence interval 0.87 to 0.97, $$P \leq 1.38$$E−03). This finding was robust in extensive sensitivity analyses. There was no clear association between the other micronutrients and the risk of infection.
### Conclusions
Our results strongly support a role of copper in the susceptibility to gastrointestinal infections.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12916-023-02780-3.
## Background
Gastrointestinal infections, pneumonia, and urinary tract infections are common causes of hospital admission and important causes of death [1]. Identifying modifiable risk factors for those infections is essential since the disease burden is projected to increase due to antibiotic resistance, an aging population, and emerging pathogens [2]. Multiple micronutrients have been established to have vital roles in the immune system and are important components for the proliferation and maturation of immune cells, cytokine release, and enzymes involved in immune cell activity for antioxidant host defense [3]. Deficiency can significantly impair host immunity, increasing susceptibility to infections [3].
Previous observational studies and randomized controlled trials have found that certain micronutrients reduce the risk of specific infections [3]. However, the results are conflicting, possibly due to factors such as high variability between studies and the use of different outcomes. It can be difficult to conduct randomized controlled trials due to logistical issues and costs, and not many adequately powered trials have evaluated the effect of micronutrients and infections. Also, it can be challenging to quantify the causal effects from traditional observational studies due to residual confounding and reverse causation [4].
Mendelian randomization (MR) provides an alternative method to determine evidence of causality. MR uses single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWASs) as genetic instruments to evaluate the effect of an exposure (e.g., blood levels of copper) on the risk of an outcome (e.g., gastrointestinal infection). GWASs have successfully identified several genetic variants involved in the metabolic pathway of several vitamins and minerals [5–15]. Importantly, since these genetic variants are allocated randomly at conception, MR studies are much less susceptible to reverse causation and confounding than traditional observational studies [4].
The aim of this study was to estimate the association between genetically predicted blood levels of micronutrients on the genetically predicted risk of infectious diseases. We identified eight micronutrients of interest that have previously been linked to the risk of infection and for which genetic instruments were available—copper, iron, selenium, zinc, beta-carotene, vitamin B12, vitamin C, and vitamin D—and evaluated the risk of the following three infections: gastrointestinal infections, pneumonia, and urinary tract infections.
## Study design
This study is reported according to the STROBE-MR (Additional file 1: Table S1) [16]. A schematic summary of the study design is given in Fig. 1. Briefly, we conducted a two-sample MR study using data from publicly available summary statistics from fourteen GWASs: eight for the exposures and six for the outcomes. Both exposure and outcome cohorts were restricted to subjects of European ancestry to reduce bias from population stratification [17]. All data used in this work are publicly available from studies with relevant participant consent and ethical approval, and ethical approval from an institutional review board was therefore not necessary for the present study. Fig. 1A schematic summary of the study design
## Data on the genetically predicted levels of circulating micronutrients
We searched for published GWASs evaluating individuals of European ancestry on the GWAS Catalog and PubMed (the last search was performed in May 2022). We did not find any GWAS conducted for vitamins B1, B2, B3, B5, B7, sulfur, iodine, chloride, and fluoride. The GWASs conducted for vitamin K, potassium, sodium, cobalt, chromium, and molybdenum were excluded because of no significant genome-wide results [8, 18, 19]. In total, fourteen micronutrients of potential interest were identified: calcium [5], copper [6], iron [7], magnesium [8], selenium [6], zinc [6], beta-carotene [9], folate [10], vitamin A [11], vitamin B6 [12], vitamin B12 [10], vitamin C [13], vitamin D [14], and vitamin E [15] (Additional file 2: Additional Text) [5–15, 20, 21]. For copper, we also identified a more recent and larger GWAS by Jäger et al. [ 20], but given that this study reported Z-scores and not beta-coefficients, we used the study by Evans et al. [ 6] in order to improve interpretability. However, the genetic instruments from the GWAS by Jäger et al. [ 20] were used in secondary analyses. Vitamin A and vitamin E were excluded because those GWASs were adjusted for body mass index (BMI) [22] which might introduce collider bias if the genetic instruments of the exposure of interest also have an effect on BMI [23].
For the main MR analysis, we included independent SNPs (r2 < 0.001 within 10,000-kb windows), strongly associated (P ≤ 5E−08) with the blood level of each micronutrient.
## Data on the genetically predicted risk of infectious diseases
Based on the disease incidence and availability of published summary statistics, we evaluated the risk of the following three infections: gastrointestinal infections, pneumonia, and urinary tract infections. We used publicly available summary statistics from two independent cohorts of European ancestry: UK Biobank (UKBiobank HRC-imputed) [24] and FinnGen Release 6 [25, 26] (Table 1). The GWAS conducted using the UKBiobank HRC-imputed data was performed using SAIGE (a generalized mixed model association test that uses the saddlepoint approximation to account for case-control imbalance), adjusted for genetic relatedness, sex, birth year, and the first four principal components [24]. The GWASs conducted on the FinnGen dataset were analyzed using SAIGE and were adjusted for sex, age, first ten principal components, and genotyping batch [25].Table 1Source of outcome genome-wide association study summary dataGastrointestinal infectionsPneumoniaUrinary tract infectionsCasesControlsPhenocode/nameCasesControlsPhenocode/nameCasesControlsPhenocode/nameUK Biobank8991399,970008 Intestinal infection6710398,538480.1 Bacterial pneumonia12,491379,936591 Urinary tract infectionsFinnGen R625,968234,437AB1 Intestinal infections9878223,587J10 Pneumobact19,479231,480N14 UrethraothMeta-analyses34,959634,407–16,588622,125–31,970611,416– In both the UK Biobank and FinnGen, cases and controls were defined based on International Classification of Diseases codes (10th revision) from hospital records (Additional file 2: Tables S2-S4) [27–32]. For each infectious disease, summary statistics were meta-analyzed using a random-effects model in METAL (version 2011-03-25) [33] (Additional file 3: Table S5). The meta-analyses included genomic control to account for residual population stratification [33]. Additionally, we conducted Cochran’s Q statistical test, included in METAL [33], to assess the heterogeneity between the two cohorts for the genetic instruments used for the outcomes.
## Statistical power
The strength of each genetic instrument was estimated using the F statistic: F = R2(N − 2)/(1 − R2), where R2 equals the proportion of variance explained by the genetic instrument and N is the effective sample size of the GWAS for the SNP-micronutrient association [34]. The R2 value was calculated using the formula 2 × MAF(1 − MAF)beta2, where beta represents the effect estimate of the genetic variant in the exposure, measured in standard deviation (SD) units, and MAF represents the minor allele frequency [35] (Table 2). The effect allele frequency was not available for the genetic instruments used for copper, selenium, and zinc, published by Evans et al. [ 6]. However, for the main analysis, none of the copper or selenium-associated SNPs were palindromic (A/T or G/C alleles), so it was clear which allele was the effect allele. For zinc, we removed one genetic instrument, rs10931753, due to being palindromic. The effect allele frequencies from the meta-analysis of FinnGen and UK Biobank were used to estimate the F statistics and R2 for copper, selenium, and zinc. Although this may result in incorrect calculations of R2 (and thus F statistic and statistical power), our results aligned well with the reported R2 from Evans et al. [ 6], with an R2 of $5\%$ for copper. We reported a lower R2 value for selenium (R2 of $2.4\%$), but we only used 1 SNP, while Evans et al. [ 6] reported a total R2 for 2 SNPs (R2 of $4\%$) and a lower R2 value for zinc (R2 of $4.25\%$), where we used 2 SNPs, while Evans et al. [ 6] reported a total R2 for 3 SNPs (R2 of $8\%$). Additionally, for the secondary analysis of copper, we observed an unlikely R2 which was explained by one extremely outlying SNP: rs12582659 (Additional file 2: Fig. S1). After removing this SNP, we found an R2 of $7.10\%$.Table 2Source of exposure genome-wide association study summary dataExposureMain analysisaSeconary analysisbPopulation ancestryReferenceNumber of SNPs% of variance explainedNumber of SNPs% of variance explainedCac20.1530.18European[5]Cu24.36QIMR67.10dEuropean[6, 20]Jäger et al.76.55EuropeanFe133.01965.37European[7]Mgc60.0180.01European[8]Se12.40QIMR610.2European.[6]ALSPAC78.06EuropeanZn24.2578.66European[6]Beta-carotene14.59––European[9]Folatec20.5830.67European[10]Vitamin B6c10.67––European[12]Vitamin B12104.36114.43European[10]Vitamin C111.90202.28European[13]Vitamin D591.98752.39European[14, 15]*Missing data* is denoted by “–”Abbreviations: Ca calcium, Cu copper, Fe iron, Mg magnesium, Se selenium, Zn zincaFor the main analyses, only independent (r2 < 0.001 within 10,000-kb windows) and strongly associated (P ≤ 5E−08) variants were usedbFor the secondary analyses, only variants (r2 < 0.01 within 10,000-kb windows) suggestively associated (P ≤ 5E−06) variants were usedcCa, Mg, and folate were excluded due to the variance explained < $1\%$dWe observed extreme values for R2, which was explained by one extremely outlying SNP; rs12582659 had an R2 of $17.80\%$ Power calculations were done using http://cnsgenomics.com/shiny/mRnd/ [36]. The statistical power was calculated to capture an odds ratio (OR) of 0.90 or 1.10 per SD change in the circulating micronutrient concentration, given the sample size used for the meta-analyses at a type 1 error of $5\%$ (Additional file 2: Table S6). In addition to the main MR analyses, we performed secondary analyses using more liberal criteria for including genetic variants to enhance statistical power; r2 < 0.01 and P ≤ 5E−06. For our study, we only considered micronutrients with an R2 > $1\%$ and/or statistical power > $50\%$ for at least one of the infectious disease outcomes, thereby excluding calcium, magnesium, folate, and vitamin B6 (Table 2 and Additional file 2: Tables S6-S7). For the remaining micronutrients, we excluded SNPs with an F statistic < 10 [4] to reduce the risk of weak instrument bias [34]. None of the included genetic instruments was shared by any of the considered micronutrients (Additional file 2: Table S7).
## MR analysis
We calculated the Wald ratio for each SNP, defined as the SNP-outcome association divided by the SNP-exposure association [37]. When multiple SNPs were available for a micronutrient, we summarized the effect calculated by the Wald ratio using an inverse-variance weighted (IVW) analysis [38]. All reported associations correspond to an OR for the outcome per SD increase in the genetically predicted circulating concentrations of the micronutrient. The MR analyses were performed separately for the outcome GWASs from UK Biobank, FinnGen, and meta-analysis of the two cohorts. If not otherwise specified, the meta-analysis was used as the outcome study. $P \leq 0.05$ was considered nominally significant, whereas the level for statistical significance corrected for multiple testing (8 exposures × 3 outcomes = 24 tests) was set at $$P \leq 0.05$$/24 = 2.08E−03.
## Sensitivity analyses
For an instrumental variable to be valid, three key assumptions must be met: the instrument must be robustly associated with the exposure, it cannot affect a confounder of the exposure-outcome association, and it must only affect the outcome through the risk factor [4]. Horizontal pleiotropy—that the SNP has multiple effects—can violate those assumptions. MR-Egger, weighted median, simple mode, and weighted mode are some of the most common sensitivity analyses to account for horizontal pleiotropy [17]. These analyses were only conducted when the number of genetic instruments was ≥ 3. The MR-Egger method allows some SNPs to affect the outcome through a pathway other than through the exposure. If the intercept term differs from zero, this indicates that not all the included instruments are valid, and the standard estimates (i.e., IVW) may be biased [39]. The weighted median method provides a valid MR estimate when up to $50\%$ of the included instruments are invalid. This method calculates the weighted median estimate by ordering the genetic variants according to the magnitude of their estimates [40]. The mode-based methods (simple mode and weighted mode) assume that the most common causal effect is consistent with the true causal effect, allowing some instruments to be invalid without biasing the estimated causal effect [41].
To evaluate if the differences in the individual effect sizes among the genetic instruments may be related to pleiotropic effects rather than chance, we conducted Cochran’s Q statistical test [42]. This test was only conducted when two or more variants were available, and a $P \leq 0.05$ was considered significant in the test for heterogeneity.
We further evaluated whether the genetic instruments were associated with other phenotypes using PhenoScanner V2 [43], available at http://www.phenoscanner.medschl.cam.ac.uk/ (accessed 30 October 2022). Additionally, we performed leave-one-out analyses for micronutrients containing > 2 SNPs. This was performed to examine the robustness of the IVW estimates and if any specific SNP drove the association (which could be due to pleiotropy) [4].
Multivariable MR was used to evaluate whether any of the identified phenotypes on PhenoScanner had introduced bias due to pleiotropy [17]. *The* genetic variants for the potentially pleiotropic phenotypes were collected from IEU OpenGWAS [44]. Based on the identified potentially pleiotropic pathways, we conducted one multivariable MR analysis of copper on the risk of gastrointestinal infections where we included erythrocyte count (GWAS identifier: ukb-d-30250_irnt) and hemoglobin concentration (GWAS identifier: ebi-a-GCST004615) in the analysis.
## Secondary analyses using less stringent criteria for the selection of genetic instruments
In secondary analyses, we included variants at a more liberal threshold of r2 < 0.01 and P ≤ 5E−06. While this could increase statistical power, it also may increase the risk of violating the MR assumptions and introduce weak instrument bias. Therefore, we included MR-RAPS, an MR method for correcting for bias introduced by weak instruments, using robust adjusted profile scores [45]. For copper, we carried out the secondary analyses both using instruments from Evans et al. [ 6] (as in the main analysis) and using instruments from Jäger et al. [ 20].
## Post hoc analyses
Finally, to validate the association between copper and gastrointestinal infections (see the “Results” section), we conducted the following two post hoc analyses: first, we retrieved the results from an additional European GWAS on gastrointestinal infections conducted by Nudel et al. [ 46] and conducted MR analyses on this independent cohort. Only one of the two copper SNPs, rs2769264, was available from this study, and no reliable proxy for the other copper SNP was available (defined by r2 > 0.9; using European ancestry in the LDproxy tool from National Cancer Institute LDlink [47]).
Second, to assess the possibility of reverse causation, we conducted MR analyses of the association between the genetically predicted risk of gastrointestinal infection on the genetically predicted blood levels of copper. We used the meta-analysis results on gastrointestinal infection as exposure in this analysis, including SNPs suggestively associated with gastrointestinal infection (r2 < 0.01 within 10,000-kb windows, P ≤ 5E−06). For the outcome, we retrieved the copper association summary-level statistics from Evans et al. [ 6].
In the post hoc analysis of copper and gastrointestinal infections, we observed a similar effect as in the main analysis when using another GWAS on gastrointestinal infections [46]: OR 0.94 ($95\%$ CI 0.81 to 1.09, $$P \leq 3.82$$E−01). The CI was wide because only one of the two SNPs was available. Meta-analyzing the results from using Nudel et al. [ 46] with the main analysis yielded an OR of 0.92 ($95\%$ CI 0.87 to 0.97, $$P \leq 1.14$$E−03).
Finally, we conducted two-sample MR analyses using gastrointestinal infections as the exposure and blood levels of copper as the outcome. We found that gastrointestinal infections did not affect circulating copper levels (beta = − 0.35, $95\%$ CI − 1.35 to 0.71, $$P \leq 5.40$$E−01) using two genetic instruments from Evans et al. [ 6], and no heterogeneity was observed (Cochran’s Q test $$P \leq 5.50$$E−01).
## Statistical analysis
All MR analyses were conducted using the TwoSampleMR package (version 0.5.6) [42] in R (version 4.0.3). METAL (version 2011-03-25) [33] was used to perform the meta-analyses of the outcomes.
## Main analyses
After correction for multiple testing, the only statistically significant micronutrient-infection association was that of genetically predicted blood levels of copper and risk of gastrointestinal infections (Fig. 2). One SD increase in genetically predicted blood levels of copper was associated with an OR of 0.91 ($95\%$ confidence interval [CI] 0.87 to 0.97, $$P \leq 1.38$$E−03), 0.89 ($95\%$ CI 0.80 to 0.98, $$P \leq 1.67$$E−02), and 0.93 ($95\%$ CI 0.87 to 0.99, $$P \leq 1.98$$E−02), in the meta-analysis, UK Biobank, and FinnGen, respectively (Fig. 2 and Additional file 2: Table S8). A nominally significant association was observed for both selenium and vitamin D on the risk of gastrointestinal infections, with an OR of 0.92 ($95\%$ CI 0.85 to 0.99, $$P \leq 2.39$$E−02) and OR of 1.11 ($95\%$ CI 1.02 to 1.21, $$P \leq 2.00$$E−02), respectively (Fig. 2 and Additional file 2: Table S8).Fig. 2Mendelian randomization analyses of circulating levels of micronutrients on the risk of gastrointestinal infections, pneumonia, and urinary tract infections. Legend: Forest plot of inverse-variance weighted Mendelian randomization analyses. The x-axis represents the results expressed as per standard deviation increase in genetically proxied levels of the exposure. Abbreviations: Cu, copper; Fe, iron; Se, selenium; UTI, urinary tract infection; Zn, zinc We observed little evidence that the circulating concentrations of iron, zinc, beta-carotene, vitamin B12, and vitamin C were associated with the risk of any of the evaluated infections (Fig. 2 and Additional file 2: Tables S8-S10).
## Sensitivity and secondary analyses
The MR-Egger, weighted median, and mode-based sensitivity analyses supported the findings from the IVW analyses. As only two instruments were used for copper, MR-Egger regression, weighted median, simple mode, and weighted mode were not carried out in the main analysis. No heterogeneity was observed in the main MR analyses for copper and vitamin D on the risk of gastrointestinal infections (Cochran’s Q test $$P \leq 5.99$$E−01 and Cochran’s Q test $$P \leq 5.56$$E−01, respectively; Additional file 2: Table S8). Heterogeneity was observed for vitamin B12 and gastrointestinal infections (Cochran’s Q test $$P \leq 2.19$$E−04): iron (Cochran’s Q test $$P \leq 1.04$$E−02) and vitamin C (Cochran’s Q test $$P \leq 3.70$$E−02) for pneumonia and vitamin D (Cochran’s Q test $$P \leq 2.27$$E−02) for urinary tract infection. For the other micronutrients, no heterogeneity was observed (Additional file 2: Tables S8-S10).
For the secondary analyses, we generally observed comparable effects as in the main analyses (Additional file 2: Table S11). However, for copper, using 6 SNPs from Evans et al. [ 6] yielded an IVW estimate of OR 1.01 ($95\%$ CI 0.96 to 1.06, $$P \leq 7.45$$E−01). There was considerable heterogeneity in this estimate (Cochran’s Q test $$P \leq 4.68$$E−03), which was explained by one extremely outlying SNP: rs12582659 (Additional file 2: Fig. S1). Excluding this SNP from the analysis yielded comparable results to the main analysis (OR 0.97, $95\%$ CI 0.90 to 1.05, $$P \leq 4.35$$E−01; Additional file 2: Table S12). However, we still found evidence of heterogeneity (Cochran’s Q test $$P \leq 1.47$$E−02), and the results should be interpreted with caution. *Using* genetic instruments from Jäger et al. [ 20] supported the main analysis (OR 0.95, $95\%$ CI 0.91 to 1.00, $$P \leq 3.07$$E−02). In both secondary analyses using data from Evans et al. [ 6] and Jäger et al. [ 20], we observed the presence of potential pleiotropy, e.g., MR-Egger OR 1.01 ($95\%$ CI 0.94 to 1.09, $$P \leq 7.18$$E−01) and OR 1.07 ($95\%$ CI 0.93 to 1.23, $$P \leq 3.99$$E−01); it is unclear whether this pleiotropy reflects the presence of pleiotropy in the main analysis.
Using PhenoScanner, we found that several of the genetic instruments used for the micronutrients have previously been reported to be associated with numerous traits and diseases (Additional file 3: Tables S13-S20). Of note, rs1175550 used for copper was strongly associated with reticulocyte count and hemoglobin concentration. We therefore performed a multivariable MR analysis between copper, reticulocyte count, and hemoglobin concentration and the risk of gastrointestinal infection. We observed a similar effect as in the main analysis with an OR of 0.96 ($95\%$ CI 0.93 to 0.99, $$P \leq 1.64$$E−02). *The* genetic instruments for vitamin D were associated with several traits related to smoking, BMI, and alcohol. In the leave-one-out analysis, the observed association of vitamin D with gastrointestinal infection did not change meaningfully, indicating that no specific SNP drove the result nor that the observed association was due to pleiotropy (Additional file 2: Table S21).
## Discussion
In this MR study of eight micronutrients and the risk of three infectious diseases, we found genetically predicted blood levels of copper to be robustly associated with the genetically predicted risk of gastrointestinal infections. We did not find a clear association between the other micronutrients and infections.
Copper plays an essential role in innate and adaptive immunity: it regulates the function of T helper cells, B cells, neutrophils, natural killer cells, and macrophages; it accumulates at sites of inflammation, including the gastrointestinal and respiratory tract and in blood and urine, and is vital for interleukin 2 production and response [3, 48]. Blood levels of copper have not previously been robustly linked to the risk of gastrointestinal infections in humans. A small randomized controlled trial (RCT) found that supplementation with high doses of copper, zinc, and selenium significantly reduced the risk of infections among hospitalized patients with severe burns [49]. Another trial found that copper supplementation increased the interleukin 2 production by blood cells in healthy individuals with low to normal copper levels, which is crucial for T helper cell proliferation and natural killer cell cytotoxicity [50]. In addition, a previous study showed that cell cultures pretreated with added Cu boosted macrophage antibacterial activity and enhanced intracellular killing of *Escherichia coli* [51]. These results align with our finding that high levels of copper have a protective effect against infectious diseases and that higher blood levels of copper might lead to increased immune response.
Regarding vitamin D, a previous MR study found that lower plasma levels of this micronutrient were associated with an increased risk of pneumonia [52], which was not supported in our study and also not supported by a systematic review of trials of vitamin D supplementation [53]. The same MR study found no evidence of an association between vitamin D and the risk of urinary tract infections or gastroenteritis [52]. While we also found no association between vitamin D and urinary tract infections, we did observe a nominally significant positive association between vitamin D and gastrointestinal infection. However, this finding may be a chance finding due to multiple testing, and it did not pass our stringent threshold for statistical significance.
Interestingly, we found no associations between genetically predicted circulating iron, zinc, beta-carotene, vitamin B12, and vitamin C and the risk of gastrointestinal infections, pneumonia, or urinary tract infection. Systematic reviews of RCTs have found limited evidence of micronutrient supplementation on the risk of infections but have also underscored the paucity of studies [54–57]. Among those reviews, one reported no difference in the incidence of diarrhea and lower respiratory tract infection in infants with zinc supplementation [54]. Another review found uncertain and limited evidence for vitamin C supplementation in preventing pneumonia [55]. Two reviews found no clear evidence that emerged in favor of selenium supplementation for developing infections [56] and the incidence of new infections [57] among critically ill patients. This may indicate that several of these micronutrients are not important risk factors for the infections considered. Finally, high levels of serum iron have in previous MR studies been associated with skin and soft tissue infections and sepsis, but we did not find any evidence of an association for the infections that we considered [58, 59]. This discrepancy may be due to organ-specific effects of iron (e.g., iron levels were also associated with damages to skin-related structures) and that the infectious diseases are not comparable (e.g., sepsis is an inflammatory syndrome in response to severe infection) [59, 60].
Our study has several strengths and limitations. By applying an MR design, we reduced the risk of confounding, which often affects observational studies. Additionally, we considerably reduced random error and increased statistical power by combining summary data from multiple cohorts [35]. However, despite the large sample sizes, several of the genetic instruments used for exposures and the outcomes, to a varying degree, suffered from low statistical power and imperfect phenotype definitions, which may contribute to the null findings of the majority of associations explored. Larger GWASs on micronutrients and infections, with more precise phenotype definitions, would be beneficial. Also, summarized data does not allow for stratification by factors such as sex, age, diet, micronutrient supplement use, or co-morbidities. Due to the use of summary-level data, we could not identify individuals with a combination of two or more infections, which might lead to bias. The quality control, genotyping, and imputation were performed using different criteria and programs for the two cohorts. Additionally, different phenotype definitions were used in the two cohorts, which may introduce heterogeneity between the association estimates. However, we observed minimal heterogeneity between the two cohorts in the meta-analysis.
*The* genetic instruments used as exposure for each micronutrient have widely been used to evaluate the association with other complex diseases or phenotypes, which supports their use in this study [61–63]. Throughout, we tried to use data on our exposures and outcomes from separate GWASs to reduce the risk of confounding bias due to overlapping samples [64], but this was not possible for vitamin D (since the other published GWASs for vitamin D adjusted for BMI) [21, 65]. To reduce the risk of population stratification, we only evaluated participants of European ancestry. However, this affects our findings’ external validity to other ancestry groups. Our findings were supported by conducting a range of sensitivity analyses, including evaluating the presence of pleiotropy, and by evaluating two distinct biobanks for each outcome (i.e., UK Biobank and FinnGen). While only two instruments were available for the main MR analysis of copper, the more liberal threshold for SNP inclusion in the secondary analyses allowed for more genetic instruments to be included; these analyses were generally consistent with the main analysis. For copper and risk of gastrointestinal infections, we conducted an extended set of sensitivity analyses to evaluate the robustness of our findings, including following up our results in an additional GWAS of gastrointestinal infections, conducting multivariable MR to account for potentially pleiotropic pathways, and conducting bi-directional MR: These analyses all supported our main finding.
## Conclusions
In conclusion, our findings support that copper may play a role in the susceptibility to gastrointestinal infections. More research is needed to evaluate whether this finding replicates in other settings and to learn more about the potential underlying mechanisms.
## Supplementary Information
Additional file 1: Table S1. STROBE MR.Additional file 2: Additional Text – Exposure GWAS cohorts. Fig. S1. Scatter plot of secondary MR analysis of copper as risk factors on the risk of gastrointestinal infections. Table S2. ICD-10 codes for gastrointestinal infections in UK Biobank and FinnGen R6. Table S3. ICD-10 codes for pneumonia in UK Biobank, and FinnGen. Table S4. ICD-10 codes for urinary tract infection UK Biobank, and FinnGen. Table S6. Power calculations. Table S7. Genetic variants used as exposure for Mendelian randomization analyses. Table S8. Main mendelian randomization analyses of micronutrients as risk factors on the risk of gastrointestinal infections. Table S9. Main mendelian randomization analyses of micronutrients as risk factors on the risk of pneumonia. Table S10. Main mendelian randomization analyses of micronutrients as risk factors on the risk of urinary tract infections. Table S11. Secondary mendelian randomization analyses of micronutrients as risk factors on the risk of gastrointestinal infections, pneumonia and urinary tract infections suggestive-significant genetic instruments. Table S12. Secondary mendelian randomization analyses of copper as risk factors on the risk of gastrointestinal infections, where rs12582659 was removed. Table S21. IVW MR regression results for the leave one SNP out analysis in the Mendelian randomization analyses of micronutrients. Additional file 3: Table S5. Results from the meta-analysis for the genetic instruments used as the outcome in the Mendelian randomization analyses. Table S13. Phenome-wide association analysis of genetic instruments for copper. Table S14. Phenome-wide association analysis of genetic instruments for iron. Table S15. Phenome-wide association analysis of genetic instruments for selenium. Table S16. Phenome-wide association analysis of genetic instruments for zinc. Table S17. Phenome-wide association analysis of genetic instruments for beta carotene. Table S18. Phenome-wide association analysis of genetic instruments for vitamin B12. Table S19. Phenome-wide association analysis of genetic instruments for vitamin C. Table S20. Phenome-wide association analysis of genetic instruments for vitamin D.
## References
1. Naghavi M, Abajobir AA, Abbafati C, Abbas KM, Abd-Allah F, Abera SF, Aboyans V, Adetokunboh O, Afshin A, Agrawal A. **Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016**. *Lancet* (2017.0) **390** 1151-1210. DOI: 10.1016/S0140-6736(17)32152-9
2. Fair RJ, Tor Y. **Antibiotics and bacterial resistance in the 21st century**. *Perspect Medicin Chem* (2014.0) **6** PMC. S14459. DOI: 10.4137/PMC.S14459
3. Gombart AF, Pierre A, Maggini S. **A review of micronutrients and the immune system–working in harmony to reduce the risk of infection**. *Nutrients* (2020.0) **12** 236. DOI: 10.3390/nu12010236
4. Davies NM, Holmes MV, Smith GD. **Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians**. *BMJ* (2018.0) **362** k601. DOI: 10.1136/bmj.k601
5. O’Seaghdha CM, Wu H, Yang Q, Kapur K, Guessous I, Zuber AM, Köttgen A, Stoudmann C, Teumer A, Kutalik Z. **Meta-analysis of genome-wide association studies identifies six new loci for serum calcium concentrations**. *PLoS Genet* (2013.0) **9** e1003796. DOI: 10.1371/journal.pgen.1003796
6. Evans DM, Zhu G, Dy V, Heath AC, Madden PA, Kemp JP, McMahon G, St Pourcain B, Timpson NJ, Golding J. **Genome-wide association study identifies loci affecting blood copper, selenium and zinc**. *Hum Mol Genet* (2013.0) **22** 3998-4006. DOI: 10.1093/hmg/ddt239
7. Bell S, Rigas AS, Magnusson MK, Ferkingstad E, Allara E, Bjornsdottir G, Ramond A, Sørensen E, Halldorsson GH, Paul DS. **A genome-wide meta-analysis yields 46 new loci associating with biomarkers of iron homeostasis**. *Commun Biol* (2021.0) **4** 156. DOI: 10.1038/s42003-020-01575-z
8. Meyer TE, Verwoert GC, Hwang S-J, Glazer NL, Smith AV, Van Rooij FJ, Ehret GB, Boerwinkle E, Felix JF, Leak TS. **Genome-wide association studies of serum magnesium, potassium, and sodium concentrations identify six loci influencing serum magnesium levels**. *PLoS Genet* (2010.0) **6** e1001045. DOI: 10.1371/journal.pgen.1001045
9. Ferrucci L, Perry JR, Matteini A, Perola M, Tanaka T, Silander K, Rice N, Melzer D, Murray A, Cluett C. **Common variation in the β-carotene 15, 15′-monooxygenase 1 gene affects circulating levels of carotenoids: a genome-wide association study**. *Am J Hum Genet* (2009.0) **84** 123-133. DOI: 10.1016/j.ajhg.2008.12.019
10. Grarup N, Sulem P, Sandholt CH, Thorleifsson G, Ahluwalia TS, Steinthorsdottir V, Bjarnason H, Gudbjartsson DF, Magnusson OT, Sparsø T. **Genetic architecture of vitamin B12 and folate levels uncovered applying deeply sequenced large datasets**. *PLoS Genet* (2013.0) **9** e1003530. DOI: 10.1371/journal.pgen.1003530
11. Mondul AM, Yu K, Wheeler W, Zhang H, Weinstein SJ, Major JM, Cornelis MC, Männistö S, Hazra A, Hsing AW. **Genome-wide association study of circulating retinol levels**. *Hum Mol Genet* (2011.0) **20** 4724-4731. DOI: 10.1093/hmg/ddr387
12. Hazra A, Kraft P, Lazarus R, Chen C, Chanock SJ, Jacques P, Selhub J, Hunter DJ. **Genome-wide significant predictors of metabolites in the one-carbon metabolism pathway**. *Hum Mol Genet* (2009.0) **18** 4677-4687. DOI: 10.1093/hmg/ddp428
13. Zheng J-S, Ja L, Sofianopoulou E, Imamura F, Stewart ID, Day FR, Pietzner M, Wheeler E, Lotta LA, Gundersen TE. **Plasma vitamin C and type 2 diabetes: genome-wide association study and mendelian randomization analysis in European populations**. *Diabetes Care* (2021.0) **44** 98-106. DOI: 10.2337/dc20-1328
14. Manousaki D, Mitchell R, Dudding T, Haworth S, Harroud A, Forgetta V, Shah RL. **Luan Ja, Langenberg C, Timpson NJ: Genome-wide association study for vitamin D levels reveals 69 independent loci**. *Am J Hum Genet* (2020.0) **106** 327-337. DOI: 10.1016/j.ajhg.2020.01.017
15. Major JM, Yu K, Wheeler W, Zhang H, Cornelis MC, Wright ME, Yeager M, Snyder K, Weinstein SJ, Mondul A. **Genome-wide association study identifies common variants associated with circulating vitamin E levels**. *Hum Mol Genet* (2011.0) **20** 3876-3883. DOI: 10.1093/hmg/ddr296
16. Skrivankova VW, Richmond RC, Woolf BAR, Davies NM, Swanson SA, VanderWeele TJ, Timpson NJ, Higgins JPT, Dimou N, Langenberg C. **Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration**. *BMJ* (2021.0) **375** n2233. DOI: 10.1136/bmj.n2233
17. Sanderson E, Glymour MM, Holmes MV, Kang H, Morrison J, Munafò MR, Palmer T, Schooling CM, Wallace C, Zhao Q. **Mendelian randomization**. *Nat Rev Methods Prim* (2022.0) **2** 1-21
18. Dashti HS, Shea MK, Smith CE, Tanaka T, Hruby A, Richardson K, Wang TJ, Nalls MA, Guo X, Liu Y. **Meta-analysis of genome-wide association studies for circulating phylloquinone concentrations**. *Am J Clin Nutr* (2014.0) **100** 1462-1469. DOI: 10.3945/ajcn.114.093146
19. Ng E, Lind PM, Lindgren C, Ingelsson E, Mahajan A, Morris A, Lind L. **Genome-wide association study of toxic metals and trace elements reveals novel associations**. *Hum Mol Genet* (2015.0) **24** 4739-4745. DOI: 10.1093/hmg/ddv190
20. 20.Jäger S, Cabral M, Kopp JF, Hoffmann P, Ng E, Whitfield JB, et al. Blood copper and risk of cardiometabolic diseases: a Mendelian randomization study. Hum Mol Genet. 2021;31(5):783–91. 10.1093/hmg/ddab275.
21. Manousaki D, Dudding T, Haworth S, Hsu Y-H, Liu C-T, Medina-Gómez C, Voortman T, Van Der Velde N, Melhus H, Robinson-Cohen C. **Low-frequency synonymous coding variation in CYP2R1 has large effects on vitamin D levels and risk of multiple sclerosis**. *Am J Hum Genet* (2017.0) **101** 227-238. DOI: 10.1016/j.ajhg.2017.06.014
22. Shim H, Chasman DI, Smith JD, Mora S, Ridker PM, Nickerson DA, Krauss RM, Stephens M. **A multivariate genome-wide association analysis of 10 LDL subfractions, and their response to statin treatment, in 1868 Caucasians**. *PloS One* (2015.0) **10** e0120758. DOI: 10.1371/journal.pone.0120758
23. Aschard H, Vilhjálmsson BJ, Joshi AD, Price AL, Kraft P. **Adjusting for heritable covariates can bias effect estimates in genome-wide association studies**. *Am J Hum Genet* (2015.0) **96** 329-339. DOI: 10.1016/j.ajhg.2014.12.021
24. Gagliano Taliun SA, VandeHaar P, Boughton AP, Welch RP, Taliun D, Schmidt EM, Zhou W, Nielsen JB, Willer CJ, Lee S. **Exploring and visualizing large-scale genetic associations by using PheWeb**. *Nat Genet* (2020.0) **52** 550-552. DOI: 10.1038/s41588-020-0622-5
25. 25.FinnGen: FinnGen documentation of R6 release. 2022. https://finngen.gitbook.io/documentation/. Accessed 20 Feb 2022.
26. 26.Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner K, et al. FinnGen: unique genetic insights from combining isolated population and national health register data. medRxiv. 2022. 10.1101/2022.03.03.22271360.
27. 27.UKBiobank HRC-imputed: 008: intestinal infection. 2018. https://pheweb.org/UKB-SAIGE/pheno/008. Accessed 6 Jan 2022.
28. 28.UKBiobank HRC-imputed: 480.1: bacterial pneumonia. 2018. https://pheweb.org/UKB-SAIGE/pheno/480.1. Accessed 6 Jan 2022.
29. 29.UKBiobank HRC-imputed: 591: urinary tract infection. 2018. https://pheweb.org/UKB-SAIGE/pheno/591. Accessed 6 Jan 2022.
30. 30.FinnGen: intestinal infectious diseases. 2022. https://r6.finngen.fi/pheno/AB1_INTESTINAL_INFECTIONS. Accessed 20 Feb 2022.
31. 31.FinnGen: bacterial pneumonia (organism specified). 2022. https://r6.finngen.fi/pheno/PNEUMOBACTKNOWN. Accessed 20 Feb 2022.
32. 32.FinnGen: other disorders of urethra and urinary system. 2022. https://r6.finngen.fi/pheno/N14_URETHRAOTH. Accessed 20 Feb 2022.
33. Willer CJ, Li Y, Abecasis GR. **METAL: fast and efficient meta-analysis of genomewide association scans**. *Bioinformatics* (2010.0) **26** 2190-2191. DOI: 10.1093/bioinformatics/btq340
34. Burgess S, Thompson SG. **Avoiding bias from weak instruments in Mendelian randomization studies**. *Int J Epidemiol* (2011.0) **40** 755-764. DOI: 10.1093/ije/dyr036
35. Burgess S, Dudbridge F, Thompson SG. **Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods**. *Stat Med* (2016.0) **35** 1880-1906. DOI: 10.1002/sim.6835
36. Brion M-JA, Shakhbazov K, Visscher PM. **Calculating statistical power in Mendelian randomization studies**. *Int J Epidemiol* (2013.0) **42** 1497-1501. DOI: 10.1093/ije/dyt179
37. Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. **Mendelian randomization: using genes as instruments for making causal inferences in epidemiology**. *Stat Med* (2008.0) **27** 1133-1163. DOI: 10.1002/sim.3034
38. Burgess S, Butterworth A, Thompson SG. **Mendelian randomization analysis with multiple genetic variants using summarized data**. *Genet Epidemiol* (2013.0) **37** 658-665. DOI: 10.1002/gepi.21758
39. Bowden J, Davey Smith G, Burgess S. **Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression**. *Int J Epidemiol* (2015.0) **44** 512-525. DOI: 10.1093/ije/dyv080
40. Bowden J, Davey Smith G, Haycock PC, Burgess S. **Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator**. *Genet Epidemiol* (2016.0) **40** 304-314. DOI: 10.1002/gepi.21965
41. Hartwig FP, Davey Smith G, Bowden J. **Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption**. *Int J Epidemiol* (2017.0) **46** 1985-1998. DOI: 10.1093/ije/dyx102
42. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R. **The MR-Base platform supports systematic causal inference across the human phenome**. *elife* (2018.0) **7** e34408. DOI: 10.7554/eLife.34408
43. Kamat MA, Blackshaw JA, Young R, Surendran P, Burgess S, Danesh J, Butterworth AS, Staley JR. **PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations**. *Bioinformatics* (2019.0) **35** 4851-4853. DOI: 10.1093/bioinformatics/btz469
44. 44.Elsworth B, Lyon M, Alexander T, Liu Y, Matthews P, Hallett J, et al. The MRC IEU OpenGWAS data infrastructure. BioRxiv. 2020. 10.1101/2020.08.10.244293.
45. Zhao Q, Wang J, Hemani G, Bowden J, Small DS. **Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score**. *Ann Stat* (2020.0) **48** 1742-1769. DOI: 10.1214/19-AOS1866
46. 46.Nudel R, Appadurai V, Schork AJ, Buil A, Bybjerg-Grauholm J, Børglum AD, et al. A large population-based investigation into the genetics of susceptibility to gastrointestinal infections and the link between gastrointestinal infections and mental illness. Hum Genet. 2020;139(5):593-604.
47. Machiela MJ, Chanock SJ. **LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants**. *Bioinformatics* (2015.0) **31** 3555-3557. DOI: 10.1093/bioinformatics/btv402
48. Focarelli F, Giachino A, Waldron KJ. **Copper microenvironments in the human body define patterns of copper adaptation in pathogenic bacteria**. *PLoS Pathog* (2022.0) **18** e1010617. DOI: 10.1371/journal.ppat.1010617
49. Berger MM, Spertini F, Shenkin A, Wardle C, Wiesner L, Schindler C, Chiolero RL. **Trace element supplementation modulates pulmonary infection rates after major burns: a double-blind, placebo-controlled trial**. *Am J Clin Nutr* (1998.0) **68** 365-371. DOI: 10.1093/ajcn/68.2.365
50. Muñoz C, López M, Olivares M, Pizarro F, Arredondo M, Araya M. **Differential response of interleukin-2 production to chronic copper supplementation in healthy humans**. *Eur Cytokine Netw* (2005.0) **16** 261-265. PMID: 16464739
51. White C, Lee J, Kambe T, Fritsche K, Petris MJ. **A role for the ATP7A copper-transporting ATPase in macrophage bactericidal activity**. *J Biol Chem* (2009.0) **284** 33949-33956. DOI: 10.1074/jbc.M109.070201
52. Çolak Y, Nordestgaard BG, Afzal S. **Low vitamin D and risk of bacterial pneumonias: Mendelian randomisation studies in two population-based cohorts**. *Thorax* (2021.0) **76** 468-478. DOI: 10.1136/thoraxjnl-2020-215288
53. Das RR, Singh M, Naik SS. **Vitamin D as an adjunct to antibiotics for the treatment of acute childhood pneumonia**. *Cochrane Database Syst Rev* (2018.0) **7** Cd011597. PMID: 30024634
54. Lassi ZS, Kurji J, Oliveira CS, Moin A, Bhutta ZA. **Zinc supplementation for the promotion of growth and prevention of infections in infants less than six months of age**. *Cochrane Database Syst Rev* (2020.0) **4** Cd010205. PMID: 32266964
55. Padhani ZA, Moazzam Z, Ashraf A, Bilal H, Salam RA, Das JK, Bhutta ZA. **Vitamin C supplementation for prevention and treatment of pneumonia**. *Cochrane Database Syst Rev* (2020.0) **4** Cd013134. PMID: 32337708
56. Allingstrup M, Afshari A. **Selenium supplementation for critically ill adults**. *Cochrane Database Syst Rev* (2015.0) **2015** Cd003703. PMID: 26214143
57. Zhao Y, Yang M, Mao Z, Yuan R, Wang L, Hu X, Zhou F, Kang H. **The clinical outcomes of selenium supplementation on critically ill patients: a meta-analysis of randomized controlled trials**. *Medicine (Baltimore)* (2019.0) **98** e15473. DOI: 10.1097/MD.0000000000015473
58. Gill D, Benyamin B, Moore LSP, Monori G, Zhou A, Koskeridis F, Evangelou E, Laffan M, Walker AP, Tsilidis KK. **Associations of genetically determined iron status across the phenome: a mendelian randomization study**. *PLoS Med* (2019.0) **16** e1002833. DOI: 10.1371/journal.pmed.1002833
59. Mohus RM, Flatby H, Liyanarachi KV, DeWan AT, Solligård E, Damås JK, Åsvold BO, Gustad LT, Rogne T. **Iron status and the risk of sepsis and severe COVID-19: a two-sample Mendelian randomization study**. *Sci Rep* (2022.0) **12** 16157. DOI: 10.1038/s41598-022-20679-6
60. Hu Y, Cheng X, Mao H, Chen X, Cui Y, Qiu Z. **Causal effects of genetically predicted iron status on sepsis: a two-sample bidirectional mendelian randomization study**. *Front Nutr* (2021.0) **8** 747547. DOI: 10.3389/fnut.2021.747547
61. Ong J-S, Dixon-Suen SC, Han X, An J, Liyanage U, Dusingize J-C, Schumacher J, Gockel I, Böhmer A, Jankowski J. **A comprehensive re-assessment of the association between vitamin D and cancer susceptibility using Mendelian randomization**. *Nat Commun* (2021.0) **12** 1-10. DOI: 10.1038/s41467-020-20368-w
62. Guo Y, Lu Y, Jin H. **Appraising the role of circulating concentrations of micro-nutrients in epithelial ovarian cancer risk: a Mendelian randomization analysis**. *Sci Rep* (2020.0) **10** 1-9. PMID: 31913322
63. Fu Y, Xu F, Jiang L, Miao Z, Liang X, Yang J, Larsson SC, Zheng J-S. **Circulating vitamin C concentration and risk of cancers: a Mendelian randomization study**. *BMC Med* (2021.0) **19** 1-14. DOI: 10.1186/s12916-021-02041-1
64. 64.Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Holmes MV, Minelli C, Relton CL, Theodoratou E. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res. 2020;4:186. 10.12688/wellcomeopenres.15555.2.
65. Jiang X, O’Reilly PF, Aschard H, Hsu Y-H, Richards JB, Dupuis J, Ingelsson E, Karasik D, Pilz S, Berry D. **Genome-wide association study in 79,366 European-ancestry individuals informs the genetic architecture of 25-hydroxyvitamin D levels**. *Nat Commun* (2018.0) **9** 1-12. PMID: 29317637
|
---
title: Association between ESR1 rs2234693 single nucleotide polymorphism and uterine
fibroids in Taiwanese premenopausal and postmenopausal women
authors:
- Yeu-Sheng Tyan
- Chao-Yu Shen
- Disline Manli Tantoh
- Shu-Yi Hsu
- Ying-Hsiang Chou
- Oswald Ndi Nfor
- Yung-Po Liaw
journal: Journal of Health, Population, and Nutrition
year: 2023
pmcid: PMC9993586
doi: 10.1186/s41043-023-00357-7
license: CC BY 4.0
---
# Association between ESR1 rs2234693 single nucleotide polymorphism and uterine fibroids in Taiwanese premenopausal and postmenopausal women
## Abstract
### Background
Uterine fibroids (UFs) are uterine smooth muscle neoplasms that affect women, especially during the reproductive stage. *Both* genetic and lifestyle factors affect the onset of the disease. We examined the association between the estrogen receptor 1 (ESR1) rs2234693 variant (whose genotypes are TT, TC, and CC) and UFs in Taiwanese premenopausal and postmenopausal women.
### Methods
We linked individual-level data of 3588 participants from the Taiwan Biobank to the National Health Insurance Research Database at the Health and Welfare Data Science Center. The association of the ESR1 rs2234693 variant and other variables with UFs was determined by multiple logistic regression, and the results were presented as odds ratios and $95\%$ confidence intervals (CIs).
### Results
The 3588 participants comprised 622 cases and 2966 controls. In all the participants, the ESR1 rs2234693 TC and CC genotypes compared to the reference genotype (TT) were associated with a lower risk of UFs. However, the results were significant only for the CC genotype (OR; $95\%$ CI = 0.70; 0.52–0.93). Noteworthy, the association of TC and CC with UFs was dose-dependent (p-trend = 0.012). Based on menopausal status, both TC and CC were significantly and dose-dependently associated with a lower risk of UFs in premenopausal women (OR; $95\%$ CI = 0.76; 0.59–0.98 for TC and 0.64; 0.43–0.95 for CC: p-trend = 0.010).
### Conclusion
The TC and CC genotypes of the ESR1 rs2234693 variant may reduce susceptibility to UFs, especially in premenopausal women.
## Background
Uterine leiomyomas, commonly known as uterine fibroids (UFs) or simply fibroids, are non-cancerous smooth muscle uterine tumors that affect women [1–3]. They are the most common gynecologic tumors and affect almost 50–$80\%$ of women of childbearing age [4–8]. They have substantial obstetrical consequences that adversely affect women’s well-being [9–11]. Some of the clinical complications of UFs include infertility, severe pelvic pain, miscarriage, abortion, and anemia due to excessive menstrual bleeding [12–15]. Even though UFs are associated with these remarkable complications, their etiology is intricate and remains poorly elucidated [4, 7, 16]. Fibroids stem from both genetic and non-genetic sources including, genetic polymorphisms, menopausal status, alcohol consumption, age, education, cigarette smoking, physical activity, body mass index (BMI), parity, diet, caffeine intake, hypertension, age at menarche, and hormones [1, 5, 7, 16–23].
Hormonal factors play a critical role in the development and progress of UFs [24, 25]. For instance, UFs are estrogen-dependent: as estrogen levels increase, the risk of the disease increases [1, 26, 27]. The rare occurrence of UFs before menarche and a low incidence after menopause support this hypothesis [25]. Moreover, fibroids contain more estrogen receptors than normal adjacent myometrial tissues [25, 28]: The UF-driving nature of estrogen paves a way for uniquely managing the disease by targeting potential estrogen receptors [25]. This is because estrogen affects some of the pathological pathways involved in the pathogenesis of UFs by binding to such receptors [29]. Therefore, fluctuations in the levels of both estrogen and estrogen receptors are implicated in the pathobiology of UFs [25]. Estrogen receptor alpha (ESRα), which is encoded by the estrogen receptor 1 (ESR1) gene [29], is mostly expressed in uterine tissues [25, 30]. This receptor is substantial in the functioning of estrogen in premenopausal and menopausal women [31, 32]. Moreover, it is believed to be among the key elements underlying the pathophysiology of gynecological disorders such as UFs and endometriosis [27, 33–35].
Genomic variations are among the prospective processes underlying the onset of UFs and could contribute to the development of unique therapeutic approaches [25]. A single nucleotide polymorphism (SNP) is a genetic variation at a specific position in a DNA sequence, where a single nucleotide (A, T, C, or G) is substituted by another in at least $1\%$ of the population [36]. Such a variation could affect the structure and function of DNA, thereby conferring disease resistance or susceptibility [37, 38]. Single nucleotide polymorphisms (SNPs) play a key role in the effective prevention of diseases because they serve as disease markers that assist in the early identification of at-risk individuals [39]. For instance, ESR1 PvuII (rs2234693), a genetic variation caused by a nucleotide change from T to C (T > C) is the most studied variant of the ESR1 gene [22, 40–42]. It has been associated with an increased risk of UFs in Taiwanese [35], Black, and White American women [33], but not in German [43], Polish [42], Italian [44, 45], Hispanic [33], and Iranian women [46]. Given this controversial relationship between the variant and UFs, further epidemiological research is needed. Furthermore, studies with large sample sizes have been recommended to clarify the relationship between rs2234693 and UFs [42]. Therefore, we carried out this study to determine the association between the ESR1 rs2234693 SNP and UFs in Taiwanese premenopausal and postmenopausal women.
## Ethical compliance
Ethical approval for this work was granted by the Institutional Review Board of the Chung Shan Medical University Hospital (CS2-20006). All participants signed an informed consent form before enrolling in the TWB project.
## Participants and datasets
We enrolled 3994 participants (1994 premenopausal and 2000 postmenopausal women with complete data) who were recruited into the Taiwan Biobank (TWB) project between 2008 and 2015. The TWB project was created to collect and integrate genetic and non-genetic data of over 200,000 Taiwanese adults aged between 30 and 70 years, with no cancer diagnosis to undertake large-scale cohort and case–control studies [47]. At the time of the current study, the TWB database contained basic demographic information (e.g., age, sex, and education), personal lifestyle habits (e.g., exercise, smoking, alcohol, tea, and coffee consumption), and genetic data (e.g., single nucleotide polymorphisms). However, information on uterine fibroids was not available in the database. Nonetheless, the National Health Insurance Research Database (NHIRD) contained data on uterine fibroids. To determine the risk of UFs, we used the participants’ identification numbers and linked the TWB database (2008–2015) to the NHIRD (1998–2015) at the Health and Welfare Data Center (HWDC). The HWDC is a data repository site established by the Ministry of Health and Welfare (MOHW). This center allows the linking and management of several databases under strict supervision to ensure data privacy and security [48]. We excluded 406 menopausal women whose menopause was not attained naturally. Finally, our study included 3588 participants, comprising 1594 premenopausal and 1994 postmenopausal women.
## Identification and definition of variables
We chose rs2234693 because it is associated with female reproduction and is one of the most commonly assessed ESR1 SNPs [40, 49]. Genotyping experiments were performed by the National Center for Genome Medicine in Academia Sinica using a custom Affymetrix Axiom Genome-Wide Array Plate (Affymetrix Inc. Santa Clara, CA, USA) called TWB chip. During genotyping, SNPs that failed quality control: had a minor allele frequency (MAF) < 0.05, a call rate < $95\%$, and deviated from the Hardy–*Weinberg equilibrium* (HWE)—p value < 1.0 × 10−3—were excluded. The MAF, call rate, and HWE p value for rs2234693 were 0.378887, $99.79\%$, and 0.2174, respectively.
Diseases in the NHIRD were identified using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and a single admission or two outpatient visits. The ICD-9-CM codes were 218.0, 218.1, 218.2, and 218.9 for UFs [50], 401–405, A260, and A269 for hypertension, and then 250 and A181 for diabetes mellitus.
Lifestyle habits, menopausal status, age, educational level, age at menarche, family history of UFs, miscarriage/abortion, and parity were self-reported. The postmenopausal subjects were women who reported an absence of menstrual flow (not due to hysterectomy or any medical condition/treatment) for at least twelve consecutive months, while the premenopausal subjects included women who were still experiencing monthly menstrual bleeding at the time of the interview. An elaborate description of the other variables has been provided elsewhere [51, 52]. In summary, we defined alcohol consumption as a weekly intake of at least 150 ml of alcohol over 6 months; smoking as regular use of cigarettes over 6 months; exercise as engaging in at least 30 min of exercise (excluding manual work) ≥ 3 times per week; coffee consumption as drinking coffee at least three times per week; and tea consumption as drinking tea at least once per day. We defined a vegetarian as someone who maintained a vegetarian lifestyle for at least 6 months prior to data collection; use of hormones as regular use of western hormonal medicine for more than 6 months; use of herbal medicine as the use of herbs (for gynecological conditions such as menstruation and menopause) for 3 months; and second-hand smoke exposure as being exposed to tobacco smoke for at least 5 min per hour. BMI was calculated as weight (kg) divided by height squared (m2).
## Statistical analyses
The t test and chi-squared test were used to evaluate the differences between continuous and non-continuous (categorical) variables, respectively. We determined the association between ESR1 rs2234693 and uterine fibroid using the multiple logistic regression analysis. Adjustments were made for menopausal status, alcohol consumption, age, education, cigarette smoking, exercise, second-hand smoke exposure, hypertension, tea/coffee consumption, vegetarian diet, age at menarche, hormone use, herbal medicine use, family history of uterine fibroid, miscarriage/abortion, BMI, and parity. SAS (version 9.4) was used to perform statistical analyses, while PLINK (version 1.09) was used for SNP quality control [53].
## Results
The participants comprised 622 cases of uterine fibroids and 2966 controls (Table 1). The difference in the rs2234693 genotype distribution between the cases and controls was significant at borderline ($$p \leq 0.052$$). Age, hypertension, diabetes, the use of herbal medicine, family history of uterine fibroids, and miscarriage/abortion were significantly different between the cases and controls ($p \leq 0.05$).Table 1Basic characteristics of participants with uterine fibroids (cases) and without uterine fibroids (controls)VariableControls ($$n = 2966$$)Cases ($$n = 622$$)p valueCategorical variablesn (%)n (%)ESR1 rs2234693 genotype0.052 TT1132 (38.17)263 (42.28) TC1392 (46.93)286 (45.98) CC442 (14.90)73 (11.74)Menopause0.678 No1653 (55.73)341 (54.82) Yes1313 (44.27)281 (45.18)Alcohol consumption0.321 No2845 (95.92)595 (95.66) Yes121 (4.08)27 (4.34)Age (years) < 0.001* 30–39685 (23.10)63 (10.13) 40–49829 (27.95)210 (33.76) 50–59838 (28.25)254 (40.84) 60–69614 (20.70)95 (15.27)Level of education0.187 Elementary school289 (9.74)55 (8.84) High school1473 (49.66)334 (53.70) University and above1204 (40.59)233 (37.46)Cigarette smoking0.766 No2845 (95.92)595 (95.66) Yes121 (4.08)27 (4.34)Exercise0.163 No1721 (58.02)342 (54.98) Yes1245 (41.98)280 (45.02)Second-hand smoke exposure0.104 No2651 (89.38)542 (87.14) Yes315 (10.62)80 (12.86)Hypertension0.004* No2404 (81.05)473 (76.05) Yes562 (18.95)149 (23.95)Diabetes0.414 No2610 (88.00)540 (86.82) Yes356 (12.00)82 (13.18)Tea consumption0.825 No2059 (69.42)429 (68.97) Yes907 (30.58)193 (31.03)Coffee consumption0.307 No1980 (66.76)402 (64.93) Yes986 (33.24)220 (35.37)Vegetarian diet0.361 No2648 (89.28)563 (90.51) Yes318 (10.72)59 (9.49)Hormone use0.069 No2558 (86.24)519 (83.44) Yes408 (13.76)103 (16.56)Herbal medicine use0.006* No2679 (90.32)539 (86.66) Yes287 (9.68)83 (13.34)Family history of uterine fibroids < 0.001* No2546 (85.84)488 (78.46) Yes420 (14.16)134 (21.54)Miscarriage/abortion < 0.001* No1170 (39.45)197 (31.67) Yes1796 (60.55)425 (68.33)Age at menarche (years)0.943Age at menarche ≤ 12603 (20.33)126 (20.26) 12 < age at menarche ≤ 13786 (26.50)172 (27.65) 13 < age at menarche ≤ 14897 (30.24)183 (29.42) Age at menarche > 14680 (22.93)141 (22.67)Continuous variables Body mass index (kg/m2)23.48 ± 3.41223.62 ± 3.2150.340 Parity2.32 ± 0.9782.29 ± 0.8880.481n sample size, ESR1 Estrogen receptor 1, SD standard deviation*Denotes statistical significance at $p \leq 0.05$ Table 2 presents the association between rs2234693 and UFs in all of the 3588 participants. Compared to the TT genotype (reference), the CC genotype was associated with a lower risk of UFs (odds ratio OR = 0.70, $95\%$ confidence interval CI 0.52–0.93), while the TC genotype was not significantly associated with the disorder (OR = 0.87, $95\%$ CI 0.72–1.05). This indicates a $30\%$ lower likelihood of having UFs in the subjects with the CC genotype compared with those with the TT genotype. Of note, the relationship of the genotypes with UFs was dose-dependent (p-trend = 0.012). A lower risk of UFs was found in the menopause group (OR = 0.69, $95\%$ CI 0.51–0.92), while a higher risk of the disorder was seen in women who were 40–49 years old (OR = 2.96, $95\%$ CI 2.16–4.06), 50–59 years old (OR = 4.55, $95\%$ CI 3.09–6.69), 60–69 years old (OR = 2.59, $95\%$ CI 1.59–4.21), hypertensive (OR = 1.32, $95\%$ CI 1.04–1.67), using herbal medicine (OR = 1.50, $95\%$ CI 1.14–1.97), having a family history of UFs (OR = 1.60, $95\%$ CI 1.28–2.00), and/or having a history of miscarriage/abortion (OR = 1.24, $95\%$ CI 1.02–1.49).Table 2Association between ESR1 rs2234693 and uterine fibroidsVariableOR ($95\%$ CI)p valueESR1 rs2234693 (ref.: TT)TC0.87 (0.72–1.05)0.147CC0.70 (0.52–0.93)0.015*p-trend0.012*Menopause (ref.: No)Yes0.69 (0.51–0.92)0.012*Alcohol consumption (ref.: No)Yes1.25 (0.71–2.21)0.445Age (ref.: 30–39 years)40–492.96 (2.16–4.06) < 0.001*50–594.55 (3.09–6.69) < 0.001*60–692.59 (1.59–4.21) < 0.001*Level of education (ref.: Elementary school)High school1.05 (0.75–1.48)0.780University and above1.00 (0.69–1.44)0.982Cigarette smoking (ref.: No)Yes1.01 (0.64–1.60)0.9708Exercise (ref.: No)Yes1.02 (0.84–1.23)0.876BMI0.99 (0.97–1.02)0.659Second-hand smoke exposure (ref.: No)Yes1.25 (0.95–1.64)0.117Diabetes (ref.: No)Yes1.06 (0.80–1.40)0.697Hypertension (ref.: No)Yes1.32 (1.04–1.67)0.021*Tea consumption (ref.: No)Yes1.03 (0.85–1.26)0.765Coffee consumption (ref.: No)Yes1.05 (0.86–1.27)0.631Vegetarian diet (ref.: No)Yes0.84 (0.62–1.13)0.254Age at menarche (ref.: age at menarche ≤ 12)12 < age at menarche ≤ 130.93 (0.72–1.21)0.61113 < age at menarche ≤ 140.87 (0.67–1.13)0.302Age at menarche > 140.88 (0.66–1.17)0.372Hormone use (ref.: No)Yes1.21 (0.94–1.55)0.147Herbal medicine use (ref.: No)Yes1.50 (1.14–1.97)0.004*Family history of uterine fibroids (ref.: No)Yes1.60 (1.28–2.00) < 0.001*Miscarriage/abortion (ref.: No)Yes1.24 (1.02–1.49)0.030*Parity0.91 (0.82–1.01)0.066ESR1 Estrogen receptor 1, OR Odds ratio, CI Confidence interval, ref. reference*Denotes statistical significance at $p \leq 0.05$ Table 3 shows the association between ESR1 rs2234693 and UFs in premenopausal and postmenopausal women. Compared to the reference genotype (TT), both the TC and CC genotypes were significantly and dose-dependently associated with a lower risk of UFs in premenopausal women (OR = 0.76, $95\%$ CI = 0.59–0.98 for TC and OR = 0.64, $95\%$ CI = 0.43–0.95 for CC: p-trend = 0.010). However, the TC and CC genotypes were not associated with the occurrence of UFs in postmenopausal women. Age at menarche had an inverse but insignificant relationship with UFs in premenopausal women. This inverse relationship was dose-dependent (p-trend = 0.030).Table 3Association between ESR1 rs2234693 and uterine fibroids stratified by menopausal statusVariableNo menopauseMenopauseOR ($95\%$ CI)p valueOR ($95\%$ CI)p valueESR1 rs2234693 (ref.: TT)TC0.76 (0.59–0.98)0.0371*1.06 (0.79–1.40)0.714CC0.64 (0.43–0.95)0.0263*0.78 (0.50–1.20)0.255P-trend0.010*0.431Alcohol consumption (ref.: No)Yes1.29 (0.63–2.64)0.4821.21 (0.46–3.17)0.698Age30–39Ref–NANA40–492.88 (2.08–3.99) < 0.001*Ref–50–594.14 (2.71–6.31) < 0.001*2.18 (0.97–4.94)0.06160–69NANA1.18 (0.51–2.74)0.693Level of education (ref.: Elementary school)High school0.93 (0.43–2.02)0.8581.07 (0.72–1.59)0.728University and above0.82 (0.37–1.80)0.6161.20 (0.76–1.88)0.437Cigarette smoking (ref.: No)Yes0.96 (0.55–1.68)0.8961.07 (0.46–2.47)0.880Exercise (ref.: No)Yes1.07 (0.82–1.40)0.6160.93 (0.70–1.22)0.585BMI1.00 (0.96–1.04)0.9940.98 (0.93–1.02)0.313Second-hand smoke exposure (ref.: No)Yes1.23 (0.87–1.74)0.2391.30 (0.82–2.06)0.271Diabetes (ref.: No)Yes1.22 (0.76–1.96)0.4170.96 (0.68–1.36)0.825Hypertension (ref.: No)Yes1.20 (0.80–1.78)0.3751.40 (1.04–1.88)0.025*Tea consumption (ref.: No)Yes0.96 (0.74–1.25)0.7631.15 (0.84–1.57)0.375Coffee consumption (ref.: No)Yes1.16 (0.90–1.50)0.2430.90 (0.66–1.22)0.481Vegetarian diet (ref.: No)Yes1.12 (0.78–1.62)0.5410.49 (0.29–0.85)0.012*Age at menarche (ref.: age at menarche ≤ 12)12 < age at menarche ≤ 130.98 (0.71–1.37)0.9110.88 (0.57–1.37)0.58313 < age at menarche ≤ 140.82 (0.58–1.15)0.2390.92 (0.61–1.41)0.712Age at menarche > 140.67 (0.44–1.00)0.0511.10 (0.72–1.68)0.656P-trend0.030*NAHormone use (ref.: No)Yes1.19 (0.79–1.79)0.4081.23 (0.89–1.69)0.217Herbal medicine use (ref.: No)Yes1.68 (1.21–2.36)0.002*1.22 (0.73–2.03)0.454Family history of uterine fibroids (ref.: No)Yes1.88 (1.41–2.51) < 0.001*1.24 (0.86–1.80)0.258Miscarriage/abortion (ref.: No)Yes1.40 (1.08–1.82)0.012*1.06 (0.80–1.40)0.704Parity0.95 (0.82–1.10)0.4880.87 (0.75–1.02)0.082ESR1 Estrogen receptor 1, OR Odds ratio, CI Confidence interval, ref. Reference, NA Not applicable*Denotes statistical significance at $p \leq 0.05$ Table 4 shows the risk of UF based on the combination of the ESR1 rs2234693 genotypes and menopausal status. Compared to the reference group (premenopausal women with the TT genotype), the risk of UF was significantly lower in the other groups including, premenopausal women with TC (OR = 0.75, $95\%$ CI 0.58–0.97), premenopausal women with CC (OR = 0.66, $95\%$ CI 0.45–0.97), postmenopausal women with TT (OR = 0.58, $95\%$ CI 0.41–0.84), postmenopausal women with TC (OR = 0.60, $95\%$ CI 0.42–0.85), and postmenopausal women with CC (OR = 0.43, $95\%$ CI 0.27–0.70).Table 4Risk of uterine fibroids based on ESR1 rs2234693 genotypes and menopausal statusVariablenOR ($95\%$ CI)p valueESR1 rs2234693 genotypes and menopausal status (ref.: TT, no menopause)775 TC, no menopause9270.75 (0.58–0.97)0.030* CC, no menopause2920.66 (0.45–0.97)0.034* TT, menopause6200.58 (0.41–0.84)0.004* TC, menopause7510.60 (0.42–0.85)0.004* CC, menopause2230.43 (0.27–0.70)0.001*Adjusted for age, level of education, cigarette smoking, exercise, BMI, second-hand smoke exposure, diabetes, hypertension, tea/coffee consumption, vegetarian diet, age at menarche, hormone/herbal medicine use, family history of uterine fibroids, miscarriage/abortion, and parityESR1 Estrogen receptor 1, OR Odds ratio, CI Confidence interval, ref. Reference*Denotes statistical significance at $p \leq 0.05$
## Discussion
In the current study, the risk of UFs was significantly lower in postmenopausal Taiwanese women compared to their premenopausal counterparts. Both the TC and CC genotypes of the ESR1 rs2234693 SNP were significantly associated with a lower risk of UFs among premenopausal women, implying that the ESR1 rs2234693 variant might protect against UFs. Each population has its unique genetic characteristics which could affect its susceptibility to diseases. As such, it is important to determine the effect of genetic variants on health outcomes in specific populations because findings from one ethnic population might not be directly applicable to another [54]. Since our study subjects were exclusively Taiwanese, our findings add to the knowledge regarding the potential genetic determinants of uterine fibroid (a non-communicable disease) in the Taiwanese population.
Most UF-related pathways are complex [55]. The role of the ESR1 gene in the pathogenesis of UFs and other gynecologic diseases has been reported [27, 33–35, 56]. Regarding UF pathobiology, the role of ESR1 is attributed, in part to rs2234693 [33, 35, 42]. *This* genetic variant alters the binding of transcription factors and affects alternative splicing of ESR1, thereby influencing its expression and functionality [29, 34, 57, 58]. Higher ESR1 expression resulting from rs2234693 C allele-induced transcription could enhance oestradiol-ESR1 binding, subsequently leading to a stronger response to estradiol in CC homozygous women compared to T allele carriers [32]. Menopausal women carrying the rs2234693 T allele have been found to have the lowest levels of estradiol while those with the CC genotypes have the highest levels [59]. Moreover, in a previous study, low levels of estrogen coupled with the T allele were associated with lower ESR1 expression [57]. In the present study, rs2234693 CC homozygosity was inversely associated with UFs. However, the association was significant only in premenopausal women. The ineffectiveness of the CC genotype in postmenopausal women could be due to estrogen deficiency and probably lower expressions of the receptor [31]. A lower risk of UFs in postmenopausal women compared to premenopausal women has been reported [60]. It is worth noting, however, that the association between ESR1 rs2234693 and UFs remains controversial [40]. In the current study, we observed an inverse association of rs2234693 TC and CC with UFs. So far, we are aware of only one study that explored the relationship between both variables in Taiwanese [35]. In their study, Hsieh and colleagues included 106 cases and 110 controls and found a moderate correlation between the rs2234693 C-related genotype and susceptibility to UFs [35]. This polymorphism was also associated with an increased risk of UFs in Indian [34], as well as black and white American women [33]. In a meta-analysis of 26,428 cases of UFs and 43,381 controls, the T allele of rs2234693 was significantly associated with a lower risk of UFs [40]. In contrast, no significant relationship existed between the polymorphism and UFs in Polish [42], German [43], Iranian [46], and Italian women [44, 45].
In the current study, age was positively associated with UFs. That is, older age was significantly associated with a higher risk of UFs, confirming the evidence that the risk of UFs increases with age, especially during the reproductive stage [1, 19, 21, 61]. However, age at menarche among premenopausal women was inversely associated with UFs in a dose-dependent manner. That is, increasing age at menarche was significantly associated with a decreasing risk of UFs. The inverse relationship between age at menarche and UFs has been previously reported [1, 18, 26, 62]. The present study suggests that vegetarian diet could be associated with a lower risk of UFs among postmenopausal women; this agrees with previous studies [63, 64]. Similar to the current findings, UFs have been positively associated with hypertension [65–67], family history [21, 42, 68–70], abortion, and miscarriage [23, 71]. More is yet to be explored regarding the effect of herbal medicine on the onset of UFs. So far, some studies have reported a lower risk of UFs among people taking Chinese traditional medicine [72, 73]. According to Li and colleagues [74], several herbal medicines and natural products are used as alternative therapies for UFs due to their antiinflammatory, antiproliferative, and antiangiogenic activities. In the current study, however, the use of herbal medicine was associated with a higher risk of UFs.
The strength of the current study is that it is the first to link two important research databases in Taiwan (TWB and NHIRD) to ascertain participants’ genetic and non-genetic information and determine the risk of UFs. However, the limitation is that the Taiwan Biobank project enrolled only Taiwanese adults aged 30–70 years. As such, this study was unable to determine the risk of UFs in women aged below 30 and above 70 years. In this sense, our results may not be generalized to all Taiwanese premenopausal and postmenopausal women. Furthermore, this study only suggests the possible association between ESR1 rs2234693 and uterine fibroids and cannot establish causality due to its design.
## Conclusions
Both the ESR1 rs2234693 TC and CC genotypes may decrease the risk of UFs, particularly in premenopausal women. However, older age, early menarche, family history of UFs, and miscarriage/abortion may increase the risk. The clinical implication of these results is that the ESR1 rs2234693 variant might protect against UFs. This study contributes to the knowledge about the role of genetic factors in the pathogenesis of UFs. We hope that our results will serve as a reference for future studies evaluating the genetic factors involved in the pathogenesis of fibroids.
## References
1. Flake GP, Andersen J, Dixon D. **Etiology and pathogenesis of uterine leiomyomas: a review**. *Environ Health Perspect* (2003.0) **111** 1037-1054. DOI: 10.1289/ehp.5787
2. Chiaffarino F, Cipriani S, Ricci E, La Vecchia C, Chiantera V, Bulfoni A, Parazzini F. **Alcohol consumption and risk of uterine myoma: a systematic review and meta analysis**. *PLoS ONE* (2017.0) **12** e0188355. DOI: 10.1371/journal.pone.0188355
3. Takala H, Yang Q, El Razek AMA, Ali M, Al-Hendy A. **Alcohol consumption and risk of uterine fibroids**. *Curr Mol Med* (2020.0) **20** 247-258. DOI: 10.2174/1566524019666191014170912
4. Yang Q, Mas A, Diamond MP, Al-Hendy A. **The mechanism and function of epigenetics in uterine leiomyoma development**. *Reprod Sci* (2016.0) **23** 163-175. DOI: 10.1177/1933719115584449
5. Baird DD, Dunson DB, Hill MC, Cousins D, Schectman JM. **High cumulative incidence of uterine leiomyoma in black and white women: ultrasound evidence**. *Am J Obstet Gynecol* (2003.0) **188** 100-107. DOI: 10.1067/mob.2003.99
6. Ezzati M, Norian JM, Segars JH. **Management of uterine fibroids in the patient pursuing assisted reproductive technologies**. *Women’s Health* (2009.0) **5** 413-421. PMID: 19586433
7. Segars JH, Parrott EC, Nagel JD, Guo XC, Gao X, Birnbaum LS, Pinn VW, Dixon D. **Proceedings from the Third National Institutes of Health International Congress on Advances in Uterine Leiomyoma Research: comprehensive review, conference summary and future recommendations**. *Hum Reprod Update* (2014.0) **20** 309-333. DOI: 10.1093/humupd/dmt058
8. Schwartz SM, Marshall LM, Baird DD. **Epidemiologic contributions to understanding the etiology of uterine leiomyomata**. *Environ Health Perspect* (2000.0) **108** 821-827. DOI: 10.1289/ehp.00108s5821
9. Guo XC, Segars JH. **The impact and management of fibroids for fertility: an evidence-based approach**. *Obstet Gynecol Clin* (2012.0) **39** 521-533. DOI: 10.1016/j.ogc.2012.09.005
10. Hervé F, Katty A, Isabelle Q, Céline S. **Impact of uterine fibroids on quality of life: a national cross-sectional survey**. *Eur J Obstet Gynecol Reprod Biol* (2018.0) **229** 32-37. DOI: 10.1016/j.ejogrb.2018.07.032
11. Sabry M, Al-Hendy A. **Medical treatment of uterine leiomyoma**. *Reprod Sci* (2012.0) **19** 339-353. DOI: 10.1177/1933719111432867
12. Donnez J, Jadoul P. **What are the implications of myomas on fertility? A need for a debate?**. *Hum Reprod* (2002.0) **17** 1424-1430. DOI: 10.1093/humrep/17.6.1424
13. Sunkara SK, Khairy M, El-Toukhy T, Khalaf Y, Coomarasamy A. **The effect of intramural fibroids without uterine cavity involvement on the outcome of IVF treatment: a systematic review and meta-analysis**. *Hum Reprod* (2010.0) **25** 418-429. DOI: 10.1093/humrep/dep396
14. Sabry M, Al-Hendy A. **Innovative oral treatments of uterine leiomyoma**. *Obstet Gynecol Int* (2012.0). DOI: 10.1155/2012/943635
15. Walker CL, Stewart EA. **Uterine fibroids: the elephant in the room**. *Science* (2005.0) **308** 1589-1592. DOI: 10.1126/science.1112063
16. Pavone D, Clemenza S, Sorbi F, Fambrini M, Petraglia F. **Epidemiology and risk factors of uterine fibroids**. *Best Pract Res Clin Obstet Gynaecol* (2018.0) **46** 3-11. DOI: 10.1016/j.bpobgyn.2017.09.004
17. Tak YJ, Lee SY, Park SK, Kim YJ, Lee JG, Jeong DW, Kim SC, Kim IJ, Yi YH. **Association between uterine leiomyoma and metabolic syndrome in parous premenopausal women: a case–control study**. *Medicine* (2016.0) **95** e5325. DOI: 10.1097/MD.0000000000005325
18. Sato F, Miyake H, Nishi M, Mori M, Kudo R. **Early normal menstrual cycle pattern and the development of uterine leiomyomas**. *J Womens Health Gend Based Med* (2000.0) **9** 299-302. DOI: 10.1089/152460900318489
19. Wise LA, Laughlin-Tommaso SK. **Epidemiology of uterine fibroids–from menarche to menopause**. *Clin Obstet Gynecol* (2016.0) **59** 2. DOI: 10.1097/GRF.0000000000000164
20. Wise LA, Palmer JR, Harlow BL, Spiegelman D, Stewart EA, Adams-Campbell LL, Rosenberg L. **Risk of uterine leiomyomata in relation to tobacco, alcohol and caffeine consumption in the Black women’s health study**. *Hum Reprod* (2004.0) **19** 1746-1754. DOI: 10.1093/humrep/deh309
21. Stewart EA, Cookson C, Gandolfo RA, Schulze-Rath R. **Epidemiology of uterine fibroids: a systematic review**. *BJOG: Int J Obstet Gynaecol* (2017.0) **124** 1501-1512. DOI: 10.1111/1471-0528.14640
22. Li L, Zhang X, Xia Q, Ma H, Chen L, Hou W. **Association between estrogen receptor alpha PvuII polymorphism and prostate cancer risk**. *Tumor Biol* (2014.0) **35** 4629-4635. DOI: 10.1007/s13277-014-1606-9
23. Lumbiganon P, Rugpao S, Phandhu-fung S, Laopaiboon M, Vudhikamraksa N, Werawatakul Y. **Protective effect of depot-medroxyprogesterone acetate on surgically treated uterine leiomyomas: a multicentre case-control study**. *BJOG: Int J Obstet Gynaecol* (1996.0) **103** 909-914. DOI: 10.1111/j.1471-0528.1996.tb09911.x
24. Ulin M, Ali M, Chaudhry ZT, Al-Hendy A, Yang Q. **Uterine fibroids in menopause and perimenopause**. *Menopause (New York, NY)* (2020.0) **27** 238
25. Borahay MA, Asoglu MR, Mas A, Adam S, Kilic GS, Al-Hendy A. **Estrogen receptors and signaling in fibroids: role in pathobiology and therapeutic implications**. *Reprod Sci* (2017.0) **24** 1235-1244. DOI: 10.1177/1933719116678686
26. Wise LA, Palmer JR, Harlow BL, Spiegelman D, Stewart EA, Adams-Campbell LL, Rosenberg L. **Reproductive factors, hormonal contraception, and risk of uterine leiomyomata in African-American women: a prospective study**. *Am J Epidemiol* (2004.0) **159** 113-123. DOI: 10.1093/aje/kwh016
27. Maruo T, Ohara N, Wang J, Matsuo H. **Sex steroidal regulation of uterine leiomyoma growth and apoptosis**. *Hum Reprod Update* (2004.0) **10** 207-220. DOI: 10.1093/humupd/dmh019
28. Hunter DS, Hodges LC, Eagon PK, Vonier PM, Fuchs-Young R, Bergerson JS, Walker CL. **Influence of exogenous estrogen receptor ligands on uterine leiomyoma: evidence from an in vitro/in vivo animal model for uterine fibroids**. *Environ Health Perspect* (2000.0) **108** 829-834. DOI: 10.1289/ehp.00108s5829
29. Jakimiuk A, Nowicka M, Bogusiewicz M, Adamiak A, Skorupski P, Miotla P, Rechberger T, Haczynski J. **Prevalence of estrogen receptor alpha PvuII and XbaI polymorphism in population of Polish postmenopausal women**. *Folia Histochem Cytobiol* (2007.0) **45** 331-338. PMID: 18165171
30. Matthews J, Gustafsson J-Å. **Estrogen signaling: a subtle balance between ERα and ERβ**. *Mol Interv* (2003.0) **3** 281. DOI: 10.1124/mi.3.5.281
31. Losordo DW, Kearney M, Kim EA, Jekanowski J, Isner JM. **Variable expression of the estrogen receptor in normal and atherosclerotic coronary arteries of premenopausal women**. *Circulation* (1994.0) **89** 1501-1510. DOI: 10.1161/01.CIR.89.4.1501
32. Pinkas J, Gujski M, Wierzbińska-Stępniak A, Owoc A, Bojar I. **The polymorphism of estrogen receptor α is important for metabolic consequences associated with menopause**. *Endokrynol Pol* (2016.0) **67** 608-614. DOI: 10.5603/EP.a2016.0058
33. Al-Hendy A, Salama SA. **Ethnic distribution of estrogen receptor-α polymorphism is associated with a higher prevalence of uterine leiomyomas in black Americans**. *Fertil Steril* (2006.0) **86** 686-693. DOI: 10.1016/j.fertnstert.2006.01.052
34. Govindan S, Shaik NA, Vedicherla B, Kodati V, Rao KP, Hasan Q. **Estrogen receptor-α gene (T/C) Pvu II polymorphism in endometriosis and uterine fibroids**. *Dis Mark* (2009.0) **26** 149-154. DOI: 10.1155/2009/580260
35. Hsieh Y-Y, Wang Y-K, Chang C-C, Lin C-S. **Estrogen receptor α-351 Xba I* G and-397 Pvu II* C-related genotypes and alleles are associated with higher susceptibilities of endometriosis and leiomyoma**. *Mol Hum Reprod* (2007.0) **13** 117-122. DOI: 10.1093/molehr/gal099
36. 36.Tang B, Preview of single nucleotide polymorphism (Snp) and potential correlation between human genomes and evolutionary history as well as religious behaviors. 2019.
37. Hyman LB, Christopher CR, Romero PA. **Competitive SNP-LAMP probes for rapid and robust single-nucleotide polymorphism detection**. *Cell Rep Methods* (2022.0) **2** 100242. DOI: 10.1016/j.crmeth.2022.100242
38. Ueki M, Kimura-Kataoka K, Fujihara J, Iida R, Kawai Y, Kusaka A, Sasaki T, Takeshita H, Yasuda T. **Evaluation of the functional effects of genetic variants-missense and nonsense SNPs, indels and copy number variations-in the gene encoding human deoxyribonuclease I potentially implicated in autoimmunity**. *Sci Rep* (2019.0) **9** 1-11. DOI: 10.1038/s41598-019-49935-y
39. Chauhan W, Fatma R, Wahab A, Afzal M. **Cataloging the potential SNPs (single nucleotide polymorphisms) associated with quantitative traits, viz. BMI (body mass index), IQ (intelligence quotient) and BP (blood pressure): an updated review**. *Egypt J Med Human Genet* (2022.0) **23** 1-24. DOI: 10.1186/s43042-022-00266-0
40. Liu X, Huang J, Lin H, Xiong L, Ma Y, Lao H. **ESR1 PvuII (rs2234693 T> C) polymorphism and cancer susceptibility: evidence from 80 studies**. *J Cancer* (2018.0) **9** 2963. DOI: 10.7150/jca.25638
41. He M, Shu J, Huang X, Tang H. **Association between estrogen receptora gene (ESR1) PvuII (T/C) and XbaI (A/G) polymorphisms and premature ovarian failure risk: evidence from a meta-analysis**. *J Assist Reprod Genet* (2015.0) **32** 297-304. DOI: 10.1007/s10815-014-0393-y
42. Ciebiera M, Wrzosek M, Wojtyła C, Łoziński T, Nowicka G, Jakiel G, Głowala M, Włodarczyk M. **Oestrogen receptor alpha PvuII polymorphism and uterine fibroid incidence in Caucasian women**. *Przeglad Menopauzalny = Menopaus Rev* (2018.0) **17** 149. DOI: 10.5114/pm.2018.81735
43. Denschlag D, Bentz E-K, Hefler L, Pietrowski D, Zeillinger R, Tempfer C, Tong D. **Genotype distribution of estrogen receptor-alpha, catechol-O-methyltransferase, and cytochrome P450 17 gene polymorphisms in Caucasian women with uterine leiomyomas**. *Fertil Steril* (2006.0) **85** 462-467. DOI: 10.1016/j.fertnstert.2005.07.1308
44. Massart F, Becherini L, Gennari L, Facchini V, Genazzani AR, Brandi ML. **Genotype distribution of estrogen receptor-α gene polymorphisms in Italian women with surgical uterine leiomyomas**. *Fertil Steril* (2001.0) **75** 567-570. DOI: 10.1016/S0015-0282(00)01760-X
45. Massart F, Becherini L, Marini F, Noci I, Piciocchi L, Del Monte F, Masi L, Falchetti A, Tanini A, Scarselli G. **Analysis of estrogen receptor (ERα and ERß) and progesterone receptor (PR) polymorphisms in uterine leiomyomas**. *Med Sci Monit* (2003.0) **9** BR25-BR30. PMID: 12552233
46. Mortezaee FT, Tabatabaiefar MA, Chaleshtori MH, Miraj S. **Lack of association between ESR1 and CYP1A1 gene polymorphisms and susceptibility to uterine leiomyoma in female patients of Iranian descent**. *Cell J (Yakhteh)* (2014.0) **16** 225
47. Fan C-T, Lin J-C, Lee C-H. **Taiwan Biobank: a project aiming to aid Taiwan’s transition into a biomedical island**. *Pharmacogenomics* (2008.0) **9** 235-246. DOI: 10.2217/14622416.9.2.235
48. Hsieh C-Y, Su C-C, Shao S-C, Sung S-F, Lin S-J. **Yang Y-HK, Lai EC-C: Taiwan’s national health insurance research database: past and future**. *Clin Epidemiol* (2019.0) **11** 349. DOI: 10.2147/CLEP.S196293
49. 49.McElroy J, Kruse RL, Taylor JF, Candidate genetic polymorphisms and haplotypes associated with endometrial cancer risk. In ISEE conference abstracts 2016.
50. Lin C-Y, Wang C-M, Chen M-L, Hwang B-F. **The effects of exposure to air pollution on the development of uterine fibroids**. *Int J Hyg Environ Health* (2019.0) **222** 549-555. DOI: 10.1016/j.ijheh.2019.02.004
51. Su C-L, Tantoh DM, Chou Y-H, Wang L, Ho C-C, Chen P-H, Lee K-J, Nfor ON, Hsu S-Y, Liang W-M. **Blood-based SOX2-promoter methylation in relation to exercise and PM2. 5 exposure among Taiwanese adults**. *Cancers* (2020.0) **12** 504. DOI: 10.3390/cancers12020504
52. Tantoh DM, Lee K-J, Nfor ON, Liaw Y-C, Lin C, Chu H-W, Chen P-H, Hsu S-Y, Liu W-H, Ho C-C. **Methylation at cg05575921 of a smoking-related gene (AHRR) in non-smoking Taiwanese adults residing in areas with different PM 2.5 concentrations**. *Clin Epigenet* (2019.0) **11** 69. DOI: 10.1186/s13148-019-0662-9
53. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, De Bakker PI, Daly MJ. **PLINK: a tool set for whole-genome association and population-based linkage analyses**. *Am J Human Genet* (2007.0) **81** 559-575. DOI: 10.1086/519795
54. Adeyemo A, Rotimi C. **Genetic variants associated with complex human diseases show wide variation across multiple populations**. *Public Health Genomics* (2010.0) **13** 72-79. DOI: 10.1159/000218711
55. Nagata C, Nakamura K, Oba S, Hayashi M, Takeda N, Yasuda K. **Association of intakes of fat, dietary fibre, soya isoflavones and alcohol with uterine fibroids in Japanese women**. *Br J Nutr* (2009.0) **101** 1427-1431. DOI: 10.1017/S0007114508083566
56. Ashton KA, Proietto A, Otton G, Symonds I, McEvoy M, Attia J, Gilbert M, Hamann U, Scott RJ. **Estrogen receptor polymorphisms and the risk of endometrial cancer**. *BJOG: Int J Obstet Gynaecol* (2009.0) **116** 1053-1061. DOI: 10.1111/j.1471-0528.2009.02185.x
57. Schuit SC, Oei H-HS, Witteman JC, van Kessel CHG, van Meurs JB, Nijhuis RL, van Leeuwen JP, de Jong FH, Zillikens MC, Hofman A. **Estrogen receptor α gene polymorphisms and risk of myocardial infarction**. *JAMA* (2004.0) **291** 2969-2977. DOI: 10.1001/jama.291.24.2969
58. Herrington DM, Howard TD, Brosnihan KB, McDonnell DP, Li X, Hawkins GA, Reboussin DM, Xu J, Zheng SL, Meyers DA. **Common estrogen receptor polymorphism augments effects of hormone replacement therapy on E-selectin but not C-reactive protein**. *Circulation* (2002.0) **105** 1879-1882. DOI: 10.1161/01.CIR.0000016173.98826.88
59. Schuit SC, de Jong FH, Stolk L, Koek WNH, van Meurs JB, Schoofs MW, Zillikens MC, Hofman A, van Leeuwen JP, Pols HA. **Estrogen receptor alpha gene polymorphisms are associated with estradiol levels in postmenopausal women**. *Eur J Endocrinol* (2005.0) **153** 327-334. DOI: 10.1530/eje.1.01973
60. Parazzini F. **Risk factors for clinically diagnosed uterine fibroids in women around menopause**. *Maturitas* (2006.0) **55** 174-179. DOI: 10.1016/j.maturitas.2006.01.013
61. Sarkodie BD, Botwe BO, Adjei DN, Ofori E. **Factors associated with uterine fibroid in Ghanaian women undergoing pelvic scans with suspected uterine fibroid**. *Fertil Res Pract* (2016.0) **2** 1-7. DOI: 10.1186/s40738-016-0022-9
62. Faerstein E, Szklo M, Rosenshein N. **Risk factors for uterine leiomyoma: a practice-based case-control study. I. African-American heritage, reproductive history, body size, and smoking**. *Am J Epidemiol* (2001.0) **153** 1-10. DOI: 10.1093/aje/153.1.1
63. Shen Y, Wu Y, Lu Q, Ren M. **Vegetarian diet and reduced uterine fibroids risk: a case–control study in Nanjing, China**. *J Obstet Gynaecol Res* (2016.0) **42** 87-94. DOI: 10.1111/jog.12834
64. He Y, Zeng Q, Dong S, Qin L, Li G, Wang P. **Associations between uterine fibroids and lifestyles including diet, physical activity and stress: a case–control study in China**. *Asia Pac J Clin Nutr* (2013.0) **22** 109-117. PMID: 23353618
65. Faerstein E, Szklo M, Rosenshein NB. **Risk factors for uterine leiomyoma: a practice-based case-control study. II. Atherogenic risk factors and potential sources of uterine irritation**. *Am J Epidem* (2001.0) **153** 11-19. DOI: 10.1093/aje/153.1.11
66. Silver MA, Raghuvir R, Fedirko B, Elser D. **Systemic hypertension among women with uterine leiomyomata: potential final common pathways of target end-organ remodeling**. *J Clin Hypertens* (2005.0) **7** 664-668. DOI: 10.1111/j.1524-6175.2005.04384.x
67. Takeda T, Sakata M, Isobe A, Miyake A, Nishimoto F, Ota Y, Kamiura S, Kimura T. **Relationship between metabolic syndrome and uterine leiomyomas: a case–control study**. *Gynecol Obstet Invest* (2008.0) **66** 14-17. DOI: 10.1159/000114250
68. Sato F, Mori M, Nishi M, Kudo R, Miyake H. **Familial aggregation of uterine myomas in Japanese women**. *J Epidemiol* (2002.0) **12** 249-253. DOI: 10.2188/jea.12.249
69. Shen Y, Xu Q, Xu J, Ren M, Cai Y. **Environmental exposure and risk of uterine leiomyoma: an epidemiologic survey**. *Eur Rev Med Pharmacol Sci* (2013.0) **17** 3249-3256. PMID: 24338469
70. Ciebiera M, Włodarczyk M, Słabuszewska-Jóźwiak A, Nowicka G, Jakiel G. **Influence of vitamin D and transforming growth factor β3 serum concentrations, obesity, and family history on the risk for uterine fibroids**. *Fertil Steril* (2016.0) **106** 1787-1792. DOI: 10.1016/j.fertnstert.2016.09.007
71. Song L, Shen L, Mandiwa C, Yang S, Liang Y, Yuan J, Wang Y. **Induced and spontaneous abortion and risk of uterine fibroids**. *J Womens Health* (2017.0) **26** 76-82. DOI: 10.1089/jwh.2016.5913
72. Hsu W-C, Tsai Y-T, Hou Y-C, Lai J-N. **Prescription of Chinese herbal products is associated with a decreased risk of uterine fibroids: a population-based cohort study**. *Medicine* (2019.0). DOI: 10.1097/MD.0000000000007918
73. Su S-Y, Muo C-H, Morisky DE. **Use of chinese medicine and subsequent surgery in women with uterine fibroid: a retrospective cohort study**. *Evid-Based Complement Altern Med* (2012.0). DOI: 10.1155/2012/617918
74. 74.Li Z-L, Huang T-Y, Ho Y, Shih Y-J, Chen Y-R, Tang H-Y, Lin H-Y, Whang-Peng J, Wang K, Herbal medicine in uterine fibroid. In Fibroids. IntechOpen; 2020
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---
title: Effect of isotemporal substitution of sedentary behavior with different intensities
of physical activity on the muscle function of older adults in the context of a
medical center
authors:
- Ting-Fu Lai
- Yung Liao
- Ming-Chun Hsueh
- Kun-Pei Lin
- Ding-Cheng Chan
- Yung-Ming Chen
- Chiung-Jung Wen
journal: BMC Geriatrics
year: 2023
pmcid: PMC9993594
doi: 10.1186/s12877-023-03819-z
license: CC BY 4.0
---
# Effect of isotemporal substitution of sedentary behavior with different intensities of physical activity on the muscle function of older adults in the context of a medical center
## Abstract
### Background
Engaging in physical activity and reducing sedentary time in daily life may enable older individuals to maintain muscle mass. This study aimed to investigate the effects of replacing sedentary behavior with light physical activity (LPA) or moderate-to-vigorous physical activity (MVPA) on the muscle function of older adults at a medical center in Taiwan.
### Methods
We recruited 141 older adults ($51.1\%$ men; 81.1 ± 6.9 years old) and asked them to wear a triaxial accelerometer on the waist to measure their sedentary behavior and physical activity. Functional performance was assessed based on handgrip strength, Timed Up and Go (TUG) test, gait speed, and five-times-sit-to-stand test (5XSST). Isotemporal substitution analysis was performed to examine the effect of substituting 60 min of sedentary time with 60 min of LPA, MVPA, and combined LPA and MVPA in different proportions.
### Results
Reallocating 60 min of sedentary behavior per day to LPA was associated with better handgrip strength (Beta [B] = 1.587, $95\%$ confidence interval [CI] = 0.706, 2.468), TUG test findings (B = -1.415, $95\%$ CI = -2.186, -0.643), and gait speed ($B = 0.042$, $95\%$ CI = 0.007, 0.078). Reallocating 60 min of sedentary behavior per day to MVPA was associated with better gait speed ($B = 0.105$, $95\%$ CI = 0.018, 0.193) and 5XSST findings (B = -0.060, $95\%$ CI = -0.117, -0.003). In addition, each 5-min increment in MVPA in the total physical activity replacing 60 min of sedentary behavior per day resulted in greater gait speed. Replacing 60 min of sedentary behavior with 30-min of LPA and 30-min of MVPA per day significantly decreased the 5XSST test time.
### Conclusion
Our study indicates that introducing LPA and a combination of LPA and MVPA to specifically replace sedentary behavior may help maintain muscle function in older adults.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-023-03819-z.
## Background
Decline in physical function is the primary reason for an increased risk of aging-associated disability. It is also the major contributor to the limited mobility of older adults in activities of daily living [1]. To prevent such functional disability, it is essential to reduce sedentary time and engage in physical activity every day, which may provide older persons with more opportunities to contract muscles to maintain the strength and function of their limbs [2]. However, studies have shown that the time spent in sedentary behavior (SB) increases with age [3] and is often accompanied by prolonged television viewing and telephone usage among older individuals [4]. Therefore, in free-living settings, appropriately reallocating the daily sedentary time to physical activity throughout waking time may improve the physical health of older individuals, as shown by a 15-year follow-up study [5]. This study revealed that substituting sedentary behavior with light-intensity physical activity (LPA) could have a positive effect on reducing both all-cause mortality and cardiovascular disease mortality [5].
As SB-related health risks in older adults are becoming apparent, a new World Health Organization (WHO) 2020 guideline on physical activity and SB recommends that older people limit the amount of time spent on sedentary behavior [6] and substitute it with physical activity of any intensity (LPA and moderate-to-vigorous physical activity [MVPA]). According to a systematic review, when MVPA was adjusted, the objectively measured (e.g., using ActiGraph) LPA was inversely associated with all-cause mortality risk and appeared to be associated with favorable health outcomes in the analyzed adult population [7]. Nevertheless, to the best of our knowledge, the association between LPA and muscle strength or functional capacity in older adults is not well established [8]. Although some previous reports have indicated the contribution of LPA to muscle function [2], there is limited preliminary evidence linking LPA with health benefits in older populations, such as decreased body mass index (BMI), greater handgrip strength (HGS), and self-reported lower extremity function [9–12]. More importantly, previous studies have not shown a positive correlation between the effect of replacing LPA with SB and physical functions. LPA has been proposed to act as a gateway to MVPA [13], which is strongly associated with better physical function [9, 10]. Therefore, owing to its acceptability and feasibility, LPA could be used instead of MVPA to promote more physical activity with less SB among older individuals. Consequently, it is necessary to understand whether replacing SB with LPA in conjunction with increase in MVPA enhances functional performance in older adults.
The isotemporal substitution and compositional isotemporal substitution models are novel statistical methodologies in epidemiology to investigate the potential relationship between substituting the time spent performing one activity by that for another activity [14]. In contrast, traditional multivariate regression has been used to examine the relationship of a single activity (i.e., SB) while controlling for the time spent in another activity (i.e., MVPA) as a covariate. Interestingly, a recent cross-sectional study suggested that the results stemming from these statistical approaches (isotemporal substitution and multivariate regression analysis) are generally similar, with interpretable differences in the association of variables, and warrant future research through data-oriented methodological resolution [15]. In the present study, isotemporal substitution has been defined as substituting time spent performing one intensity of physical activity with another. This enabled us to not only simulate the possible outcomes of daily behavior changes but also capture the potential health-gaining effects of lifestyle changes. Therefore, isotemporal substitution might be a more realistic approach for achieving easily interpretable and real-life public health evidence. Further, several studies on isotemporal substitution of physical functions [16–18] have shown that replacing SB with either LPA or MVPA benefitted the upper extremities (i.e., HGS). Therefore, we aimed to investigate whether replacing SB with LPA and MVPA improves the muscle strength and function of both upper and lower limbs in older adults.
## Participants
We recruited 208 older adults aged ≥ 65 years who visited the geriatric outpatient clinic at a medical center in Taipei City, Taiwan, between September 2020 and March 2021 for treatment or health check-up. The detailed inclusion and exclusion criteria were as described in our previous study [19]. Briefly, older adults who could walk independently and had no severe hearing or visual impairment affecting communication were included. Those who lived in institutions with severe dementia or functional impairment were excluded. Of 208 participants, we excluded 15 older adults who did not undergo the full health examination and physical function checkup in the first stage. We also excluded 52 participants did not meet the criteria for wearing of the accelerometer, and 141 participants were included in the final analysis.
## Performance of physical functions
Physical function performance, including upper extremity strength (i.e., HGS), basic functional mobility (i.e., “Timed Up and Go” test, TUG) [20], gait speed (GS), and lower limb strength (i.e., five-times-sit-to-stand test, 5XSST) [21], was assessed using the BabyBot vital data recording system (Netown Corporation, Taiwan) [22]. It includes a 68-item self-reported questionnaire and tests for physical function performance [20, 21]. The HGS of the dominant hand was measured using a hydraulic hand dynamometer, with the elbow flexed while sitting. The best performance with the highest strength was selected from among three attempts, with a 1-min break between each attempt. In the TUG test, participants were instructed to rise from a standard chair, walk 3 m forward, return to the chair, and sit back down. In addition, each participant was asked to walk one-way for 6 m at their usual pace to measure the GS. A shorter time spent on the test indicated a better GS performance. Finally, lower-limb strength was measured using the 5XSST. Participants were asked to sit on a standard chair, stand up, and sit back down five times and as fast as possible.
## Physical activity and sedentary behavior
We used a waist-worn triaxial accelerometer (ActiGraph GT3X+, Pensacola, FL, USA) to measure the time spent for SB (≤ 99 counts/min), LPA (100–2019 counts/min), and MVPA (≥ 2020 counts/min) [23]. The data from this monitor were downloaded using ActiLife software (version 6.0, Pensacola, FL, USA) with 60-second epochs and a sampling frequency of 30 Hz. Using the data collection and processing criteria suggested by a systematic review of standard protocols for accelerometers [24], a period of 600 min or more of monitor wear time was defined as a valid day and zero counts of physical activity for 60 consecutive min and the sleep duration were classified as non-wear time. Sleep duration was excluded from sleep logs. Participants were requested to remove the accelerometer when they engaged in water activities, such as bathing and swimming. Data from participants who recorded at least four valid days (three weekdays and one weekend) were included in the analysis.
## Covariates
Demographic characteristics, including age, sex, education level (tertiary education or not), and living status (living alone or not), were self-reported. We calculated BMI using the weight and height formula and categorized participants as normal weight (18.5–24 kg/m2) or overweight (> 24 kg/m2). Participants were asked about their cigarette and alcohol use habits and whether they had been diagnosed with hypertension, hyperlipidemia, or diabetes (yes or no). Nutritional status was assessed using the Mini Nutritional Assessment (Short Form) [25]. The participants were categorized as having normal nutritional status (12–14 points)” or being “at risk of malnutrition (≤ 11 points).” Finally, average monitor wear time was calculated by using accelerometer and the ActiLife software.
## Statistical analyses
Descriptive analyses were conducted using the SPSS software (version 23.0; SPSS Inc., IBM, Chicago, IL, USA). Three multiple linear regression models, including the single model, partition model, and isotemporal substitution model, were used to examine the associations between SB, LPA, and MVPA for each performance of the four physical function tests. First, in the single physical activity parameter model, each activity type was assessed separately (e.g., only LPA or MVPA), without other activities, after adjusting for total wear time and covariates. Second, in the partition model, all activity types and covariates were simultaneously examined, without adjusting for the total wear time. The results of the single physical activity parameter and partition models are listed in Appendix 1. The outcomes of isotemporal substitution modeling could either be continuous or dichotomous [14]. In the final analyses, multiple linear regression models were used to examine the effect of substituting 60 min of SB with 60 min of LPA and MVPA. All activity variables (LPA and MVPA), except sedentary time, were entered into the models simultaneously, while the total wear time variable and covariates were kept constant. By including the total wear time variable, the isotemporal substitution is performed; hence, the regression estimate for each activity variable in the model reflects the effect of substituting a 60-min bout of SB with a 60-min bout of LPA and MVPA.
To simulate routine daily life, we followed the method used in previous study that used a different isotemporal substitution model [16]. The model was based on substituting 60 min of SB with 60 min of physical activity comprising both LPA and MVPA in varying proportions and increasing the duration of MVPA by 5 min every day (i.e., from 0 min/day of MVPA and 60 min/day of LPA to 60 min/day of MVPA and 0 min/day of LPA through increment of MVPA duration by 5 min/day) while controlling the total wear time and covariates.
## Participant characteristics
The analyses included 141 older adults ($51.1\%$ men; 81.1 ± 6.9 years old) (Table 1). Most participants lived with others ($90.1\%$) and had tertiary educational attainment ($61.0\%$). Additionally, most participants did not have a habit of cigarette use ($92.9\%$) or alcohol use ($89.4\%$), and the average BMI (23.7 ± 3.3 kg/m2) indicating that the participants were borderline obese. Nearly half ($48.2\%$) of the participants had been diagnosed with hypertension, and $31.9\%$ and $26.9\%$ had been diagnosed with hyperlipidemia or diabetes, respectively. The average performances of the participants in HGS, TUG, GS and 5XSST activities were 23.8 ± 0.3 kg, 9.6 ± 5.7 s, 1.2 ± 0.4 m/s and 11.3 ± 5.2 s, respectively. The average durations of SB, LPA, MVPA, and wear time were 606.7 ± 74.8 min/day, 250.9 ± 79.8 min/day, 18.8 ± 26.8 min/day, and 876.6 ± 79.4 min/day, respectively.
Table 1Characteristics of Participants ($$n = 141$$)VariablesMean ± SDCategoriesn%Age (years)81.9 ± 6.9SexMen$7251.1\%$Living statusLiving with others$12790.1\%$Living alone$149.9\%$Educational levelLower than university$5539.0\%$University$8661.\%$BMI (kg/m2)23.7 ± 3.3Normal$7855.3\%$Overweight$6344.7\%$DrinkingNo$12689.4\%$SmokingNo$13192.9\%$HypertensionYes$6848.2\%$HyperlipidemiaYes$4531.9\%$DiabetesYes$3826.9\%$Nutritional status10.1 ± 1.2Normal$11682.3\%$At risk of malnutrition$2517.7\%$Handgrip strength (kg)23.8 ± 7.5Timed up & go test (second)9.6 ± 5.7Gait speed (m/s)1.2 ± 0.4Five-time sit to stand test (s)11.3 ± 5.2MVPA (min/day)18.8 ± 26.8LPA (min/day)250.9 ± 79.8SB (min/day)606.7 ± 74.8Wear time (min/day)876.4 ± 79.4SB: sedentary behavior; MVPA: moderate-to-vigorous physical activity; LPA: light physical activity
## Isotemporal substitution model
Table 2 shows the results of substituting 60 min of SB with LPA and MVPA on the physical function tests using the isotemporal substitution model. Substituting 60 min of SB per day to LPA was significantly associated with better performance in the HGS ($B = 1.587$, $95\%$ CI = 0.706, 2.468), TUG (B = -1.415, $95\%$ CI = -2.186, -0.643), and GS ($B = 0.042$, $95\%$ CI = 0.007, 0.078) tests. Substituting 60 min of SB per day to MVPA was associated with better GS ($B = 0.105$, $95\%$ CI = 0.018, 0.193) and lower limb strength (B = -0.060, $95\%$ CI = -0.117, -0.003).
Table 2Isotemporal Substitution Models Examining the Associations of Replacing 60 min Sedentary Behavior, LPA and MVPA on Physical Function Test ($$n = 141$$)Analysis MethodSBLPAMVPAB$95\%$CI p B$95\%$CI p B$95\%$CI p Handgrip strength (kg)Replace SB withDropped 1.587 (0.706, 2.468) < 0.01* 1.113(− 1.055, 3.282)0.311Replace LPA with −1.587 (− 2.468, − 0.706) < 0.01* Dropped−0.474(− 2.895, 1.947)0.699Replace MVPA with−1.113(− 3.282, 1.055)0.3110.474(− 1.947, 2.895)0.699DroppedTimed up & go test (s)Replace SB withDropped -1.415 (-2.186, -0.643) < 0.001* -1.387(-3.256, 0.482)0.144Replace LPA with 1.415 (0.643, 2.186) < 0.001* Dropped0.028(-2.070, 2.126)0.979Replace MVPA with1.387(-0.482, 3.256)0.144-0.028(-2.126, 2.070)0.979DroppedGait speed (m/s)Replace SB withDropped 0.042 (0.007, 0.078) 0.02* 0.105 (0.018, 0.193) 0.019* Replace LPA with −0.042 (− 0.078, − 0.007) 0.02* Dropped0.063(− 0.035, 0.160)0.204Replace MVPA with −0.105 (− 0.193, − 0.018) 0.019* −0.063(− 0.160, 0.035)0.204Droppeda Five-time sit to stand testReplace SB withDropped−0.009(− 0.032, 0.014)0.435 −0.060 (− 0.117, − 0.003) 0.030* Replace LPA with0.009(− 0.014, 0.032)0.435Dropped−0.051(− 0.114, 0.013)0.117Replace MVPA with 0.060 (0.003, 0.117) 0.030* 0.051(− 0.013, 0.114)0.117DroppedAdjusted for sociodemographics (age, sex, education, living status), health status (BMI, hyperlipidemia, hypertension, diabetes, alcohol, smoking and nutritional status.) and monitor wear time; *$p \leq 0.05$a Log-transformedSB: sedentary behavior; MVPA: moderate-to-vigorous physical activity; LPA: light physical activity
## Mixed redistribution–substitution model
The association between substituting 60 min of SB with a combination of LPA and MVPA and the GS and 5XSST findings is presented in Figs. 1 and 2. We found that substituting SB with LPA, but not MVPA, was associated with a better performance in the HGS and TUG tests; thus, we did not perform a mixed redistribution–substitution model using either of these tests. *In* general, the substitution of SB with any combination of LPA and MVPA tended to improve the GS. Each 5-min increment in MVPA in the 60-min physical activity substituting SB resulted in greater GS improvement. Specifically, substituting 60 min of SB with 30 min of each LPA and MVPA per day showed statistically significant improvement and probable changes in the 5XSST (B = -0.044, $95\%$ CI= -0.087, -0.002).
Fig. 1Substitution regression model for the effect of replacing SB with various ratios of LPA and MVPA on the gait speed (0–60 min). The values indicate the parameter estimate and $95\%$ CI adjusted for patient sociodemographic characteristics (age, sex, education, living status), health status (BMI, hyperlipidemia, hypertension, diabetes, alcohol, smoking and nutritional status.), and average monitored wear time SB: sedentary behavior; MVPA: moderate-to-vigorous physical activity; LPA: light physical activity Fig. 2Substitution regression model for the effect of replacing SB with various ratios of LPA and MVPA on the five-times-sit-to-stand test (0–60 min). The values indicate the parameter estimate and $95\%$ CI adjusted for patient sociodemographic characteristics (age, sex, education, living status), health status (BMI, hyperlipidemia, hypertension, diabetes, alcohol, smoking and nutritional status.), and average monitored wear timea Log-transformed SB: sedentary behavior; MVPA: moderate-to-vigorous physical activity; LPA: light physical activity
## Discussion
To our knowledge, this is the first study to demonstrate that substituting SB with 60-min LPA per day among the elderly has a positive relationship with their HGS and performance in the TUG and GS tests. This indicates that substituting 60 min of SB with LPA among older adults is mathematically associated with better muscle function in 4 limbs. Moreover, we also observed that substituting 60 min of SB with MVPA is mathematically associated with a better performance of lower limb functions (e.g., GS and 5XSST). On substituting 60 min of SB with mixed type of physical activity with LPA and MVPA, the results showed an overall positive relationship with GS.
In the past, few attempts of isotemporal substitution of SB in older adults with LPA have been reported, and they were unable to conclusively link LPA with the improvement of muscle strength and function in older adults. In our study, however, we found that replacing SB with LPA improved HGS, TUG test performance, and GS. In terms of HGS, while previous systematic reviews and meta-analyses suggest that LPA and HGS have a positive relationship, including four studies representing 3,215 individuals [2], our results indicate that a daily 60-min substitution of SB with LPA is associated with better HGS among older adults.
In addition to HGS, the function of the lower limbs was also positively correlated with the 60-min LPA substitution, as measured by the TUG and GS tests, which may imply that a lower intensity of walking has a beneficial impact on the lower limbs [2]. Previous studies on isotemporal substitution analysis to investigate the relationship between SB, physical activity, and lower limb strength and function only showed that substitution with MVPA, and not LPA, was positively correlated with improvement of lower limb function [16, 18]. In our study, we observed positive effects on the lower limb function on substituting 60-min SB with not only 60-min MVPA but also with 60-min LPA (GS test, Table 2). Specifically, our results indicate that replacing SB with LPA would suffice for improvement of lower limb function mathematically. Older age in this study (81.9 years vs. 70.7 and 74.4 years) may account for the observed differences since adults with advanced age were less like to be engaged in MVPA. [ 16, 18]. Previous studies have highlighted the importance of interrupting SB to prevent a decline in physical function over a 12-month follow-up [26] or in a large cohort [27]; these studies have also shown that sit-to-stand transitions may be adequate in improving lower extremity strength. Our findings may further inform the importance of replacing SB with just 1 h of LPA in the context of hospitals and relatively older adults (80 + years) and may provide greater motivation to aged individuals to undertake LPA to improve lower limb performance.
Interestingly, when SB was replaced with MVPA instead of LPA, a similar association with HGS was not observed (Table 2). We reasoned that as MVPA may be heavily reliant on lower-limb-related physical activity for its higher intensity, such as in dancing, bicycling, or running [28], the intensity of LPA may be associated with daily living activities, such as household chores, with lower intensity of walking behavior in older adults, leading to better HGS [29]. Additionally, WHO guidelines recommend an optimal MVPA duration 150 to 300 min/week for older adults [6]. Our clinically recruited participants, who were mostly aged ≥ 75 years, may have found it difficult to attain the suggested allocation. Regardless of reports suggesting that engaging in MVPA increases the risk of injury among older persons, it is our belief that increasing LPA is a reasonable and attainable strategy for improving upper limb strength in older individuals.
Our results indicate that reallocating 60 min of SB toward a combination of LPA and MVPA in different proportions tended to mathematically improve both strength and function of the lower limbs in older adults (Figs. 1 and 2). With respect to muscle function (Fig. 1), we found that replacing 60 min of SB with a combination of LPA and MVPA and increasing the proportion of MVPA by increments of 5 min was significantly associated with the GS, which is consistent with a previous report [16]. However, the lower limb strength as measured by 5XSST (Fig. 2) only showed mathematical improvement when the 60-min SB was reallocated to at least 30 min, rather than 10 min, of MVPA, as has been previously described by Lerma et al. [ 16]. This is likely due to the significant difference in the mean age in this study (81.9 years) and that of Lerma et al. ( 70.7) [16]; our cohort was older and would have needed to exert more effort to build muscles.
We and other researchers have shown that substituting SB with MVPA mathematically improves lower limb strength and function among aged persons. They may remain reluctant to perform higher intensity physical activity as suggested [30, 31], considering that in an aging society, older adults are often afflicted with comorbidities or functional decline [32]. Importantly, our findings on substituting SB with LPA, which was positively associated with upper limb strength and lower limb function, may pave the way for designing a feasible regimen of combined LPA and MVPA in varying proportions for older adults.
To the best of our knowledge, only one study has used isotemporal substitution analysis to investigate the association between substituting SB with a combination of LPA and MVPA and lower limb function [16]. Notably, this is the second such study; however, it has a larger sample size and was performed among medically enrolled older adults. Nonetheless, our study had some limitations. First, owing to the cross-sectional design of our study, we could not interpret the causality between substitutions of physical activity and function of the tested subjects. For instance, the results of the mixed redistribution–substitution model may reflect the findings of the isotemporal substitution model, which showed a positive relationship between MVPA and lower limb strength. Second, the small sample of clinically recruited participants was not representative of the community-dwelling population as a whole. Further studies with a longitudinal design and representative sample size are therefore necessary. Third, there are some drawbacks of using the hip-worn Actigraph GT3X + to measure physical activity among older people [33]. For example, in case of popular activities such as swimming, the device was not wear. Moreover, the gadget may overestimate or underestimate the SB status owing to failure in measuring body posture, which leads to the inability in detecting the physical activity of standing, lying down, and taking an afternoon nap. Finally, the cutoff point to differentiate LPA from MVPA is arbitrary. As the intensity of physical activity occurs on a continuum, it is important to carefully interpret the upper limit of what is considered LPA and the lower limit of what is considered MVPA.
## Conclusion
It is certain that reducing SB from the activities of daily living of older individuals is the key to preserving limb strength and function. We demonstrates substituting 60 min of SB with LPA boosted upper limb strength and lower limb function in older adults. In addition, replacing 60-min SB with a combination of LPA and MVPA everyday enhanced both limb strength and function of the older individuals. LPA may be more appealing to the elderly population, and our study suggests suitable alternatives to promote the physical health of older adults based on the intensity and feasibility of physical activity.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1. Appendix 1. Single Models and Partition Models Examining the Associations of SB, LPA and MVPA on Physical Function Test ($$n = 141$$)
## References
1. Vincent HK, Vincent KR, Lamb KM. **Obesity and mobility disability in the older adult**. *Obes reviews: official J Int Association Study Obes* (2010.0) **11** 568-79. DOI: 10.1111/j.1467-789X.2009.00703.x
2. Ramsey KA, Rojer AGM, D’Andrea L, Otten RHJ, Heymans MW, Trappenburg MC. **The association of objectively measured physical activity and sedentary behavior with skeletal muscle strength and muscle power in older adults: a systematic review and meta-analysis**. *Ageing Res Rev* (2021.0) **67** 101266. DOI: 10.1016/j.arr.2021.101266
3. Diaz KM, Howard VJ, Hutto B, Colabianchi N, Vena JE, Blair SN. **Patterns of sedentary behavior in US Middle-Age and older adults: the REGARDS Study**. *Med Sci Sports Exerc* (2016.0) **48** 430-8. DOI: 10.1249/MSS.0000000000000792
4. Ferrari GLdM, Oliveira Werneck A, Rodrigues da Silva D, Kovalskys I, Gómez G, Rigotti A. **Socio-Demographic correlates of total and domain-specific sedentary behavior in Latin America: a Population-Based study**. *Int J Environ Res Public Health* (2020.0) **17** 5587. DOI: 10.3390/ijerph17155587
5. Dohrn IM, Kwak L, Oja P, Sjöström M, Hagströmer M. **Replacing sedentary time with physical activity: a 15-year follow-up of mortality in a national cohort**. *Clin Epidemiol* (2018.0) **10** 179-86. DOI: 10.2147/CLEP.S151613
6. Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G. **World Health Organization 2020 guidelines on physical activity and sedentary behaviour**. *Br J Sports Med* (2020.0) **54** 1451-62. DOI: 10.1136/bjsports-2020-102955
7. Amagasa S, Machida M, Fukushima N, Kikuchi H, Takamiya T, Odagiri Y. **Is objectively measured light-intensity physical activity associated with health outcomes after adjustment for moderate-to-vigorous physical activity in adults? A systematic review**. *Int J Behav Nutr Phys Act* (2018.0) **15** 65. DOI: 10.1186/s12966-018-0695-z
8. Ku PW, Hamer M, Liao Y, Hsueh MC, Chen LJ. **Device-measured light-intensity physical activity and mortality: a meta-analysis**. *Scand J Med Sci Sports* (2020.0) **30** 13-24. DOI: 10.1111/sms.13557
9. Yuki A, Otsuka R, Tange C, Nishita Y, Tomida M, Ando F. **Daily physical activity predicts Frailty Development among Community-Dwelling older japanese adults**. *J Am Med Dir Assoc* (2019.0) **20** 1032-6. DOI: 10.1016/j.jamda.2019.01.001
10. Amagasa S, Fukushima N, Kikuchi H, Takamiya T, Oka K, Inoue S. **Light and sporadic physical activity overlooked by current guidelines makes older women more active than older men**. *Int J Behav Nutr Phys Act* (2017.0) **14** 59. DOI: 10.1186/s12966-017-0519-6
11. Osuka Y, Yabushita N, Kim M, Seino S, Nemoto M, Jung S. **Association between habitual light-intensity physical activity and lower-extremity performance: a cross-sectional study of community-dwelling older japanese adults**. *Geriatr Gerontol Int* (2015.0) **15** 268-75. DOI: 10.1111/ggi.12268
12. Jefferis BJ, Parsons TJ, Sartini C, Ash S, Lennon LT, Wannamethee SG. **Does duration of physical activity bouts matter for adiposity and metabolic syndrome? A cross-sectional study of older british men**. *Int J Behav Nutr Phys Activity* (2016.0) **13** 36. DOI: 10.1186/s12966-016-0361-2
13. Hill K, Gardiner PA, Cavalheri V, Jenkins SC, Healy GN. **Physical activity and sedentary behaviour: applying lessons to chronic obstructive pulmonary disease**. *Intern Med J* (2015.0) **45** 474-82. DOI: 10.1111/imj.12570
14. 14.Mekary RA, Ding EL. Isotemporal Substitution as the Gold Standard Model for Physical Activity Epidemiology: Why It Is the Most Appropriate for Activity Time Research. 2019;16(5):797.
15. Biddle GJH, Henson J, Biddle SJH, Davies MJ, Khunti K, Rowlands AV. **Modelling the reallocation of Time spent sitting into physical activity: Isotemporal Substitution vs. compositional Isotemporal Substitution**. *Int J Environ Res Public Health* (2021.0) **18** 6210. DOI: 10.3390/ijerph18126210
16. Lerma NL, Cho CC, Swartz AM, Miller NE, Keenan KG, Strath SJ. **Isotemporal Substitution of Sedentary Behavior and physical activity on function**. *Med Sci Sports Exerc* (2018.0) **50** 792-800. DOI: 10.1249/MSS.0000000000001491
17. Sánchez-Sánchez JL, Mañas A, García-García FJ, Ara I, Carnicero JA, Walter S. **Sedentary behaviour, physical activity, and sarcopenia among older adults in the TSHA: isotemporal substitution model**. *J Cachexia Sarcopenia Muscle* (2019.0) **10** 188-98. DOI: 10.1002/jcsm.12369
18. Yasunaga A, Shibata A, Ishii K, Koohsari MJ, Inoue S, Sugiyama T. **Associations of sedentary behavior and physical activity with older adults’ physical function: an isotemporal substitution approach**. *BMC Geriatr* (2017.0) **17** 280. DOI: 10.1186/s12877-017-0675-1
19. Hsiao C, Wen CJ, Yen HY, Hsueh MC, Liao Y. **Association between accelerometer-measured light-intensity physical activity and cognitive function in older adults**. *J Nutr Health Aging* (2022.0) **26** 230-5. DOI: 10.1007/s12603-022-1749-0
20. Herman T, Giladi N, Hausdorff JM. **Properties of the ‘timed up and go’ test: more than meets the eye**. *Gerontology* (2011.0) **57** 203-10. DOI: 10.1159/000314963
21. 21.de Abreu DCC, Porto JM, Tofani PS, Braghin RMB, Freire Junior RC. Prediction of reduced gait speed using 5-Time sit-to-stand test in healthy older adults. Journal of the American Medical Directors Association; 2021.
22. 22.Shih T-Y. Medical Cloud Service Innovation-The Case of Netown Company. 2020.
23. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. **Physical activity in the United States measured by accelerometer**. *Med Sci Sports Exerc* (2008.0) **40** 181-8. DOI: 10.1249/mss.0b013e31815a51b3
24. 24.Migueles JH, Cadenas-Sanchez C, Ekelund U, Delisle Nyström C, Mora-Gonzalez J, Löf M et al. Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations. Sports medicine (Auckland, NZ). 2017;47(9):1821-45.
25. Guigoz Y, Vellas B, Garry PJ. **Assessing the nutritional status of the elderly: the Mini Nutritional Assessment as part of the geriatric evaluation**. *Nutr Rev* (1996.0) **54** 59-65
26. Slaughter SE, Wagg AS, Jones CA, Schopflocher D, Ickert C, Bampton E. **Mobility of vulnerable Elders study: effect of the sit-to-stand activity on mobility, function, and quality of life**. *J Am Med Dir Assoc* (2015.0) **16** 138-43. DOI: 10.1016/j.jamda.2014.07.020
27. Wilson JJ, McMullan I, Blackburn NE, Skjødt M, Caserotti P, Giné-Garriga M. **Associations of sedentary behavior bouts with community-dwelling older adults’**. *Phys function* (2021.0) **31** 153-62
28. Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett DR, Tudor-Locke C. **2011 Compendium of Physical Activities: a second update of codes and MET values**. *Med Sci Sports Exerc* (2011.0) **43** 1575-81. DOI: 10.1249/MSS.0b013e31821ece12
29. 29.Farren L, Belza B, Allen P, Brolliar S, Brown DR, Cormier ML et al. Peer Reviewed: Mall Walking Program Environments, Features, and Participants: A Scoping Review. 2015;12.
30. Kim HR, Lee H, Seong Y, Lee E, Jung H-W, Park YR. **Longitudinal trajectory of disability in community-dwelling older adults: an observational cohort study in South Korea**. *BMC Geriatr* (2020.0) **20** 430. DOI: 10.1186/s12877-020-01834-y
31. Krell-Roesch J, Syrjanen JA, Bezold J, Trautwein S, Barisch-Fritz B, Boes K. **Physical activity and trajectory of cognitive change in older persons: Mayo Clinic Study of Aging**. *J Alzheimers Dis* (2021.0) **79** 377-88. DOI: 10.3233/JAD-200959
32. Chen JL, Chuang SC, Chang HY, Chiang CC. **Muscle strength and balance to the Elderly from the perspective of the WHO 2020 guidelines on physical activity**. *J Sports Perform* (2022.0) **9** 79-92
33. Strain T, Fitzsimons C, Kelly P, Mutrie N. **The forgotten guidelines: cross-sectional analysis of participation in muscle strengthening and balance & co-ordination activities by adults and older adults in Scotland**. *BMC Public Health* (2016.0) **16** 1108. DOI: 10.1186/s12889-016-3774-6
|
---
title: 'Diagnostic accuracy of circular RNA for diabetes Mellitus: a systematic review
and diagnostic Meta-analysis'
authors:
- Hojat Dehghanbanadaki
- Pooria Asili
- Abdolkarim Haji Ghadery
- Maryam Mirahmad
- Ali Zare Dehnavi
- Amirhossein Parsaei
- Hamid Reza Baradaran
- Mobin Azami
- Gustavo Jose Justo da Silva
- Reza Parvan
- Yousef Moradi
journal: BMC Medical Genomics
year: 2023
pmcid: PMC9993609
doi: 10.1186/s12920-023-01476-0
license: CC BY 4.0
---
# Diagnostic accuracy of circular RNA for diabetes Mellitus: a systematic review and diagnostic Meta-analysis
## Abstract
### Background
This study aimed to investigate the pooled diagnostic ability of circular RNA (circRNA) molecules for diabetes mellitus.
### Methods
We searched PubMed, Scopus, and Web of Science for relevant studies. A total of 2070 participants, including 775 diabetic patients and 1295 healthy individuals, from five studies were included in this meta-analysis. True positive, true negative, false positive, and false negative data were extracted to calculate pooled sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and area under the receiver operating characteristics curve. The Deeks’ funnel plot was applied for publication bias assessment, Cochran’s Q test and I2 index were applied for inter-study heterogeneity assessment. Besides, a subgroup analysis was performed for determining the source of heterogeneity between studies. P value < 0.05 was considered significance. All analysis were done by STATA version 14.
### Results
CircRNA presented a sensitivity of $76\%$ ($95\%$ confidence interval [$95\%$CI]: 66-$84\%$), specificity of $77\%$ ($95\%$CI: 58-$89\%$), positive LR of 3.25 ($95\%$CI: 1.69–6.23), negative LR of 0.31 ($95\%$CI: 0.21–0.46), DOR of 10.41 ($95\%$CI: 4.26–25.41), and AUC of 0.82 ($95\%$CI: 0.79–0.85) for diabetes mellitus detection. More specifically, hsa_circ_0054633 showed a sensitivity of $67\%$ ($95\%$CI: 53-$81\%$) and a specificity of $82\%$ ($95\%$CI: 63-$100\%$).
### Conclusion
CircRNAs show highly accurate diagnostic capability for type 2 diabetes mellitus and gestational diabetes mellitus. High sensitivity of circRNAs introduces them as potential noninvasive biomarkers for early diagnosis of diabetes mellitus and their high specificity introduces them as potential therapeutic targets by regulation of their expression.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12920-023-01476-0.
## Introduction
Diabetes mellitus (DM) is a chronic and progressive condition defined by hyperglycemia caused by abnormalities in insulin production, insulin receptor sensitivity, or both [1]. Due to its long-term consequences, diabetes is now one of the leading causes of death in the world [2]. The International Expert Committee indicated that evaluating glycated hemoglobin (HbA1c) is a valid approach for diabetes diagnosis [3]. The World Health Organization (WHO) and the American Diabetes Federation stated that fasting blood glucose (FBG) and oral glucose tolerance test (OGTT) are both gold standards for diabetes diagnosis [4, 5]. Mechanistically, different molecular pathways and genetic factors are recognized to be related to some pathological conditions that cause hyperglycemia [6]. Several studies have documented that utilizing circRNAs can be effective in understanding the pathogenesis of various DM-related complications including diabetic retinopathy (DR), diabetic nephropathy (DN), and diabetic cardiomyopathy (DC) [7–9].Therefore, an accurate diagnosis of these associated molecules or genetic factors, along with the appropriate use of blood glucose tests and other potential factors influencing the disease progress, may lead to the development of comprehensive approaches for the early diagnosis of diabetes, and a guided treatment of DM and its complications [10, 11].
More recently, a great progress has been made towards the identification and employment of novel biomarkers to aid in the early detection of the disease, exploration of medical response, and the assessment of treatment benefits in a variety of diseases, including diabetes [12–14]. Various biomarkers including blood sugar, proteins, and particular nucleic acids can be employed as potential disease predictors [15]. Circular RNAs (circRNAs) are a new type of non-coding RNA molecules with a covalently closed loop that loses both polarity and a polyadenylated tail [16]. Circular RNAs are classified into three types: exonic circular RNA, intronic circular RNA, and intergenic circular RNA [17]. Exonic circular RNA is the most frequent type of circular RNA and usually is termed circRNA. The steadiness, abundancy, and evolutionary preservation of circRNAs through the species suggest that they could play a significant regulatory function [18]. Therefore, recent studies suggest that circRNAs may act as miRNA sponges because of the competitive endogenous RNA (ceRNA) system [19]. The remarkable biological stability of circRNAs makes it ideal for usage as a disease biomarker. The detection of circRNA in the blood stream has been associated with the control of insulin production as well as the development of diabetes [20]. The changing production of circRNA in the blood of type 2 diabetes patients has been confirmed, and some circRNAs such as hsa_circ_0054633 were recommended as possible diabetes diagnostic biomarkers [21]. Even though several studies have reported diagnostic value of circRNAs [22–26], the efficacy of circRNAs in the early detection of diabetes remains uncertain due to the inconsistency or heterogeneity in the reported performances of these studies. Thus, we conducted a comprehensive systematic review and meta-analysis of currently available data to investigate the accuracy of circRNAs in diabetes mellitus diagnosis.
## Search strategy
We conducted a systematic review and meta-analysis in accordance with the Preferred Reporting Items for Systematic and Meta-analyses (PRISMA) statements, and registered its protocol in the International Prospective Register of Systematic Reviews (PROSPERO, CRD42022295996). We searched PubMed, Scopus, and Web of Science databases to retrieve all relevant studies investigating the diagnostic accuracy of circRNA for diabetes mellitus. All mentioned databases were searched without language restrictions to identify eligible studies based on the following main study keywords including “Diabetes”, “Circular RNA”, “Diagnostic Values”, and their synonyms. Gray Literature was then searched to access unpublished articles and dissertations or international reports. In addition, after the final selection of articles, a manual search was performed by reviewing the references of related articles. The search strategy in international databases was independently conducted by two researchers (HDB and PA) and the disputes were resolved by a third person (YM).
## Study selection
Eligible studies were independently selected by two reviewers (AH and PA) based on the following inclusion and exclusion criteria.
## Inclusion criteria
All cohort studies, with no language restrictions, which have investigated the diagnostic accuracy of circRNA for diabetes mellitus were included. The population of interest was patients with diabetes mellitus including type 1 diabetes mellitus, type 2 diabetes mellitus, and gestational diabetes mellitus. The index test of interest was circRNAs. The reference test of interest was the gold standard laboratory tests for diabetes mellitus based on the American Diabetes Association (ADA) standards (FPG ≥ 126 mg/dL [7.0 mmol/L], 2-hour plasma glucose ≥ 200 mg/dL [11.1 mmol/L], HA1C ≥ $6.5\%$, and in a patient with classic symptoms of hyperglycemia or hyperglycemic crisis, a random plasma glucose ≥ 200 mg/dL [11.1 mmol/L]) [27]. The target condition of interest was diagnostic performance of test including sensitivity and specificity.
## Exclusion criteria
Case reports, case series, reviews, and meta-analyses were excluded. Studies in which false positive (FP), false negative (FN), true positive (TP), and true negative (TN) values of test were not reported or could not be calculated due to no data of sensitivity, specificity, the number of diabetic patients, and the number of healthy controls.
## Data extraction and quality assessment
Data extraction was independently performed by two reviewers (AH and PA). The following items were recorded: author, circRNA name, year, country, number of study center, study design, period of enrollment, reference standard for diabetes detection, number of patients, type of diabetes, sample source of circRNA, method of circRNA detection, reference gene, cutoff value regarding sensitivity and specificity Besides, two reviewers (AH and PA) independently evaluated the quality of studies according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool [28]. This tool investigated the risk of bias and concerns regarding the applicability of the included studies. Any discrepancies were resolved by a third investigator (HD).
## Statistical analysis
All statistical analyses were performed in STATA (version 14.1). First, TP, TN, FP, and FN values were extracted from the studies or calculated based on sensitivity, specificity, number of diabetic patients, and number of controls. For preservation of two-dimensional nature of original data, we took the advantage of bivariate random-effects model analysis (BRMA) following by hierarchical summary receiver operating characteristic (HSROC) model. Then, the MIDAS module was applied for determining the pooled diagnostic ability of circRNA including sensitivity, specificity, positive and negative likelihood ratios (LRs), diagnostic odds ratio (DOR), area under the receiver operating characteristics curve (AUC). The Deeks’ funnel plot was applied for publication bias assessment [29], and Cochran’s Q test and I2 index were applied for inter-study heterogeneity assessment. Besides, a subgroup analysis was performed for determining the source of heterogeneity between studies. Additionally, we sub-grouped the studies based on the circRNA (hsa_circ_0054633 vs. other circRNAs), study design (prospective vs. retrospective), number of participants (≥ 100 vs. < 100), publication year (≥ 2020 vs. < 2020), type of diabetes mellitus (Type 2 diabetes vs. gestational diabetes), the reference gene (beta actin vs. GAPDH). P value < 0.05 was considered significance.
## Literature search
The initial search yielded 632 results. After the removal of duplications, 327 articles remained and were screened based on their title and abstract. 289 articles were excluded because of irrelevancy, and 38 articles were screened with full-text. Finally, 5 studies [22–26] met all eligibility criteria and were considered for meta-analysis. Figure 1 shows the PRISMA flowchart of the literature search.
Fig. 1PRISMA chart of the included studies
## General features of the included studies
Table 1 shows the baseline characteristics of the included studies. A total of 1284 participants (382 diabetic patients and 902 healthy individuals) were included in this meta-analysis. Study participants were enrolled from April 2015 to October 2019. Out of 5 studies, three [22, 24, 26] were prospective cohorts and two [23, 25] were retrospective cohorts. Type 2 diabetes patients were investigated in three of the studies [22, 25, 26], and gestational diabetes in the other two studies [23, 24]. Of note, based on our search strategy, we did not find any study on type 1 diabetes with use of circRNAs. Besides, hsa_circRNA_0054633 was investigated in three studies [22, 23, 25]. Additional file 1 shows the quality of included studies. All included studies had a low risk of bias regarding flow and timing, reference standard, and patient selection as well as low concerns of applicability regarding reference standard and patient selection. However, there were moderate concerns of applicability regarding the index test.
Table 1Baseline characteristics of included studiesAuthor, yearcountryStudy designNo. of centersPeriod of enrollmentSample size(DM / non-DM)Type of diabetesSample sourceDetection methodReferenceGenecircRNA NameZhao Z. et al. 2016 [22], ChinaProspective cohort,Single-centerJuly 2015 -June $\frac{201664}{60}$T2DMwhole bloodqRT PCR (SYBR)NRhsa_circ_0054633hsa_circ_0068087Wu H. et al. 2019 [23], ChinaRetrospective Cohort,Single-centerJuly 2017 -February $\frac{201865}{65}$GDMMaternal serum in the second and third trimester, umbilical cord bloodqRT PCR (SYBR)Beta actinhsa_circ_0054633hsa_circ_063981hsa_circ_102682hsa_circ_103410Yang H et al. 2020 [24], ChinaProspective cohort,Single-centerApril 2015 -July 2016106/ 630GDMPlasmaGoTaq qPCR Master Mix (Promega) on a ViiA 7 Real-time PCR System (Applied Biosystems)NRhsa_circ_102893Liang H. et al. 2021 [25], ChinaRetrospective Cohort,Single-centerJanuary 2018 -March $\frac{201944}{44}$T2DMSerumqRT PCR (SYBR)NRhsa_circ_0054633Yingying Z. et al. 2021 [26], ChinaProspective cohort,Single-centerJanuary 2019 -October $\frac{2019103}{103}$T2DMwhole bloodBoth the Prime Script™ RT reagent Kit with gDNA Eraser and Q-PCR kits were equipped with Takara Bio Inc, JapanGAPDHhsa_circ_0071106GDM: gestational diabetes mellitus, T2DM: type 2 diabetes mellitus, DM: diabetes mellitus, NR: Not Reported
## Diagnostic accuracy of circRNA for diabetes mellitus
The pooled analyses on sensitivity and specificity of circRNA are depicted in Fig. 2a and b, respectively. CircRNAs had pooled sensitivity of $76\%$ ($95\%$ CI: 66–$84\%$; Cochran’s Q: 105.42; I2 index: $86.72\%$) and pooled specificity of $77\%$ ($95\%$ CI: 58–$89\%$; Cochran’s Q: 391.08; I2 index: $96.42\%$) for diagnosis of diabetes mellitus. The ROC analysis (Fig. 3a) shows high accuracy (AUC = 0.82; $95\%$ CI: 0.79–0.85) for circRNA as a factor for diagnosis of diabetes mellitus. The pooled positive LR was estimated as 3.25 ($95\%$ CI: 1.69–6.23; Cochran’s Q: 158.06; I2 index = $87.80\%$), the pooled negative LR was estimated as 0.31 ($95\%$ CI: 0.21–0.46; Cochran’s Q: 85.34; I2 index = $83.60\%$; Additional file 2a and 2b, respectively), the pooled diagnostic score was estimated as 2.34 ($95\%$ CI: 1.45–3.24; Cochran’s Q: 55.47; I2 index = $74.76\%$), and the pooled DOR was estimated as 10.41 ($95\%$ CI: 4.26–25.41; Cochran’s Q: 2.2e + 9; I2 index = $100\%$; Additional file 3a and 3b, respectively).
Fig. 2The pooled sensitivity (a) and pooled specificity (b) of circRNA for diabetes mellitus detection Fig. 3(a) The receiver operating characteristic (ROC) curve of circRNA for diabetes mellitus detection, (b) The Deek’s funnel-plot asymmetry test of circRNA for diabetes mellitus detection. The horizontal axis represents the diagnostic odds ratio (DOR) as an indicator of the diagnostic accuracy, and the vertical axis represents the inverse of the square root of the effective sample size (1/root (ESS)). This figure shows symmetrical effect size measures (DOR) over different sample sizes, indicating no presence of publication bias
## Publication bias assessment
Figure 3b shows the Deeks’ funnel plot asymmetry test, assessing the presence of publication bias in this meta-analysis. The X-axis represents DOR as an indicator of the diagnostic accuracy, and the Y-axis represents the inverse of the square root of the effective sample size (1/root (ESS)). Through a qualitative assessment of publication bias, we found a symmetrical effect size measures (DOR) over different sample sizes (1/root (ESS)), indicating publication bias is unlikely. Besides, the quantitative assessment of publication bias through Egger’s regression line displays a p-value of 0.07, confirming the absence of publication bias.
## Subgroup analysis
The subgroup analysis showed that type of circRNA was the source of heterogeneity for sensitivity between studies. For instance, hsa_circRNA_0054633 had a sensitivity of $67\%$ ($95\%$ CI: 53–$81\%$) while other types of circRNA had a sensitivity of $82\%$ ($95\%$ CI: 73–$91\%$; p value = 0.01). Besides, we found that study design, sample size of studies, publication year, type of diabetes mellitus, reference gene did not influence the sensitivity and specificity of circRNA for diabetes mellitus detection. The meta-regression and subgroup results are shown in Table 2.
Table 2Subgroup analysis of the diagnostic performance of circRNA for diabetes diagnosisSubgroupsCovariatesNo. of studiesNo. of reports on circRNA accuracyPooled sensitivity [$95\%$ CI]P valuePooled specificity [$95\%$ CI]P value Type of circRNA hsa_circ_0054633370.67 [0.53–0.81]0.010.82 [0.63–1.00]0.65Other types480.82 [0.73–0.91]0.71 [0.47–0.95] Study design Retrospective290.76 [0.64–0.88]0.250.73 [0.51–0.95]0.70Prospective360.76 [0.63–0.90]0.81 [0.60–1.00] Sample size ≥ 100470.74 [0.61–0.87]0.110.81 [0.62–1.00]0.66< 100380.78 [0.67–0.90]0.71 [0.47–0.96] Year of publication ≥ 2020340.77 [0.61–0.92]0.350.91 [0.79–1.00]0.11< 20202110.76 [0.65–0.86]0.68 [0.47–0.88] Type of diabetes GDM290.77 [0.66–0.89]0.390.80 [0.61–0.98]0.73T2DM360.74 [0.60–0.89]0.71 [0.43–0.99] Reference *Gene beta* Actin180.76 [0.62–0.89]0.810.66 [0.53–0.80]0.62GAPDH120.74 [0.49–1.00]0.53 [0.25–0.82]GDM: gestational diabetes mellitus, T2DM: type 2 diabetes mellitus
## Discussion
To the best of our knowledge, this is the first meta-analysis that is particularly directed to evaluate the diagnostic value of circRNAs for diabetes mellitus. We have screened a total of five cohort studies and included 1284 participants including 382 diabetic patients and 902 healthy individuals in this systematic review and meta-analysis. The quality of the five included studies was relatively high with no significant publication bias. The overall performance based on the pooled results demonstrated pooled sensitivity of $76\%$, pooled specificity of $77\%$, and AUC of 0.82. Based on subgroup analyses, the circRNA54633 was less sensitive than other types of circRNAs (has_circ_0068087, 0054633, 063981, 102,682, 103,410, 102,893, 0054633, 0071106). The pooled positive LR of 3 indicates that diabetic individuals had a 3-fold greater possibility of dysregulated expression of circRNAs than healthy controls. Similarly, a pooled negative LR of 0.31 suggests that individuals with normal expression of circRNAs have a $31\%$ chance of having diabetes. The pooled diagnostic statistics showed moderate diagnostic tool accuracy suggesting that circRNAs have enough statistical power to distinguish diabetic patients from non-diabetic individuals regardless of the type of diabetes and the target gene.
Prior studies suggested the role of circRNAs in the pathogenesis of diabetes mellitus [30]. Thousands of circRNAs are expressed in human pancreatic islets [31]. CircRNAs (CiRS-7 and circHIPK3) were shown to enhance β-cell function and improve insulin granule secretion by microRNA sponging activities [20, 31]. Evidence from included studies revealed different expression levels of distinct circRNAs were detected in serum, plasma, or umbilical cord blood of patients with type 2 diabetes mellitus and pregnant women with gestational diabetes compared to healthy controls [22–26]. For instance, the expression levels of hsa_circRNA_0054633 [23], hsa_circ_0054633 [25], hsa_circ_0071106, hsa_circ_0071271, and hsa_circ_0000284 [26] were found increased in the circulation in diabetic patients. While the hsa_circRNA_102893 [24] was downregulated in women with gestational diabetes. Bioinformatics analysis showed that downregulation of hsa_circRNA_102893 induces upregulation of hsa- miR-197-3p [32], and the level of hsa-miR-197-3p was associated with impaired β-cell function and insulin resistance in diabetes mellitus [33].
Nevertheless, these findings should be interpreted with caution since all the included studies have been exclusively performed in China and this may result in population selection bias. More investigation on greater geographical and ethnic diversity among participants is highly recommended. Additionally, there were some restrictions in the “index test” due to not specified cut-off points. More precisely, the study by Wu H et al. [ 23] did not report the cut-off points at which sensitivity and specificity were calculated for hsa_circ_0054633, hsa_circ_063981, hsa_circ_102682, and hsa_circ_103410 in detecting diabetes mellitus. Other limitations include the limited number of enrolled studies, the small sample size of some of the studies, and considerable heterogeneity between studies. Likewise, some confounding information including age, gender proportion, and physical health of participants was present. Owing to the restrictions of this meta-analysis, high-quality multi-center prospective studies with larger sample sizes are warranted to further validate the clinical application of circRNAs as a diagnostic biomarker of diabetes mellitus.
Despite its limitations, this work offers a comprehensive analysis of a panel of circRNAs as a diagnostic tool for the early detection of diabetes mellitus. Future well-designed research using a combination of circRNAs, or in combination with microRNAs and other clinical diagnostic biomarkers would improve the accuracy of the test as a biomarker of prognosis, diagnosis, or treatment of diabetes mellitus.
## Conclusion
CircRNAs are identified as highly accurate diagnostic biomarkers for type 2 diabetic mellitus and gestational diabetes mellitus. The high sensitivity of these circRNAs introduces them as good biomarkers for early detection of diabetes mellitus and high specificity of these circRNAs introduces them as good therapeutic targets for the management of diabetes mellitus through the regulation of these circRNAs expression. This study suggests the possibility of the utility of circRNAs as potential noninvasive biomarkers for better understanding of the diabetes mellitus pathogenesis.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1 Supplementary Material 2
## References
1. Banday MZ, Sameer AS, Nissar S. **Pathophysiology of diabetes: an overview**. *Avicenna J Med* (2020.0) **10** 174-88. DOI: 10.4103/ajm.ajm_53_20
2. Dehghanbanadaki H, Aazami H, Ejtahed HS, Sohrabi A, Raftar SKA, Tarashi S. **The global scientific publications on gut microbiota in type 2 diabetes; a bibliometric, scientometric, and descriptive analysis**. *J Diabetes Metabolic Disorders* (2022.0) **21** 13-32. DOI: 10.1007/s40200-021-00920-1
3. **Role of the A1C assay in the diagnosis of diabetes**. *Diabetes Care* (2009.0) **32** 1327-34. DOI: 10.2337/dc09-9033
4. 4.World Health Organization. Use of glycated haemoglobin (HbA1c) in diagnosis of diabetes mellitus: abbreviated report of a WHO consultation. World Health Organization; 2011.
5. **2. Classification and diagnosis of diabetes**. *Diabetes Care* (2017.0) **40** 11-s24. DOI: 10.2337/dc17-S005
6. **Diagnosis and classification of diabetes mellitus**. *Diabetes Care* (2014.0) **37** 81-S90. DOI: 10.2337/dc14-S081
7. 7.Fan W, Pang H, Xie Z, Huang G, Zhou ZJFiE.Circular RNAs in diabetes mellitus and its complications. 2022;13.
8. Crawford TN, Alfaro DV, Kerrison JB. **Jablon EPJCdr**. *Diabet retinopathy angiogenesis* (2009.0) **5** 8-13
9. 9.Xu B, Wang Q, Li W, Xia L, Ge X, Shen L et al. Circular RNA circEIF4G2 aggravates renal fibrosis in diabetic nephropathy by sponging miR-218. 2022;26(6):1799–805.
10. 10.Will new diagnostic criteria for diabetes mellitus change phenotype of patients with diabetes? Reanalysis of European epidemiological data. DECODE Study Group on behalf of the European Diabetes Epidemiology Study GroupBMJ (Clinical research ed)19983177155371510.1136/bmj.317.7155.371. *BMJ (Clinical research ed)* (1998.0) **317** 371-5. DOI: 10.1136/bmj.317.7155.371
11. Wang J. **Electrochemical biosensors: towards point-of-care cancer diagnostics**. *Biosens Bioelectron* (2006.0) **21** 1887-92. DOI: 10.1016/j.bios.2005.10.027
12. Dehghanbanadaki H, Aazami H, Razi F, Nasli-Esfahani E, Norouzi P, Hashemi E. **The global trend of exosome in diabetes research: a bibliometric approach**. *Diabetes & Metabolic Syndrome: Clinical Research & Reviews* (2022.0) **16** 102450. DOI: 10.1016/j.dsx.2022.102450
13. Hosseinkhani S, Aazami H, Hashemi E, Dehghanbanadaki H, Adibi-Motlagh B, Razi F. **The trend in application of omics in type 2 diabetes researches; a bibliometric study**. *Diabetes & Metabolic Syndrome: Clinical Research & Reviews* (2021.0) **15** 102250. DOI: 10.1016/j.dsx.2021.102250
14. Hosseinkhani S, Dehghanbanadaki H, Aazami H, Pasalar P, Asadi M, Razi F. **Association of circulating omega 3, 6 and 9 fatty acids with gestational diabetes mellitus: a systematic review**. *BMC Endocr Disorders* (2021.0) **21** 1-9. DOI: 10.1186/s12902-021-00783-w
15. 15.Marrugo-Ramírez J, Mir M, Samitier J. Blood-Based Cancer Biomarkers in Liquid Biopsy: A Promising Non-Invasive Alternative to Tissue Biopsy. International journal of molecular sciences. 2018;19(10).
16. Chen Z, Zhang L, Han G, Zuo X, Zhang Y, Zhu Q. **A Meta-analysis of the diagnostic accuracy of circular RNAs in Digestive System Malignancy**. *Cell Physiol Biochem* (2018.0) **45** 962-72. DOI: 10.1159/000487291
17. 17.Lasda E, Parker R. Circular RNAs: diversity of form and function. RNA (New York, NY). 2014;20(12):1829-42.
18. Shao T, Pan Y-h. **Xiong X-d. Circular RNA: an important player with multiple facets to regulate its parental gene expression**. *Mol Therapy-Nucleic Acids* (2021.0) **23** 369-76. DOI: 10.1016/j.omtn.2020.11.008
19. Barrett SP, Salzman J. **Circular RNAs: analysis, expression and potential functions**. *Development* (2016.0) **143** 1838-47. DOI: 10.1242/dev.128074
20. Xu H, Guo S, Li W, Yu P. **The circular RNA Cdr1as, via miR-7 and its targets, regulates insulin transcription and secretion in islet cells**. *Sci Rep* (2015.0) **5** 12453. DOI: 10.1038/srep12453
21. Zhang Z, Yang T, Xiao J. **Circular RNAs: promising biomarkers for Human Diseases**. *EBioMedicine* (2018.0) **34** 267-74. DOI: 10.1016/j.ebiom.2018.07.036
22. Zhao Z, Li X, Jian D, Hao P, Rao L, Li M. **Hsa_circ_0054633 in peripheral blood can be used as a diagnostic biomarker of pre-diabetes and type 2 diabetes mellitus**. *Acta Diabetol* (2017.0) **54** 237-45. DOI: 10.1007/s00592-016-0943-0
23. Wu H, Wu S, Zhu Y, Ye M, Shen J, Liu Y. **Hsa_circRNA_0054633 is highly expressed in gestational diabetes mellitus and closely related to glycosylation index**. *Clin epigenetics* (2019.0) **11** 1-13. DOI: 10.1186/s13148-019-0610-8
24. Yang H, Ye W, Chen R, Zeng F, Long Y, Zhang X. **Circulating expression of Hsa_circRNA_102893 contributes to early gestational diabetes mellitus detection**. *Sci Rep* (2020.0) **10** 1-9. DOI: 10.1038/s41598-020-76013-5
25. Liang H, Hou L, Wang Q, Zhou X, Sha L, Xu L. **Serum hsa_circ_0054633 is elevated and correlated with clinical features in type 2 diabetes Mellitus**. *Annals of Clinical & Laboratory Science* (2021.0) **51** 90-6. PMID: 33653785
26. Yingying Z, Yongji Y, Qiuting C, Rifang L, Zhuanping Z. **has_circ_0071106 can be used as a diagnostic marker for type 2 diabetes**. *Int J Med Sci* (2021.0) **18** 2312. DOI: 10.7150/ijms.52575
27. 27.American Diabetes Association. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes—2020. Diabetes care. 2020;43(Supplement 1):S14-S31.
28. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB. **QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies**. *Ann Intern Med* (2011.0) **155** 529-36. DOI: 10.7326/0003-4819-155-8-201110180-00009
29. Deeks JJ, Macaskill P, Irwig L. **The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed**. *J Clin Epidemiol* (2005.0) **58** 882-93. DOI: 10.1016/j.jclinepi.2005.01.016
30. Zaiou M. **circRNAs signature as potential diagnostic and prognostic biomarker for diabetes mellitus and related cardiovascular complications**. *Cells* (2020.0) **9** 659. DOI: 10.3390/cells9030659
31. Stoll L, Sobel J, Rodriguez-Trejo A, Guay C, Lee K, Venø MT. **Circular RNAs as novel regulators of β-cell functions in normal and disease conditions**. *Mol metabolism* (2018.0) **9** 69-83. DOI: 10.1016/j.molmet.2018.01.010
32. Horton JD, Goldstein JL, Brown MS. **SREBPs: activators of the complete program of cholesterol and fatty acid synthesis in the liver**. *J Clin Investig* (2002.0) **109** 1125-31. DOI: 10.1172/JCI0215593
33. Zampetaki A, Kiechl S, Drozdov I, Willeit P, Mayr U, Prokopi M. **Plasma microRNA profiling reveals loss of endothelial miR-126 and other microRNAs in type 2 diabetes**. *Circul Res* (2010.0) **107** 810-7. DOI: 10.1161/CIRCRESAHA.110.226357
|
---
title: 'Impact of vitamin D receptor gene polymorphisms (TaqI and BsmI) on the incidence
and severity of coronary artery disease: a report from southern Iran'
authors:
- Boshra Akhlaghi
- Negar Firouzabadi
- Farzaneh Foroughinia
- Marzieh Nikparvar
- Pouyan Dehghani
journal: BMC Cardiovascular Disorders
year: 2023
pmcid: PMC9993610
doi: 10.1186/s12872-023-03155-5
license: CC BY 4.0
---
# Impact of vitamin D receptor gene polymorphisms (TaqI and BsmI) on the incidence and severity of coronary artery disease: a report from southern Iran
## Abstract
### Background
The association of vitamin D level and vitamin D receptor (VDR) gene polymorphisms with the prevalence of coronary artery disease (CAD) has been evaluated in various studies; however, the reported results were inconsistent. Hence, we aimed to investigate the impact of two VDR gene polymorphisms, TaqI (rs731236) and BsmI (rs1544410), on the incidence and severity of CAD in Iranian population.
### Methods
Blood samples were collected from 118 CAD patients underwent elective percutaneous coronary intervention (PCI) and 52 control subjects. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was performed for genotyping. SYTNAX score (SS) was calculated as a grading tool for complexity of CAD by an interventional cardiologist.
### Results
TaqI polymorphism of VDR was not associated with the incidence of CAD. A significant difference was observed between CAD patients and controls regarding BsmI polymorphism of VDR ($p \leq 0.001$). GA and AA genotypes was significantly associated with a decreased risk of CAD ($$p \leq 0.01$$, p-adjusted = 0.01 and $p \leq 0.001$, p-adjusted = 0.001 respectively). A allele of BsmI polymorphism was shown to have a protective effect against CAD ($p \leq 0.001$, p-adjusted = 0.002). No association was found between TaqI and BsmI polymorphisms of VDR and SS as a measure of CAD severity.
### Conclusion
Association of BsmI genotypes with the incidence of CAD revealed that the genetic variation of VDR might play a role in the pathogenesis of CAD.
## Background
Cardiovascular disorders are one of the most important causes of mortality with the prevalence of about $48\%$ among people over the age of 20 [1]. Death from cardiovascular disease (CVD) in 2015 was estimated to be 17.9 million people, of which 7.3 million were caused by coronary artery disease (CAD) [2]. CAD is the most common CVD identified as one of the leading causes of death worldwide. The rising prevalence of CAD in both developed and developing countries imposes a heavy financial burden on these countries [3]. *In* general, coronary artery occlusion followed by impaired oxygen delivery to the heart muscle leads to angina pain in CAD patients [4, 5]. The major risk factors for CAD include diabetes, high blood pressure, obesity, dyslipidemia, smoking, alcohol consumption, inflammation, diet, and lack of physical activity. Furthermore, vitamin D deficiency can also play a substantial role as a risk factor for CAD [6–8].
Vitamin D is a regulatory hormone that plays an important role in various biological processes including calcium and phosphorus regulation, bone metabolism, and immune and anti-inflammatory responses [9]. Evidence from various studies over the past decade indicates that vitamin D deficiency is associated with an increased risk of CVD, including CAD [10, 11]. Vitamin D exerts its protective cardiovascular effects through various mechanisms. One of these mechanisms is its inhibitory effect on renin biosynthesis, which is involved in the pathogenesis of hypertension. Vitamin D can also have a protective effect against atherosclerosis development [12].
Along the conventional risk factors that predict approximately $50\%$ of the hazards of cardiovascular events [13, 14]; genetics plays the remaining part [15]. To date, different studies have been conducted to recognize numerous genetic variants involved in CAD [16–18]; among which, vitamin D receptor (VDR) gene has been identified as a possible contributor to CVD [19]. So far, a large number of single nucleotide polymorphisms (SNPs) of the VDR have been identified; among which TaqI (rs731236), FokI (rs2228570), BsmI (rs1544410), and ApaI (rs7975232) have been investigated meticulously for their effects on various diseases such as CAD [20].
The SYNTAX score (SS) (synergy between percutaneous coronary intervention with taxus and cardiac surgery) is an angiographic tool to grade the extent and the complexity of lesions in CAD. It helps interventionists to decide the optimal strategy for revascularization. Moreover, SS is a powerful stratification system that provides the possibility for homogenous evaluation of CAD severity [21]. A higher SS demonstrates a more severe coronary involvement and poorer prognosis following coronary intervention [22].
Although there are some papers about the association of VDR gene polymorphisms with CAD, further studies on various ethnic groups needs to investigate this association. This study aimed to investigate the association between two VDR gene polymorphisms (Bsm I and Taq I) and the incidence and severity of CAD in Iranian population, along with studying the association between these SNPs and SS as a powerful tool in stratification of CAD severity for the first time.
## Materials and methods
The study protocol was reviewed and approved by the Ethics Committee of Shiraz University of Medical Sciences (SUMS, Iran) (No: IR.SUMS.REC.1399.1316) and was in accordance with ethical principles of the World Medical Association (Helsinki Declaration). All participants signed the written informed consent prior to participating into the study.
## Subjects
One-hundred and eighteen patients who were admitted to a tertiary cardiac care hospital of SUMS (Ghalb-Al-Zahra hospital) with the diagnosis of CAD were recruited into the study. fifty-two subjects with normal coronary angiography or non-significant CAD (< $50\%$ coronary stenosis) who visited a tertiary care clinic for more CAD evaluation were enrolled in the study as the control group. All of the protocols conformed to the ethical guidelines of the Helsinki Declaration.
Inclusion criteria for the CAD group were as follows: Ages between 18 and 80 years old, confirmed diagnosis of CAD (> $50\%$ luminal stenosis in at least one major coronary artery in angiography) and successful percutaneous coronary intervention (PCI). Exclusion criteria for both groups include severe liver disease, active malignancies, chronic inflammatory disease, history of surgery, or severe trauma in the last month, and administration of immunosuppressive drugs.
All demographic and clinical data were obtained from patients’ histories and medical records. Participants who actively smoked cigarettes were considered as smokers. No documented history of CAD duration before hospitalization was available.
## Coronary angiography
The procedure was performed in a cardiac catheterization laboratory (Cath lab). After local anesthesia, a catheter was guided into the coronary arteries through the femoral or radial artery. Patients received aspirin, clopidogrel, and heparin according to standard protocols prior to coronary angioplasty [23]. All angiographic variables were assessed by an experienced interventional cardiologist who was blinded to other data. SYNTAX score was calculated using the calculator provided by the SYNTAX score website [24]. Patients were divided into two groups based on the SYNTAX score: low risk (SS ≤ 15) and intermediate/ high risk (SS > 15).
## Biological samples and genotyping
Genomic DNAs were extracted from whole blood using DNA extraction kit (Yekta tajhiz, Iran). To identify VDR gene polymorphisms (TaqI and BsmI), polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was performed using specific primers listed in Table 1 [25, 26].
Table 1List of Polymorphisms, primers and digested fragmentsPolymorphismPrimersPCR product (enzyme digestion product)TaqI(Forward) 5’-CAGAGCATGGACAGGGAGCAA-3’(Reverse) 3’-GCAACTCCTCATGGCTGAGGTCTC-5’740 bp (T: 495 bp + 245 bp; C: 290 bp + 245 bp + 205 bp)BsmI(Forward) 5’-CAACCAAGACTACAAGTACGCGTCAGTGA-3’(Reverse) 5’-AACCAGCGGGAAGAGGTCAAGGG-3’825 bp (G: 825 bp; A: 650 bp + 175 bp) PCR protocol for TaqI genotyping was as follows: initial denaturation at 94 °C for 4 min, followed by 35 cycles of denaturation at 94 °C for 50 s, annealing at 66 °C for 50 s, elongation at 72 °C for 50 s, and a final extension at 72 °C for 7 min. PCR conditions for BsmI was similar to TaqI except for the annealing temperature, which was adjusted at 60 °C. The total volume of PCR reaction mixture was 20 µl containing 3 µl nuclease-free water, 1 µl of each reverse and forward primer, 5 µl genomic DNA, and 10 µl PCR Master Mix (Ampliqon).
After amplification, 18 µl nuclease-free water, 2 µl 10x buffer TaqI, and 1 µl TaqI restriction enzyme (Thermo Scientific) were added to 10 µl of PCR product of TaqI and incubated at 65 °C for 2 h. Two µl of 10x buffer R, and 0.5 µl of BsmI restriction enzyme (Thermo Scientific) were added to 10 µl of PCR product of BsmI and incubated at 37 °C for 3 h. Following enzyme digestion, products were electrophoresed on $2.5\%$ agarose gel at 100 V for 35 min and visualized using a UV transilluminator (Figs. 1 and 2).
Fig. 1Agarose gel electrophoresis of the PCR–RFLP products digested with TaqI restriction enzyme. TT wild (495 bp, 245 bp), TC heterozygote (495 bp, 290 bp, 245 bp, 205 bp), CC homozygote (290 bp, 245 bp, 205 bp) Fig. 2Agarose gel electrophoresis of the PCR–RFLP products digested with BsmI restriction enzyme. GG wild (825 bp), GA heterozygote (825 bp, 650 bp, 175 bp), AA homozygote (650 bp, 175 bp)
## Study endpoints
The primary endpoint of this study was investigation the association between Bsm I and Taq I VDR gene polymorphisms and the incidence and severity of CAD. The incidence of Major adverse cardiac Events over a period of 30 days after coronary angioplasty (30-day MACE) was reported as a secondary endpoint. MACE is defined as myocardial infarction, revascularization treatment, and all-cause death.
According to our previous study [26], there was no association between vitamin D levels and VDR polymorphisms. Therefore; this relationship was not investigated as an endpoint of this study.
## Statistical analysis
Data analysis was carried out using SPSS software version 21 (SPSS Inc, Chicago, USA). All continuous variables were reported as mean ± SD and categorical variables were reported as absolute numbers and percentages. Continuous variables were assessed for normal distribution by the Kolmogorov–Smirnov test and were compared using t-test or Mann–Whitney U test as appropriate. Categorical variables were compared with Chi-square or Fisher exact test as appropriate. Variables with p-value less than 0.2 in the univariate analysis were entered in logistic regression analysis model. Crude and adjusted odds ratios (ORs) were reported with $95\%$ confidence intervals ($95\%$CI). Multiple logistic regression analysis was applied to determine the association between the incidence of CAD and VDR gene polymorphisms and to adjust for the cardiovascular risk factors. P-value < 0.05 was considered significant.
## Results
Totally, One-hundred and seventy-one individuals participated in this study. Patient flow diagram is presented in Fig. 3.
Fig. 3Patient flow diagram *Demographic data* and clinical characteristics of controls and CAD patients are described in Table 2. There were significant differences between the two groups in terms of age, BMI, sex, diabetes mellitus, hypertension, dyslipidemia, and smoking (all $p \leq 0.05$). The mean number of arteries with stents implanted was one vessel for $73.6\%$, two vessels for $21.8\%$, and three vessels for $4.5\%$ of patients.
No cases of MACE were found in both groups during 30-day follow up, therefore; evaluation of the impact of VDR gene polymorphisms and patient’s outcomes is not possible.
Table 2The demographic data and clinical characteristics of controls and CAD patients in univariate analysisVariablesControl($$n = 52$$)Patient($$n = 118$$)P-valueAge, years, mean ± SD51.58 ± 10.4857.62 ± 10.540.001BMI, kg/m2, mean ± SD25.20 ± 4.7627.06 ± 3.940.01SexMale, N (%)27 (51.90)80 (67.80)0.04Female, N (%)25 (48.10)38 (32.20)Diabetes Mellitus, N (%)6 (11.50)34 (28.80)0.01Hypertension, N (%)16 (30.80)63 (53.40)0.006Dyslipidemia, N (%)13 [25]48 (40.70)0.04Smoker, N (%)8 (15.40)48 (40.70)0.001Family history of CAD, N (%)21 (40.40)43 (36.40)0.62 Genotypes and allele frequencies of TaqI and BsmI gene polymorphisms of CAD patients and controls are shown in Table 3. No significant differences were observed neither in genotypes nor in allele frequencies of the TaqI gene polymorphism between the two groups, whereas a significant difference was observed between CAD patients and controls in both genotypes and allele frequencies of the BsmI polymorphism ($p \leq 0.001$, OR = 0.14, $95\%$CI = 0.05–0.35 and $p \leq 0.001$, OR = 0.31, $95\%$CI = 0.19–0.50 respectively). BsmI GG genotype and G allele were more frequent in CAD patients while BsmI AA genotype and A allele were frequently higher in the control group.
Table 3Associations between CAD the incidence and VDR gene polymorphisms (TaqI and BsmI)Genotypes and allelesControl($$n = 52$$)Patient($$n = 118$$)P-value TaqI Genotype, N (%)TT28 (53.80)59 [50]TC21 (40.40)45 (38.10)0.47CC3 (5.80)14 (11.90)Allele, N (%)T77 [74]163 (69.10)C27 [26]73 (30.90)0.35 BsmI Genotype, N (%)GG10 (19.20)58 (49.20)GA21 (40.40)43 (36.40)< 0.001AA21 (40.40)17 (14.40)Allele, N (%)G41 (39.40)159 (67.40)A63 (60.60)77 (32.60)< 0.001 *Demographic data* and clinical characteristics of CAD patients based on SYNTAX score are described in Table 4. As demonstrated, no significant differences were observed between the two groups (all $p \leq 0.05$).
Table 4Demographic data and clinical characteristics of CAD patients based on syntax score in univariate analysisVariablesSyntax score ≤ 15($$n = 71$$)Syntax score > 15($$n = 47$$)P-valueAge, years, mean ± SD56.57 ± 10.4359.20 ± 10.610.19BMI, kg/m2, mean ± SD27.14 ± 3.9426.92 ± 3.970.78SexMale, N (%)47 (66.2)33 (70.2)0.64Female, N (%)24 (33.8)14 (29.8)Diabetes Mellitus, N (%)20 (28.2)14 (29.8)0.84Hypertension, N (%)38 (53.5)25 (53.2)0.97Dyslipidaemia, N (%)30 (42.3)41 (57.7)0.66Smoker, N (%)27 (38.0)21 ($44.7\%$)0.47Family history of CAD, N (%)29 (40.8)14 (29.8)0.22 No association was found between SS and TaqI genotypes and alleles. There was a significant association between SS and BsmI alleles ($$p \leq 0.002$$, OR = 2.29, $95\%$CI = 1.36–3.85) but not with genotypes (Table 5). The frequency of G allele was higher in CAD patients with SS ≤ 15, while A allele was more frequent in patients with SS > 15.
Table 5The association between SYNTAX score and VDR gene polymorphisms (TaqI and BsmI) in CAD patientsGenotypes and allelesSyntax score ≤ 15($$n = 71$$)Syntax score > 15($$n = 47$$)P-value TaqI Genotype, N (%)TT32 (45.10)27 (57.40)0.38TC29 (40.80)16 [34]CC10 (14.10)4 (8.50)Allele, N (%)T93 (65.50)70 (74.50)0.14C49 (34.50)24 (25.50) BsmI Genotype, N (%)GG39 (54.90)19 (40.40)0.30GA23 (32.40)20 (42.60)AA9 (12.70)17 (14.40)Allele, N (%)G101 (71.10)58 (51.80)0.002A41 (28.90)54 (48.20) Table 6 represents cardiovascular risk factors and VDR gene polymorphisms associated with the incidence of CAD. BsmI GA and AA genotypes were significantly associated with a decreased risk of CAD with adjustment for conventional risk factors (pa=0.01, OR = 0.30, $95\%$ CI = 0.11–0.81 and pa=0.001, OR = 0.16, $95\%$ CI = 0.05–0.48 respectively). According to the results represented in Table 7, after adjustment for cardiovascular risk factors, A allele demonstrated a protective effect against CAD development (pa=0.002, OR = 0.23, $95\%$CI = 0.09–0.59).
Table 6The association between the incidence of CAD and cardiovascular risk factors and BsmI genotypesVariablesP-valueCrude OR ($95\%$ CI)Pa-valueAdjusted OR ($95\%$ CI)Sex (male)0.041.94 (1.00–3.79)0.042.60 (1.00-6.75)Age0.0011.05 (1.02–1.09)0.011.05 (1.01–1.09)BMI0.011.11 (1.02–1.21)0.041.11 (1.00-1.23)Diabetes Mellitus0.013.10 (1.21–7.93)0.052.98 (0.97–9.20)Hypertension0.0072.57 (1.29–5.14)0.122.15 (0.81–5.70)Dyslipidemia0.052.05 (0.99–4.25)0.341.59 (0.60–4.21)Smoking0.0023.77 (1.63–8.71)0.0044.72 (1.62–13.79)BsmI genotypeGGreference 0.01reference 0.01GA0.010.35 (0.15–0.82)0.010.30 (0.11–0.81)AA< 0.0010.14 (0.05–0.35)0.0010.16 (0.05–0.48)Pa: adjusted p-value; OR: Odds Ratio; CI: Confidence Interval Table 7The association between the incidence of CAD and cardiovascular risk factors and BsmI allelesvariablesP-valueCrude odds ratio ($95\%$CI)Pa-valueAdjusted odds ratio ($95\%$ CI)Sex (male)0.041.94 (1.00–3.79)0.042.66 (1.03–6.87)Age0.0011.05 (1.02–1.09)0.0061.05 (1.01–1.10)BMI0.011.11 (1.02–1.21)0.031.11 (1.01–1.23)Diabetes Mellitus0.013.10 (1.21–7.93)0.053.04 (0.99–9.35)Hypertension0.0072.57 (1.29–5.14)0.132.07 (0.78–5.44)Dyslipidemia0.052.05 (0.99–4.25)0.381.53 (0.58-4.00)Smoking0.0023.77 (1.63–8.71)0.0034.90 (1.69–14.25)BsmI alleleGreferencereferenceA< 0.0010.24 (0.11–0.53)0.0020.23 (0.09–0.59)Pa: adjusted p-value; OR: Odds Ratio; CI: Confidence Interval Moreover, the power analysis was estimated $98\%$ to detect an effect size of 0.7893 using 5 degrees of freedom chi-square test with a significance level (α) of 0.05 for BsmI genotypes in patients and control group.
As demonstrated in Table 8, no significant association was found between SS and BsmI alleles after logistic regression analyses.
Table 8The association between SYNTAX score and cardiovascular risk factors and VDR gene polymorphismsvariablesP-valueCrude OR ($95\%$CI)Pa-valueAdjusted OR ($95\%$ CI)Age0.191.02 (0.98–1.06)0.111.03 (0.99–01.07)BsmI alleleGreferencereferenceA0.121.79 (0.85–3.79)0.062.09 (0.94–4.63)Pa: adjusted p-value; OR: Odds Ratio; CI: Confidence Interval
## Discussion
The present study was conducted to investigate the association between TaqI and BsmI polymorphisms and the incidence and severity of CAD in Iranian population. This is the first study in which the association of TaqI and BsmI polymorphisms of VDR and SS has been evaluated in CAD patients. Based on our findings, no significant association was found between SS and BsmI polymorphism. However, a significant association was found between BsmI genotypes and alleles and the incidence of CAD. GA and AA genotype carriers showed a lower risk of developing CAD and the A allele was found to have a protective effect against CAD.
Association between vitamin D deficiency and the prevalence of CVD risk has been investigated in various studies [27–30]. Evidence from Framingham Offspring Study demonstrated a higher rate of serious cardiovascular events (up to $80\%$) in participants who were vitamin D deficient [31]. Also, a cohort study carried out in India indicated a normal level of vitamin D in less than $5\%$ of patients with CAD. Concordantly, it was suggested that vitamin D deficiency is very common in CAD [32]. It is shown that a low level of vitamin D is associated with a rise in blood pressure and cardiovascular risk [6]. Vitamin D may play a role in the development of atherosclerosis through affecting calcification or various pathways influential in the process of inflammation [33]. A study on Iranian patients under coronary computed tomography angiography (CCTA) showed that there was a correlation between vitamin D deficiency and coronary artery calcification as well as stenosis severity [34]. Results of a study showed that a low level of vitamin D was associated with structural changes in the heart including systolic and diastolic dysfunction [35]. Another study demonstrated that the levels of matrix metalloproteinase-9 (MMP-9), a contributor to atherosclerosis process and heart remodelling, were higher in CAD patients with lower vitamin D levels [36]. In addition, vitamin D suppresses the expression of the renin gene and down-regulates the renin-angiotensin system (RAS) which is a major contributor to hypertension and cardiac remodelling [37].
It is reported in several studies that vitamin D exerts its physiological effects through VDR [38]. VDR is the intracellular receptor of vitamin D that binds to the active form of this hormone and exerts various biological effects by interacting with particular nucleotide sequences in targeted genes [39]. Inactivation of VDR results in an increase in RAS activity, endothelial dysfunction, hypertension, and cardiac hypertrophy [40]. The effect of vitamin D supplements on reducing angiotensin II levels, plasma renin activity, and blood pressure has been demonstrated in clinical studies [41, 42]. Among the identified SNPs of VDR, BsmI, TaqI, FokI, and ApaI have gained much attention owing to their possible impact on predicting a variety of pathophysiologic or physiologic phenotypes including CVDs [25]. However, controversies on the association of VDR polymorphisms with susceptibility to CVD have been reported in various studies [43, 44]. Based on our findings, a significant association was found between SS and alleles of BsmI; however, after including the confounding factors using logistic regression models, no association was observed between BsmI alleles and severity of CAD. Moreover, a significant association was found between VDR BsmI genotypes as well as associated alleles and the incidence of CAD. After evaluating the effects of confounding factors by multivariate logistic regression analysis, it was found that GA and AA genotype carriers have a lower risk of developing CAD ($$p \leq 0.01$$, OR = 0.30 and $$p \leq 0.001$$, OR = 0.16 respectively) and the A allele was found to have a protective effect against CAD ($$p \leq 0.002$$, OR = 0.23). In line with our findings, Ortlepp et al. concluded that carriers of GG genotype were at higher risk of CAD and type 2 diabetes [45]. In addition to these studies, a meta-analysis study demonstrated that carriers of the AA genotype were at a lower risk of hypertension in comparison to those carrying the GG or GA genotypes concluding that AA genotype plays a protective role against CVD [46]. Eweida et al. and Raljević et al. reported that GA genotype was less frequent in CAD patients compared to healthy individuals, whereas AA genotype was more frequent in CAD patients [27, 47]. The authors suggested that carriers of AA genotype are more susceptible to CAD while GA genotype plays a protective role [47]. Moreover, the G allele was assumed to have a protective effect against CVD, and the A allele was reported as a potential predictor of CVD risk, which is in contrast with our findings [27]. In line with these studies, a study on a population from west of Iran proposed the A allele as a possible predictor for CAD development [48]. On the other hand, some studies and meta-analyses did not find any significant association between BsmI polymorphism and the incidence or severity of CAD [20, 33, 44]. Discrepancies between results of these studies can be ascribed to different ethnicities of the study populations, different sample sizes, and heterogeneity of CAD severity and CAD definition.
Results of our study revealed that TaqI polymorphism was not associated with the incidence and severity of CAD. This finding is supported by several other studies. He et al. reported no association between TaqI polymorphism and risk of CAD [49]. Similar result was observed in Egyptian males [50]. A meta-analysis conducted in an Iranian population also revealed no significant association between TaqI polymorphism and CAD [20]. In contrast, results of a French study reported that the C allele (minor allele) of TaqI polymorphism was associated to an increased risk of CAD in patients with type 2 diabetes [51]. Moreover, the results of a study demonstrated that C allele and TC genotype significantly predict the risk of CAD development [52]. In another study, CC genotype was more prominent among the CAD patients who had experienced myocardial infarction [47].
It is not clear how VDR gene polymorphisms play a role in the pathogenesis of CAD. It is thought that vitamin D binding sites may be altered by some VDR gene polymorphisms including TaqI polymorphism, which affects the function of VDR and may lead to inflammatory responses participating in an increased risk of developing atherosclerosis and CAD [52]. The BsmI polymorphism which is located in intron 8 near the 3′ end of the VDR gene, does not change the VDR protein amino acid sequence. Nevertheless, it can alter mRNA stability, disrupt mRNA transcription splice sites, or change intronic regulatory elements which may lead to alteration of gene expression. The TaqI polymorphism in exon 9 does not alter the VDR protein but it plays a role in the regulation of mRNA stability [53].
Our study has some strengths as well as limitations. It is noteworthy that using SS, as a powerful stratification system, in order to provide a homogenous evaluation of CAD severity and recruiting studied groups from an ethnically homogenous population were the strengths of this study. One of the limitation of this study was the small sample size of enrolled participants which was attributed to the limited number of participants with normal coronary angiography.
Considering the controversial results with respect to the association of VDR gene polymorphisms with the incidence and severity of CAD, further studies on various ethnic groups with larger sample sizes are urged. The mechanistic pathway by which these polymorphisms affect CAD also needs to be investigated in future pharmacological studies.
## Conclusion
Association of BsmI genotypes with the incidence of CAD revealed that along with vitamin D level, the genetic variation of its receptors might also play a role in the pathogenesis of CAD and may propose this variant as a marker of risk assessment for CAD. Therefore, assessment of BsmI polymorphism may be considered as a new approach for the assessment of CAD risk and may be a helpful measure in designing a better clinical approach for the prevention and management of CAD patients.
## References
1. Wang Y, Zhang Y, Wei J, Du W, Ding J, Zhang Y. **Vitamin D for inflammation biomarkers in coronary artery disease: a protocol for systematic review and meta-analysis**. *Med (Baltim)* (2020.0) **99** e21407. DOI: 10.1097/MD.0000000000021407
2. Legarth C, Grimm D, Krüger M, Wehland M, Infanger M. **Potential beneficial effects of vitamin d in coronary artery disease**. *Nutrients* (2020.0) **12** 1-22
3. 3.Bahrami LS, Ranjbar G, Norouzy A, Arabi SM. Vitamin D supplementation effects on the clinical outcomes of patients with coronary artery disease: a systematic review and meta-analysis. Sci Rep [Internet]. 2020;10(1):1–10. Available from: 10.1038/s41598-020-69762-w
4. 4.Chakravarty S. Pathogenesis of atherosclerosis. Handbook of Lipidology. Volume 28. JP Medical Ltd; 2016.
5. 5.Libby P, Theroux P. Pathophysiology of coronary artery disease [Internet]. Vol. 111, Circulation. Lippincott Williams & Wilkins; 2005 [cited 2022 Jan 31]. p. 3481–8. Available from: https://www.ahajournals.org/doi/abs/10.1161/CIRCULATIONAHA.105.537878
6. 6.Wang TJ, Pencina MJ, Booth SL, Jacques PF, Ingelsson E, Lanier K et al. Vitamin D deficiency and risk of cardiovascular disease.Circulation. 2008Jan;117(4):503–11.
7. 7.Iyngkaran P, Chan W, Liew D, Zamani J, Horowitz JD, Jelinek M et al. Risk stratification for coronary artery disease in multi-ethnic populations: Are there broader considerations for cost efficiency? World J Methodol [Internet]. 2019 Jan 18;9(1):1–19. Available from: https://pubmed.ncbi.nlm.nih.gov/30705870
8. 8.Kunadian V, Ford GA, Bawamia B, Qiu W, Manson JE. Vitamin D deficiency and coronary artery disease: A review of the evidence. Am Heart J [Internet]. 2014;167(3):283–91. Available from: https://www.sciencedirect.com/science/article/pii/S0002870313008338
9. 9.Holick MF. Vitamin D deficiency. N Engl J Med. 2007 Jul;357(3):266–81.
10. 10.Cigolini M, Iagulli MP, Miconi V, Galiotto M, Lombardi S, Targher G. Serum 25-Hydroxyvitamin D3 Concentrations and Prevalence of Cardiovascular Disease Among Type 2 Diabetic Patients. Diabetes Care [Internet]. 2006 Mar 1;29(3):722–4. Available from: 10.2337/diacare.29.03.06.dc05-2148
11. 11.Mirhosseini N, Rainsbury J, Kimball SM, Vitamin D, Supplementation. Serum 25(OH)D Concentrations and Cardiovascular Disease Risk Factors: A Systematic Review and Meta-Analysis [Internet]. Vol. 5, Frontiers in Cardiovascular Medicine. 2018. Available from: https://www.frontiersin.org/article/10.3389/fcvm.2018.00087
12. Das B, Kumar Mishra T, Narayan Routray S, Satpathy C, Mishra H. **Vitamin D deficiency: a new risk factor for cardiovascular disease. Journal**. *Indian Acad Clin Med* (2013.0) **14** 247-52
13. 13.Onrat ST, Akci Ö, Söylemez Z, Onrat E, Avşar A. Prevalence of myocardial infarction polymorphisms in Afyonkarahisar, Western Turkey. Mol Biol Rep [Internet]. 2012;39(9):9257–64. Available from: 10.1007/s11033-012-1799-1
14. 14.Szabó GV, Kunstár A, Acsády G. Methylentetrahydrofolate Reductase and Nitric Oxide Synthase Polymorphism in Patients with Atherosclerosis and Diabetes. Pathol Oncol Res [Internet]. 2009;15(4):631. Available from: 10.1007/s12253-009-9163-z
15. 15.Isordia-Salas I, Trejo-Aguilar A, Valadés-Mejía MG, Santiago-Germán D, Leaños-Miranda A, Mendoza-Valdéz L et al. C677T Polymorphism of the 5,10 MTHFR Gene in Young Mexican Subjects with ST-Elevation Myocardial Infarction. Arch Med Res [Internet]. 2010;41(4):246–50. Available from: https://www.sciencedirect.com/science/article/pii/S0188440910000998
16. 16.Firouzabadi N, Tajik N, Bahramali E, Bakhshandeh H, Maadani M, Shafiei M. Gender specificity of a genetic variant of angiotensin-converting enzyme and risk of coronary artery disease. Mol Biol Rep. 2013 Aug;40(8):4959–65.
17. 17.Bahramali E, Firouzabadi N, Jonaidi-Jafari N, Shafiei M. Renin-angiotensin system genetic polymorphisms: Lack of association with CRP levels in patients with coronary artery disease. JRAAS - J Renin-Angiotensin-Aldosterone Syst [Internet]. 2014 Dec 17 [cited 2022 May 8];15(4):559–65. Available from: https://journals.sagepub.com/doi/full/10.1177/1470320312474051
18. 18.Firouzabadi N, Tajik N, Bahramali E, Bakhshandeh H, Ebrahimi SA, Maadani M et al. Association of angiotensin-converting enzyme polymorphism with coronary artery disease in Iranian patients with unipolar depression. Clin Biochem [Internet]. 2012 Nov [cited 2022 May 8];45(16–17):1347–52. Available from: https://pubmed.ncbi.nlm.nih.gov/22683751/
19. 19.Prabhakar P, Majumdar V, Kulkarni GB, Christopher R. Genetic variants of vitamin D receptor and susceptibility to ischemic stroke. Biochem Biophys Res Commun [Internet]. 2015;456(2):631–6. Available from: https://www.sciencedirect.com/science/article/pii/S0006291X1402172X
20. Alizadeh S, Djafarian K, Alizadeh H, Mohseni R, Shab-Bidar S. **Common variants of vitamin D receptor gene polymorphisms and susceptibility to coronary artery disease: a systematic review and meta-analysis**. *J Nutrigenet Nutrigenomics* (2017.0) **10** 9-18. PMID: 28351026
21. Cerit L, Cerit Z. **Vitamin d deficiency is not associated with higher levels of syntax score**. *Brazilian J Cardiovasc Surg* (2019.0) **34** 57-61. DOI: 10.21470/1678-9741-2018-0178
22. 22.Foroughinia F, Dehghani P, Dianatpour M, Amiri A, Jamhiri I, Ghasemiyeh P. The association between estrogen receptor 2 gene polymorphism and complexity of coronary artery disease: an analysis in elective percutaneous coronary intervention patients. BMC Cardiovasc Disord [Internet]. 2021;21(1):1–8. Available from: 10.1186/s12872-021-02088-1
23. Foroughinia F, Nouri BM, Kojuri J, Ostovan MA. **Impact of Omega-3 supplementation on high sensitive C-reactive protein level and 30-day major adverse cardiac events after the implementation of coronary stent in patients with chronic kidney disease: a randomized clinical study**. *Adv Pharm Bull* (2018.0) **8** 471-8. DOI: 10.15171/apb.2018.055
24. 24.References. [Internet]. [cited 2022 May 24]. Available from: https://syntaxscore.org/index.php/references
25. 25.Sharifi Ardani P, Foroughinia F, Dianatpour M, Jamhiri I. Genotype and allele frequencies of two Vitamin-D receptor gene polymorphisms (ApaI and BsmI) in patients undergoing elective percutaneous coronary intervention in Iranian population. Trends Pharm Sci [Internet]. 2020;6(2):105–12. Available from: https://tips.sums.ac.ir/article_46653.html
26. 26.Foroughinia F, Morovati N, Safari A, Dianatpour M, Jamhiri I, Heydari ST et al. Association between Fok1 and TaqI polymorphisms of vitamin D receptor gene with the severity of stenosis and calcification in carotid bulb in patients with ischemic stroke. J Clin Neurosci [Internet]. 2022 Mar 1 [cited 2022 Apr 24];97:115–20. Available from: https://pubmed.ncbi.nlm.nih.gov/35091316/
27. 27.Eweida SM, Salem A, Shaker YM, Samy N, Yassen I, Mohamed RH. Vitamin D levels and vitamin D receptor genetic variants in Egyptian cardiovascular disease patients with and without diabetes.Egypt J Med Hum Genet. 2021;22(1).
28. 28.Hossein-Nezhad A, Eshaghi SM, Maghbooli Z, Mirzaei K, Shirzad M, Curletto B et al. The Role of Vitamin D Deficiency and Vitamin D Receptor Genotypes on the Degree of Collateralization in Patients with Suspected Coronary Artery Disease. Kamboh MI, editor. Biomed Res Int [Internet]. 2014;2014:304250. Available from: 10.1155/2014/304250
29. Sokol SI, Tsang P, Aggarwal V, Melamed ML, Srinivas VS. **Vitamin D status and risk of cardiovascular events: lessons learned via systematic review and meta-analysis**. *Cardiol Rev* (2011.0) **19** 192-201. DOI: 10.1097/CRD.0b013e31821da9a5
30. 30.Wang L, Song Y, Manson JAE, Pilz S, März W, Michaëlsson K et al. Circulating 25-Hydroxy-Vitamin D and risk of cardiovascular disease: A meta-analysis of prospective studies. Circ Cardiovasc Qual Outcomes [Internet]. 2012 Nov [cited 2022 Jan 31];5(6):819–29. Available from: https://pubmed.ncbi.nlm.nih.gov/23149428/
31. Mohammad AM, Shammo NA, Jasem JA. **Vitamin D status in acute myocardial infarction: a case-control study**. *Cardiovasc Endocrinol Metab* (2018.0) **7** 93-6. DOI: 10.1097/XCE.0000000000000160
32. 32.Akhtar T, Aggarwal R, Jain SK. Serum Vitamin D Level in Patients with Coronary Artery Disease and Association with Sun Exposure: Experience from a Tertiary Care, Teaching Hospital in India. Veledar E, editor. Adv Med [Internet]. 2019;2019:6823417. Available from: 10.1155/2019/6823417
33. Ortlepp JR, Von Korff A, Hanrath P, Zerres K, Hoffmann R. **Vitamin D receptor gene polymorphism BsmI is not associated with the prevalence and severity of CAD in a large-scale angiographic cohort of 3441 patients**. *Eur J Clin Invest* (2003.0) **33** 106-9. DOI: 10.1046/j.1365-2362.2003.01124.x
34. Moradi M, Foroutanfar A. **Evaluation of vitamin D levels in relation to coronary CT angiographic findings in an iranian population**. *Vasc Health Risk Manag* (2017.0) **13** 361-7. DOI: 10.2147/VHRM.S142721
35. 35.Pekkanen MP, Ukkola O, Hedberg P, Piira OP, Lepojärvi S, Lumme J et al. Serum 25-hydroxyvitamin D is associated with major cardiovascular risk factors and cardiac structure and function in patients with coronary artery disease. Nutr Metab Cardiovasc Dis [Internet]. 2015;25(5):471–8. Available from: 10.1016/j.numecd.2015.02.005
36. 36.Foroughinia F, Mirjalili M. Association between serum vitamin D concentration status and matrix metalloproteinase-9 in patients undergoing elective percutaneous coronary intervention. Iran J Pharm Res [Internet]. 2020 [cited 2022 Apr 24];19(4):135–42. Available from: https://pubmed.ncbi.nlm.nih.gov/33841529/
37. 37.Li YC, Kong J, Wei M, Chen Z-F, Liu SQ, Cao L-P. 1,25-Dihydroxyvitamin D3 is a negative endocrine regulator of the renin-angiotensin system. J Clin Invest [Internet]. 2002 Jul 15;110(2):229–38. Available from: 10.1172/JCI15219
38. 38.Chatterjee M. Vitamin D and genomic stability. Mutat Res Mol Mech Mutagen [Internet]. 2001;475(1):69–87. Available from: https://www.sciencedirect.com/science/article/pii/S002751070100080X
39. 39.Raimondi S, Johansson H, Maisonneuve P, Gandini S. Review and meta-analysis on vitamin D receptor polymorphisms and cancer risk. Carcinogenesis [Internet]. 2009 Jul 1;30(7):1170–80. Available from: 10.1093/carcin/bgp103
40. 40.Pilz S, Tomaschitz A, März W, Drechsler C, Ritz E, Zittermann A et al. Vitamin D, cardiovascular disease and mortality [Internet]. Vol. 75, Clinical Endocrinology. John Wiley & Sons, Ltd; 2011 [cited 2022 Jan 31]. p. 575–84. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-2265.2011.04147.x
41. 41.Zittermann A, Schleithoff SS, Koerfer R. Putting cardiovascular disease and vitamin D insufficiency into perspective. Br J Nutr [Internet]. 2007/03/08. 2005;94(4):483–92. Available from: https://www.cambridge.org/core/article/putting-cardiovascular-disease-and-vitamin-d-insufficiency-into-perspective/1DF316D177154AA8A54E57CA892561BC
42. 42.Pfeifer M, Begerow B, Minne HW, Nachtigall D, Hansen C. Effects of a Short-Term Vitamin D3 and Calcium Supplementation on Blood Pressure and Parathyroid Hormone Levels in Elderly Women1. J Clin Endocrinol Metab [Internet]. 2001 Apr 1;86(4):1633–7. Available from: 10.1210/jcem.86.4.7393
43. 43.Nakhl S, Sleilaty G, Chouery E, Salem N, Chahine R, Farès N. FokI vitamin D receptor gene polymorphism and serum 25-hydroxyvitamin D in patients with cardiovascular risk. Arch Med Sci Atheroscler Dis [Internet]. 2019 Dec 31;4:e298–303. Available from: https://pubmed.ncbi.nlm.nih.gov/32368685
44. Pan XM, Li DR, Yang L, Wang EY, Chen TY, Liu YJ. **No association between vitamin D receptor polymorphisms and coronary artery disease in a chinese population**. *DNA Cell Biol* (2009.0) **28** 521-5. DOI: 10.1089/dna.2009.0908
45. Ortlepp JR, Lauscher J, Hoffmann R, Hanrath P, Joost HG. **The vitamin D receptor gene variant is associated with the prevalence of type 2 diabetes mellitus and coronary artery disease**. *Diabet Med* (2001.0) **18** 842-5. DOI: 10.1046/j.1464-5491.2001.00585.x
46. Zhu YB, Li ZQ, Ding N, Yi HL. **The association between vitamin D receptor gene polymorphism and susceptibility to hypertension: a meta-analysis**. *Eur Rev Med Pharmacol Sci* (2019.0) **23** 9066-74. PMID: 31696497
47. 47.Raljević D, Peršić V, Markova-Car E, Cindrić L, Miškulin R, Žuvić M et al. Study of vitamin D receptor gene polymorphisms in a cohort of myocardial infarction patients with coronary artery disease. BMC Cardiovasc Disord [Internet]. 2021;21(1):1–9. Available from: 10.1186/s12872-021-01959-x
48. 48.Kiani A, Mohamadi-Nori E, Vaisi-Raygani A, Tanhapour M, Elahi-Rad S, Bahrehmand F et al. Vitamin D-binding protein and vitamin D receptor genotypes and 25-hydroxyvitamin D levels are associated with development of aortic and mitral valve calcification and coronary artery diseases. Mol Biol Rep [Internet]. 2019;46(5):5225–36. Available from: 10.1007/s11033-019-04979-1
49. He L, Wang M. **Association of vitamin d receptor-a gene polymorphisms with coronary heart disease in Han Chinese**. *Int J Clin Exp Med* (2015.0) **8** 6224-9. PMID: 26131229
50. Maaty AE, Hassanein MA, Sleem SI, Gad HM. **Vitamin D receptor gene polymorphisms (TaqI and ApaI) in relation to 25-hydroxyvitamin D levels and coronary artery disease incidence**. *J Recept Signal Transduct* (2015.0) **35** 391-5. DOI: 10.3109/10799893.2014.959593
51. Ferrarezi DAF, Bellili-Muñoz N, Dubois-Laforgue D, Cheurfa N, Lamri A, Reis AF. **Allelic variations of the vitamin D receptor (VDR) gene are associated with increased risk of coronary artery disease in type 2 diabetics: the DIABHYCAR prospective study**. *Diabetes Metab* (2013.0) **39** 263-70. DOI: 10.1016/j.diabet.2012.11.004
52. Kulsoom U, Khan A, Saghir T, Nawab SN, Tabassum A, Fatima S. **Vitamin D receptor gene polymorphism TaqI (rs731236) and its association with the susceptibility to coronary artery disease among pakistani population**. *J Gene Med* (2021.0) **23** 1-9. DOI: 10.1002/jgm.3386
53. Triantos C, Aggeletopoulou I, Kalafateli M, Spantidea PI, Vourli G, Diamantopoulou G. **Prognostic significance of vitamin D receptor (VDR) gene polymorphisms in liver cirrhosis**. *Sci Rep* (2018.0) **8** 1-14. DOI: 10.1038/s41598-018-32482-3
|
---
title: Association between triglyceride glucose index and subclinical left ventricular
systolic dysfunction in patients with type 2 diabetes
authors:
- Yanyan Chen
- Jianfang Fu
- Yi Wang
- Ying Zhang
- Min Shi
- Cheng Wang
- Mengying Li
- Li Wang
- Xiangyang Liu
- Shengjun Ta
- Liwen Liu
- Zeping Li
- Xiaomiao Li
- Jie Zhou
journal: Lipids in Health and Disease
year: 2023
pmcid: PMC9993628
doi: 10.1186/s12944-023-01796-1
license: CC BY 4.0
---
# Association between triglyceride glucose index and subclinical left ventricular systolic dysfunction in patients with type 2 diabetes
## Abstract
### Background
The triglyceride glucose (TyG) index has been considered a new biomarker for the diagnosis of angiocardiopathy and insulin resistance. However, the association of the TyG index with subclinical left ventricular (LV) systolic dysfunction still lacks comprehensive exploration. This study was carried out to examine this relationship in patients with type 2 diabetes mellitus (T2DM).
### Methods
A total of 150 T2DM patients with preserved LV ejection fraction (LVEF ≥ $50\%$) from June 2021 to December 2021 were included in this study. The subclinical LV function was evaluated through global longitudinal strain (GLS), with the predefined GLS < $18\%$ as the cutoff for subclinical LV systolic dysfunction. The TyG index calculation was obtained according to ln (fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2), which was then stratified into quartiles (TyG index—Q).
### Results
Analyses of clinical characteristics in the four TyG indexes-Q (Q1 (TyG index ≤ 8.89) $$n = 38$$, Q2 (8.89 < TyG index ≤ 9.44) $$n = 37$$, Q3 (9.44 < TyG index ≤ 9.83) $$n = 38$$, and Q4 (TyG index > 9.83) $$n = 37$$) were conducted. A negative correlation of the TyG index with GLS (r = -0.307, $P \leq 0.001$) was revealed according to correlation analysis. After gender and age were adjusted in multimodel logistic regression analysis, the higher TyG index (OR 6.86; $95\%$ CI 2.44 to 19.30; $P \leq 0.001$, Q4 vs Q1) showed a significant association with GLS < $18\%$, which was still maintained after further adjustment for related clinical confounding factors (OR 5.23, $95\%$ CI 1.12 to 24.51, $$p \leq 0.036$$, Q4 vs Q1). Receiver operator characteristic analysis indicated a diagnostic capacity of the TyG index for GLS < $18\%$ (area under curve: 0.678; $P \leq 0.001$).
### Conclusions
A higher TyG index had a significant association with subclinical LV systolic dysfunction in T2DM patients with preserved ejection fraction, and the TyG index may have the potential to exert predictive value for myocardial damage.
## Background
A causal relationship exists between diabetes mellitus and heart failure. For instance, diabetic cardiomyopathy as a diffuse cardiomyopathy resulting from glucose metabolism disorder has received increasing interest in recent years, which generally manifests as pathological cardiac remodeling and systolic and diastolic dysfunction and may eventually develop into overt heart failure [1, 2]. Studies have demonstrated insulin resistance and/or hyperinsulinemia as the source of the cascade that contributes to diabetic cardiomyopathy [3, 4]. The early stages of diabetic cardiomyopathy are usually ignored and underestimated in clinics. Some studies have shown the existence of asymptomatic systolic LV dysfunction in patients with type 2 diabetes mellitus (T2DM) with hidden cardiac disease manifestations [5] and suggest the emergence of reduced global longitudinal strain (GLS) at an early stage of this disorder process prior to the detectability of ejection fraction (EF) transformations [6]. Therefore, early identification followed by punctual intervention is of vital significance for individuals at high risk of diabetic cardiomyopathy. The TyG index is indicated to serve as an alternative biomarker of insulin resistance, which is convenient and reliable [7–9] and is closely related to cardiovascular disease [10]. However, there is not yet enough evidence to evaluate the clinical value of the TyG index on subclinical LV dysfunction in diabetes. Accordingly, our objective was to explore the correlation of the TyG index with LV longitudinal systolic function in T2DM patients without heart disease.
## Study subjects
A total of 165 hospitalized patients with T2DM categorized by World Health *Organization criteria* [11] at the Department of Endocrinology of Xijing Hospital of Air Force Medical University during the period of June 2021 to December 2021 were enrolled. The exclusion criteria were as follows: [1] LVEF < $50\%$; [2] moderate-to-severe aortic/mitral valve stenosis or insufficiency; [3] a coronary artery disease history or other heart disease; [4] arrhythmia such as left bundle-branch block, frequent ventricular premature complexes or atrial fibrillation; and [5] too poor speck tracking image quality for analysis. According to the exclusion criteria, 15 participants were excluded. Ultimately, 150 patients with T2DM were included in the present study.
## Data collection
Demographic data were provided by the electronic medical record system, with diabetes duration, gender, age, systolic and diastolic pressures, and medication covered. All patients were measured for height and weight through specially assigned personnel on the admission day, with body mass index (BMI) defined as weight/height2 (kg/m2). Hypertension was described as systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg without antihypertensive medication or with a previous hypertension physician identification. After an at least 8-h overnight fast, an evaluation of total cholesterol, triglycerides, high- and low-density lipoprotein cholesterol, and uric acid was performed with an automatic biochemical analyzer. The fasting glucose index was detected with glycosylated hemoglobin (HbA1c) checked by high-performance liquid chromatography. Immunoturbidimetry depending on a COBAS INTEGRA 400 plus autoanalyzer (Germany) was carried out to determine the urinary albumin-to-creatinine ratio (UACR). The ln[fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2] [7] was used to identify the TyG index, and the patients were grouped into Q1 (TyG index ≤ 8.89), Q2 (8.89 < TyG index ≤ 9.44), Q3 (9.44 < TyG index ≤ 9.83), and Q4 (TyG index > 9.83) based on the TyG index levels.
## Conventional echocardiography
The ultrasound measurements of all subjects were collected according to the guidelines of the American Society of Echocardiography [12]. Two-dimensional (2D) echocardiography (Philips Healthcare, iE33 system, X5-1 probe) was performed on each subject accompanied by electrocardiogram. LV fractional shortening (LVFS), LVEF, heart rate, and stroke volume of each subject were analyzed. Afterwards, the pulse Doppler sampling volume was located under the mitral valve to perform the measurement on the early diastolic blood flow velocity (E peak) and late diastole (A peak) to calculate the E/A ratio, then to measure the early diastolic mitral annulus motion velocity (E' peak) to calculate the E/E' ratio by placing it at the septal side of the mitral annulus.
## Speck-tracking echocardiography
Echocardiography images for 2D speck-tracking echocardiography (STE) with a rate of 60–90 frames/s in three cardiac cycles were acquired. Then, the LV views of the apical four-chamber, apical two-chamber and apical three-chamber were analyzed using QLAB 8.1 2D imaging system. The trace of the endocardial border at end-diastole was performed in a manual manner to obtain the 2D strain‒time curve and bull's-eye plot of 17 segments of LV (Fig. 1). The trace was adjusted based on a visual assessment of the tracking quality by observer, and the untraceable images of spots elicited by atrial fibrillation were excluded from the subsequent analysis. The LV GLS for the average value of the three peak strains in systole was calculated for subclinical LV systolic function evaluation. Based on previous studies and the latest guidelines of the European Association of Cardiovascular Imaging, the predefined cutoff of GLS < $18\%$ was adopted to evaluate subclinical LV systolic dysfunction [12–14].Fig. 1Acquisition of left ventricular apical four-chamber strain imaging by two-dimensional speckle tracking echocardiography in type 2 diabetic patients
## Statistical analyses
A Kolmogorov‒Smirnov test was performed to test the normality of data, and a nonparametric test was implemented where data did not meet a normal distribution. Data satisfying normal distribution were expressed as the means ± SDs, and conversely, as median and interquartile range. Categorical variables were described in the form of percentages n (%). One-way ANOVA or the Kruskal‒Wallis test was used to test for differences between groups. The Bonferroni test was adopted for post hoc comparisons. Least Pearson's Chi-square test was performed on categorical variable comparisons. The correlation between the TyG index and GLS was evaluated according to Pearson correlation coefficients. Three forced-entry logistic regression models were performed to determine the independent association of GLS < $18\%$ with TyG index: model 1 (an unadjusted model), mode 2 (a multivariable model) adjusted for age and gender, and model 3 (a multivariable model) adjusted for age, gender, diabetes duration, systolic pressure, HbA1c, BMI, hypertension, heart rate, logarithmic microalbuminuria, LVEF and insulin therapy. To confirm an alternative index for identifying reduced GLS < $18\%$, models covering the TyG index and HbA1c were established, followed by a comparison of both according to the area under the receiver operating characteristic (ROC) curves (AUC). The measurement of GLS was performed by one professional physician to avoid interobserver and intraobserver variability. To prevent the ambiguity of negative size to a value, the GLS was given in the absolute value form. All statistical analyses were carried out on IBM SPSS statistics 26.0, with a P value < 0.05 considered statistically significant.
## Clinical characteristics
A total of 150 T2DM patients (mean age: 53.4 ± 13.8 years, diabetes duration: 10.25 ± 7.22 years, male: 96 ($64.0\%$)) were included, with the clinical features across quartiles of the TyG index listed in Table 1, which indicated an increased hypertension prevalence in subjects with a higher TyG index, accompanied by higher levels of systolic pressure, fasting glucose, HbA1c, heart rate, triglycerides, low-density lipoprotein cholesterol, and total cholesterol (all $P \leq 0.05$). Moreover, insulin therapy statistically varied across the TyG index quartiles ($$P \leq 0.025$$) without obvious differences in sex, age, diabetes duration or BMI among groups ($P \leq 0.05$).Table 1Clinical characteristics of participants by quartiles of TyG indexCharacteristicQ1 (TyG index ≤ 8.89) $$n = 38$$Q2 (8.89 < TyG index ≤ 9.44) $$n = 37$$Q3 (9.44 < TyG index ≤ 9.83) $$n = 38$$Q4 (TyG index > 9.83) $$n = 37$$ P value Male, n (%)23 (60.5)26 (70.3)26 (68.4)21 (56.8)0.573Age, years52.5 ± 15.355.8 ± 13.352.6 ± 11.253.1 ± 15.30.692BMI, kg/m2 23.29 ± 3.8823.87 ± 3.5424.82 ± 3.1024.51 ± 4.110.273Diabetes duration, years10.79 ± 7.088.57 ± 6.979.17 ± 6.7812.29 ± 7.790.115Heart rate, bpm72.45 ± 8.7373.65 ± 12.1277.74 ± 2.7181.31 ± 4.02ce 0.008 SBP, mmHg127.08 ± 15.29135.08 ± 14.53131.26 ± 14.62139.59 ± 24.44c 0.019 DBP, mmHg76.16 ± 9.3078.97 ± 9.5377.39 ± 9.2080.43 ± 14.860.359HbA1c, (%)7.93 ± 1.648.49 ± 1.849.05 ± 1.969.75 ± 2.46ce 0.001 FPG, mmol/l8.15 (6.38–9.40)10.00 (7.85–14.45)a 11.50(10.10–13.73)b 14.50(10.25–18.15)ce < 0.001 Total cholesterol, mmol/l3.57 ± 1.053.72 ± 1.104.07 ± 0.904.83 ± 1.57cef < 0.001 HDL-C, mmol/l1.19 ± 0.321.10 ± 0.531.09 ± 0.471.03 ± 0.360.426LDL-C, mmol/l1.95 ± 0.912.17 ± 0.972.61 ± 1.27b 2.76 ± 1.22ce 0.006 Triglyceride, mmol/l0.70 (0.61–0.99)1.16 (0.87–1.45)a 1.58 (1.29–1.97) bd 2.77 (2.13–3.81)cef < 0.001 Apolipoproteins A1, g/l1.27 ± 0.221.14 ± 0.18a 1.16 ± 0.181.23 ± 0.21 0.028 Apolipoproteins B, g/l0.56 ± 0.170.63 ± 0.210.81 ± 0.49b 0.82 ± 0.31c 0.001 Albuminuria, mg/l11.30 (8.13–15.18)12.85 (8.83–20.08)9.20 (8.00–15.00)14.90 (8.30–105.00)0.109UACR, mg/mmol1.45 (0.77–2.42)1.45 (1.03–3.55)1.70 (0.75–2.73)2.41(1.12–16.47)0.172Uric acid, umol/l301.43 ± 66.61315.00 ± 80.30327.30 ± 87.99341.92 ± 88.450.177hypertension, n (%)8 (21.1)20 (54.1)a 19 (50.0)b 21(56.8)c 0.006 Diabetic nephropathy, n (%)5 (13.2)8 (21.6)8 (21.1)14 (37.8)0.081Diabetic retinopathy, n (%)3 (7.9)3 (8.1)5 (13.2)10 (27.0)0.058Medical treatmentACEI/ARB, n (%)4 (10.5)8 (21.6)11 (28.9)11 (29.7)0.163CCB, n (%)5 (13.2)10 (27.0)8 (21.1)8 (21.6)0.523Statin, n (%)8 (21.1)7 (18.9)10 (26.3)10 (27.0)0.805Insulin, n (%)23 (60.5)12 (32.4)a 24 (63.2)22 (59.5) 0.025 SGLT-2I, n (%)4 (10.5)3 (8.1)4 (10.5)3 (8.1)0.968GLP-1RA, n (%)5 (13.2)2 (5.4)4 (10.5)4 (10.8)0.723Metformin, n (%)27 (71.1)26 (70.3)27 (71.1)25 (67.6)0.986αGI, n (%)15 (39.5)13 (35.1)12 (31.6)13 (35.1)0.914Values are presented as the mean ± SD, median (interquartile range) or n (%). Bold indicates values of $P \leq 0.05$Abbreviations: TyG Triglyceride glucose, BMI Body mass index, SBP Systolic blood pressure, DBP Diastolic blood pressure, FPG *Fasting plasma* glucose, TC Total cholesterol, HDL-C High-density lipoprotein cholesterol, LDL-C Low-density lipoprotein cholesterol, UACR Urinary albumin-to-creatinine ratio, CCB Calcium channel blocker, ACEI Angiotensin-converting enzyme inhibitor, ARB Angiotensin II receptor blocker, SGLT-2I Sodium glucose cotransporter 2 inhibitor, GLP-1RA Glucagon-like peptide-1 receptor agonist, α-GI α-glucosidase inhibitor. The Bonferroni test was adopted for post hoc comparisons a $P \leq 0.05$ between the 1st quartile and 2nd quartile b $P \leq 0.05$ between the 1st quartile and 3rd quartile c $P \leq 0.05$ between the 1st quartile and 4th quartile d $P \leq 0.05$ between the 2nd quartile and 3rd quartile e $P \leq 0.05$ between the 2nd quartile and 4th quartile f $P \leq 0.05$ between the 3rd quartile and 4th quartile
## Association of the TyG index with GLS
Table 2 displays the characteristics of LV function stratified by quartiles of the TyG index. No statistically significant differences were observed in traditional echocardiographic parameters, that is, LVEF, LVFS, stroke volume, E, E', E/A and E/E' across TyG index quartiles (all $P \leq 0.05$). However, the GLS exhibited a stepwise decrease in line with the increase in TyG index quartile (19.23 ± 3.28 vs 18.87 ± 2.79 vs 17.22 ± 3.56 vs 16.67 ± 3.31, $$P \leq 0.001$$). Pearson correlation analysis indicated strong and negative correlations of the TyG index (r = -0.307, $P \leq 0.001$) and HbA1c (r = -0.470, $P \leq 0.001$) with GLS.Table 2Characteristics of LV function stratified by quartiles of the TyG indexEchocardiographic indexesQ1 (TyG index ≤ 8.89) $$n = 38$$Q2 (8.89 < TyG index ≤ 9.44) $$n = 37$$Q3 (9.44 < TyG index ≤ 9.83) $$n = 38$$Q4 (TyG index > 9.83) $$n = 37$$ P value LV EF, %60.14 ± 3.8960.19 ± 5.5560.13 ± 5.0159.91 ± 4.300.995LV FS, %31.94 ± 3.3632.09 ± 4.3432.41 ± 3.9231.73 ± 3.860.912Stroke volume, ml47.22 ± 8.5948.47 ± 9.6448.22 ± 9.9145.61 ± 7.500.561E,cm/s67.12 ± 19.0167.90 ± 14.1265.97 ± 13.7464.55 ± 15.030.850A,cm/s73.48 ± 19.1780.40 ± 21.1875.07 ± 19.8183.26 ± 19.400.181E',cm/s7.92 ± 3.017.01 ± 2.417.74 ± 2.756.77 ± 2.440.303E/A ratio1.00 ± 0.381.36 ± 2.820.94 ± 0.360.83 ± 0.390.508E/E' ratio10.36 ± 4.2310.22 ± 3.499.52 ± 3.0110.14 ± 3.110.825GLS, %19.23 ± 3.2818.87 ± 2.7917.22 ± 3.56a 16.67 ± 3.31bc 0.001 Bold indicates $P \leq 0.05$Abbreviations: TyG Triglyceride glucose, LVEF Left ventricular ejection fraction, LVFS, LV fractional shortening, EPeak early diastolic mitral flow velocity, A Peak late diastolic mitral flow velocity, E/A Peak early diastolic (E-wave) and late diastolic (A-wave) velocity ratio, E/E' Mitral inflow E and mitral E' annular velocity ratio, GLS Global longitudinal strain. The Bonferroni test was adopted for post hoc comparisons a $P \leq 0.05$ between the 1st quartile and 3rd quartile b $P \leq 0.05$ between the 1st quartile and 4th quartile c $P \leq 0.05$ between the 2nd quartile and 4th quartile Table 3 displays the logistic regression results based on the TyG index quartile. Taking Q1 as the reference, the risks in the Q3 and Q4 groups of GLS < $18\%$ were found to be significantly higher in comparison to the Q1 group in the univariate model (Q3 vs Q1: OR 4.29, $95\%$ CI 1.63 to 11.35, $$P \leq 0.003$$; Q4 vs Q1: OR 5.83, $95\%$ CI 2.15 to 15.82, $P \leq 0.001$, respectively). After gender and age adjustment, the relation of the TyG index of the 3rd quartile and 4th quartile with GLS < $18\%$ still existed (Q3 vs Q1: OR 4.87, $95\%$ CI 1.78 to 13.28, $$P \leq 0.002$$; Q4 vs Q1: OR 6.86, $95\%$ CI 2.44 to 19.30, $P \leq 0.001$, respectively). With further adjustment for confounders of age, gender, diabetes duration, systolic pressure, HbA1c, BMI, hypertension, heart rate, logarithmic microalbuminuria, LVEF and insulin therapy, the higher quartile of the TyG index remained an independent risk indicator related to GLS < $18\%$ (Q3 vs Q1: OR 4.52, $95\%$ CI 1.12 to 18.27, $$P \leq 0.034$$; Q4 vs Q1: OR 5.23, $95\%$ CI 1.12 to 24.51, $$P \leq 0.036$$).Table 3Logistic regression analysis of GLS < $18\%$ by TyG index quartiles Model 1 Model 2 Model 3 OR ($95\%$CI) P value OR ($95\%$CI) P value OR ($95\%$CI) P value Q1 (TyG index ≤ 8.89)Reference—Reference—Reference—Q2 (8.89 < TyG index ≤ 9.44)1.34 (0.50, 3.64)0.5611.59 (0.57, 4.48)0.3801.28 (0.30, 5.55)0.741Q3 (9.44 < TyG index ≤ 9.83)4.29 (1.63, 11.35) 0.003 4.87 (1.78, 13.28) 0.002 4.52 (1.12, 18.27) 0.034 Q4 (TyG index > 9.83)5.83 (2.15, 15.82) < 0.001 6.86 (2.44, 19.30) < 0.001 5.23 (1.12, 24.51) 0.036 Bold indicates $P \leq 0.05.$ Abbreviations: TyG, triglyceride glucose; OR, odds ratio; CI, confidential interval. Model 1: unadjusted; Model 2: adjusted for age and gender; Model 3: further adjusted for age, gender, diabetes duration, systolic pressure, HbA1c, BMI, hypertension, heart rate, logarithmic microalbuminuria, LVEF and insulin therapy The ROC curves depicted in Fig. 2 demonstrate the diagnostic validity of the TyG index in identifying subclinical LV systolic dysfunction (GLS < $18\%$). Notably, the TyG index with a cutoff value of 9.6 (AUC: 0.678; $P \leq 0.001$) displayed a sensitivity of $73.8\%$ with a specificity of $54.3\%$ for predicting GLS < $18\%$. HbA1c also exhibited a high AUC of 0.742, a sensitivity of $62.9\%$ and a specificity of $76.2\%$ for reduced GLS < $18\%$ ($P \leq 0.001$). Subsequently, the composite variable with the TyG index and HbA1c combined showed increased AUC and diagnostic values (AUC: 0.770; sensitivity: $65.7\%$, specificity: $80.0\%$, $P \leq 0.001$).Fig. 2Receiver‑operating characteristic curves for the prediction of reduced GLS (< $18\%$) in patients with type 2 diabetes using the TyG index and HbA1c. Abbreviations: GLS: global longitudinal strain; HbA1c: glycosylated hemoglobin Figure 3 shows three bull’s eye plots of representative cases with reduced GLS with high TyG index quartiles (Q2〜Q4).Fig. 3Three bull’s eye plots of representative cases with reduced GLS with high TyG index quartiles (Q2〜Q4). Abbreviations: GLS: global longitudinal strain; Q: quartile
## Discussion
To the best of our knowledge, the present study was the first to explore the association of the TyG index with LV longitudinal myocardial function in patients with type 2 diabetes and preserved ejection fraction. The results demonstrated a close relationship of the increased TyG index with an elevated risk of LV longitudinal myocardial dysfunction. Moreover, it was found that the composite parameters of the TyG index and HbA1c exhibited a certain value for the identification of reduced GLS < $18\%$.
The correlation between heart failure and diabetes has been notably confirmed by epidemiological and clinical studies [15–17]. However, among diabetes-related complications, diabetic cardiomyopathy, as an "entity", remains poorly understood. An increasing number of studies have indicated a hidden subclinical period in diabetic cardiomyopathy, featuring subtle abnormalities in function and structure [18]. In this context, asymptomatic LV dysfunction, defined as abnormal diastolic or systolic function without clinically detectable heart disease, is frequently reported in T2DM patients, which is expected to be between 50 and $70\%$ [19] and presented as LV systolic dysfunction in one-third of patients [20]. Recently, GLS has been adopted as a preferred indicator to evaluate global LV systolic function, considering the longitudinal subendocardial fibers as the most vulnerable fibers that are first damaged by metabolic disorders in the early stage of diabetic heart disease [21, 22]. Ernande et al. also proposed the presence of LV longitudinal dysfunction in T2DM patients with preserved LVEF but normal LV diastolic function, which was defined as GLS < $18\%$ [23]. The specific mechanism of this disorder remains to be uncovered. Metabolic characterizations have indicated that impaired insulin metabolic signaling is a contributing pathophysiological abnormality associated with diabetic cardiomyopathy [3, 4].
To date, the standard estimation of insulin resistance, the hyperinsulinemic-euglycemic clamp (HEGC) test, still requires diagnostic technology, which is expensive and is not available for basic-level hospital utilization. The TyG index, as an ideal surrogate of insulin resistance regardless of insulin treatment status, has been widely validated to be robustly related to cardiovascular events [24–26]. However, evidence on the validity of the TyG index on LV longitudinal myocardial function in those without prominent symptoms of heart failure is still not sufficient. Indeed, individuals with insulin resistance tend to develop systematic metabolic disorders, including dyslipidemia, hyperglycemia, and hypertension, which were also reported by the present study, as the highest quartile of the TyG index tended to be associated with a high prevalence of hypertension, poor blood glucose control and lipid levels. These interactions will significantly promote insulin resistance. Notably, these patients were more prone to suffering from reduced GLS than those in the lowest quartile. Subsequently, the multimodel logistics regression analysis demonstrated the independent association of a higher TyG index (ORs: 4.52 and 5.23 in the Q3 and Q4 groups compared with the Q1 group) with subclinical LV systolic dysfunction assessed by GLS < $18\%$. This result was consistent with the view of Ikonomidis et al., who reported that insulin resistance was related to GLS and resulted in LV longitudinal dysfunction in the immediate family of T2DM patients [27]. In fact, reduced coronary flow reserve has been shown to be a crucial determinant of LV longitudinal subendocardial myocardial fiber deformation. In addition, insulin resistance may induce myocardial injury through various other mechanisms, including oxidative stress, fibrosis, autonomic nervous dysfunction and inefficient energy metabolism [28, 29]. Accordingly, the TyG index may serve as the contributing reference for detecting cardiac involvement at a relatively earlier stage of diabetic heart disease.
In a study with a large cohort followed for 10 years, Sánchez-Íñigo et al. first proposed a positive correlation of the TyG index (AUC: 0.708) with heart events [30]. Similarly, the ROC curve plotted here indicated the clinical validity of the TyG index (AUC: 0.678) for reduced GLS. More interestingly, compared to HbA1c and the composite index, the TyG index with a cutoff value of 9.6 showed the highest sensitivity but the lowest specificity in predicting subclinical LV systolic dysfunction. This finding is in agreement with previous studies in which insulin resistance has been recognized as both a pathogenic trigger and a predictor of cardiovascular events [10]. Moreover, the AUC of the TyG index binding to HbA1c (AUC: 0.770) observed in this study provided an incremental predictive value for poor cardiac outcomes. Despite the absence of an absolute illustration of the underlying mechanisms of this relationship, it has been determined that the TyG index represents the combined effect of "glycotoxicity" and "lipid toxicity", which prominently contribute to the reduced endocardial collateral flow density and the impaired coronary microcirculation in patients with T2DM [4, 31]. Consequently, it is not unexpected to observe systemic lipid disturbances, including elevated total cholesterol, triglycerides, low-density lipoprotein cholesterol levels, and apolipoproteins in the present study, which in turn evoke oxidative stress and inflammation, with the potential to elicit lipotoxic cardiomyopathy [32]. These pathologies further support the triggering role of insulin resistance in the early initiation of myocardial function changes in diabetic patients, such as hyperglycemia and dysfunction of lipid oxidation and utilization [33].
## Study strengths and limitations
Previous studies have mostly focused on patients with existing symptoms of heart failure. In contrast, more emphasis was placed on early attention to the LV subclinical phase of T2DM patients in the present study. The relatively time-consuming requirement, high cost, and professional features of speckle tracking echocardiography and the accumulated training to perform effective measurement and analysis may limit its application in daily clinical practice for general diabetes physicians without enough experience with this technique. As stated above, the hyperglycemia elicited by insulin resistance activates the cascade of diabetic heart dysfunction, which induces metabolic disorders, followed by endothelial dysfunction, cardiac hypertrophy and fibrosis [34, 35]. HEGC has been confirmed to be closely related to poor prognosis in type 2 diabetes, exerting a praisable validity for the prevention and treatment of those patients. However, the complex operation and uneconomical test limit its wide availability in clinical practice. The homeostasis model assessment of insulin resistance (HOMA-IR) is considered another preferential index, which requires the measurement of fasting insulin. Because of the absence of a standardized method for insulin measurement or the recognized cutoff value, it is relatively difficult to apply in primary hospitals. Promisingly, the TyG index has been validated as a more accurate novel measurement of insulin resistance compared to HOMA⁃IR [36]. More prominently, this index is available and inexpensive in reality. The outcomes of this study demonstrated the validity of the TyG index to assist clinicians in screening people at high risk of cardiovascular events, exerting a more prominent role in the prevention and intervention of diabetic cardiomyopathy. Thus, it is recommended to perform punctual monitoring of the TyG index as soon as possible. For people in the T2DM population with a high TyG index in particular, metabolic disorders are suggested to be controlled earlier, and advanced hypoglycemic medicines for cardiovascular protection should be administered to effectively avoid and delay the occurrence and development of diabetic heart disease and ultimately bring clinical benefits to patients.
The limitations of this study should also be considered. First, as a result of the cross-sectional study, the specific causal relationship of the TyG index with reduced GLS remains unclear. Second, not all patients without coronary artery disease have undergone invasive coronary angiography. Third, despite the analysis on the medication of the patients, the underlying contributions of medications for lipid-lowering and LV function improvements failed to be controlled in this study. Fourth, insulin resistance parameters were not analyzed in this study. Last, covariates involved in the multivariable regression models were taken as potential confounders based on previous studies [37, 38] or their biological plausibility, which partly limits the further application of the research findings.
## Conclusions
In conclusion, the present study demonstrated a close relationship between an elevated TyG index and decreased GLS, which could be adopted as a sensitive and practical index to predict subclinical LV systolic dysfunction. Therefore, the TyG index should be punctually monitored for T2DM patients with preserved LVEF, which is expected to effectively identify the occurrence of diabetic heart disease and delay its development.
## References
1. Tan Y, Zhang Z, Zheng C, Wintergerst KA, Keller BB, Cai L. **Mechanisms of diabetic cardiomyopathy and potential therapeutic strategies: preclinical and clinical evidence**. *Nat Rev Cardiol* (2020.0) **17** 585-607. DOI: 10.1038/s41569-020-0339-2
2. Peterson LR, Gropler RJ. **Metabolic and Molecular Imaging of the Diabetic Cardiomyopathy**. *Circ Res* (2020.0) **126** 1628-1645. DOI: 10.1161/CIRCRESAHA.120.315899
3. Jia G, Whaley-Connell A, Sowers JR. **Diabetic cardiomyopathy: a hyperglycaemia- and insulin-resistance-induced heart disease**. *Diabetologia* (2018.0) **61** 21-28. DOI: 10.1007/s00125-017-4390-4
4. Jia G, Demarco VG, Sowers JR. **Insulin resistance and hyperinsulinaemia in diabetic cardiomyopathy**. *Nat Rev Endocrinol* (2016.0) **12** 144-153. DOI: 10.1038/nrendo.2015.216
5. Faden G, Faganello G, De Feo S, Berlinghieri N, Tarantini L, Di Lenarda A. **The increasing detection of asymptomatic left ventricular dysfunction in patients with type 2 diabetes mellitus without overt cardiac disease: data from the SHORTWAVE study**. *Diabetes Res Clin Pract* (2013.0) **101** 309-316. DOI: 10.1016/j.diabres.2013.07.004
6. Minciună IA, Hilda Orășan O, Minciună I, Lazar AL, Sitar-Tăut AV, Oltean M. **Assessment of subclinical diabetic cardiomyopathy by speckle-tracking imaging**. *Eur J Clin Invest* (2021.0) **51** e13475. DOI: 10.1111/eci.13475
7. Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, Martínez-Abundis E, Ramos-Zavala MG, Hernández-González SO. **The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp**. *J Clin Endocrinol Metab* (2010.0) **95** 3347-51. DOI: 10.1210/jc.2010-0288
8. Dikaiakou E, Vlachopapadopoulou EA, Paschou SA, Athanasouli F, Panagiotopoulos Ι, Kafetzi M. **Τriglycerides-glucose (TyG) index is a sensitive marker of insulin resistance in Greek children and adolescents**. *Endocrine* (2020.0) **70** 58-64. DOI: 10.1007/s12020-020-02374-6
9. Sánchez-García A, Rodríguez-Gutiérrez R, Mancillas-Adame L, González-Nava V, Díaz González-Colmenero A, Solis RC. **Diagnostic Accuracy of the Triglyceride and Glucose Index for Insulin Resistance: A Systematic Review**. *Int J Endocrinol* (2020.0) **2020** 4678526. DOI: 10.1155/2020/4678526
10. Tao LC, Xu JN, Wang TT, Hua F, Li JJ. **Triglyceride-glucose index as a marker in cardiovascular diseases: landscape and limitations**. *Cardiovasc Diabetol* (2022.0) **21** 68. DOI: 10.1186/s12933-022-01511-x
11. Alberti KG, Zimmet PZ. **Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation**. *Diabet Med* (1998.0) **15** 539-53. DOI: 10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S
12. Lang RM, Badano LP, Mor-Avi V, Afilalo J, Armstrong A, Ernande L. **Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging**. *J Am Soc Echocardiogr* (2015.0) **28** 1-39.e14. DOI: 10.1016/j.echo.2014.10.003
13. Ernande L, Bergerot C, Girerd N, Thibault H, Davidsen ES, Gautier Pignon-Blanc P. **Longitudinal myocardial strain alteration is associated with left ventricular remodeling in asymptomatic patients with type 2 diabetes mellitus**. *J Am Soc Echocardiogr* (2014.0) **27** 479-488. DOI: 10.1016/j.echo.2014.01.001
14. Marwick TH, Leano RL, Brown J, Sun JP, Hoffmann R, Lysyansky P. **Myocardial strain measurement with 2-dimensional speckle-tracking echocardiography: definition of normal range**. *JACC Cardiovasc Imaging* (2009.0) **2** 80-84. DOI: 10.1016/j.jcmg.2007.12.007
15. Kenny HC, Abel ED. **Heart Failure in Type 2 Diabetes Mellitus**. *Circ Res* (2019.0) **124** 121-141. DOI: 10.1161/CIRCRESAHA.118.311371
16. Mchugh K, Devore AD, Wu J, Matsouaka RA, Fonarow GC, Heidenreich PA. **Heart Failure With Preserved Ejection Fraction and Diabetes: JACC State-of-the-Art Review**. *J Am Coll Cardiol* (2019.0) **73** 602-611. DOI: 10.1016/j.jacc.2018.11.033
17. Jankauskas SS, Kansakar U, Varzideh F, Wilson S, Mone P, Lombardi A. **Heart failure in diabetes**. *Metabolism* (2021.0) **125** 154910. DOI: 10.1016/j.metabol.2021.154910
18. Falcão-Pires I, Leite-Moreira AF. **Diabetic cardiomyopathy: understanding the molecular and cellular basis to progress in diagnosis and treatment**. *Heart Fail Rev* (2012.0) **17** 325-344. DOI: 10.1007/s10741-011-9257-z
19. Natali A, Nesti L, Fabiani I, Calogero E, Di Bello V. **Impact of empagliflozin on subclinical left ventricular dysfunctions and on the mechanisms involved in myocardial disease progression in type 2 diabetes: rationale and design of the EMPA-HEART trial**. *Cardiovasc Diabetol* (2017.0) **16** 130. DOI: 10.1186/s12933-017-0615-6
20. Cioffi G, Giorda CB, Chinali M, Di Lenarda A, Faggiano P, Lucci D. **Analysis of midwall shortening reveals high prevalence of left ventricular myocardial dysfunction in patients with diabetes mellitus: the DYDA study**. *Eur J Prev Cardiol* (2012.0) **19** 935-943. DOI: 10.1177/1741826711417759
21. Ikonomidis I, Tzortzis S, Triantafyllidi H, Parissis J, Papadopoulos C, Venetsanou K. **Association of impaired left ventricular twisting-untwisting with vascular dysfunction, neurohumoral activation and impaired exercise capacity in hypertensive heart disease**. *Eur J Heart Fail* (2015.0) **17** 1240-1251. DOI: 10.1002/ejhf.403
22. Dillmann WH. **Diabetic Cardiomyopathy**. *Circ Res* (2019.0) **124** 1160-1162. DOI: 10.1161/CIRCRESAHA.118.314665
23. Ernande L, Bergerot C, Rietzschel ER, De Buyzere ML, Thibault H, Pignonblanc PG. **Diastolic dysfunction in patients with type 2 diabetes mellitus: is it really the first marker of diabetic cardiomyopathy?**. *J Am Soc Echocardiogr* (2011.0) **24** 1268-75.e1. DOI: 10.1016/j.echo.2011.07.017
24. Garofolo M, Gualdani E, Scarale MG, Bianchi C, Aragona M, Campi F. **Insulin Resistance and Risk of Major Vascular Events and All-Cause Mortality in Type 1 Diabetes: A 10-Year Follow-up Study**. *Diabetes Care* (2020.0) **43** e139-e141. DOI: 10.2337/dc20-0433
25. Laakso M, Kuusisto J. **Insulin resistance and hyperglycaemia in cardiovascular disease development**. *Nat Rev Endocrinol* (2014.0) **10** 293-302. DOI: 10.1038/nrendo.2014.29
26. Lebovitz HE. **Insulin resistance–a common link between type 2 diabetes and cardiovascular disease**. *Diabetes Obes Metab* (2006.0) **8** 237-249. DOI: 10.1111/j.1463-1326.2005.00521.x
27. Ikonomidis I, Pavlidis G, Lambadiari V, Kousathana F, Varoudi M, Spanoudi F. **Early detection of left ventricular dysfunction in first-degree relatives of diabetic patients by myocardial deformation imaging: The role of endothelial glycocalyx damage**. *Int J Cardiol* (2017.0) **233** 105-112. DOI: 10.1016/j.ijcard.2017.01.056
28. Zhao CT, Wang M, Siu CW, Hou YL, Wang T, Tse HF. **Myocardial dysfunction in patients with type 2 diabetes mellitus: role of endothelial progenitor cells and oxidative stress**. *Cardiovasc Diabetol* (2012.0) **11** 147. DOI: 10.1186/1475-2840-11-147
29. Witteles RM, Fowler MB. **Insulin-resistant cardiomyopathy clinical evidence, mechanisms, and treatment options**. *J Am Coll Cardiol* (2008.0) **51** 93-102. DOI: 10.1016/j.jacc.2007.10.021
30. Sánchez-Íñigo L, Navarro-González D, Fernández-Montero A, Pastrana-Delgado J, Martínez JA. **The TyG index may predict the development of cardiovascular events**. *Eur J Clin Invest* (2016.0) **46** 189-197. DOI: 10.1111/eci.12583
31. Adameova A, Dhalla NS. **Role of microangiopathy in diabetic cardiomyopathy**. *Heart Fail Rev* (2014.0) **19** 25-33. DOI: 10.1007/s10741-013-9378-7
32. Nakamura M, Sadoshima J. **Cardiomyopathy in obesity, insulin resistance and diabetes**. *J Physiol* (2020.0) **598** 2977-2993. DOI: 10.1113/JP276747
33. Pan Y, Zhong S, Zhou K, Tian Z, Chen F, Liu Z. **Association between Diabetes Complications and the Triglyceride-Glucose Index in Hospitalized Patients with Type 2 Diabetes**. *J Diabetes Res* (2021.0) **2021** 8757996. DOI: 10.1155/2021/8757996
34. El Hayek MS, Ernande L, Benitah JP, Gomez AM, Pereira L. **The role of hyperglycaemia in the development of diabetic cardiomyopathy**. *Arch Cardiovasc Dis* (2021.0) **114** 748-760. DOI: 10.1016/j.acvd.2021.08.004
35. 35.Avagimyan A, Popov S, Shalnova S. The Pathophysiological Basis of Diabetic Cardiomyopathy Development. Curr Probl Cardiol. 2022;47:101156.
36. Son DH, Lee HS, Lee YJ, Lee JH, Han JH. **Comparison of triglyceride-glucose index and HOMA-IR for predicting prevalence and incidence of metabolic syndrome**. *Nutr Metab Cardiovasc Dis* (2022.0) **32** 596-604. DOI: 10.1016/j.numecd.2021.11.017
37. Mochizuki Y, Tanaka H, Matsumoto K, Sano H, Toki H, Shimoura H. **Clinical features of subclinical left ventricular systolic dysfunction in patients with diabetes mellitus**. *Cardiovasc Diabetol* (2015.0) **14** 37. DOI: 10.1186/s12933-015-0201-8
38. Yamauchi Y, Tanaka H, Yokota S, Mochizuki Y, Yoshigai Y, Shiraki H. **Effect of heart rate on left ventricular longitudinal myocardial function in type 2 diabetes mellitus**. *Cardiovasc Diabetol* (2021.0) **20** 87. DOI: 10.1186/s12933-021-01278-7
|
---
title: 'Associations of risk factor burden and genetic predisposition with the 10-year
risk of atrial fibrillation: observations from a large prospective study of 348,904
participants'
authors:
- Junguo Zhang
- Ge Chen
- ChongJian Wang
- Xiaojie Wang
- Zhengmin (Min) Qian
- Miao Cai
- Michael G. Vaughn
- Elizabeth Bingheim
- Haitao Li
- Yanhui Gao
- Gregory Y. H. Lip
- Hualiang Lin
journal: BMC Medicine
year: 2023
pmcid: PMC9993634
doi: 10.1186/s12916-023-02798-7
license: CC BY 4.0
---
# Associations of risk factor burden and genetic predisposition with the 10-year risk of atrial fibrillation: observations from a large prospective study of 348,904 participants
## Abstract
### Background
Understanding the effects of risk factor burden and genetic predisposition on the long-term risk of atrial fibrillation (AF) is important to improve public health initiatives. However, the 10-year risk of AF considering risk factor burden and genetic predisposition is unknown.
### Methods
A total of 348,904 genetically unrelated participants without AF at baseline from the UK were categorized into three groups: index ages 45 years ($$n = 84$$,206), 55 years ($$n = 117$$,520), and 65 years ($$n = 147$$,178). Optimal, borderline, or elevated risk factor burden was determined by body mass index, blood pressure, diabetes mellitus, alcohol consumption, smoking status, and history of myocardial infarction or heart failure. Genetic predisposition was estimated using the polygenic risk score (PRS), constructed using 165 predefined genetic risk variants. The combined effects of risk factor burden and PRS on the risk of incident AF in 10 years were estimated for each index age. Fine and Gray models were developed to predict the 10-year risk of AF.
### Results
The overall 10-year risk of AF was $0.67\%$ ($95\%$ CI: 0.61–$0.73\%$) for index age 45 years, $2.05\%$ ($95\%$ CI: 1.96–$2.13\%$) for index age 55 years, and $6.34\%$ ($95\%$ CI: 6.21–$6.46\%$) for index age 65 years, respectively. An optimal risk factor burden was associated with later AF onset regardless of genetic predisposition and sex ($P \leq 0.001$). Significant synergistic interactions were observed for risk factor burden with PRS at each index age ($P \leq 0.05$). Participants with an elevated risk factor burden and high PRS had the highest 10-year risk of AF in reference to those who had both an optimal risk factor burden and a low PRS. At younger ages, optimal risk burden and high PRS might also lead to later onset of AF, compared to the joint effect of elevated risk burden and low/intermediate PRS.
### Conclusions
Risk factor burden together with a genetic predisposition is associated with the 10-year risk of AF. Our results may be helpful in selecting high-risk individuals for primary prevention of AF and facilitating subsequent health interventions.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12916-023-02798-7.
## Background
Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmias and poses a substantial health and economic burden worldwide [1]. Along with the population aging, a rapid increase in AF prevalence and incidence has been observed [2]. Given that one third of the total AF population is asymptomatic [3], and there is a high mortality risk at the first ($20\%$) and fifth ($50\%$) year after an AF diagnosis, it is critical to identify high-risk population groups at risk of developing AF who are more likely to benefit from preventive interventions [4].
The European Society of Cardiology has advocated the use of risk prediction models to determine individuals who could benefit from specific preventive treatment, thus improving cost-effectiveness and enhancing healthcare [5]. Additionally, estimating individual risk over a 10-year period is the most commonly used risk model of primary prevention [6]. However, both the 10-year risk of AF developed by the Framingham Heart Study [7] and the CHARGE-AF consortium short-term risk tool [8] were developed more than a decade ago, which might not be systematically ‘recalibrated’ to contemporary AF rates. Furthermore, considering the change in the prevalence of risk factors for AF and the failure to include genetic information, these conventional risk scores might not be able to accurately represent the current impact on AF [9]. The 10-year risk developed from recent data will aid in identifying susceptible populations with appropriately predicted risk probability [10].
The estimate of the 10-year risk of AF should integrate information on widely available, easy to measure, and conventional risk factors. In addition to aging, several modifiable risk factors for AF prevention have been well established and described, including lifestyle risk factors and concomitant cardiovascular diseases [11–13]. These risk factors, whether at borderline or elevated levels, have the potential to increase the risk of AF, especially in conjunction with one another [14]. Moreover, the occurrence of AF is also affected by genetic predisposition, with approximately $11\%$ of the variation in AF explained by total genome-wide genetic variation [15]. *While* genetic predisposition is often assumed to be deterministic, there is evidence that risk factor burden can attenuate high genetic risk [16, 17]. Integrating both modifiable risk factors and genetic predisposition could help improve AF risk prediction and result in more effective strategies [18]. However, standing in the way of this goal is a lack of evidence on the relationship between these two components and their association with the short-term probability of developing AF.
Leveraging data from the UK Biobank, which covers a wide range of modifiable risk factors as well as genetic data, the primary aim of this study is to estimate the 10-year risk of AF in various subgroups with different genetic and clinical factors, and second, to evaluate the combined effects and potential interactions of risk factors and genetic predisposition on AF incidence.
## Study design and population
Data from the UK Biobank (Application Number: 69550) were applied for the present analysis. We included all participants aged 40–69 years enrolled in 2006 to 2010 with complete baseline assessments, including information on demographic and socioeconomic factors. The detailed UK Biobank protocol is available elsewhere [19].
Participants who were diagnosed with AF at baseline ($$n = 6748$$), had missing data for the risk factors of interest ($$n = 46$$,400, mainly due to missing blood pressure and glycated hemoglobin), missing quality-controlled genotyping data ($$n = 95$$,175), or withdrew from the study ($$n = 1298$$) were excluded from the current analysis. The resulting sample included 352,804 participants (Fig. 1). The detailed information on quality-controlled genotyping data was presented in Additional file 1: Text S1.Fig. 1Selection of the study sample with index age years Participants were divided into three categories: index ages 45, 55, and 65 years, according to the ages at assessment: 40–49 ($$n = 84$$,506), 50–59 ($$n = 117$$,520), and 60–69 ($$n = 147$$,178) years. For example, at index age 55 years, the criteria for selecting the participants were that they did not develop AF and were still alive before 55 years old. If participants were recruited between 50 and 55 years old or between 55 and 59 years old, the follow-up times of those started at 55 years or at the specific age when participant attained, respectively. The same approach was applied for participants with index ages 45 years and 65 years.
## Follow-up and outcome ascertainment
Participants free of AF were followed up from the later dates of their index age until the first AF occurrence, death, loss to follow-up, end of the ten-year follow-up, or March 31, 2021, whichever occurred first.
The outcome of the present study was the incidence of AF including both atrial fibrillation and flutter. Physician diagnosis of incident AF occurring during the 10-year follow-up was identified through the primary care system, hospital inpatient records, and death registry [19]. The date of death was ascertained by linking to death registries of the National Health Service Information Centre. Ascertainment of AF in the UK Biobank (ICD code I48) was mainly extracted from the “first occurrence” of health outcomes, which incorporates the diagnoses of AF at different time points from recruitment. We also collected the cases directly from the following codes: [1] non-cancer illness code, self-reported [1471, 1483]; [2] Operation code [1524], [3] diagnoses – main/secondary ICD10 (I48, I48.0–4, I48.9); [4] underlying (primary/secondary) cause of death: ICD10 (I48, I48.0–4, I48.9); [5] diagnoses – main/secondary ICD9 [4273]; [6] operative procedures – main/secondary OPCS (K57.1, K62.1–4).
## Definition of risk factors burden and profiles
In the current study, risk factor burden for each participant was constructed using a series of risk factors for AF, which was in accordance with the widely used CHARGE-AF risk score and previous literature [7, 20, 21]. The risk factors under analysis include BMI, alcohol consumption, smoking status, blood pressure, diabetes mellitus, and history of myocardial infarction or heart failure. Each risk factor was classified into two (optimal and elevated) or three (elevated, borderline, or optimal) categories, as described in Table 1. Details of the assessment of covariates are shown in Additional file 1: Text S2 [22]. An overall risk factor burden was calculated and then categorized into three levels: optimal (all risk factors were optimal), borderline (the borderlines were presented but no elevated factors were present), and elevated (any one of the risk factors was elevated) [20].Table 1Definitions of risk factorsRisk factor and categoryDefinitionSmoking OptimalNever smoker BorderlineFormer smoker ElevatedCurrent smokerAlcohol consumption OptimalConsumption of < 6 standard drinks/day for women or < 8 standard drinks/day for men ElevatedConsumption of ≥ 6 standard drinks/day for women or ≥ 8 standard drinks/day for menBody mass index Optimal< 25 Borderline25–29 Elevated≥ 30Blood pressure OptimalSystolic blood pressure < 120 mm Hg and diastolic blood pressure < 80 mm Hg, and no treatment or history for hypertension BorderlineSystolic blood pressure 120-139 mm Hg or diastolic blood pressure 80-89 mm Hg, and no treatment or history for hypertension ElevatedSystolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥ 90 mm Hg, and/or treatment or history for hypertensionDiabetes mellitus (type 1 or 2) OptimalGlycated hemoglobin (HbA1c) < 6.5 and no treatment or history for diabetes ElevatedGlycated hemoglobin (HbA1c) ≥ 6.5 or treatment or history for diabetesHistory of heart failure or myocardial infarction OptimalNo history of heart failure or myocardial infarction ElevatedHistory of heart failure or myocardial infarction Furthermore, a risk factor profile was constructed to reflect the risk factor burden with different numbers of borderline/elevated risk factors, which was categorized into 7 groups: “$\frac{0}{0}$,” “$\frac{0}{1}$,” “$\frac{0}{2}$,” “0/≥3,” “1/any,” “2/any,” and “≥3/any.”
## Definition of genetic predisposition
To estimate the genetic predisposition of AF, we constructed a polygenic risk score for each participant, which was derived from the current optimal genetic risk variant list from the meta-analyses excluding the UK Biobank participants ($$n = 165$$ SNPs, Additional file 1: Text S3, and Table S1) [15]. Briefly, most of the genome-wide significant risk variants for AF fall in genes that cause serious heart defects in humans (e.g., PITX2, TBX5) or near genes important for striated muscle function and integrity (e.g., CFL2, MYH7), which are crucial for the function of cardiac ion channels and calcium signaling. According to the number of risk alleles, we used imputed data to calculate the PRS through multiplying by the regression coefficient obtained from the previous study [23]: PRS = (β1 × SNP1 + β2 × SNP2 + … + β165 × SNP165). Furthermore, we classified each participant into three categories: low (lowest quartile), intermediate (mid two quartiles), and high (highest quartile) genetic AF risk groups.
## Statistical analysis
Baseline characteristics including risk factors and PRS were reported as a frequency (proportion) and were compared across different index ages using the chi-square test. To investigate the potential interactions between risk factor burden and PRS on both additive and multiplicative scales within strata of index age and sex, a new term with nine categories representing nine combinations (3×3) of risk factor burden levels and PRS levels was created.
To determine an additive interaction, both the attributable proportion (AP) and relative excess risks due to interaction (RERI), and their $95\%$ confidence intervals (CIs) with the reference group of optimal risk factor burden/profiles and low PRS were calculated, and these measures represented the combined excess risk in both exposed groups [24]. The P-values for the additive scale in each index age were adjusted by FDR (false discovery rate). For the multiplicative scale, likelihood tests were performed to compare the model with and without the multiplicative interaction term.
The 10-year risk for incident AF from index ages of 45, 55, and 65 years up to a maximum of 10 years was calculated for each risk factor burden/profile and each risk factor separately, and then we performed subgroup analyses stratified by sex and PRS. To account for the competing death risk and to avoid inflating the cumulative incidence, a modified Kaplan-Meier estimate with age as the time scale was used to compute the 10-year risks of AF and associated $95\%$ CIs [25]. Z ratio tests using low level as the reference were compared to the 10-year risks in the elevated and borderline risk groups.
Multivariable Fine and Gray models for each index age were fitted to predict the 10-year risk of AF (full details in Additional file 1: Text S4) [26, 27]. Calibration plots and C-index were used to evaluate the performance of the models. The predicted 10-year risks of AF for 16 risk profiles were presented in different sexes and PRS separately. All analyses were conducted using SAS software, version 9.4 (SAS Institute), and R software 4.1.1 (R Development Core Team, Vienna, Austria).
## Characteristics at index ages 45, 55, and 65 years
A total of 348,904 participants were included in statistical analysis defined by index ages of 45 ($$n = 84$$,206), 55 ($$n = 117$$,520), and 65 ($$n = 147$$,178) years (Table 2 and Fig. 1). Among participants at index ages 45, 55, and 65 years, 924 ($1.1\%$), 3883 ($3.3\%$), and 12,924 ($8.8\%$) developed AF during follow-up, respectively. Within each respective group, $45.6\%$, $43.6\%$, and $46.9\%$ were men and $90.1\%$, $94.2\%$, and $96.9\%$ were white. As index age increased, the proportion of participants that reported smoking decreased, while blood pressure, prevalent diabetes, and history of MI or HF increased. Elevated blood pressure was the most common risk factor ($33.8\%$ at age 45 years, $52.5\%$ at 55 years, and $70.9\%$ at 65 years). The PRS of AF ranged from 7.10 to 11.67 with a median of 9.29. Differences in baseline characteristics between low, intermediate, and high PRS at different index ages are presented in Additional file 1: Table S2-4.Table 2Characteristics of participantsVariables45 years($$n = 84$$,206)55 years($$n = 117$$,520)65 years($$n = 147$$,178)p valueSex<.0001 Female45,841 (54.4)66,283 (56.4)78,119 (53.1) Male38,365 (45.6)51,237 (43.6)69,059 (46.9)Ethnicity<.0001 Non-White8320 (9.9)6783 (5.8)4564 (3.1) White75,886 (90.1)110,737 (94.2)142,614 (96.9)Smoking<.0001 Optimal51,565 (61.2)66,321 (56.4)74,110 (50.4) Borderline20,966 (24.9)38,239 (32.5)61,075 (41.5) Elevated11,675 (13.9)12,960 (11.0)11,993 (8.1)Alcohol consumption<.0001 Optimal81,845 (97.2)114,010 (97.0)143,757 (97.7) Elevated2361 (2.8)3510 (3.0)3421 (2.3)BMI<.0001 Optimal32,605 (38.7)39,883 (33.9)44,801 (30.4) Borderline33,340 (39.6)48,407 (41.2)66,694 (45.3) Elevated18,261 (21.7)29,230 (24.9)35,683 (24.2)Blood pressure<.0001 Optimal21,228 (25.2)16,632 (14.2)9135 (6.2) Borderline34,490 (41.0)39,243 (33.4)33,749 (22.9) Elevated28,488 (33.8)61,645 (52.5)104,294 (70.9)Diabetes mellitus<.0001 Optimal81,713 (97.0)111,303 (94.7)135,551 (92.1) Elevated2493 (3.0)6217 (5.3)11,627 (7.9)Heart history<.0001 Optimal83,751 (99.5)115,620 (98.4)141,774 (96.3) Elevated455 (0.5)1900 (1.6)5404 (3.7)Polygenic risk score<.0001 Low21,118 (25.0)29,380 (25.0)37,730 (25.6) Intermediate41,910 (49.8)58,811 (50.1)74,193 (50.4) High21,178 (25.2)29,329 (25.0)35,255 (24.0)
## Single risk factors, risk factor burden, and 10-year risk of atrial fibrillation
The overall 10-year risks of AF were $0.67\%$ ($95\%$ CI: $0.61\%$ to $0.73\%$) for index age 45 years, $2.05\%$ ($95\%$ CI: $1.96\%$ to $2.13\%$) for index age 55 years, and $6.34\%$ ($95\%$ CI: $6.21\%$ to $6.46\%$) for index age 65 years. The 10-year risk of AF increased gradually at each index age with an increase in each single risk factor profile. History of MI or HF had the strongest association with a 10-year risk of AF at each index age, followed by diabetes mellitus and alcohol consumption (Additional file 1: Table S5).
Table 3, Fig. 2, and Additional file 2: Figure S1-S2 show the 10-year risks of AF for index ages 45, 55, and 65 years, respectively, with the elevated, borderline, or optimal risk factor burden. The 10-year risk was the lowest for the participants with optimal factors, whereas the risk was higher for participants with elevated factors for index age 45, 55, and 65 years (Table 3). The 10-year AF risks were all higher in men than in women (Additional file 1: Table S6 & S7).Table 3Ten-year risk (%) of atrial fibrillation in all and different PRS according to risk factor burden, after the adjustment for competing risk of deathStudy sample (index age) and risk factor burdenAllLow PRSIntermediate PRSHigh PRSNo. of atrial fibrillation cases/total10-year risk ($95\%$ CI)No. of atrial fibrillation cases/total10-year risk ($95\%$ CI)No. of atrial fibrillation cases/total10-year risk ($95\%$ CI)No. of atrial fibrillation cases/total10-year risk ($95\%$ CI)45 years Optimal$\frac{30}{79130.25}$ (0.13, 0.36)$\frac{5}{19740.26}$ (0.03, 0.50)$\frac{10}{39040.18}$ (0.03, 0.32)$\frac{15}{20350.37}$ (0.10, 0.64) Borderline$\frac{237}{32}$,1840.44 (0.36, 0.51)*$\frac{30}{81090.27}$ (0.15, 0.38)$\frac{94}{16}$,0560.35 (0.25, 0.44)*$\frac{113}{80190.79}$ (0.59, 0.99)* Elevated$\frac{657}{44}$,1090.92 (0.83, 1.01)*$\frac{90}{11}$,0350.54 (0.39, 0.68)*$\frac{283}{21}$,9500.74 (0.63, 0.86)*$\frac{284}{11}$,1241.65 (1.40, 1.90)*55 years Optimal$\frac{88}{57191.13}$ (0.84, 1.41)$\frac{9}{14440.43}$ (0.09, 0.78)$\frac{41}{28280.98}$ (0.60, 1.35)$\frac{38}{14472.10}$ (1.34, 2.86) Borderline$\frac{698}{35}$,4051.29 (1.17, 1.41)$\frac{80}{88650.61}$ (0.44, 0.78)$\frac{294}{17}$,7781.02 (0.87, 1.18)$\frac{324}{87622.52}$ (2.17, 2.86) Elevated$\frac{3097}{76}$,3962.47 (2.35, 2.58)*$\frac{415}{19}$,0711.28 (1.11, 1.45)*$\frac{1421}{38}$,2052.18 (2.03, 2.33)*$\frac{1261}{19}$,1204.22 (3.92, 4.52)*65 years Optimal$\frac{111}{28672.93}$ (2.29, 3.57)$\frac{24}{7762.27}$ (1.16, 3.38)$\frac{35}{13801.71}$ (1.00, 2.42)$\frac{52}{7116.02}$ (4.23, 7.81) Borderline$\frac{1551}{29}$,6863.88 (3.65, 4.11)*$\frac{253}{75412.43}$ (2.07, 2.79)$\frac{693}{15}$,0563.44 (3.14, 3.74)*$\frac{605}{70896.35}$ (5.76, 6.94) Elevated$\frac{11262}{11}$,46257.05 (6.90, 7.20)*$\frac{1751}{29}$,4133.91 (3.68, 4.14)*$\frac{5448}{57}$,7576.72 (6.51, 6.93)*$\frac{4063}{27}$,45511.10 (10.72, 11.49)*Abbreviations: CI confidence interval, PRS polygenic risk score*Test comparing lifetime risk in borderline and elevated risk groups with optimal risk group by z ratio test (that is, the difference in lifetime risk between two groups divided by its standard error). Significant differences between subgroup effects with p-values < 0.05Fig. 2Cumulative risk (%) for atrial fibrillation according to risk factor burdens (optimal, borderline, or elevated) at index age 55 years. Shading = $95\%$ confidence intervals. Participants entered the study sample between ages 55 and < 60 years; therefore, the number at risk increased from age 55 years to < 60 years
## Potential interactions between risk factor burden and PRS on the AF incidence
Table 4 showed the combined health impact of risk factor burden and PRS on AF incidence. At each index age, compared to those with optimal risk factor burden or low PRS, participants with higher risk factor burden or higher PRS generally had a higher risk of AF. Furthermore, compared with those who had optimal risk factor burden and low PRS, participants with an elevated risk factor burden and high PRS had the highest risk of developing AF at index age 45 years (HR: 7.32; $95\%$ CI: 3.02 to 17.70), 55 years (HR: 8.41; $95\%$ CI: 4.37 to 16.20), and 65 years (HR: 4.12; $95\%$ CI: 2.76 to 6.14).Table 4Combined effects of risk factor burden and polygenic risk score and atrial fibrillation incidenceStudy sample (index age) and risk factor burdenPRS levels (HR, $95\%$ CI)aRERI bP for interactioncLowIntermediateHighIntermediateHigh45 years Optimal1.001.01 (0.35, 2.96)2.89 (1.05, 7.95)*0.63 Borderline1.14 (0.44, 2.93)1.81 (0.74, 4.46)4.45 (1.82, 10.88)*0.65 (−0.25, 1.55)1.15 (−0.55, 2.84) Elevated2.28 (0.93, 5.62)3.65 (1.51, 8.84)*7.32 (3.02, 17.70)*1.43 (0.43, 2.42)*3.35 (0.61, 6.08)*55 years Optimal1.002.37 (1.15, 4.86)*4.32 (2.09, 8.94)*0.08 Borderline1.24 (0.62, 2.47)2.28 (1.17, 4.42)*5.17 (2.66, 10.02)*−0.35 (−1.56, 0.85)0.42 (−1.13, 1.97) Elevated2.67 (1.38, 5.16)*4.61 (2.40, 8.89)*8.41 (4.37, 16.20)*0.63 (−0.21, 1.48)2.44 (0.69, 4.20)*65 years Optimal1.000.83 (0.50, 1.40)2.40 (1.49, 3.89)*0.002 Borderline0.97 (0.64, 1.47)1.33 (0.89, 2.00)2.56 (1.70, 3.84)*0.53 (0.16, 0.90)*0.18 (−0.59, 0.95) Elevated1.54 (1.03, 2.30)*2.51 (1.68, 3.73)*4.12 (2.76, 6.14)*1.13 (0.83, 1.43)*1.16 (0.49, 1.84)*Abbreviations: PRS polygenic risk score, HR hazard ratio, CI confidence interval, RERI relative excess risk due to interactionaAll results were calculated after adjusting for sexbOn the additive scale, the estimates of RERI were calculated based on the reference group with optimal factor burden and low PRScOn the multiplicative scale, likelihood tests were applied to test the significance of the interaction term by comparing the model with and without the interaction term*P-values < 0.05, and the P-values for additive scale in each index age were adjusted by FDR (false discovery rate) On an additive scale, positive interactions were observed for risk factor burden (optimal versus elevated levels) with the PRS at each index age (Padditive-scale < 0.05, Table 4, Additional file 1: Table S8). For example, for participants at index age 55 with elevated risk factor burden and high PRS, the RERI and AP were 2.44 and 0.29, suggesting that a 2.44 relative excess risk was due to the additive interaction. This accounted for $29\%$ of the risk of AF in participants who had elevated risk factor burden and high PRS. The values of RERIs and APs tended to decrease as index age increased in participants with high PRS and risk factor burden. On a multiplicative scale, significant interactions were found between risk factor burden and PRS in relation to AF risk at index age 65 years (Pmultiplicative-scale < 0.05). Similar results were also observed in the subgroup of sex (Additional file 1: Table S9 and S10).
## Risk factor burden and 10-year risk of AF stratified by genetic predisposition
Among participants without AF at age index age 45, 55, and 65 years, those in optimal risk burdens and low PRS had 10-year risks of AF of $0.26\%$, $0.43\%$, and $2.27\%$ separately, whereas those in the elevated risk burdens and high PRS had 10-year risks of AF of $1.65\%$, $4.22\%$, and $11.10\%$, respectively (Table 3).
For individuals at index ages 55 and 65 with optimal risk factor burden and high PRS of AF, the 10-year risks were $2.10\%$ and $6.02\%$, with values higher than those in other elevated clinical risk factor burden strata at the same index age. At the index age of 45 years, compared with the participants with the optimal burden and high PRS, participants with an elevated risk factor burden and low/intermediate PRS had higher 10-year AF risk. At the same strata of risk factor burden, gradients of increased AF risk were observed with increasing risk of PRS. Stratification analyses by sex were conducted, yielding results that were generally consistent with our main findings (Additional file 1: Table S6 & S7).
## Risk factor profile borderline or multiple elevated risk factors and 10-year risk of AF
Additional file 1: Table S11 shows the distributions of risk factor profiles (number of elevated/borderline risk factors) at age index age 45, 55, and 65 years. The participants with optimal risk factors decreased from $9.4\%$ to $4.9\%$ and $2.0\%$ with the index age changed from 45 to 55 and 65 years, respectively.
As age increased, the number of participants with one or more borderline risk factors decreased, and gradually transitioned to having one to three elevated risk factors. Significant interactions were observed between risk factor profile and PRS in relation to AF risk (Additional file 1: Table S12). Table 5 and Additional file 1: Tables S13 and S14 show the 10-year risk among the participants with elevated or multiple borderline risk factors when the participants were separated into low, intermediate, and high PRS, respectively. The results were similar to the 10-year risks associated with risk factor burden. Especially, the 10-year risks were higher in men with optimal risk factor profiles and high PRS compared to those with elevated risk factor profiles (≥3/any) and low PRS at the index age of 65 years. In contrast, the opposite results were observed in the index age 45 years. Table 5Ten-year risk (%) of atrial fibrillation by risk factor profiles and number of elevated/borderline risk factors after the adjustment for competing risk of deathRisk factor profile and number of elevated/borderline risk factorsIndex age 45 yearsIndex age 55 yearsIndex age 65 yearsNo. of atrial fibrillation cases/total10-year risk ($95\%$ CI)No. of atrial fibrillation cases/total10-year risk ($95\%$ CI)No. of atrial fibrillation cases/total10-year risk ($95\%$ CI)Optimal $\frac{0}{030}$/79130.25 (0.13, 0.36)$\frac{88}{57191.13}$ (0.84, 1.41)$\frac{111}{28672.93}$ (2.29, 3.57)Borderline $\frac{0}{190}$/15,2310.36 (0.26, 0.46)$\frac{252}{14}$,9921.08 (0.91, 1.26)$\frac{537}{11}$,0383.63 (3.27, 4.00) $\frac{0}{2119}$/13,3560.48 (0.35, 0.60)$\frac{318}{15}$,2261.37 (1.18, 1.56)$\frac{667}{12}$,9473.81 (3.47, 4.15) 0/>=$\frac{328}{35970.60}$ (0.34, 0.86)$\frac{128}{51871.65}$ (1.29, 2.01)$\frac{347}{57014.53}$ (3.97, 5.08)Elevated 1/any$\frac{281}{28}$,1660.62 (0.52, 0.71)$\frac{1302}{45}$,4751.76 (1.63, 1.88)$\frac{5460}{69}$,2835.55 (5.38, 5.73) 2/any$\frac{255}{12}$,6891.27 (1.07, 1.48)$\frac{1160}{23}$,8202.96 (2.73, 3.18)$\frac{3997}{34}$,4848.38 (8.08, 8.69) >= 3/any$\frac{121}{32542.16}$ (1.64, 2.69)$\frac{635}{71015.36}$ (4.82, 5.91)$\frac{1805}{10}$,85812.33 (11.69, 12.97)Abbreviations: CI confidence interval
## Predicted 10-year risk of AF
Additional file 1: Table S15-S17 presented three multivariable predictive models of the 10-year risk of AF at index ages 45, 55, and 65 years. The 10-year risk of AF is shown in Fig. 3 and Additional file 2: Figure S3-S6 for 16 different risk profiles for both men and women. Fig. 3A Predicted 10-year risk (%) of atrial fibrillation at index age 55 years across 16 risk profiles, in men with different polygenic risk scores (PRS; low, intermediate, or high). Profiles were defined according to treatment for hypertension (yes or no), body mass index (25 or 35), alcohol (elevated (E) or optimal (O)), and history of myocardial infarction or heart failure (yes or no). For each profile, white participants with a borderline blood pressure level (systolic blood pressure 128 mmHg and diastolic blood pressure 80 mmHg), who never smoked, and who had no diabetes were considered. B Predicted 10-year risk (%) of atrial fibrillation at index age 55 years across 16 risk profiles in women with different polygenic risk scores (low, intermediate, or high) Higher predicted 10-year risks of AF were observed in men and in those with high PRS. Participants with high PRS, history of MI or HF, elevated alcohol consumption, a BMI of 35 or higher, and hypertension treatment had the greatest 10-year risks in both men and women. The C-indexes of the predicted model at index ages 45, 55, and 65 years were $73.5\%$ ($95\%$ CI: $71.7\%$ to $75.2\%$), $71.0\%$ ($95\%$ CI: $70.1\%$ to $71.9\%$), and $67.1\%$ ($95\%$ CI: $66.2\%$ to $68.1\%$), respectively. Additional file 2: Figures S7-S9 show the calibration plots of the predictive models at index ages 45, 55, and 65 years.
## Discussion
To the best of our knowledge, this is the largest prospective cohort study to illustrate the 10-year risk of AF which takes into account both risk factor burden and genetic predisposition. Thus, overall findings were consistent with our study hypothesis, more specifically, among participants aged 45, 55, and 65 years, the overall 10-year risks of AF were $0.67\%$, $2.05\%$, and $6.34\%$, respectively. The 10-year risk of AF at index ages 45, 55, and 65 years ranged from $0.26\%$, $0.43\%$, and $2.27\%$ among participants in the lowest tertiles of risk factor burden and genetic predisposition, rising to almost $1.65\%$, $4.22\%$, and $11.10\%$ among those in the highest tertiles of risk factor burden and genetic predisposition. The 10-year risk was highest in men with high PRS and elevated risk factor burden at each index age.
There are three key clinical implications to be drawn from this analysis. First, risk factor burden/profiles, genetic predisposition, and their interactions play pivotal roles in the risk of AF, especially among male participants and those of a younger age. Especially, the risk of AF attributable to a risk factor burden/profile increased along with the increased risk due to genetic predisposition. Second, age was the most prominent risk factor for AF. Along with aging, the contributions of risk factors and genetic predisposition on AF risk decreased, especially among men. Third, at younger ages, elevated risk burden/profiles with low/intermediate PRS might lead to earlier onset of AF, compared to the joint effect of high PRS and optimal risk burden/profiles. Hence, an optimal risk burden is important for AF prevention, especially for individuals who are younger or with high genetic predisposition of AF.
## Comparison with other studies
The Framingham Heart Study estimated short-term and lifetime AF risk among 9764 participants without AF at ages 55 to 75 years [21]. Though instrumental in value, cohort studies are needed to further refine the joint effect of both polygenic risk and other risk factors. Our updated 10-year risks, estimated by the contribution of both risk factors burden and genetic predisposition, developed from a substantially larger prospective cohort study, offers superior generalizability to contemporary populations and helps identify high-risk populations [10].
While previous literature has reported that the 10-year risk of AF is $2.0\%$, $4.5\%$, and $7.6\%$ among participants in the index age of 55 years with low, intermediate, and high polygenic risk [21], the observed lower 10-year risk in our study may be attributable to a relatively healthier cohort in UK Biobank. Furthermore, the incidence of AF increases rapidly after the age of 40, and the 10-year AF risk varies with age [7].
Previous studies have largely focused on middle-aged and older adults (aged ≥ 55 years old) to investigate the 10-year risk of AF, and it is uncertain of the applicability to younger individuals (age ≤ 55). Indeed, prospectively identifying these relatively young individuals at risk for AF might be beneficial, allowing for early intervention measures [28]. Our study included younger participants (index age 45 years) and observed that the joint effect of elevated risk factors burden/profiles and high inherited predisposition on AF incidence declined with advancing age. Furthermore, among the younger age group, compared to genetic predisposition, risk factor burden/profiles have a larger effect on AF development. Our results suggest that a more favorable risk burden/profile might delay the onset of AF risk, especially among young people and men. Similar to a previous study [21], participants with high polygenic risk had twice the 10-year risk than those with low PRS, underscoring the independent impact of an inherited predisposition to AF risk.
Three AF multivariable predictive models had moderate discrimination (C-index $67.1\%$ to $73.5\%$) but suboptimal calibration. The performances of our models were comparable to other models reported in prior studies [20, 29]. In our predictive models, the strongest risk factors were sex, history of MI or HF, and PRS. Both the 10-year and predicted risks of AF were higher in men than women, as previously noted [30]. One possible explanation might be that women had more favorable risk burdens compared to men. Furthermore, considering the main outcomes in the present study, which include both AF and flutter, risk factor burden and genetic predisposition might have an independent prognostic impact in terms of different types of AF [31].
Compared to the performances of previously used models, we included ethnicity as an important covariate in the AF prediction models due to the accumulated evidence that white individuals have a higher AF risk than those who are not white [32]. Furthermore, the preponderance of AF cases in our population ($56\%$, $71\%$, and $82\%$ in the index age of 45, 55, and 65 years, respectively) developed in participants with a history of MI, HF, hypertension, or diabetes, suggesting that comorbidities contributed more to AF risk in older individuals. Additionally, lifestyle risk factors including BMI, alcohol consumption, and smoking, which are modifiable, also played important roles in 10-year risk and our predictive models. Alcohol consumption led to the highest 10-year risk of AF among lifestyle risk factors. There is growing evidence on the adverse effects of alcohol on left atrial function, electrical remolding, and structure. These effects would contribute to AF [33]. Considering the close association of risk factors with the risk of AF, these modifiable factors may be better targets for AF prevention [34].
Furthermore, we hypothesize that our prediction models could be used to further inform targeted screening of individuals at risk, which may be more cost-effective than routinely screening all patients aged ≥65 years [35, 36]. The risk factor burden of AF is an easily interpretable and accessible tool of structured management, which can be readily measured with existing and future data. Moreover, with the increasing ubiquity of genomic data, both in the clinical setting and via direct-to-consumer testing, the current optimal variant list of a PRS for AF could potentially provide a more accurate personalized risk assessment [15, 21]. For individuals at an index age of 45, 55, and 65 with elevated risk factor burden and high PRS, the 10-year risks of AF were $1.65\%$, $4.22\%$, and $11.10\%$, respectively, which could potentially be used as thresholds. The application of the prediction models could be used to inform thresholds for screening in individuals with high predicted 10-year risks, potentially leading to earlier detection, guidance on preventive approaches, and individualized counseling.
## Strengths and limitations
The strengths of this study include the uniquely large sample size, including younger participants; long-term follow-up; detailed information on behavioral, clinical profile, and genetic data; the large number of AF cases included in the analysis; and the estimation of 10-year risks incorporating both risk burden/profiles and genetic predisposition with adjustment for competing risks. Prediction models and potential thresholds were developed in our study to help implement early and targeted interventions.
Our study also has some limitations. Importantly, biomarkers, electrocardiographic data, anemia, electrolyte imbalance, and other potential risk factors for AF were not included in our study [37]. Nonetheless, some studies have pointed out that electrocardiogram data may not improve the performance of predictive models [8, 38]. Some AF events caused by acute events, such as trauma, surgery, etc., may have been missed since the majority of events were ascertained using hospital inpatient data, thus the true risk of AF might be underestimated since undiagnosed AF is common [3]. In addition, measurements of risk factor burden/profiles were attained at baseline and could not reflect changes over time. Participants recruited in the UK Biobank are healthier and with a higher socioeconomic status than the general UK population, which might result in healthy volunteer bias [39]. Given that an adequate number of participants with various levels of exposure were evaluated with high internal validity, this might not have an impact on the valid estimates of relationships [39]. The UK Biobank does not record close relationships or not release the kinship coefficients of all participants estimated from genetic data, which prevents us from implementing mixed-effects models that account for the relatedness among the samples. Finally, our study used only one cohort to predict 10-year risks and predictive models of AF, and whether our findings can be generalized to other ethnic groups needs further investigation.
## Conclusions
An optimal risk factor burden, including maintaining a normal body weight, no smoking, moderate to no alcohol consumption, and treatment for hypertension and diabetes mellitus, may be important for AF prevention, especially among men, participants with high genetic predisposition, or those at a young age. Our predictive model would be helpful to identify high-risk populations for primary AF prevention and facilitate preventive measures.
## Supplementary Information
Additional file 1: Text S1. The detail information on quality-controlled genotyping data. Text S2. Details of the assessment of covariates. Text S3. Detailed definition of genetic predisposition. Text S4. Details of multivariable Fine and Gray models and C-index. Table S1. Individual atrial fibrillation SNP association atrial fibrillation odds. Table S2. Characteristics of participants at the index age of 45 years according to PRS, divided into Low, Intermediate, and High. Table S3. Characteristics of participants at the index age of 55 years according to PRS, divided into Low, Intermediate, and High. Table S4. Characteristics of participants at the index age of 65 years according to PRS, divided into Low, Intermediate, and High. Table S5. 10-year risk (%) of atrial fibrillation by individual risk factors, after adjustment for competing risk of death. Table S6. 10-year risk (%) of atrial fibrillation in PRS in men according to the risk factor burden, after adjustment for competing risk of death. Table S7. 10-year risk (%) of atrial fibrillation in PRS in women according to the risk factor burden, after adjustment for competing risk of death. Table S8. Attributable proportion of risk factor burdens and PRS and AF incidence in overall and by sex. Table S9. Combined effects of risk factor burdens and PRS and AF incidence in men. Table S10. Combined effects of risk factor burdens and PRS and AF incidence in women. Table S11. Distribution of risk factor profiles. Table S12. Additive and multiplicative interactions between risk factor profiles and PRS in relation to AF incidence. Table S13. 10-year risk (%) of atrial fibrillation in men by risk factor profiles (number of elevated/borderline risk factors) and PRS, after adjustment for competing risk of death. Table S14. 10-year risk (%) of atrial fibrillation in women by risk factor profiles (number of elevated/borderline risk factors) and PRS, after adjustment for competing risk of death. Table S15. Multivariable prediction model of the 10-year risk of atrial fibrillation at index age 45 years. Table S16. Multivariable prediction model of the 10-year risk of atrial fibrillation at index age 55 years. Table S17. Multivariable prediction model of the 10-year risk of atrial fibrillation at index age 65 years. Additional file 2: Central illustration. Figure S1. Cumulative risk (%) for atrial fibrillation according to risk factor burdens (optimal, borderline, or elevated) at the index age of 45 years. Figure S2. Cumulative risk (%) for atrial fibrillation according to risk factor burdens (optimal, borderline, or elevated) at the index age of 65 years. Figure S3. Predicted 10-year risk (%) of atrial fibrillation at index age 45 years across 16 risk profiles, in men with different polygenic risk score (low, intermediate, or high). Figure S4. Predicted 10-year risk (%) of atrial fibrillation at index age 45 years across 16 risk profiles, in women with different polygenic risk score (low, intermediate, or high). Figure S5. Predicted 10-year risk (%) of atrial fibrillation at index age of 65 years across 16 risk profiles, in men with different polygenic risk score (low, intermediate, or high). Figure S6. Predicted 10-year risk (%) of atrial fibrillation at index age of 65 years across 16 risk profiles, in women with different polygenic risk score (low, intermediate, or high). Figure S7. Calibration plot of the prediction model for predicting 10-year risk at index age of 45 years. Figure S8. Calibration plot of the prediction model for predicting 10-year risk at index age 55 years. Figure S9. Calibration plot of the prediction model for predicting 10-year risk at index age 65 years.
## References
1. Kornej J, Borschel CS, Benjamin EJ, Schnabel RB. **Epidemiology of atrial fibrillation in the 21st century: novel methods and new insights**. *Circ Res* (2020.0) **127** 4-20. DOI: 10.1161/CIRCRESAHA.120.316340
2. Di Carlo A, Bellino L, Consoli D, Mori F, Zaninelli A, Baldereschi M. **Prevalence of atrial fibrillation in the Italian elderly population and projections from 2020 to 2060 for Italy and the European Union: the FAI project**. *Europace* (2019.0) **21** 1468-1475. DOI: 10.1093/europace/euz141
3. Svennberg E, Engdahl J, Al-Khalili F, Friberg L, Frykman V, Rosenqvist M. **Mass screening for untreated atrial fibrillation: the STROKESTOP study**. *Circulation* (2015.0) **131** 2176-2184. DOI: 10.1161/CIRCULATIONAHA.114.014343
4. Piccini JP, Hammill BG, Sinner MF, Hernandez AF, Walkey AJ, Benjamin EJ. **Clinical course of atrial fibrillation in older adults: the importance of cardiovascular events beyond stroke**. *Eur Heart J* (2014.0) **35** 250-256. DOI: 10.1093/eurheartj/eht483
5. Rossello X, Dorresteijn JA, Janssen A, Lambrinou E, Scherrenberg M, Bonnefoy-Cudraz E. **Risk prediction tools in cardiovascular disease prevention: a report from the ESC prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the acute cardiovascular care association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)**. *Eur J Prev Cardiol* (2019.0) **26** 1534-1544. DOI: 10.1177/2047487319846715
6. **Third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) final report**. *Circulation* (2002.0) **106** 3143-3421. DOI: 10.1161/circ.106.25.3143
7. Schnabel RB, Sullivan LM, Levy D, Pencina MJ, Massaro JM, D'Agostino RB. **Development of a risk score for atrial fibrillation (Framingham heart study): a community-based cohort study**. *Lancet* (2009.0) **373** 739-745. DOI: 10.1016/S0140-6736(09)60443-8
8. Alonso A, Krijthe BP, Aspelund T, Stepas KA, Pencina MJ, Moser CB. **Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium**. *J Am Heart Assoc* (2013.0) **2** e000102. DOI: 10.1161/JAHA.112.000102
9. Matheny M, McPheeters ML, Glasser A, Mercaldo N, Weaver RB, Jerome RN, Walden R, McKoy JN, Pritchett J, Tsai C. **U.S. preventive services task force evidence syntheses, formerly systematic evidence reviews**. (2011.0)
10. Yang X, Li J, Hu D, Chen J, Li Y, Huang J. **Predicting the 10-year risks of atherosclerotic cardiovascular disease in Chinese population: the China-PAR project (prediction for ASCVD risk in China)**. *Circulation* (2016.0) **134** 1430-1440. DOI: 10.1161/CIRCULATIONAHA.116.022367
11. Rahman F, Kwan GF, Benjamin EJ. **Global epidemiology of atrial fibrillation**. *Nat Rev Cardiol* (2014.0) **11** 639-654. DOI: 10.1038/nrcardio.2014.118
12. Ding M, Wennberg A, Gigante B, Walldius G, Hammar N, Modig K. **Lipid levels in midlife and risk of atrial fibrillation over 3 decades-experience from the Swedish AMORIS cohort: a cohort study**. *PLoS Med* (2022.0) **19** e1004044. DOI: 10.1371/journal.pmed.1004044
13. Zhao M, Song L, Zhao Q, Chen Y, Li B, Xie Z. **Elevated levels of body mass index and waist circumference, but not high variability, are associated with an increased risk of atrial fibrillation**. *BMC Med* (2022.0) **20** 215. DOI: 10.1186/s12916-022-02413-1
14. Huxley RR, Lopez FL, Folsom AR, Agarwal SK, Loehr LR, Soliman EZ. **Absolute and attributable risks of atrial fibrillation in relation to optimal and borderline risk factors: the atherosclerosis risk in communities (ARIC) study**. *Circulation* (2011.0) **123** 1501-1508. DOI: 10.1161/CIRCULATIONAHA.110.009035
15. Nielsen JB, Thorolfsdottir RB, Fritsche LG, Zhou W, Skov MW, Graham SE. **Biobank-driven genomic discovery yields new insight into atrial fibrillation biology**. *Nat Genet* (2018.0) **50** 1234-1239. DOI: 10.1038/s41588-018-0171-3
16. Khera AV, Emdin CA, Drake I, Natarajan P, Bick AG, Cook NR. **Genetic risk, adherence to a healthy lifestyle, and coronary disease**. *N Engl J Med* (2016.0) **375** 2349-2358. DOI: 10.1056/NEJMoa1605086
17. Said MA, Verweij N, van der Harst P. **Associations of combined genetic and lifestyle risks with incident cardiovascular disease and diabetes in the UK biobank study**. *JAMA Cardiol* (2018.0) **3** 693-702. DOI: 10.1001/jamacardio.2018.1717
18. Fawzy AM, Lip GYH. **Cardiovascular disease prevention: risk factor modification at the heart of the matter**. *Lancet Region Health Western Pacific* (2021.0) **17** 100291. DOI: 10.1016/j.lanwpc.2021.100291
19. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J. **UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age**. *PLoS Med* (2015.0) **12** e1001779. DOI: 10.1371/journal.pmed.1001779
20. Staerk L, Wang B, Preis SR, Larson MG, Lubitz SA, Ellinor PT. **Lifetime risk of atrial fibrillation according to optimal, borderline, or elevated levels of risk factors: cohort study based on longitudinal data from the Framingham heart study**. *BMJ* (2018.0) **361** k1453. DOI: 10.1136/bmj.k1453
21. Weng LC, Preis SR, Hulme OL, Larson MG, Choi SH, Wang B. **Genetic predisposition, clinical risk factor burden, and lifetime risk of atrial fibrillation**. *Circulation* (2018.0) **137** 1027-1038. DOI: 10.1161/CIRCULATIONAHA.117.031431
22. Tu SJ, Gallagher C, Elliott AD, Linz D, Pitman BM, Hendriks JML. **Risk thresholds for Total and beverage-specific alcohol consumption and incident atrial fibrillation**. *JACC Clin Electrophysiol* (2021.0) **7** 1561-1569. DOI: 10.1016/j.jacep.2021.05.013
23. Wang N, Sun Y, Zhang H, Wang B, Chen C, Wang Y. **Long-term night shift work is associated with the risk of atrial fibrillation and coronary heart disease**. *Eur Heart J* (2021.0) **42** 4180-4188. DOI: 10.1093/eurheartj/ehab505
24. VanderWeele TJ, Knol MJ. **A tutorial on interaction**. *J Epidemiol Methods* (2014.0) **3** 33-72
25. Beiser A, D'Agostino RB, Seshadri S, Sullivan LM, Wolf PA. **Computing estimates of incidence, including lifetime risk: Alzheimer's disease in the Framingham study. The practical incidence estimators (PIE) macro**. *Stat Med* (2000.0) **19** 1495-1522. DOI: 10.1002/(SICI)1097-0258(20000615/30)19:11/12<1495::AID-SIM441>3.0.CO;2-E
26. Gerds TA, Schumacher M. **Efron-type measures of prediction error for survival analysis**. *Biometrics* (2007.0) **63** 1283-1287. DOI: 10.1111/j.1541-0420.2007.00832.x
27. Mogensen UB, Ishwaran H, Gerds TA. **Evaluating random forests for survival analysis using prediction error curves**. *J Stat Softw* (2012.0) **50** 1-23. DOI: 10.18637/jss.v050.i11
28. Kolek MJ, Graves AJ, Xu M, Bian A, Teixeira PL, Shoemaker MB. **Evaluation of a prediction model for the development of atrial fibrillation in a repository of electronic medical records**. *JAMA Cardiol* (2016.0) **1** 1007-1013. DOI: 10.1001/jamacardio.2016.3366
29. Sinner MF, Stepas KA, Moser CB, Krijthe BP, Aspelund T, Sotoodehnia N. **B-type natriuretic peptide and C-reactive protein in the prediction of atrial fibrillation risk: the CHARGE-AF consortium of community-based cohort studies**. *Europace* (2014.0) **16** 1426-1433. DOI: 10.1093/europace/euu175
30. Magnussen C, Niiranen TJ, Ojeda FM, Gianfagna F, Blankenberg S, Njolstad I. **Sex differences and similarities in atrial fibrillation epidemiology, risk factors, and mortality in community cohorts: results from the BiomarCaRE consortium (biomarker for cardiovascular risk assessment in Europe)**. *Circulation* (2017.0) **136** 1588-1597. DOI: 10.1161/CIRCULATIONAHA.117.028981
31. Nuzzi V, Cannata A, Manca P, Castrichini M, Barbati G, Aleksova A. **Atrial fibrillation in dilated cardiomyopathy: outcome prediction from an observational registry**. *Int J Cardiol* (2021.0) **323** 140-147. DOI: 10.1016/j.ijcard.2020.08.062
32. 32.Alonso A, Roetker NS, Soliman EZ, Chen LY, Greenland P, Heckbert SR. Prediction of atrial fibrillation in a racially diverse cohort: the multi-ethnic study of atherosclerosis (MESA). J Am Heart Assoc. 2016;5(2):e003077.
33. Voskoboinik A, Wong G, Lee G, Nalliah C, Hawson J, Prabhu S. **Moderate alcohol consumption is associated with atrial electrical and structural changes: insights from high-density left atrial electroanatomic mapping**. *Heart Rhythm* (2019.0) **16** 251-259. DOI: 10.1016/j.hrthm.2018.10.041
34. Schnabel RB, Aspelund T, Li G, Sullivan LM, Suchy-Dicey A, Harris TB. **Validation of an atrial fibrillation risk algorithm in whites and African Americans**. *Arch Intern Med* (2010.0) **170** 1909-1917. DOI: 10.1001/archinternmed.2010.434
35. Hindricks G, Potpara T, Dagres N, Arbelo E, Bax JJ, Blomström-Lundqvist C. **2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): the task force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) developed with the special contribution of the European heart rhythm association (EHRA) of the ESC**. *Eur Heart J* (2021.0) **42** 373-498. DOI: 10.1093/eurheartj/ehaa612
36. Jones NR, Taylor CJ, Hobbs FDR, Bowman L, Casadei B. **Screening for atrial fibrillation: a call for evidence**. *Eur Heart J* (2020.0) **41** 1075-1085. DOI: 10.1093/eurheartj/ehz834
37. Kim M, Hong M, Kim JY, Kim IS, Yu HT, Kim TH. **Clinical relationship between anemia and atrial fibrillation recurrence after catheter ablation without genetic background**. *Int J Cardiol Heart Vasc* (2020.0) **27** 100507. PMID: 32258364
38. Aronson D, Shalev V, Katz R, Chodick G, Mutlak D. **Risk score for prediction of 10-year atrial fibrillation: a community-based study**. *Thromb Haemost* (2018.0) **118** 1556-1563. DOI: 10.1055/s-0038-1668522
39. Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T. **Comparison of Sociodemographic and health-related characteristics of UK biobank participants with those of the general population**. *Am J Epidemiol* (2017.0) **186** 1026-1034. DOI: 10.1093/aje/kwx246
|
---
title: miR-34a/DRP-1-mediated mitophagy participated in cisplatin-induced ototoxicity
via increasing oxidative stress
authors:
- Haiyan Wang
- Hanqing Lin
- Weibiao Kang
- Lingfei Huang
- Sisi Gong
- Tao Zhang
- Xiaotong Huang
- Feinan He
- Yongyi Ye
- Yiyang Tang
- Haiying Jia
- Haidi Yang
journal: BMC Pharmacology & Toxicology
year: 2023
pmcid: PMC9993635
doi: 10.1186/s40360-023-00654-1
license: CC BY 4.0
---
# miR-34a/DRP-1-mediated mitophagy participated in cisplatin-induced ototoxicity via increasing oxidative stress
## Abstract
### Purpose
Cisplatin is a widely used and effective chemotherapeutic agent for most solid malignant tumors. However, cisplatin-induced ototoxicity is a common adverse effect that limits the therapeutic efficacy of tumors in the clinic. To date, the specific mechanism of ototoxicity has not been fully elucidated, and the management of cisplatin-induced ototoxicity is also an urgent challenge. Recently, some authors believed that miR34a and mitophagy played a role in age-related and drug-induced hearing loss. Our study aimed to explore the involvement of miR-34a/DRP-1-mediated mitophagy in cisplatin-induced ototoxicity.
### Methods
In this study, C57BL/6 mice and HEI-OC1 cells were treated with cisplatin. MiR-34a and DRP-1 levels were analyzed by qRT‒PCR and western blotting, and mitochondrial function was assessed via oxidative stress, JC-1 and ATP content. Subsequently, we detected DRP-1 levels and observed mitochondrial function by modulating miR-34a expression in HEI-OC1 cells to determine the effect of miR-34a on DRP-1-mediated mitophagy.
### Results
MiR-34a expression increased and DRP-1 levels decreased in C57BL/6 mice and HEI-OC1 cells treated with cisplatin, and mitochondrial dysfunction was involved in this process. Furthermore, the miR-34a mimic decreased DRP-1 expression, enhanced cisplatin-induced ototoxicity and aggravated mitochondrial dysfunction. We further verified that the miR-34a inhibitor increased DRP-1 expression, partially protected against cisplatin-induced ototoxicity and improved mitochondrial function.
### Conclusion
MiR-34a/DRP-1-mediated mitophagy was related to cisplatin-induced ototoxicity and might be a novel target for investigating the treatment and protection of cisplatin-induced ototoxicity.
## Introduction
Cisplatin is one of the earliest approved platinum compounds and is a widely used and highly effective chemotherapeutic medicine for many types of tumors including ovarian, uterine, testicular malignant tumors, head and neck cancer and other solid tumors [1, 2], greatly improve the survival rate and quality of life of tumor patients. However, it was reported that the incidence of ototoxicity is approximately $40\%$-$80\%$ [3, 4]. Cisplatin causes bilateral, progressive and irreversible sensorineural hearing loss, often associated with vertigo and tinnitus [3]. To date, there is a lack of available prevention and treatment for ototoxicity. Therefore, ototoxicity limits the maximum treatment effect of tumors and negatively affects the quality life and long-term survival of tumor survivors, particularly children and adolescents with cancer [5]. To date, the precise molecular mechanism of cisplatin-induced ototoxicity has been incompletely elucidated. Thus, further elucidating the pathogenesis of cisplatin-induced ototoxicity is an important research objective for developing a novel therapy. At present, it is widely accepted that the accumulation of reactive oxygen species (ROS) in the cochlea plays an important role in the process, possibly involving inflammation, apoptosis, pyroptosis, ER stress, autophagy and necroptosis [6, 7]. Hearing loss results at least in part from excessive ROS generation in cochlear cells, leading to mitochondrial damage, metabolic disruption, and cell death [4].
Therefore, it is necessary to further investigate the mechanism of cisplatin-induced ototoxicity.
MicroRNAs (miRNAs) are endogenous RNAs of approximately 22 nt that can play important regulatory roles in animals [8]. They regulate gene and protein expression by binding to target mRNA, leading to mRNA degradation or inhibition of translation [9]. Therefore, they have a negative regulatory effect on the relevant gene or protein expression. MiRs are involved in multiple cellular processes, such as development, differentiation, proliferation, autophagy, mitophagy and apoptosis [10, 11].
Recently, miRs were observed to be highly expressed in various cells of the animal cochlea and associated with inner ear development and pathogenesis [12, 13]. MiR-34a is involved in senescence, apoptosis, autophagy and cell death [14, 15]. Previous studies have suggested that miR-34a plays an important role in acquired sensorineural hearing loss, such as age-related hearing loss [8, 12, 16] and antibiotic-induced ototoxicity [17]. It has also been reported that miR-34a can serve as a potential biomarker to evaluate CDDP-related nephrotoxicity [18]. However, the role of miR-34a in cisplatin-induced ototoxicity remains unclear.
Mitochondria are highly dynamic organelles in eukaryotic cells that regularly fuse and divide themselves to maintain a balance, known as mitochondrial dynamics [19]. An abnormal balance between mitochondrial fission and fusion has been linked to various diseases, including cardiac diseases, neurologic diseases, cancer, and diabetes [20]. Mitochondrial dynamics participate in the oxidative stress response. It is well known that mitochondria are the main sources of ROS, the release of ROS causes further damage to mitochondrial components, and ROS-induced oxidative stress is involved in cochlear damage [21, 22]. Abnormal mitochondria can be eliminated through mitophagy. Dynamin-related protein 1 (Drp1), a GTPase enzyme, is an essential mediator of mitochondrial fission [23] to initiate mitophagy. Yoshiyuki Ikeda. et al. demonstrated that inhibition of mitophagy was caused by downregulation of Drp1, leading to mitochondrial accumulation [24].
According to the TargetScan database and the literature [25], DRP-1 is a target gene of miR-34a. To date, the role of miR-34a/DRP1 in cisplatin-induced ototoxicity has not been elucidated. The present study investigated the effect of miR-34a/DRP1 on mitophagy in the process of cisplatin-induced ototoxicity and the change in mitochondrial function to elucidate the possible mechanisms of cisplatin-induced ototoxicity.
## Materials
Cisplatin (CDDP, Selleck, S1166, USA), DRP1(immunoway, YT1414, USA), LC3B (ABclonal, A19665, China), β-actin（cell signalingtechnology, #4970, USA), Lipofectamine™ 3000 Transfection Reagent (Invitrogen, L3000001, USA), Myosin VIIa (Abcam, ab150386, England), BCA protein assay kit (Beyotime, cx00098, China), CCK8 (APExBio, K1018, USA), JC-1 (Beyotime, C2006, China), DCFH-DA (Beyotime, S0033S, China), miR-34amimic/inhibitor/control (Ruibo, PA20201227003, China), and DAPI (Solarbio, C0065-10, China).
## Animals and cisplatin ototoxicity model
A total of 40 male C57BL/6 mice (18–20 g, 6 weeks old) were purchased from Guangdong Yaokang Biotechnology Company (Foshan, China). The mice were housed in the Animal Center of Jinan University at 23 ± 2 °C and 50–$60\%$ relative humidity with a 12 h light/dark cycle and free access to food and water. After adapting to the environment for 10 days and ABR measurement, the mice were randomly divided into a control group ($$n = 20$$) and a cisplatin group ($$n = 20$$). Three cycles of the cisplatin administration regimen were used according to previous studies to simulate the clinical administration of cisplatin to establish the ototoxicity model [26]. Briefly, cisplatin was dissolved in saline solution at a concentration of 1 mg/mL. The mice in the cisplatin groups were received 3.0 mg/kg cisplatin once daily (intraperitoneal injection) for 4 days, followed by 10 days for recovery as a cycle for a total of three cycles. The control groups were injected with normal saline (3 mg/kg.d) on the same schedule. All procedures of animal experiments were approved by the Committee on the Ethics of Animal Care and Use of Jinan University (Guangzhou, China, Permit NO. IACUC-20210426–02). All animals received research according to the criteria outlined in the “Guide for the Care and Use of Laboratory Animals” prepared by the National Academy of Sciences and published by the National Institutes of Health. All methods were reported in accordance with ARRIVE guidelines.
## Auditory Brainstem Response (ABR)
The mice were anesthetized using a mixture of ketamine (100 mg/kg) and xylazine (10 mg/kg). ABR testing was measured using Tucker-Davis Technologies (TDT system III, Alachua, FL, USA) 3 days before and at the end of cisplatin administration. Three subcutaneous needle electrodes were inserted at the vertex (active), under the pinna of the left ear (reference), and in the middle of the back (ground). The earphone was placed on the left auricle of the mice, and the sound stimuli were presented directly into the ear canal in the acoustic shielding room. The auditory waveforms within 10 ms (ms) were recorded after tone bursts with a 1 ms rise/fall time at frequencies of 8, 16, and 32 kHz. The average response to 1000 stimuli was obtained by reducing the sound intensity at 5 dB intervals from 100 to 0 dB SPL. The ABR threshold was defined as the lowest stimulation intensity that produced a replicable waveform response.
## Tissue preparation
At the end of 3 cycles of cisplatin administration, the deeply anesthetized mice were sacrificed by cervical dislocation after ABR detection and then decapitated, and the cochlea were collected. The temporal bones were washed with fresh ice-cold $4\%$ PBS and then placed into a 30 mm diameter Petri dish containing fresh ice-cold $4\%$ PBS. Under a dissection microscope, fine forceps were used to remove the stapes and tissue. The volute was scanned from the oval window parallel to the spiral of the basilar membrane using Venus scissors, and then a fracture line was cut from the bottom to the apical turn along with the spiral plane at the edge of the volute. The volute was gently removed with a fine forceps and needle, and the basilar membrane tissue was immediately placed into a centrifuge tube, snap frozen in liquid nitrogen, and stored at -80 °C for subsequent RNA or protein extraction. On the other hand, the cochlea was removed from the skull, the stapes was removed, a small hole was made in the apical turn of the cochlea, the round window was pierced, and $4\%$ paraformaldehyde was perfused. Then, the cochlea was immersed in $4\%$ paraformaldehyde overnight at 4 °C and decalcified in $10\%$ sodium ethylenediaminetetraacetic acid for 48 h at room temperature on a shaker. The basilar membrane was dissected under a microscope for immunofluorescence staining.
## Hair cell counting
The basilar membrane samples were permeabilized with $2.5\%$ Triton X-100 in 1X PBS for 15 min at room temperature on a shaker. Then, the specimens were washed 3 times with PBS and blocked in $10\%$ goat serum solution for 1 h at room temperature. After washing with PBS three times, cochlear sections were incubated with phalloidin (1:200) for 2 h at room temperature in the dark, counterstained with DAPI for 8 min and washed three times with PBS. The samples were mounted on glass slides in 10 µl anti-fluorescence quenching agent. Hair cells were visualized using an Olympus BX63 fluorescence microscope from the apex to the base of the cochlea, and then the outer hair cells were counted.
## HEI-OC1 cell culture
House Ear Institute-Organ of Corti 1 (HEI-OC1) auditory cells were obtained from Lin Baixin Medical Research Central. Cells were cultured in Dulbecco’s modified Eagle’s medium (Gibco, USA) containing $10\%$ fetal bovine serum (Gibco, USA) without antibiotics in a 33 °C incubator supplemented with $5\%$ CO2 in air.
## Cell transfection
miR34a mimic or inhibitor and negative control were purchased from Ruibo Biology Technology Company (Ruibo, Guangzhou, China). HEI-OC1 cells were plated into 6-well plates at a density of 1.5 × 105/well. Cells were grown to $50\%$ confluence and then transfected with 5 µM miR34a mimic or 10 µM inhibitor using serum-free Opti-MEM (Gibco, USA) and Lipofectamine 3000 transfection agent (Invitrogen, USA) following the manufacturer’s instructions. Negative controls were generated using mimic or inhibitor control with the same procedure. Cells were incubated with the transfection mixture for 8 h at 37 °C, then replaced with normal DMEM and further incubated for 40 h.
## CCK-8 cell viability analysis
Cells were seeded in 96-well plates at a density of 5 × 103 cells per well overnight. They were treated with various concentrations (10, 20, 30, and 40 µM) of cisplatin for different times (8, 16, 24, and 48 h) and 20 µM cisplatin for 24 h following transfection for 48 h. Cell viability was detected using Cell Counting Kit-8 (CCK-8) according to the manufacturer’s protocol. At the indicated time, 10 µl/well CCK-8 reagent was added and then incubated at 37 °C for 2.5 h in the dark. A microplate reader (Thermo Fisher Scientific, USA) was used to detect the absorbance at 450 nm.
## Intracellular ROS level detection assay
Intracellular ROS levels were detected by a Reactive Oxygen Species Assay Kit/DCFH-DA (2′,7′-Dichlorofluorescin diacetate) (Beyotime, S0033S, China), a fluorescent probe for living cells used according to the manufacturer’s protocols. The FACS Calibur system (BD Biosciences) was used to measure the green fluorescence intensity.
## Mitochondrial membrane potential (MMP) assay
Mitochondrial membrane potential assay kit with JC-1 was used to measure the MMP following the manufacturer’s instructions. JC-1 staining solution was diluted at a ratio of 1:1000 to the working concentration. After treatment with 20 µM cisplatin for 24 h, the cells were harvested, and then 1 mL of JC-1 working solution was added and incubated in the incubator at 37 °C for 20 min in the dark. Then, the cells were washed and analyzed using a FACS Calibur flow cytometer (Becton Dickinson, USA Bioscience).
## ATP content analysis
An Enhanced ATP Assay Kit was used according to the manufacturer’s protocols. The chemiluminescence intensity was measured by a SpectraMax M5 microplate reader (Molecular Devices). The concentration of ATP in the sample was calculated according to the standard curve. The protein concentration was measured with the BCA protein quantification kit and normalized to nmol/mg.
## Real-time polymerase chain reaction (RT‒PCR)
Total RNA was extracted with an EZ-press RNA Purification Kit (EZBioscience, USA). Total RNA was reverse transcribed to cDNA using the Color Reverse Transcription Kit (EZBioscience, USA) following the manufacturer’s instructions. Reverse transcription was performed at 42 °C for 15 min and 95 °C for 30 s.
MiR34a expression was measured with Color SYBR Green qPCR Master Mix (EZBioscience, USA) by using the Roche LightCycler96 Real-Time PCR system (Roche Applied Science, Rotkreuz, Switzerland). The amplification program was 40 cycles of denaturing at 95 °C for 10 s, annealing at 60 °C for 30 s, and extension at 60 °C for 30 s. MiR-34a and U6 were purchased from RiboBio (Guangzhou, China). The sequences of specific primers were used as follows: miR-34a forward:5’-ACACTCCAGCTGGGTGGCAGTGTCTTAGCTGGT-3’, Reverse:5’-CTCAACTGGTGTCGTGGA-3’;U6forward:5’-GCTTCGGCAGCACATATACTAA-3’, reverse: 5’-AACGCTTCACGAATTTGCGT-3’. The expression level of miR-34a was defined from the Ct. U6 was used as an endogenous control. The 2−ΔΔt method was used for relative quantification after normalization.
## Protein extraction and Western blot analysis
Protein was extracted from HEI-OC1 cells and cochlear tissue according to the manufacturer’s instructions. Protein samples (20 µg) were loaded in a $12.5\%$ SDS‒PAGE gel and transferred to polyvinylidene fluoride membranes (Millipore, Burlington, MA, USA), followed by blocking with $5\%$ nonfat milk in TBST buffer at room temperature for 1 h. The membrane was cut according to the target protein and then hybridized with the following primary antibody cocktail: anti-DRP1 (1:1000, immunoway), anti-LC3B (1:1000, ABclonal) and anti-β actin.
(1:2000, Cell Signaling Technology) at 4 °C overnight on a shaker. The strips were washed 3 times in $0.05\%$ TBST for 10 min each time before incubation with HRP-conjugated anti-rabbit secondary antibody (Proteintech, 1:3000) for 1 h at room temperature. The protein intensity value was normalized by comparison with β-actin using ImageJ software (U.S. National Institutes of Health (NIH), Bethesda, MD, USA).
## Statistical analysis
The results were presented as data from at least three independent experiments and expressed as the mean ± S.E. (standard error of the mean). Statistical analysis was performed using two-way ANOVA with Duncan’s test or t test. SPSS 21.0 was used for statistical analyses. A value of $p \leq 0.05$ was considered significant.
## Cisplatin caused ototoxicity in C57BL/6 mice
The 3 cycles of cisplatin administration were performed according to previous studies [26]. ABR testing was used to evaluate the hearing level of C57BL/6 mice. The cisplatin group resulted in a greater hearing threshold shift than the control group, particularly at high frequency. The mean hearing threshold shift of the cisplatin group was 16.5 dB ± 5.29 at 8 kHz, 18.5 ± 5.29 dB at 16 kHz and 43 ± 18.59 dB at 32 kHz compared to 2.08 ± 4.5 dB at 8 kHz, 2.92 ± 5.82 dB at 16 kHz and 4.58 ± 4.98 dB at 32 kHz in the control group. The difference between the two groups was statistically significant at each frequency, but hearing loss was most significant at the high frequency of 32 kHz. ( Fig. 1A, $$n = 10$$, $p \leq 0.0001$).Fig. 1Three cycles of cisplatin treatment caused ototoxicity in C57BL/6 mice. A Hearing threshold shifts were observed in C57BL/6 mice at 8, 16 and 32 kHz. $$n = 10$$ per group. B Immunofluorescent staining (myosin VIIa) of the basilar membrane from a representative cochlear section. C. Outer hair cell counting obtained from five independent cochlear dissections at the apical, middle and basal turns. Scale bar = 20 µM. $$n = 5$$, ****$P \leq 0.0001$ We dissected the cochlea to observe the morphological changes in the basilar membrane and identified the localization of hair cell loss using immunofluorescence staining. The results showed that 1 row of inner hair cells (IHCs) and 3 rows of outer hair cells (OHCs) were arranged neatly, without missing from the apical to basal cochlear turns in the control group (Fig. 1B). Thus, in C57BL/6 mice treated with cisplatin, the missing outer hair cells were mainly located at the basal turn (Fig. 1B), and the survival rate of outer hair cells was 54.98 ± $1.9\%$ (Fig. 1C, $$n = 5$$, $P \leq 0.05$). The 3 cycles of cisplatin administration in C57BL/6 mice were similar to the clinical medication regimen. The results of decreased hearing and hair cell loss were consistent with previous studies [26] in the mouse model and indicated that cisplatin caused ototoxicity in C57BL/6 mice.
## The expression of miR34a/DRP1 in the cochlea of C57BL/6 mice after 3 cycles of cisplatin treatment
In this study, we found that miR34a expression was significantly upregulated in mice treated with cisplatin via RT‒PCR (Fig. 2A, $P \leq 0.05$). Western blot analysis showed that DRP1 protein levels were decreased, whereas LC3-II/I levels were elevated in the cisplatin group (Fig. 2B, C, D, $P \leq 0.05$). Thus, we speculated that miR-34a/DRP-1 may be involved in the process of cisplatin-induced ototoxicity in C57BL/6 mice. Fig. 2The expression of miR-34a and DRP1 in C57BL/6 mice treated with cisplatin. A RT‒PCR showed that the expression of miR-34a was increased in the cisplatin group. B-D The levels of DRP1 and LC3II/I were detected using Western blotting in C57BL/6 mice. B Representative Western blot analysis of DRP1 and LC3-II/I. C-D Relative expression of DRP1 and LC3-II/I. Data are presented as the mean ± SEM of three independent experiments. * $p \leq 0.05$, **$p \leq 0.01$
## Cisplatin induced cytotoxicity via mitochondrial dysfunction in HEI-OC1 cells
HEI-OC1 cells were treated with various concentrations of cisplatin (10, 20, 30, and 40 µM), and cell viability was detected at 8, 16, 24, and 48 h after exposure. The CCK-8 assay indicated that cisplatin exposure induced the cytotoxicity of HEI-OC1 cells in a dose- and time-dependent manner (Fig. 3A). The cell viability was approximately $45.2\%$ in HEI-OC1 cells treated with 20 µM cisplatin for 24 h. Therefore, we chose 20 µM cisplatin for 24 h as the exposure concentration and time for subsequent experiments. Fig. 3Cisplatin reduced cell viability and affected mitochondrial function in HEI-OC1 cells. A Cell viability was determined using the CCK-8 assay. HEI-OC1 cells were treated with various concentrations (0, 10, 20, 30, 40 µM) of cisplatin for 8, 16, 24, and 48 h. Cell viability decreased with cisplatin treatment in a time-dose-dependent manner. B, C FITC fluorescence intensity was measured using flow cytometry. Cisplatin exposure increased ROS levels. D, E, F Cisplatin treatment impaired mitochondrial function. Data are presented as the mean ± SEM of three independent experiments. * $p \leq 0.05$;**$p \leq 0.01$;***$p \leq 0.001$ ROS formation is an important marker of oxidative stress, and mitochondria are the main sites of oxidative stress in cells. A previous study revealed that cisplatin application increased the generation of ROS [27]. The ROS level in HEI-OC1 cells after 20 µM cisplatin treatment for 24 h was assessed by DCFH-DA staining. The FACS results showed that the green fluorescence intensity was significantly increased after cisplatin treatment (Fig. 3B, C).
Mitochondrial membrane potential (MMP, ∆Ψm) is an indicator of mitochondrial integrity and bioenergetic function [28]. JC-1 is a widely used fluorescent probe in the detection of MMP. The red and green fluorescence intensities were detected by flow cytometry. The red fluorescence intensity was significantly decreased in the cisplatin group (Fig. 3D, E). Meanwhile, the ATP content also declined in the cisplatin group (Fig. 3F).
These data demonstrated that cisplatin induced cytotoxicity in HEI-OC1 cells through mitochondrial dysfunction.
## The expression of miR34a/DRP1 in HEI-OC1 cells after cisplatin treatment
In HEI-OC1 cells treated with 20 µM cisplatin for 24 h, RT‒PCR showed that miR34a expression was significantly upregulated (Fig. 4A), DRP1 protein expression was decreased, and LC3-II/I levels were elevated, as shown by western blot analysis (Fig. 4B-D), in line with the in vivo results. Based on the above data, we speculated that miR-34a/DRP-1 may play an important role in cisplatin-induced ototoxicity via mitochondrial dysfunction. Next, we investigated the effect of miR-34a/DRP1 on mitophagy and the underlying mechanism. Fig. 4The expression of miR-34a and DRP1 in HEI-OC1 cells treated with 20 µM cisplatin for 24 h. A RT‒PCR showed that the expression of miR-34a was increased in the cisplatin group. B-D The levels of DRP1 and LC3II/I were detected using Western blotting in HEI-OC1 cells. B Representative Western blot analysis of DRP1 and LC3-II/I. C, D Relative expression of DRP1 and LC3-II/I. Data are presented as the mean ± SEM of three independent experiments. * $p \leq 0.05$, **$p \leq 0.01$
## miR-34a modulated DRP1 expression and mitophagy
By searching the TargetScan database and the literature [25], we found that DRP1 might be a target protein of miR-34a (Fig. 5A). Modulation of miR-34a in HEI-OC1 cells further investigated the effect of miR-34a on DRP1 expression and mitophagy. Fig. 5MiR-34a modulated DRP1 expression and mitophagy in HEI-OC1 cells. A The putative binding site of miR-34a-5p on the 3’-UTR of DRP1 as predicted in the TargetScan database. B, C qRT‒PCR showed the level of miR34a in HEI-OC1 cells transfected with miR-34a mimic inhibitor or control. D-F Western blot analysis showed DRP1 and LC3-II/I expression in HEI-OC1 cells after miR34a mimic, inhibitor or control transfection RT‒PCR showed the transfection effect (Fig. 5B, C). Western blot analysis showed that DRP1 decreased and LC3-II/I expression increased in HEI-OC1 cells overexpressing miR-34a; however, the results were reversed in HEI-OC1 cells treated with the miR-34a inhibitor (Fig. 5D-F).
## miR-34a mediated cisplatin-induced ototoxicity via the regulation of mitochondrial function
To further investigate the effect of miR-34a on cisplatin-induced ototoxicity, HEI-OC1 cells were transfected with miR-34a mimic or inhibitor and the corresponding negative control and then exposed to 20 µM cisplatin for 24 h, and cell viability, ROS level and ATP content were detected (Fig. 6). Compared with the negative control miRNA, miR-34a overexpression resulted in a decrease in cell viability and ATP content and an increase in ROS levels in HEI-OC1 cells. Therefore, we speculated that the increase in miR-34a levels might aggravate ototoxicity by enhancing oxidative stress and mitochondrial dysfunction after cisplatin exposure. Furthermore, inhibiting miR-34a exerted the opposite tendency, significantly improving cell viability and ATP content and decreasing ROS levels relative to the miR-34a mimic group after cisplatin treatment, indicating that decreased miR-34a can alleviate ototoxicity by reducing oxidative stress and improving mitochondrial function. Because the MMP of the negative group decreased too much after JC-1 staining, the MMP result was not included in this part. Fig. 6Role of miR-34a/DRP1 in cisplatin-induced ototoxicity in HEI-OC1 cells. Cells were transfected with miR-34a mimic or inhibitor and negative control miRNA and then incubated with 20 µM cisplatin for 24 h. A Cell viability was detected by CCK8 assay. B, C The green fluorescence intensity of ROS detected by flow cytometry. D ATP content was detected by chemiluminescence. E Model summarizing the relationship between miR-34a/DRP1 and mitophagy in cisplatin-induced ototoxicity. Data are presented as the mean ± SEM of three independent experiments. * $p \leq 0.05$;**$p \leq 0.01$;***$p \leq 0.001$;****$p \leq 0.0001$
## Discussion
Currently, cisplatin-induced ototoxicity is a major obstacle that limits the maximum efficacy for tumor patients. Previous studies have demonstrated that miRNAs are closely associated with hearing loss and are considered promising therapeutic targets [29]. A recent study reported that mitophagy protected HEI-OC1 cells against cisplatin-induced ototoxicity [30], but the precise molecular mechanism remains to be further studied. In the present study, we investigated whether miR-34a/DRP1-mediated mitophagy contributed to cisplatin-induced ototoxicity and sought to determine the underlying mechanism.
Three cycles of cisplatin treatment resulted in increased miR-34a and decreased DRP1 in the cochlea of C57BL/6 mice, accompanied by significant hearing threshold elevation and outer hair cell loss. Meanwhile, cisplatin caused ototoxic damage and mitochondrial dysfunction in HEI-OC1 cells, and the changes in miR-34a and DRP-1 expression were consistent with the results in C57BL/6 mice. Based on these results, we considered that miR-34a and DRP-1 were involved in the process of cisplatin-induced ototoxicity with mitochondrial dysfunction and that autophagy may be activated in some way. Current studies on the role of autophagy in cisplatin-induced ototoxicity are contradictory [31, 32]. In this study, we focused on the role of mitophagy, a selective autophagy that plays an important role in removing damaged mitochondria and maintaining the dynamic stability of the mitochondrial network, thereby protecting the cell [33].
Mitochondrial homeostasis is maintained through a dynamic balance of fusion and fission [34]. DRP-1 is a cytosolic GTPase that regulates mitochondrial fission, which is important for mitochondrial renewal, proliferation, and redistribution to maintain mitochondrial morphology, number and functionality [35, 36]. By searching the TargetScan database and the literature, we found that DRP-1 might be a target protein of miR-34a. Herein, we further verified the effect of miR-34a on DRP-1 expression and found that miR-34a overexpression led to decreased DRP-1 expression and an increased LC3 II/I ratio in HEI-OC1 cells. Nevertheless, inhibiting miR-34a expression can reverse these results. Therefore, we proposed that miR-34a contributed to cisplatin-induced ototoxicity by negatively regulating the expression of DRP-1 and damaging mitophagy.
Subsequently, we investigated the effect of miR-34a regulation on mitochondrial function during cisplatin treatment in HEI-OC1 cells. We found that cell viability and mitochondrial function decreased in HEI-OC1 cells overexpressing miR-34a compared with the negative control group after cisplatin exposure. In addition, inhibition of miR-34a expression could reduce the damage to cell viability and mitochondrial function after cisplatin exposure. The results indicated that modulating miR-34a expression can improve ototoxic damage by regulating mitochondrial function.
Taken together, the results of this study revealed that miR-34a/DRP1 played an important role in cisplatin-induced ototoxicity and was probably related to abnormal mitophagy. We speculated that the increase in miR-34a expression resulted in a decrease in DRP1 expression and led to abnormal mitophagy, ultimately causing ototoxicity during cisplatin treatment. Furthermore, inhibiting miR-34a expression alleviated cisplatin-induced ototoxicity, which was probably linked to the improvement of DRP1-mediated mitophagy, thus removing abnormal mitochondria and improving mitochondrial function. Therefore, miR-34a/DRP-mediated mitophagy may be a novel target for investigating the treatment and protection of cisplatin-induced ototoxicity.
## Limitations
There are still limitations that need to be further studied regarding this research. We can try to regulate the expression of miR-34a in cochlear explants or use transgenic mice to determine the effect of miR-34a/DRP1 on cisplatin-induced ototoxicity, and the results would be more convincing. Furthermore, direct regulation of DRP1 expression determined the role of DRP1 in the development of cisplatin ototoxicity.
## References
1. Wang D, Lippard SJ. **Cellular processing of platinum anticancer drugs**. *Nat Rev Drug Discovery* (2005) **4** 307-320. DOI: 10.1038/nrd1691
2. Hanigan MH, Devarajan P. **Cisplatin nephrotoxicity:molecular mechanisms**. *Cancer Ther* (2003) **1** 47-61. PMID: 18185852
3. Rybak LP. **Mechanisms of cisplatin ototoxicity and progress in otoprotection**. *Curr Opin Otolaryngol Head Neck Surg* (2007) **15** 364-369. DOI: 10.1097/MOO.0b013e3282eee452
4. Knight KR, Chen L, Freyer D. **Group-wide, prospective study of ototoxicity assessment in children receiving cisplatin chemotherapy (ACCL05C1): a report from the Children’s Oncology Group**. *J Clin Oncol* (2017) **35** 440-445. DOI: 10.1200/JCO.2016.69.2319
5. Freyer DR, Brock PR, Chang KW. **Prevention of cisplatin-induced ototoxicity in children and adolescents with cancer: a clinical practice guideline**. *Lancet Child Adolesc Health.* (2020) **4** 141-150. DOI: 10.1016/S2352-4642(19)30336-0
6. Gentilin E, Simoni E, Candito M. **Cisplatin-Induced Ototoxicity: Updates on Molecular Targets**. *Trends Mol Med* (2019) **25** 1123-1132. DOI: 10.1016/j.molmed.2019.08.002
7. Dos Santos NA, Ferreira RS, Dos Santos AC. **Overview of cisplatin-induced neurotoxicity and ototoxicity, and the protective agents**. *Food Chem Toxicol* (2020) **1** 111079. DOI: 10.1016/j.fct.2019.111079
8. David PB. **MicroRNAs: genomics, biogenesis, mechanism, and function**. *Cell* (2004) **116** 281-297. DOI: 10.1016/S0092-8674(04)00045-5
9. Behm-Ansmant I, Rehwinkel J, Izaurralde E. **MicroRNAs silence gene expression by repressing protein expression and/or by promoting mRNA decay**. *Cold Spring Harb Symp Quant Biol* (2006) **71** 523-530. DOI: 10.1101/sqb.2006.71.013
10. Chen HHR, Wijesinghe P, Nunez DA. **MicroRNAs in acquired sensorineural hearing loss**. *J Laryngol Otol* (2019) **133** 650-7. DOI: 10.1017/S0022215119001439
11. Strappazzon F. **A global view of the miRNA-mitophagy connexion**. *Prog Mol Biol Transl Sci* (2020) **172** 37-54. DOI: 10.1016/bs.pmbts.2020.03.006
12. Rudnicki A, Avraham KB. **MicroRNAs: the art of silencing in the ear**. *EMBO Mol Med* (2012) **4** 849-859. DOI: 10.1002/emmm.201100922
13. Pang JQ, Xiong H, Yang HD. **Circulating miR-34a levels correlate with age-related hearing loss in mice and humans**. *Exp Gerontol* (2016) **76** 58-67. DOI: 10.1016/j.exger.2016.01.009
14. Yang J, Chen D, He Y. **MiR-34 modulates Caenorhabditis elegans lifespan via repressing the autophagy gene atg9**. *Age (Dordr)* (2013) **35** 11-22. DOI: 10.1007/s11357-011-9324-3
15. Liu N, Landreh M, Cao K. **The microRNA miR-34 modulates aging and neurodegeneration in Drosophila**. *Nature* (2012) **482** 519-523. DOI: 10.1038/nature10810
16. Qang PJ, Xiong H, Lin P. **Activation of miR-34a impairs autophagic flux and promotes cochlear cell death via repressing ATG9A: implications for age-related hearing loss**. *Cell Death Dis* (2017) **8** e3079. DOI: 10.1038/cddis.2017.462
17. Yu L, Tang H, Jiang XH. **Involvement of calpain-I and microRNA34 in kanamycin-induced apoptosis of inner ear cells**. *Cell Biol Int* (2010) **34** 1219-1225. DOI: 10.1042/CBI20100515
18. Lee CG, Kim JG, Kim HJ. **Discovery of an integrative network of microRNAs and transcriptomics changes for acute kidney injury**. *Kidney Int* (2014) **86** 943-53. DOI: 10.1038/ki.2014.117
19. Chan DC. **Mitochondria:dynamic or organelles in disease, aging, and development**. *Cell* (2006) **125** 1241-1252. DOI: 10.1016/j.cell.2006.06.010
20. Archer SL. **Mitochondrial dynamics—Mitochondrial fission and fusion in human diseases**. *N Engl J Med* (2013) **369** 2236-2251. DOI: 10.1056/NEJMra1215233
21. Wong AC, Ryan AF. **Mechanisms of sensorineural cell damage, death and survival in the cochlea**. *Front Aging Neurosci* (2015) **7** 58. DOI: 10.3389/fnagi.2015.00058
22. Bottger EC, Schacht J. **The mitochondrion: a perpetrator of acquired hearing loss**. *Hear Res* (2013) **303** 12-19. DOI: 10.1016/j.heares.2013.01.006
23. Nishimura A, Shimauchi T, Tanaka T. **Hypoxia-induced interaction of filamin with Drp1causes mitochondrial hyperfission–associated myocardial senescence**. *Sci Signal* (2018) **11** 5185. DOI: 10.1126/scisignal.aat5185
24. Ikeda Y, Shirakabe A, Maejima Y. **Endogenous Drp1 mediates mitochondrial autophagy and protects the heart against energy stress**. *Circ Res* (2015) **116** 264-278. DOI: 10.1161/CIRCRESAHA.116.303356
25. Chen KH, Dasgupta A, Lin JH. **Epigenetic dysregulation of the dynamin-related protein 1 binding partners mid49 and mid51 increases mitotic mitochondrial fission and promotes pulmonary arterial hypertension: mechanistic and therapeutic implications**. *Circulation* (2018) **138** 287-304. DOI: 10.1161/CIRCULATIONAHA.117.031258
26. Fernandez K, Wafa T, Fitzgerald TS. **An optimized, clinically relevant mouse model of cisplatin-induced Ototoxicity**. *Hear Res* (2019) **375** 66-74. DOI: 10.1016/j.heares.2019.02.006
27. Fujimoto C, Yamasoba T. **Mitochondria-targeted antioxidants for treatment of hearing loss: a systematic review**. *Antioxidants (Basel)* (2019) **8** 109-127. DOI: 10.3390/antiox8040109
28. Suomalainen NA. **Mitochondria: in sickness and in health**. *Cell* (2012) **148** 1145-1159. DOI: 10.1016/j.cell.2012.02.035
29. Ying L, Shen JJ, Li DF. **MiR-34a contributes to diabetes-related cochlear hair cell apoptosis via SIRT1/HIF-1α signaling**. *Gen Comparative Endocrinol.* (2017) **246** 63-70. DOI: 10.1016/j.ygcen.2017.02.017
30. Cho SI, Jo ER, Song H. **Mitophagy Impairment Aggravates Cisplatin-Induced Ototoxicity**. *BioMed Res Int* (2021) **2021** 5590973. DOI: 10.1155/2021/5590973
31. Fang B, Xiao H. **Rapamycin alleviates cisplatin-induced ototoxicity in vivo**. *Biochem Biophys Res Commun* (2014) **448** 443-7. DOI: 10.1016/j.bbrc.2014.04.123
32. Youn CK, Kim J, Park JH. **Role of autophagy in cisplatin-induced ototoxicity**. *Int J Pediatr Otorhinolaryngol* (2015) **79** 1814-9. DOI: 10.1016/j.ijporl.2015.08.012
33. Twig G, Elorza A, Molina AJ, Mohamed H. **Fission and selective fusion govern mitochondrial segregation and elimination by autophagy**. *EMBO J.* (2008) **27** 433-46. DOI: 10.1038/sj.emboj.7601963
34. Atkins K, Dasgupta A, Chen KH. **The role of Drp1 adaptor proteins MiD49 and MiD51 in mitochondrial fifission: implications for human disease**. *Clin Sci* (2016) **130** 1861-1874. DOI: 10.1042/CS20160030
35. Hu CX, Huang Y, Li LJ. **Drp1-Dependent mitochondrial fifission plays critical roles in physiological and pathological progresses in mammals**. *Int J Mol Sci* (2017) **18** 144. DOI: 10.3390/ijms18010144
36. Otera H, Ishihara N, Mihara K. **New insights into the function and regulation of mitochondrial fifission**. *Biochim Biophys Acta* (2013) **1833** 1256-1268. DOI: 10.1016/j.bbamcr.2013.02.002
|
---
title: Effect of helicobacter pylori infection eradication on serum level of anti-tissue
transglutaminase in children with celiac disease
authors:
- Pejman Rohani
- Maryam Monajam Zadeh
- Hosein Alimadadi
- Mohammad Hassan Sohouli
journal: BMC Pediatrics
year: 2023
pmcid: PMC9993645
doi: 10.1186/s12887-023-03934-1
license: CC BY 4.0
---
# Effect of helicobacter pylori infection eradication on serum level of anti-tissue transglutaminase in children with celiac disease
## Abstract
### Background
Evidence shows the increase of anti-tissue transglutaminase (tTG) levels in various conditions, including infectious agents, independently of celiac disease (CD). The aim of this study was to investigate the effect of helicobacter pylori (H.pylori) infection eradication on serum level of tTG in children with CD.
### Methods
This study was conducted on children aged 2 to 18 who referred to reference hospitals for diagnosis of CD. After upper endoscopy and biopsy to confirm CD and H.pylori infection, the children were divided into three groups (including group one: 16 CD patients with positive H. pylori; group two: 16 non-CD patients with positive H. pylori; and group three: 56 CD patients with negative H. pylori), respectively. The tTG level in study groups were compared after the eradication of H.pylori.
### Results
The mean age of the subjects in the group one, two, and three was 9.7 ± 3.33, 11.8 ± 3.14, and 7.6 ± 3.32 years, respectively. Our results showed that in group one, mean tTG increased after eradication of H.pylori infection, however, these changes were not significant (182.43 vs. 157.18, $$P \leq 0.121$$). In the second group, although unlike the first group, mean tTG decreased after eradication of the infection, but still these changes were not significant (9.56 vs. 22.18, $$P \leq 0.449$$). Furthermore, at the baseline level, the mean tTG in the group three was closer to the mean tTG in the first group.
### Conclusion
Our findings showed that the eradication of H.pylori infection does not have a significant effect on tTG levels in children with and without CD.
## Introduction
Celiac disease (CD) is known as an immune-mediated disease that can develop in susceptible individuals after exposure to gluten [1]. CD is the most common enteropathy affects $1\%$ of population worldwide [2]. CD is characterized by a set of serologic features, histological changes in intestinal tissue, and gluten-related intestinal and extraintestinal symptoms that can vary from person to person [3]. However, in order to screen this disease in the general population and population at high risk of IgA and symptomatic, it is recommended to use a very sensitive test called Anti-tissue transglutaminase (anti-tTG) IgA antibody, which like other immune-mediated disease, mechanism of anti-tTG production in patients with CD has been precisely determined [3, 4].
In recent years, some evidence shows the role of infectious agents in the development of autoimmune diseases, including CD, as well as some of the characteristics of this disease, including increased anti-tTG production [5, 6]. So that it has been suggested that infectious agents increase this antibody through the conversion of tTG and gluten peptides into macromolecular aggregates and as a result tissue changes related to gluten [6, 7]. In the study of Ferrara et al., it was also shown the independent role of these infectious agents in the production of anti-tTG antibodies [8]. It was also stated in this study that anti-tTG produced by infection have the same effect on tissue changes, inflammation, and other damages in the body [8].
H.pylori infection is common infection especially in developing country [9]. The precise prevalence of pediatric H.pylori and CD is not defined in Iran. But it seems that both conditions are prevalent [9]. A lot of study was performed about association of H.pylori infection and CD and different conclusion were achieved [10–12]. So that, it has been shown that H.pylori infection and CD have overlapping pathological features [10, 12, 13]. This study was conducted with the assumption that the eradication of H.pylori can be helpful in children with positive CD serology and reduce the amount of common serological tests despite the continuous consumption of gluten.
Therefore, the purpose of this study is to investigate the effect of eradication of H.pylori infection on the serum level of common serological tests, including IgA anti tTG.
## Data source and subjects
This study was designed to explain the probable involvement of H. pylori in mimicking celiac disease pathology and, as a result, influencing screening test results. To do so, we selected three groups of children between 2 and 18 years old were referred to Mofid children hospital and Children’s Medical Center, tertiary medical centers of children in Tehran/Iran for diagnosis of CD.
IgA EMA was measured by an indirect immunofluorescence method ((EmA Kit Biosystems, Genova, Italy). IgA anti TTG was measured by ELISA (ImmuLisa, Immco, USA).
Patients referred for upper endoscopy for confirming diagnosis of CD. Five biopsy samples (4 from D2 and 1 from bulb) were sent. In addition four gastric biopsy samples were sent (2 from antrum, 1 from cardia and 1 from body). Two pathologists expert in the field have reviewed the tissues.
The two groups who had moderate to heavy colonization with H.pylori infection according to gastric biopsy results. These patients introduced to ethics committee of research institute for children health. After written informed consent they referred to pediatric gastroenterologist for treatment of H pylori infection. Two weeks course of treatment with antibiotics and proton pomp inhibitor was performed. All patients had examined for H.pylori infection eradication by stool antigen 6 weeks later. During these 6 weeks, the patients were not subjected to a gluten-free diet, but after the 6th week and re-examination and eradication of H.pylori infection in the stool, based on the confirmation of celiac disease, they were subjected to the desired diet. Furthermore, the anti-endomysial antibody (EMA-IgA(mg/dL)) test was checked before and after therapy in the first group. This test was also performed in the third group of celiac patients with negative H. pylori, but not in the second group of non-celiac patients with positive H.pylori because it was not scientifically necessary.
## Statistical analysis
The normality of the data was initially assessed in order to select the suitable test. If the data between the two groups were normal, a paired or unpaired T-Test was used; if they were not normal, Mann-whitney or Wilcoxon tests were used. For comparisons between more than two groups, either analysis of variance or the Kruskal-Wallis test was considered. In all cases, the significance level was less than 0.05.
## Results
This study was conducted between 2021 and 2022 and on children aged 2–18 years who referred to children’s hospital. The first group included 16 CD patients who tested positive for H. pylori. The second group included 16 children who had H. pylori but did not have CD. The third group included 56 celiac disease patients who tested negative for H.pylori. In the first group of our samples (positive H. pylori, celiac patients), the mean age of the subjects was 9.7 ± 3.33 years. In the second group (positive H. pylori, non-celiac patients), the mean age of the subjects was 11.8 ± 3.14 years, and finally, in the third group (celiac patients, negative H. pylori) the mean age was 7.6 ± 3.32 years. In the first group, nine children were girls and seven were boys. In second group, nine children were boys and seven were girls. In third group, 26 children were boys and 30 were girls (Fig. 1). As shown in Table 1, the histological distributions of the subjects in groups one and three were almost similar so they often had marsh 2 or/and 3. The histological distribution of two groups in the March 3 class was also no different (chi2 = 6.0, $$p \leq 0.199$$).
Fig. 1Study summery Table 1Histology findings of patients in group 1 and 3 Group Marsh 1 Marsh 2 Marsh 3 One ($$n = 16$$) 0 5 $A = 6$, $B = 5$, $C = 0$ Three ($$n = 56$$) 0 6 $A = 18$, $B = 24$, $C = 8$
## Celiac patients with positive H. pylori (group one)
The mean tTG value after eradication of *Helicobacter pylori* was higher than before eradication (182.43 vs. 157.18, $$P \leq 0.121$$) which was not statistically significant (Table 2; Fig. 2b). Conversely, EMA mean score was lower after eradication (0.021 vs. 0.022, $$P \leq 0.831$$) and was not significant (Table 2; Fig. 2a). The mean tTG in boys and girls after treatment was lower than before treatment (girls, 150.7 vs. 170.2 and boys, 165.4 vs. 198.1) which was not significant. Subsequently, the mean EMA was higher in girls after eradication but lower in boys (girls, 0.023 vs. 0.021 and boys, 0.0196 vs. 0.0205). These findings might point to a possible involvement for H. pylori in the findings of two screening tests, tTG and EMA, and, ultimately, the diagnosis of celiac disease. However, it can also show a gender-confusing role.
Table 2Mean tTG-IgA and EMA-IgA levels among study groups before and after *Helicobacter pylori* eradication tTG-IgA(U/mL) P-value EMA-IgA(mg/dL) P-value Group Before After Before After One 157.18(76.28) 182.43(51.46) 0.121 0.022(0.031) 0.021(0.031) 0.831 Two 22.18(56.21) 9.56(8.27) 0.449 - - - Three 202.50(63.06) - - 0.014(0.014) - - Data are reported as mean (SD).
Fig. 2Mean tTG-IgA and EMA-IgA levels in the groups one and three before and after *Helicobacter pylori* eradication
## Non-Celiac patients with positive H. pylori (group two)
In this case, the mean amount of tTG after H. pylori eradication was lower than before eradication (9.56 vs. 22.18, $$P \leq 0.449$$), although the difference was not statistically significant as demonstrated in Table 2; Fig. 3. The mean level of tTG after treatment for H. pylori eradication was lower in girls (11.28 vs. 12) but greater in boys (30.66 vs. 7.66). Similarly, these findings might point to an involvement for H. pylori as well as a sex-confounding role.
Fig. 3Mean tTG-IgA levels in the group two before and after *Helicobacter pylori* eradication
## Celiac patients with negative H. pylori (group three)
At the baseline level, the mean tTG in this group was closer to the mean tTG in the first group (202.50 vs. 182.43) Table 2; Fig. 2a. This similarity might reassure us about H. pylori’s probable involvement in this test. This group’s mean EMA value was 0.014, which differed from the first group’s values both before and after treatment Table 2; Fig. 2b.
## Discussion
In recent years, significant progress has been made in improving the diagnosis of CD with the development of new diagnostic tools. Although it is still the use of celiac-related antibody detection tests as the first step of screening in symptomatic patients [3], however, the increase in serum levels of anti-tTG in various diseases and conditions such as autoimmune diseases, diabetes mellitus, infections, tumors, injuries myocardium, liver disorders, and psoriasis are observed without any evidence of CD, which can be an obstacle for the differential diagnosis of CD and make its diagnosis challenging [14–19]. On the other hand, it has been shown that the histopathological changes of CD may be associated with various conditions, including intestinal infections, drugs, autoimmune enteropathy, immunodeficiency, eosinophilic enteropathy, and small intestinal bacterial overgrowth, and these changes are nonspecific. Some findings also indicate an increase in intra-epithelial lymphocytes in the duodenum with a normal villi structure, which is often associated with H. pylori gastritis and may improve with the eradication of this infection [20–23]. In previous studies, a cause-and-effect relationship between this infection and CD has been reported, however, this evidence is limited to epidemiological studies and has resulted in contradictory results, which can be caused by the difference in the type of diagnostic method and the lack of proper selection of the control group [6, 12, 24–26].
Our results showed that in group 1 (patients with celiac disease and positive for H.pylori), mean tTG increased after eradication of H.pylori infection, however, these changes were not significant. In the second group (non-Celiac patients with positive H. pylori), although unlike the first group, mean tTG decreased after eradication of the infection, but still these changes were not significant. The observation of an increase in mean tTG in the first group after eradication can support the hypothesis that H.pylori infection has a protective role against CD, so that this hypothesis has been mentioned in several studies [27–29] and the results observed in the third group of our study can also show this issue. In group 3, it showed that at the baseline level, the mean tTG in this group was closer to the mean tTG in the first group (202.50 vs. 182.43). However, these results and changes were not significantly reported in our study, and in other studies, this hypothesis was contradictory and cannot confirm this result. However, the results reported from the second group contradicted this hypothesis and the results of the second group and indicated the small effect of eradicating this infection in reducing mean tTG. A recent study has also been conducted in this regard [30]. So that in line with our results in non-celiac patients who tested positive for H.pylori, in $80\%$ of these patients the mean tTG level significantly decreased after treatment of this infection with follow-up 6 month [30]. However, the observation of non-significant results in our study can be due to the small subjects and the small follow-up of patients, and on the other hand, different tests and diagnostic kits have been used to test positive for H.pylori infection and tTG which can affect the results. Also, the difference in the follow-up of patients can be another reason for explaining this contradiction in the results. Furthermore, In the study of Akkelle et al. [ 30], unlike our study, all patients with CD were subjected to a gluten-free diet, which can significantly affect the results and cause a decrease in mean tTG in people with CD and with a positive H. pylori test after treatment of this infection. In another study conducted in 2020 by Gungor et al [31]. in order to link between H.pylori and CD in pediatrics, contradicting our results, the findings showed that after the eradication of H.pylori, okutransglutaminas antibody level (DTG) and EMA) serology significantly decreased in children with CD potential. However, in the mentioned study, DTG test was used instead of tTG, which seems to have a different sensitivity than glucose. On the other hand, in Gungor’s study, significant findings were observed after eradication of H.pylori in children with celiac potential that according to the author’s statement, tissue biopsy has not yet confirmed celiac disease, which can affect the findings.
Our findings also indicated a gender-dependent role in mean tTG levels. So that in some studies, it confirms these results [32]. So that in one study, it was observed that female patients with CD have significantly higher autoantibody titers compared to male patients [32] and in various studies, the role of sexual bias in the sensitivity and severity of autoimmune diseases is well known [33]. In addition, the prevalence of CD disease is more common in women and its clinical manifestations are more severe and faster [34]. In animal models, it shows that this gender difference in autoimmune diseases can be caused by a TH1 or TH2 helper lymphocyte, which ultimately causes a stronger immune response and thus more antibody production in women [33].
Among the strengths of this study was the use of valid diagnostic tests to diagnose H.pylori infection and CD, including histological, and endoscopic evidence. On the other hand, we tried to consider all necessary potential include and exclude criteria and all patients entered the study with full knowledge and consent. In addition, the data related to this study was collected from a reference hospital to increase the generalizability of the results and to minimize the confounding factors regarding the patients. However, the small sample size and the short duration of the intervention were among the limitations of our study. One of the reasons for the short duration of the intervention was to comply with the ethics of patients with CD who were included in the study without common treatment during the study. Although all patients with CD entered the study with full consent and according to common guidelines, 6 weeks of not receiving common treatment has no effect on any of the disease factors [3]. Another limitation of this study was the lack of adjustment for potential confounders such as individual characteristics of patients and genetic factors. Furthermore, IgA anti-tTG test is a highly sensitive screening test for celiac disease that it specifically detects the gliadin peptides in the blood. In case there is an H. pylori infection, and it entangles with gluten peptides, we believe the IgA anti-tTG test will not confuse it. However, in such conditions, a novel microbial tTG (mTG) test can be performed that is specially designed for this purpose.
## Conclusion
Our findings generally showed that the eradication of H.pylori infection does not have a significant effect on tTG levels in any of the studied groups in children with and without CD. However, more studies and better design and longer intervention duration are needed to investigate these findings.
## References
1. McCarty TR, O’Brien CR, Gremida A, Ling C, Rustagi T. **Efficacy of duodenal bulb biopsy for diagnosis of celiac disease: a systematic review and meta-analysis**. *Endoscopy Int open* (2018.0) **6** E1369-78. DOI: 10.1055/a-0732-5060
2. Raiteri A, Granito A, Giamperoli A, Catenaro T, Negrini G, Tovoli F. **Current guidelines for the management of celiac disease: a systematic review with comparative analysis**. *World J Gastroenterol* (2022.0) **28** 154. DOI: 10.3748/wjg.v28.i1.154
3. Al-Toma A, Volta U, Auricchio R, Castillejo G, Sanders DS, Cellier C, Mulder CJ, Lundin KE. **European Society for the study of Coeliac Disease (ESsCD) guideline for coeliac disease and other gluten-related disorders**. *United Eur Gastroenterol J* (2019.0) **7** 583-613. DOI: 10.1177/2050640619844125
4. Husby S, Koletzko S, Korponay-Szabó I, Kurppa K, Mearin ML, Ribes-Koninckx C, Shamir R, Troncone R, Auricchio R, Castillejo G. **European Society Paediatric Gastroenterology, Hepatology and Nutrition guidelines for diagnosing coeliac disease 2020**. *J Pediatr Gastroenterol Nutr* (2020.0) **70** 141-56. DOI: 10.1097/MPG.0000000000002497
5. Goodwin G. **Type 1 diabetes mellitus and celiac disease: distinct autoimmune disorders that share common pathogenic mechanisms**. *Hormone Res paediatrics* (2019.0) **92** 285-92. DOI: 10.1159/000503142
6. Jiang Hy, Zhang X, Zhou Yy J, Cm S. **Infection, antibiotic exposure, and risk of celiac disease: a systematic review and meta-analysis**. *J Gastroenterol Hepatol* (2020.0) **35** 557-66. DOI: 10.1111/jgh.14928
7. Hanevik K, Wik E, Langeland N, Hausken T. **Transient elevation of anti-transglutaminase and anti-endomysium antibodies in Giardia infection**. *Scand J Gastroenterol* (2018.0) **53** 809-12. DOI: 10.1080/00365521.2018.1481522
8. Ferrara F, Quaglia S, Caputo I, Esposito C, Lepretti M, Pastore S, Giorgi R, Martelossi S, Dal Molin G, Di Toro N. **Anti-transglutaminase antibodies in non-coeliac children suffering from infectious diseases**. *Clin Experimental Immunol* (2010.0) **159** 217-23. DOI: 10.1111/j.1365-2249.2009.04054.x
9. Zamani M, Ebrahimtabar F, Zamani V, Miller W, Alizadeh-Navaei R, Shokri‐Shirvani J, Derakhshan M. **Systematic review with meta‐analysis: the worldwide prevalence of Helicobacter pylori infection**. *Aliment Pharmacol Ther* (2018.0) **47** 868-76. DOI: 10.1111/apt.14561
10. Amlashi FI, Norouzi Z, Sohrabi A, Shirzad-Aski H, Norouzi A, Ashkbari A, Gilani N, Fatemi SA, Besharat S. **A systematic review and meta-analysis for association of Helicobacter pylori colonization and celiac disease**. *PLoS ONE* (2021.0) **16** e0241156. DOI: 10.1371/journal.pone.0241156
11. Dore MP, Salis R, Loria MF, Villanacci V, Bassotti G, Pes GM. **Helicobacter pylori infection and occurrence of celiac disease in subjects HLA-DQ 2/DQ 8 positive: a prospective study**. *Helicobacter* (2018.0) **23** e12465. DOI: 10.1111/hel.12465
12. Maxim R, Pleşa A, Stanciu C, Gîrleanu I, Moraru E, Trifan A. **Helicobacter pylori prevalence and risk factors among children with celiac disease**. *Turkish J Gastroenterol* (2019.0) **30** 284. DOI: 10.5152/tjg.2018.18181
13. Dai Y, Zhang Q, Olofson AM, Jhala N, Liu X. **Celiac disease: updates on pathology and differential diagnosis**. *Adv Anat Pathol* (2019.0) **26** 292-312. DOI: 10.1097/PAP.0000000000000242
14. Bizzaro N, Tampoia M, Villalta D, Platzgummer S, Liguori M, Tozzoli R, Tonutti E. **Low specificity of anti-tissue transglutaminase antibodies in patients with primary biliary cirrhosis**. *J Clin Lab Anal* (2006.0) **20** 184-9. DOI: 10.1002/jcla.20130
15. Bizzaro N, Villalta D, Tonutti E, Tampoia M, Bassetti D, Tozzoli R. **Association of celiac disease with connective tissue diseases and autoimmune diseases of the digestive tract**. *Autoimmun rev* (2003.0) **2** 358-63. DOI: 10.1016/S1568-9972(03)00055-7
16. Waisbourd-Zinman O, Hojsak I, Rosenbach Y, Mozer-Glassberg Y, Shalitin S, Phillip M, Shamir R. **Spontaneous normalization of anti-tissue transglutaminase antibody levels is common in children with type 1 diabetes mellitus**. *Dig Dis Sci* (2012.0) **57** 1314-20. DOI: 10.1007/s10620-011-2016-0
17. Bakhshipour A, Kaykhaei MA, Moulaei N, Mashhadi MA. **Prevalence of coeliac disease in patients with non-alcoholic fatty liver disease**. *Arab J Gastroenterol* (2013.0) **14** 113-5. DOI: 10.1016/j.ajg.2013.08.001
18. Piacentini M, Fesus L, Farrace M, Ghibelli L, Piredda L, Melino G. **The expression of” tissue” transglutaminase in two human cancer cell lines is related with the programmed cell death (apoptosis)**. *Eur J Cell Biol* (1991.0) **54** 246-54. PMID: 1679009
19. Singh S, Sonkar GK, Singh S. **Celiac disease-associated antibodies in patients with psoriasis and correlation with HLA Cw6**. *J Clin Lab Anal* (2010.0) **24** 269-72. DOI: 10.1002/jcla.20398
20. Hammer ST, Greenson JK. **The clinical significance of duodenal lymphocytosis with normal villus architecture**. *Arch Pathol Lab Med* (2013.0) **137** 1216-9. DOI: 10.5858/arpa.2013-0261-RA
21. Simondi D, Ribaldone DG, Bonagura GA, Foi S, Sapone N, Garavagno M, Villanacci V, Bernardi D, Pellicano R, Rizzetto M. **Helicobacter pylori in celiac disease and in duodenal intraepithelial lymphocytosis: active protagonist or innocent bystander?**. *Clin Res Hepatol Gastroenterol* (2015.0) **39** 740-5. DOI: 10.1016/j.clinre.2015.03.005
22. Brown I, Mino-Kenudson M, Deshpande V, Lauwers GY. **Intraepithelial lymphocytosis in architecturally preserved proximal small intestinal mucosa: an increasing diagnostic problem with a wide differential diagnosis**. *Arch Pathol Lab Med* (2006.0) **130** 1020-5. DOI: 10.5858/2006-130-1020-ILIAPP
23. Goldstein N. **Proximal small-bowel mucosal villous intraepithelial lymphocytes**. *Histopathology* (2004.0) **44** 199-205. DOI: 10.1111/j.1365-2559.2004.01775.x
24. Aydogdu S, Cakir M, Ali Yuksekkaya H, Tumgor G, Baran M, Arikan C, Yagci RV. **Helicobacter pylori infection in children with celiac disease**. *Scand J Gastroenterol* (2008.0) **43** 1088-93. DOI: 10.1080/00365520802101846
25. Narang M, Puri AS, Sachdeva S, Singh J, Kumar A, Saran RK. **Celiac disease and Helicobacter pylori infection in children: is there any association?**. *J Gastroenterol Hepatol* (2017.0) **32** 1178-82. DOI: 10.1111/jgh.13654
26. Rostami-Nejad M, Villanacci V, Mashayakhi R, Molaei M, Bassotti G, Zojaji H, Mirstatari D, Rostami K, Zali MR. **Celiac disease and hp infection association in Iran**. *Rev Esp Enferm Dig* (2009.0) **101** 850. DOI: 10.4321/S1130-01082009001200004
27. Lebwohl B, Blaser MJ, Ludvigsson JF, Green PH, Rundle A, Sonnenberg A, Genta RM. **Decreased risk of celiac disease in patients with Helicobacter pylori colonization**. *Am J Epidemiol* (2013.0) **178** 1721-30. DOI: 10.1093/aje/kwt234
28. Jozefczuk J, Bancerz B, Walkowiak M, Glapa A, Nowak J, Piescikowska J, Kwiecien J, Walkowiak J. **Prevalence of Helicobacter pylori infection in pediatric celiac disease**. *Age [years]* (2015.0) **6** 30
29. Uyanikoglu A, Dursun H, Yenice N. **Is there any Association between Celiac Disease and Helicobacter pylori?**. *Euroasian J Hepato-gastroenterology* (2016.0) **6** 103. DOI: 10.5005/jp-journals-10018-1179
30. Akkelle BS, Sengul OK, Tutar E, Volkan B, Celikel C, Ertem D. **Low Titer tissue transglutaminase antibodies: a link to Helicobacter pylori infection?**. *Dig Dis* (2022.0) **40** 168-74. DOI: 10.1159/000516479
31. 31.Gungor S, Köylü AA. Effects of Helicobacter pylori infection on serology and intestinal mucosal changes in pediatric patients with celiac disease: A retrospective cohort study.Cureus2020, 12(10).
32. Tiberti C, Bao F, Bonamico M, Verrienti A, Picarelli A, Di Tola M, Ferri M, Vecci E, Dotta F, Eisenbarth G. **Celiac disease-associated transglutaminase autoantibody target domains at diagnosis are age and sex dependent**. *Clin Immunol* (2003.0) **109** 318-24. DOI: 10.1016/j.clim.2003.08.010
33. Whitacre CC. **Sex differences in autoimmune disease**. *Nat Immunol* (2001.0) **2** 777-80. DOI: 10.1038/ni0901-777
34. Mäki M, Mustalahti K, Kokkonen J, Kulmala P, Haapalahti M, Karttunen T, Ilonen J, Laurila K, Dahlbom I, Hansson T. **Prevalence of celiac disease among children in Finland**. *N Engl J Med* (2003.0) **348** 2517-24. DOI: 10.1056/NEJMoa021687
|
---
title: Dimethyl itaconate is effective in host-directed antimicrobial responses against
mycobacterial infections through multifaceted innate immune pathways
authors:
- Young Jae Kim
- Eun-Jin Park
- Sang-Hee Lee
- Prashanta Silwal
- Jin Kyung Kim
- Jeong Seong Yang
- Jake Whang
- Jichan Jang
- Jin-Man Kim
- Eun-Kyeong Jo
journal: Cell & Bioscience
year: 2023
pmcid: PMC9993662
doi: 10.1186/s13578-023-00992-x
license: CC BY 4.0
---
# Dimethyl itaconate is effective in host-directed antimicrobial responses against mycobacterial infections through multifaceted innate immune pathways
## Abstract
### Background
Itaconate, a crucial immunometabolite, plays a critical role in linking immune and metabolic functions to influence host defense and inflammation. Due to its polar structure, the esterified cell-permeable derivatives of itaconate are being developed to provide therapeutic opportunities in infectious and inflammatory diseases. Yet, it remains largely uncharacterized whether itaconate derivatives have potentials in promoting host-directed therapeutics (HDT) against mycobacterial infections. Here, we report dimethyl itaconate (DMI) as the promising candidate for HDT against both *Mycobacterium tuberculosis* (Mtb) and nontuberculous mycobacteria by orchestrating multiple innate immune programs.
### Results
DMI per se has low bactericidal activity against Mtb, M. bovis Bacillus Calmette–Guérin (BCG), and M. avium (Mav). However, DMI robustly activated intracellular elimination of multiple mycobacterial strains (Mtb, BCG, Mav, and even to multidrug-resistant Mtb) in macrophages and in vivo. DMI significantly suppressed the production of interleukin-6 and -10, whereas it enhanced autophagy and phagosomal maturation, during Mtb infection. DMI-mediated autophagy partly contributed to antimicrobial host defenses in macrophages. Moreover, DMI significantly downregulated the activation of signal transducer and activator of transcription 3 signaling during infection with Mtb, BCG, and Mav.
### Conclusion
Together, DMI has potent anti-mycobacterial activities in macrophages and in vivo through promoting multifaceted ways for innate host defenses. DMI may bring light to new candidate for HDT against Mtb and nontuberculous mycobacteria, both of which infections are often intractable with antibiotic resistance.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13578-023-00992-x.
## Background
Mycobacterium tuberculosis (Mtb), the pathogen of human tuberculosis (TB), is a global health burden with high morbidity and mortality rates [1]. Despite the development of new antimicrobial strategies for TB, little progress has been made in improving or replacing short-term chemotherapy (directly observed treatment, short course), which comprises isoniazid (INH), rifampicin, pyrazinamide, and ethambutol during the initial 2 months, followed by INH and rifampicin for 4 months, as the first-line regimen for drug-sensitive TB [2, 3]. The treatment of drug-resistant cases is difficult and can require > 2 years of drugs with severe side effects. The increasing incidences of multidrug-resistant (MDR) and extremely drug-resistant TB are worrisome [2, 4, 5]. Moreover, nontuberculous mycobacteria (NTM) include > 170 species and are major pathogens of emerging respiratory infections in immunocompromised and immunocompetent subjects [6, 7]. The prevalence and incidence of NTM pulmonary infections are increasing worldwide and often intractable to treat [8–10]. NTM treatment with standard antimicrobial regimens is challenging, takes longer than TB treatment, and has a low cure rate because of antibiotic resistance and toxicity [11–13]. There is an urgent need for innovative host-directed therapeutics (HDT) based on deciphering an in-depth molecular mechanisms underlying host–pathogen interactions that could be geared for efficient protective responses against TB and NTM infections.
Immune-metabolic adaptations are rewired in macrophages and immune cells during mycobacterial infections. The remodeling of immunometabolism can shape the host defensive responses to intracellular mycobacteria, thus affecting the outcomes of infections [14–16]. Early studies highlighted the function of the immunometabolite itaconic acid (also known as methylenesuccinic acid) in decarboxylating cis-aconitate, which exerts an antimicrobial effect on *Salmonella enterica* and Mtb by covalent inhibition of bacterial isocitrate lyase [17, 18]. Immune-responsive gene 1 (IRG1), a mitochondrial enzyme that catalyzes the production of itaconate in myeloid cells, controls Mtb infection by preventing excessive neutrophil-driven immunopathology [19]. In addition, autocrine/paracrine signaling by tumor necrosis factor (TNF)-α and interleukin (IL)-6, which are generated by bystander cells, leads to the juxtaposition of bacterial phagosomes with mitochondria, thereby activating IRG1 signaling and an itaconate-mediated antimicrobial effect against M. avium (Mav) infection [20]. Furthermore, increasing efforts have been made to develop more cell-permeable derivatives of itaconate than the natural itaconate with polar structure, while they retain the immunoregulatory function [21–23]. Indeed, dimethyl itaconate (DMI) and 4-octyl itaconate (OI) ameliorate excessive inflammation and pathologic responses in models of autoimmune and inflammatory disorders including sepsis, mastitis, neuroinflammation, and psoriasis [22–26]. Mounting evidence showed that DMI is able to induce a robust electrophilic stress response, and activates NF-E2 p45-related factor 2 (Nrf2) and the expression of genes encoding its downstream biomolecules including Nqo1 and Hmox1 in the lipopolysaccharide (LPS)-stimulated murine macrophages [24, 25]. In addition, DMI showed a strong inhibitory effect upon the production of IL-6, IL-10, and interferon (IFN)-β, although these effects are independent of Nrf2 [24, 25]. DMI-mediated anti-inflammatory response is also mediated through a well-known negative regulator of toll-like receptor signaling, activating transcription factor 3/IκBζ pathway [24]. Interestingly, DMI is not directly metabolized into itaconate, but it potentiates an increase in the itaconate level in LPS-stimulated macrophages [26]. Despite this, it remains largely undefined whether and how DMI modulates antimicrobial host defense against Mtb, NTM, and drug-resistant mycobacterial infections.
In this study, we aimed to evaluate whether DMI exerts host defensive functions and how it achieved protective immune reactions during mycobacterial infections. We examined whether DMI increases antimicrobial host responses against Mtb, M. bovis Bacillus Calmette–Guérin (BCG), Mav, and multidrug-resistant (MDR)-Mtb infections. Although DMI per se did not exhibit direct antimicrobial effects against Mtb, BCG, or Mav, it showed potent antimicrobial activities in macrophages and in vivo. Mechanistically, DMI played multiple roles in the activation of innate immune defenses, i.e., maintenance of inflammatory homeostasis, signal transducer and activator of transcription 3 (STAT3) signaling, and activation of autophagy. These data offer DMI as an effective therapeutic candidate of HDT against Mtb and NTM infections by modulating multifaceted innate immune pathways.
## Mycobacterial strains and cultivation
Mtb H37Rv was supplied by R.L. Friedmann (University of Arizona, Tucson, AZ). BCG, MDR-Mtb (KMRC-00116-00150), and Mav (ATCC 25291) were acquired from the Korean Mycobacterium Resource Center in the Korean Institute of Tuberculosis (Osong, South Korea). Mycobacteria were cultured in Middlebrook 7H9 (Difco, 271310) medium supplemented with $10\%$ oleic albumin dextrose catalase (OADC; BD Biosciences, San Diego, CA, 212240), $0.5\%$ glycerol, and $0.05\%$ Tween-80 (7H9-OADC) on a rotary shaking incubator (140 rpm) at 37°C to an OD600 of 0.4–0.6. Mtb expressing red fluorescent protein (Mtb-ERFP) was cultivated in 7H9-OADC supplemented with 50 µg/ml kanamycin (Sigma-Aldrich, St. Louis, MO, 60615). Bacterial cultures were harvested, and the pellets were washed with phosphate-buffered saline (PBS; LPS solution, Daejeon, Korea, CBP007B) by sequential centrifugation at 2090×g (3000 rpm) for 30 min. To separate bacteria into single cells, pellets resuspended in PBS with $0.1\%$ Tween-80 were subjected to repeated rounds of sonication. The resulting bacterial suspensions were aliquoted and stored at −80°C. Colony-forming units (CFUs) were counted on Middlebrook 7H10 agar (Difco, 262710).
## Mice
Wild-type (WT) C57BL/6 mice (Samtako Bio, Gyeonggi-do, South Korea) were obtained at 6–8 weeks of age and maintained under a 12 h:12 h light:dark cycle and specific-pathogen-free conditions. Information on Atg7-floxed mice and Atg7-lacking mice is available elsewhere [27]. The mice were 6–8 weeks old at the time of the experiments and were matched by sex. The animal experiments and handling were conducted following the ethical guidelines of Chungnam National University School of Medicine and were approved by the Institutional Animal Care and Use Committee (202109A-CNU-180; Daejeon, South Korea) and the South Korean Food and Drug Administration.
## Isolation of bone marrow-derived macrophages (BMDMs) and peritoneal macrophages (PMs)
BMDMs were collected from the femur and tibia of 6–8-week-old mice and cultured for 4–5 days in Dulbecco’s modified Eagle’s medium (DMEM; Lonza, Walkersville, USA, BE12-60fF) supplemented with $10\%$ fetal bovine serum (Gibco, Grand Island, NY, 16000-044) and penicillin streptomycin amphotericin mixture (Lonza, 17-745E) containing 25 ng/ml macrophage colony-stimulating factor (R&D Systems, Minneapolis, MN) at 37°C in $5\%$ CO2. For the isolation of PMs from Atg7-floxed and Atg7-lacking mice (8-week-old), intraperitoneal injection of mice were performed using 1 ml of $3\%$ Brewer thioglycollate (BD Biosciences, 211716). After 3 days from injection, the isolation of cells were conducted by flushing out the peritoneal cavity with 10 ml of Dulbecco's PBS (DPBS; Cytiva HycloneTM, Marlborough, MA, SH30028.02) containing $10\%$ fetal bovine serum. The appropriate number of cells were seeded and incubated for 1 day in DMEM supplemented with $10\%$ fetal bovine serum and penicillin streptomycin amphotericin mixture.
## Experimental infection
Bacterial cells stored at −80°C were thawed and diluted in DPBS containing $0.05\%$ Tween-80. Vial containing bacterial cells was sonicated to a bath sonicator 3 times for 30 s. Infection of BMDMs and PMs at the indicated multiplicities of infection (MOI) of Mtb, BCG, or Mav was conducted for 4 h. The remaining bacteria around cells were removed by washing with DPBS, and infected cells were incubated in fresh DMEM for the indicated times. For in vivo infection, mice were anesthetized and intranasally infected with mycobacteria (Mtb: 5 × 104 CFU/mouse; BCG: 1 × 107 CFU/mouse; Mav: 1 × 107 CFU/mouse; MDR-Mtb: 5 × 103 CFU/mouse). To estimate the bacterial burden, mice were euthanized at 7 days post infection (dpi), and lungs were harvested, homogenized in DPBS, serially diluted, and spotted on 7H10 agar. After incubation for 2–3 weeks, colonies were counted.
## Reagents and antibodies
DMI [592498], bafilomycin A1 (Baf-A1; B1793), d-(+)-glucose (glucose; G7021), sodium acetate (S8750), and β-cyclodextrin (H5784) were purchased from Sigma-Aldrich (St. Louis, MO). For Western blotting, anti-pSTAT3 (9145S), anti-STAT3 (9139S), anti-microtubule-associated protein 1 light chain 3 β (LC3) (L7543), anti-ACTIN (5125S), anti-mouse IgG (7076S), and anti-rabbit IgG (7074S) antibodies were purchased from Cell Signaling Technology (Danvers, MA). For immunofluorescence analysis, an anti-LC3 (PM036) antibody was obtained from Medical & Biological Laboratories International, and an anti-lysosomal-associated member protein 1 (LAMP1; SC-19992) antibody was obtained from Santa Cruz Biotechnology. Alexa Fluor 488-conjugated anti-rabbit IgG (A11034) and Alexa Fluor 594-conjugated anti-rat IgG (A21209) antibodies were from Invitrogen (Waltham, MA). Fluoromount-G with 4′-6-diamidino-2-phenylindole (DAPI) [00-4959-52] was obtained from Invitrogen (Waltham, MA).
## CFU assay
To analyze bacterial survival in murine macrophages, Mtb-, BCG-, or Mav-infected cells (MOI 1) were incubated for 4 h and washed with DPBS to discard extracellular bacteria. The infected cells were incubated in fresh medium for the indicated periods. Thereafter, the cells were lysed in sterile distilled water for 40 min, and intracellular bacteria were collected. Cell lysates were diluted with DPBS and spotted on Middlebrook 7H10 agar containing $10\%$ OADC. Colonies were counted to assess intracellular bacterial viability after 2–3 weeks.
## Histology and immunohistochemistry
Lungs were removed from Mtb-infected mice, fixed in $10\%$ formalin, and embedded in paraffin wax. Paraffin blocks were sectioned (4 µm) and stained with hematoxylin and eosin (H&E) as described previously [28]. The inflamed area was quantified by total field scanning of lung tissues, and the mean fluorescence intensity of the red threshold was examined using FIJI software.
## Determination of dose–response curves
The half-maximal inhibitory concentration (IC50) values were determined following the CLSI guidelines [29]. Mtb, BCG, and Mav were cultured in 7H9-OADC to an OD600 of 0.4–0.6 followed by harvesting, washing twice with PBS supplemented with $0.02\%$ Tween-80, and vortexing 3 times for 5 s with 30–50 glass beads of diameter 2 mm. Finally, low-speed centrifugation (250×g) leaves only single bacterial cells in supernatant. Inoculum was prepared from this supernatant. All wells contained 7H9-OADC, 7H9 medium supplemented with $0.5\%$ glycerol, $0.02\%$ Tween 80 and 10 mM glucose (7H9-glucose), or 10 mM sodium acetate (7H9-acetate). Then all wells were added by each inoculum of Mtb, BCG, or Mav within each media with an OD600 of 0.005/ml except for the negative control (medium only). DMI, β-cyclodextrin (SC), and INH were two-fold serially diluted 20 times (DMI, β-cyclodextrin) or 10 times (INH) in flat-bottom clear 96-well plates, ranging from 50 mM to 95 nM (DMI) or from 10 μM to 19.5 nM (INH) containing Mtb, BCG, or Mav in a final volume of 100 µl, and then incubated for 5 to 7 days (7H9-OADC and -glucose) or 10 to 14 days (7H9-acetate) at 37°C. OD600 values were determined using the VersaMax microplate reader (Molecular devices, Sunnyvale, CA). IC50 values were calculated from the OD600 values using Prism 8.0 software (GraphPad Inc., La Jolla, CA).
## RNA preparation and quantitative real-time PCR (qRT-PCR)
Total RNA was isolated from BMDMs, PMs, or lung tissue homogenates using TRIzol reagent (Invitrogen, Waltham, MA, 15596026) in accordance with the manufacturer’s instructions. cDNA was prepared from total RNA using Reverse Transcription Master Premix (ELPIS Biotech, Daejeon, South Korea, EBT-1515) following the manufacturer’s protocols. qRT-PCR was conducted on the Rotor-Gene A 2plex System (Qiagen, Hilden, Germany, 9001620) using cDNA, primer pairs specific to the genes of interest, and SYBR Green Master Mix (Qiagen, 218073) according to the manufacturer’s instructions. Relative mRNA levels were analyzed by the 2−ΔΔ threshold cycle method and normalized to those of Gapdh. The primer sequences used are as follows: Il1b forward: 5′-TGACGGACCCCAAAAGATGA-3′, reverse: 5′-AAAGACACAGGTAGCTGCCA-3′; Il6 forward: 5′-ACAAAGCCAGAGTCCTTCAGA-3′, reverse: 5′-TGGTCCTTAGCCACTCCTTC-3′; Il10 forward: 5′-GCTCTTGCACTACCAAAGCC-3′, reverse: 5′-CTGCTGATCCTCATGCCAGT-3′; Tnf forward: 5′-CCCACGTCGTAGCAAACCAC-3′, reverse: 5′-GCAGCCTTGTCCCTTGAAGA-3′; Ifng forward: 5′-CGGCACAGTCATTGAAAGCC-3′, reverse: 5′-TGCATCCTTTTTCGCCTTGC-3′; Csf2 forward: 5-CTG GCC CCA TGT ATA GCT GA-3′, reverse: 5′-TCC TCC TCA GGA CCT TAG CC-3′; Gapdh forward: 5′-TGGCAAAGTGGAGATTGTTGCC-3′, reverse: 5′-AAGATGGTGATGGGCTTCCCG-3′.
## Enzyme-linked immunosorbent assay (ELISA)
The supernatant of mouse BMDMs or lung lysates was stored at −80°C. The levels of the proinflammatory cytokines IL-6 [555240] and IL-10 [555252] were estimated using the Mouse BD OptEIA Set ELISA Kit (BD Biosciences) according to the manufacturer’s instructions.
## Immunofluorescence analysis
BMDMs were grown on coverslips in 24-well plates followed by infection with Mtb-ERFP (MOI 5) for 4 h. The solvent control (SC), DMI (100 μM) or Baf-A1 (100 nM) was then treated within the freshly changed media for the indicated times. After treatment, the upper medium was discarded, and cells on coverslips were washed three times in DPBS followed by fixation in $4\%$ paraformaldehyde for 10 min. Thereafter, the cells were permeabilized in $0.25\%$ Triton X-100 for 10 min and incubated overnight at 4℃ with anti-LC3 (1:400 diluted) and anti-LAMP1 (1:400 diluted) primary antibodies. The cells were washed three times with DPBS and reacted for 2 h with the secondary antibody at room temperature. Fluoromount-G with DAPI was used to stain nuclei and mount the cells. For imaging acidic states of phagolysosome, BMDMs infected with Mtb-ERFP (MOI 5) were treated with SC or DMI (100 μM) for the indicated times, followed by the incubation of cells with LysoView 633 (Biotium, Fremont, USA; 70058) for 30 min at 37°C. Cells were DPBS-washed two times and mounted by fluoromount-G with DAPI. The excitation of LysoView 633 was performed by the 633 nm laser, and pictured at 645–750 nm. To determine the autophagic flux in BMDMs, cells were transduced with retroviruses expressing a tandem-tagged mCherry-enhanced green fluorescent protein (EGFP)-LC3B for 24 h and then treated with DMI in the presence or absence of Baf-A1 for 24 h. Cells with tandem LC3B plasmid was detected by confocal microscopy.
## Transmission electron microscopy (TEM)
Cells were scraped from plates and collected by centrifugation. Samples were sequentially fixed in $3\%$ glutaraldehyde and $1\%$ osmium tetroxide, cooled on ice for 1 h, washed with 0.1 M cacodylate buffer (pH 7.2) containing $0.1\%$ CaCl2, and dehydrated in an ethanol and propylene oxide series. Next, samples were embedded in Epon 812 mixture and polymerized at 60°C for 36 h. Using the ULTRACUT UC7 ultramicrotome (Leica Biosystems, Vienna, Austria), sections of 70 nm thickness were cut and mounted on 75-mesh copper grids. Sections were counterstained with uranyl acetate and lead citrate for 10 min and 7 min, respectively, and examined using the KBSI Bio-High Voltage EM (JEM-1400 Plus at 120 kV and JEM-1000BEF at 1000 kV; JEOL Ltd., Tokyo, Japan).
## Western blotting
BMDM lysates were collected in Protein 5× Sample Buffer (ELPIS BIOTECH, EBA-1052) diluted with RIPA buffer (150 mM sodium chloride, $1\%$ Triton X-100, $0.1\%$ SDS, $1\%$ sodium deoxycholate, 50 mM Tris-Cl at pH 7.5, and 2 mM EDTA) supplemented with protease [04693132001] and phosphatase [11836170001] inhibitor cocktails (Roche, Mannheim, Germany). Samples were boiled for 10 min on a heating block and cooled on ice for 10 min. The samples were resolved by SDS-PAGE and transferred to a nitrocellulose (Pall Corporation, NY, 66485) or PVDF (Millipore, Burlington, MA, IPVH0001) membrane at 200 mA for 2 h. To prevent nonspecific binding, membranes were incubated in blocking solution with $1\%$ BSA in TBST for 30 min at room temperature and reacted overnight with anti-pSTAT3, -STAT3, or -ACTIN primary antibody at 4°C. The membranes were reacted with the appropriate horseradish peroxidase-conjugated secondary antibodies for 1 h at room temperature. Immunoreactive bands were visualized with ECL reagent from the Chemiluminescence Assay Kit (Millipore, WBKL S0500), and the appropriate bands were detected using the UVitec Alliance mini-chemiluminescence device (UVitec, Rugby, UK). Band intensities were measured using Image J software and normalized to that of ACTIN.
## Statistical analysis
Statistical analysis was conducted using Prism 8.0 for Windows (GraphPad Software Inc., San Diego, CA). The unpaired Student’s t-test or Mann–Whitney U test was used to compare two groups and one-way ANOVA for three or more groups. Data are means ± standard deviation (SD) or ± standard error of the mean (SEM). Statistical significance is indicated as *$p \leq 0.05$, **$p \leq 0.01$, and ***$p \leq 0.001.$
## DMI induces antimicrobial activity against Mtb, BCG, and Mav in vitro and in vivo
IRG1, a mitochondrial enzyme that produces itaconate via decarboxylation of cis-aconitate [17], protects against Mtb infection in a mouse model by controlling excessive pathological inflammatory responses and neutrophil recruitment [19]. To assess the antimicrobial effect of DMI on Mtb, BCG, and NTM infections, we infected murine BMDMs with Mtb H37Rv, BCG, or Mav at an MOI of 1 and monitored bacterial survival at 72 h post-infection. DMI treatment caused dose-dependent suppression of intracellular Mtb, BCG, and Mav (Fig. 1A). To examine whether DMI directly kills mycobacteria, we first determined the IC50 values and generated dose–response curves in culture conditions of standard medium 7H9-OADC. The IC50 values of DMI for Mtb, BCG, and Mav were 866 µM, 1.2 mM, and 3.8 mM, respectively, whereas the IC50 of INH was 154 nM. Compared with INH, the IC50 values of DMI were over 5000-fold for all tested mycobacterial strains (Additional file 1: Fig. S1). Isocitrate lyase, a target of itaconate, is an essential component of glyoxylate shunt found in many pathogens including mycobacteria [30–32]. This metabolic pathway is activated under carbon-limiting conditions through a bypass of TCA cycle [33, 34]. We thus determined whether DMI induces direct antibacterial activities against Mtb, BCG, and Mav, depending on culture conditions containing different carbon sources, i.e., glucose or acetate. When IC50 values were compared between glucose- and acetate-containing culture conditions, DMI showed significantly greater IC50 values for Mtb (7H9-glucose, 2.9 mM; 7H9-acetate, 3.3 mM), for BCG (7H9-glucose, 3 mM; 7H9-acetate, 1.5 mM), and for Mav (7H9-glucose, 5 mM; 7H9-acetate, 4.1 mM), when compared to those of INH for Mtb (7H9-glucose, 183 nM; 7H9-acetate, 190 nM) (Additional file 1: Fig. S2). Therefore, the intracellular antimicrobial responses to DMI may not be caused by its direct inhibition of mycobacterial growth. Fig. 1DMI-treatment inhibits the both in vitro and in vivo mycobacterial survival. A Intracellular survival of mycobacteria in BMDMs infected with Mtb, BCG, or Mav (MOI 1). BMDMs were infected with Mtb (left panel), BCG (mid panel), or Mav (right panel). After 4 h, cells were washed with pre-warmed DPBS and treated with SC or indicated concentration of DMI. At 3 dpi, cells were lysed and used to a CFU assay to examine the intracellular survival of Mtb, BCG, or Mav. B Mice were intranasally infected with Mtb (5 × 104 CFU, $$n = 7$$–8 per group), BCG (1 × 107 CFU, $$n = 7$$–8 per group), MDR-Mtb (5 × 103 CFU, $$n = 5$$ per group), or Mav (1 × 107 CFU, $$n = 5$$ per group), followed by treatment with vehicle or DMI (50 mg/kg) by intraperitoneal (i.p.) injection, and euthanized as depicted schematic diagram of experimental schedule (left panel). The dissected lungs from mice were subjected to analyze the bacterial burden by CFU assay. C, D Mice ($$n = 3$$ per group) were infected with Mtb (5 × 104 CFU) followed by treatment with vehicle or DMI (50 mg/kg) by i.p. injection. At 28 dpi, lungs were harvested to determine the inflamed area. Representative histopathological images (C, scale bar = 300 μm) and quantitative analysis for the inflamed area of the lung tissues from mice using H&E (D). Statistical analysis was determined with one-way ANOVA test with Tukey’s multiple comparisons (A) and Mann–Whitney U test (B, D). Data are representative of at least three independent experiments, and error bars denote ± SD (A) or ± SEM (B, D). CFU colony forming unit, DMI dimethyl itaconate, MOI multiplicities of infection, dpi days post infection, MDR-TB MDR-Mtb. * $p \leq 0.05$, **$p \leq 0.01$, and ***$p \leq 0.001$ C57BL/6 mice were infected intranasally with Mtb (5 × 104 CFU/mouse), BCG (1 × 107 CFU/mouse), MDR-Mtb (5 × 103 CFU/mouse), or Mav (1 × 107 CFU/mouse) and treated intraperitoneally with 50 mg/kg DMI or the vehicle control for 4 days (Fig. 1B, left). We found that DMI treatment significantly enhanced in vivo antimicrobial effects during infection with Mtb, BCG, MDR-Mtb, or Mav in mouse lung tissues (Fig. 1B). Pronounced differences were observed in the in vivo bacterial loads of all four different strains in the lung tissues from the infected mice between DMI-treated and vehicle-treated groups (Fig. 1B). In addition, DMI significantly inhibited granulomatous lesions in the lung tissues of Mtb-infected mice (Fig. 1C, D). Taken together, these data strongly suggest that DMI significantly suppresses the pulmonary bacterial loads and inflammatory lesions in mice during mycobacterial infections.
## DMI modulates inflammatory and protective cytokine generation in macrophages and in the lung tissues from infected mice
Because DMI reduced the bacterial burden and lung pathological lesions at the early (7 dpi) stages of infection (Fig. 1), we examined its effect on lung inflammatory responses during mycobacterial infection. We thus compared the expression levels of proinflammatory cytokines Tnf, Il6, Il1b, and the anti-inflammatory cytokine Il10, in the lung tissues between the DMI-treated and vehicle-treated control mice during infection. The Il6, Il10, and Il1b levels were significantly suppressed, whereas Tnf mRNA level was not reduced, by DMI treatment in the lung tissues from mice infected with Mtb and BCG (Fig. 2A, B; at 7 dpi). Consistent with the mRNA data, the IL-6 and IL-10 protein levels were markedly reduced in the supernatants of lung lysates from DMI-treated compared with control mice infected with BCG and Mav (Fig. 2C, D). Thus, both IL-6 and IL-10 levels were specifically modulated by DMI treatment in vivo during mycobacterial infection. We next assessed whether DMI modulates the mRNA levels of IFN-γ and granulocyte–macrophage colony-stimulating factor (GM-CSF), which are known as protective cytokines associated with anti-mycobacterial host defense [35–38] in the infected lungs from mice during Mtb and BCG infections. As shown in Fig. 2E, F, the Csf2 levels were significantly upregulated by DMI treatment in both Mtb- and BCG-infected lung tissues. However, the mRNA level of Ifng was significantly reduced in Mtb-infected lungs, whereas it was increased in the lung tissues from BCG-infected mice (Fig. 2E, F). These data suggest that DMI treatment differentially modulated protective cytokine generation in the lung tissues depending on the mycobacterial strains. Fig. 2The treatment with DMI suppresses the expression of inflammatory cytokines and increases the level of protective cytokines in lung tissues from mycobacteria-infected mice. WT mice ($$n = 5$$–6 per group) were infected intranasally with Mtb (5 × 104 CFU), BCG (1 × 107 CFU), or Mav (1 × 107 CFU) followed by treatment with vehicle or DMI (50 mg/kg) in accordance with experimental schedule, and monitored at 7 dpi. Lung tissues from Mtb- (A) or BCG- (B) infected mice were used to qRT-PCR analysis to estimate the mRNA expression of Il6, Il10, Il1b, and Tnf. C, D The supernatants from lung lysates separated from BCG- (C) or Mav- (D) infected mice were used to ELISA analysis. Lung tissues from Mtb- (E) or BCG- (F) infected mice were used to qRT-PCR anlaysis to examine the mRNA expression of Ifng and Csf2. Mann–Whitney U test was used to examine the statistical analysis and the results were shown as means ± SEM from at least three independent experiments performed. DMI dimethyl itaconate, n.s. not significant, a.u. arbitrary unit. * $p \leq 0.05$, **$p \leq 0.01$, and ***$p \leq 0.001$ We further examined the effect of DMI on Mtb-induced mRNA levels of inflammatory cytokines (Tnf, Il1b, and Il6) in macrophages (Fig. 3A and Additional file 1: Fig. S3A). Mtb-induced Tnf expression was significantly increased by DMI at 6 h, but decreased at 18 h, in BMDMs (Additional file 1: Fig. S3A). However, Mtb-induced Il1b and Il16 levels were markedly suppressed by DMI at 3, 6, and 18 h (Fig. 3A). Additionally, the Mav-induced Il6 and Il1b levels were markedly decreased at 3–18 h, as with Mtb infection (Additional file 1: Fig. S3B). Moreover, DMI markedly suppressed IL-6 and IL-10 production in BMDMs infected with Mtb, BCG, or Mav, compared with those induced by the infection alone (Fig. 3B). Therefore, DMI treatment regulates inflammatory homeostasis in vivo and in macrophages during mycobacterial infection. Fig. 3DMI-treatment reduces the expression level of inflammatory cytokines during various mycobacterial infections. A Mtb (MOI 3)-infected BMDMs were incubated in the freshly changed media treated with SC or 100 μM of DMI. Cells were lysed at the indicated time points (3, 6, or 18 h) and used to qRT-PCR analysis to estimate the expression level of Il1b and Il6. B The supernatants from the BMDMs infected with Mtb (left panel), BCG (mid panel), or Mav (right panel) were harvested at 18 h post-infection and used to ELISA to examine the cytokine level of IL-6 and IL-10. Statistical analysis was conducted with one-way ANOVA test with Tukey’s multiple comparisons (A) and unpaired Student’s t-test (B). Data shown as means ± SD from two independent experiments conducted in triplicate. DMI, dimethyl itaconate. * $p \leq 0.05$ and ***$p \leq 0.001$
## DMI promotes the activation of autophagy and autophagic flux in BMDMs
DMI induces cellular autophagy to inhibit NLRP3-mediated pyroptosis [39]. We thus examined whether DMI activates autophagy to enhance antibacterial responses during infection. DMI robustly increased LC3 punctate structures in BMDMs in a time-dependent manner (Fig. 4A). In addition, Western blotting revealed that DMI caused slight but significant conversion of cytosolic LC3-I to autophagosome-associated LC3-II (Fig. 4B). Moreover, pre-treatment of BMDMs with Baf-A1 significantly increased the level of lipidated LC3-II, indicating that DMI increases autophagic flux (Fig. 4B, lane 4).Fig. 4DMI-treatment increases the activation of autophagy and autophagic flux. A BMDMs were treated with SC or DMI (100 μM) for the indicated times and stained with anti-LC3 (green) and DAPI (for nuclei; blue). Representative immunofluorescence microscopy images (left panel) and quantitation of LC3 puncta per cell (right panel). At least 100 cells in independent 8 fields were counted in each group from two different experiments. Scale bar, 2 μm. B BMDMs were pre-incubated with or without Baf-A1 (100 nM) for 2 h and followed by treatment with SC or DMI (100 μM) for 8 h. LC3 and ACTIN levels were evaluated by Western blot analysis. C, D BMDMs were treated with SC or DMI (100 μM) for 18 h. Representative TEM image (C) and quantitation of autophagic vesicles per cell (D). E, F BMDMs were transduced with retroviruses expressing a tandem-tagged mCherry-EGFP-LC3B. After 24 h, cells were pre-incubated with or without Baf-A1 (100 nM) for 2 h and followed by treatment with SC or DMI (100 μM) for 24 h. Cells were collected and mCherry or EGFP expressing LC3B were detected by confocal microscopy. Representative immunofluorescence microscopy images (E) and quantitation of LC3 dots per cell (F). Scale bars, 2 μm. Statistical analysis was determined with one-way ANOVA test with Tukey’s multiple comparisons (A), unpaired Student’s t-test (D), and two-way ANOVA test with Sidak’s multiple comparisons (F). Data are representative of at least three independent experiments, and error bars denote ± SD. SC solvent control, DMI dimethyl itaconate, Baf-A1 bafilomycin A1, N nucleus. ** $p \leq 0.01$ and ***$p \leq 0.001$ *Ultrastructural analysis* by TEM showed that DMI significantly increased the number of autophagic vesicles (autophagosomes and autolysosomes) in BMDMs (Fig. 4C, D). We further transduced BMDMs with a retroviral vector containing a mCherry-EGFP-LC3B prior to DMI treatment. The number of red punctate structures (mCherry; acid stable) denoting the state of autolysosomes (acidic pH quenches GFP fluorescence) were significantly increased. However, pre-treatment with Baf-A1, an inhibitor of the lysosomal V-ATPase, prevented this effect in DMI-treated BMDMs (Fig. 4E, F). These data confirm that DMI promotes the activation of autophagy and autophagic flux in BMDMs.
## DMI-mediated autophagy enhances phagosomal maturation of Mtb
We next examined the effect of DMI on phagosomal maturation of Mtb in BMDMs. To examine this, we performed immunostaining and assessed the bacteria colocalization with LC3-positive autophagosomes and lysosomal structures in the Mtb-ERFP-infected BMDMs. DMI treatment in Mtb-ERFP-infected BMDMs significantly increased the colocalization of endogenous LC3-positive autophagosomal structures and Mtb-ERFP at 6 (early) and 18 (late) h (Fig. 5A, B). Additionally, DMI significantly increased the colocalization of Mtb-ERFP with LAMP1-positive lysosomal vesicles in BMDMs at the same time points (Fig. 5C, D). Moreover, we assessed whether DMI enhanced the Mtb captured in acidic compartment by staining with LysoView 633 dye, which is a highly sensitive pH sensor of the acidified lysosomes [40, 41]. As shown in Fig. 5E, F, we found that DMI treatment results in the significantly increased colocalization of Mtb-ERFP and LysoView 633+ acidic compartment. Fig. 5DMI enhances antibacterial autophagy against infection with Mtb in BMDMs. A–D BMDMs were infected with Mtb-ERFP (MOI 5) and followed by treatment with SC or DMI (100 μM) for the indicated times. Mtb-ERFP (red), Alexa Fluor 488-conjugated LC3 (green, A) or LAMP1 (green, C), DAPI (for nuclei, blue) were detected by confocal microscopy. Representative immunofluorescence images (A for LC3, C for LAMP1) and quantitation of colocalization of Mtb-ERFP with LC3 (B) or LAMP1 (D) were shown. Scale bars, 2 μm. E, F Mtb-ERFP-infected (MOI 5) BMDMs were treated with SC or DMI (100 μM) for the indicated times. Mtb-ERFP (red), Lysoview 633 (skyblue), and DAPI (for nuclei, blue) were detected by confocal microscopy. Representative immunofluorescence images (E) and quantitation of colocalization of Mtb-ERFP with LysoView 633 (F) using Manders’ coefficient were assessed. G, H BMDMs were infected with Mtb (MOI 5) and followed by treatment with SC or DMI (100 μM) for 18 h. Representative TEM images (G) and quantitation of bacteria in compartment (H). Bacteria in cytosol (light green), autophagosomes (orange), and phagosomes (pale blue) were marked as indicated. Unpaired Student’s t-test was used to examine the statistical analysis and the results were shown as means ± SD from at least three independent experiments performed. N nucleus, DMI dimethyl itaconate, n.s. not significant, Cyto cytosol, Auto autophagosomal/autolysosomal structure, Phag phagosome. * $p \leq 0.05$, **$p \leq 0.01$, and ***$p \leq 0.001$ We next examined the changes of ultrastructural findings by DMI treatment in Mtb-infected macrophages. TEM analysis showed that bacteria-containing autophagosomal–autolysosomal structures, where bacteria are localized within the host vesicles surrounded by 2 or more encircling membranes [42, 43], were significantly increased in Mtb-infected BMDMs at 18 h (Fig. 5G, H). However, there was no significant difference in cytosolic bacteria, which were counted as those without surrounding host membranes [42], between DMI-treated and -untreated conditions (Fig. 5H). Bacteria-containing phagosomes enclosed by a single membrane-bound organelle [42, 43] were significantly downregulated in Mtb-infected BMDMs treated with DMI (Fig. 5H). Therefore, DMI promotes the association of Mtb with autophagosomes and autolysosomes, thereby boosting phagosomal maturation during infection.
## DMI-induced autophagy is partly required for antimicrobial responses to Mtb, BCG, and Mav infection
We next assessed whether autophagy inhibition affects DMI-mediated antimicrobial responses in BMDMs by comparing the DMI-induced intracellular survival of Mtb and BCG in BMDMs from WT mice (Atg7fl/fl) versus mice with myeloid cell-specific disruption of the crucial autophagy gene Atg7 (Atg7fl/fl; LysMcre+; conditional homozygous knockout, cKO) (Fig. 6A). Notably, DMI significantly inhibited intracellular Mtb and BCG growth in Atg7 WT and cKO BMDMs (Fig. 6A). However, Atg7 cKO BMDMs showed significantly increased bacterial growth compared with Atg7 WT BMDMs under all conditions at 3 dpi in the presence or absence of DMI. Similar pattern was observed in Atg7 cKO PMs under all conditions in the presence or absence of DMI (Fig. 6B). Notably, the numbers of intracellular Mtb and BCG CFUs were comparable between Atg7 cKO macrophages treated with 100 μM of DMI and SC-treated Atg7 WT macrophages (Fig. 6A, B, for BMDMs and PMs, respectively).Fig. 6Autophagy is partially involved in the effect of DMI on antimicrobial responses in macrophages. Intracellular survival assay after Mtb, BCG, or Mav (MOI 1) in the presence or absence of DMI. A BMDMs from Atg7 WT or Atg7 cKO mice were infected with Mtb (left panel) or BCG (right panel) and treated with indicated concentration of DMI for 3 days. Cells were lysed and used to a CFU assay to examine the intracellular survival of Mtb or BCG. B PMs from Atg7 WT or Atg7 cKO mice were infected with Mtb (left panel) or BCG (right panel) and treated with indicated concentration of DMI for 3 days. Cells were lysed and used to a CFU assay to examine the intracellular survival of Mtb or BCG. C BMDMs from Atg7 WT or Atg7 cKO mice were infected with Mav (MOI 1) and treated with indicated concentration of DMI for 3 days. Cells were lysed and used to a CFU assay to examine the intracellular survival of Mav. Statistical analysis was conducted with one-way ANOVA test with Tukey’s multiple comparisons. Data shown as means ± SD from two independent experiments conducted in triplicate. CFU colony forming unit, n.s. not significant, DMI dimethyl itaconate. * $p \leq 0.05$ and ***$p \leq 0.001$ DMI-mediated suppression of intracellular Mav growth was observed in Atg7 WT and Atg7 cKO BMDMs (Fig. 6C). The number of intracellular Mav CFUs was significantly higher in Atg7 cKO than Atg7 WT BMDMs in the presence or absence of DMI (Fig. 6C). As with the Mtb-infected conditions, there was no difference in intracellular Mav growth between Atg7 cKO BMDMs treated with 100 μM of DMI and SC-treated Atg7 WT BMDMs (Fig. 6C). Therefore, autophagy is partly required for DMI-induced antimicrobial responses in macrophages against Mtb, BCG, and Mav infections.
## DMI modulates the activation of STAT3 in mycobacterium-infected macrophages
Given that DMI suppresses IL-6 and IL-10 and induces autophagy in macrophages during infection, we evaluated its effect on the activation of the transcription factor STAT3, which modulates gene expression in response to IL-6 and IL-10 [44] and the suppression of autophagy [45, 46]. In addition, the STAT3 signaling pathway may play a detrimental role in the host defensive responses through the enhanced intracellular Mtb survival, blockade of apoptosis, and aggravation of inflammatory responses during infection and inflammation [47–49]. We thus evaluated the effect of DMI on STAT3 phosphorylation and expression. Although the activation patterns differed over time, Mtb, BCG, or Mav infection significantly increased the phosphorylation of STAT3 at Tyr-705, which was highly phosphorylated at 18 h in BMDMs (Fig. 7A–C). When BMDMs were exposed to DMI, the phosphorylated STAT3 levels were significantly downregulated at 18 h during infections of all three strains tested (Fig. 7A–C). These data suggest that DMI significantly suppresses the activation of STAT3 which signaling might be related to detrimental effects upon host responses during Mtb and NTM infections. Fig. 7DMI treatment inhibits the activation of STAT3 in mycobacteria-infected macrophages. BMDMs were infected with Mtb (A), BCG (B), or Mav (C) (MOI 3) for 4 h and washed with DPBS followed by incubation with SC or DMI (100 μM) in the fresh media. The cells were harvested at the indicated times. The p-STAT3 and STAT3 levels were evaluated by Western blot analysis. Densitometry analysis of p-STAT3 and STAT3 Western blot represented in right panels. Statistical analysis was determined with unpaired Student’s t-test and shown as means ± SD from three independent experiments conducted in duplicate. n.s. not significant, SC solvent control, DMI dimethyl itaconate. * $p \leq 0.05$, **$p \leq 0.01$, and ***$p \leq 0.001$
## Discussion
Mycobacteria–host interactions are complex and dynamic and influence the outcome of infection. Mtb and NTM have evolved multiple strategies to modulate and evade host innate signaling pathways and immune clearance [50–52]. These include interruption of phagosomal maturation [53, 54], disruption of apoptosis [55, 56] and autophagy [57], evasion of reactive oxygen species (ROS) [58, 59], deleterious type I IFN secretion [60], excessive production of inflammatory cytokines [61], and immunosuppression with increased IL-10 production [62, 63]. These events can modulate the host–pathogen relationship to favor mycobacterial survival in macrophages. Several drug candidates for HDT can reportedly overcome the immune-evasion mechanisms of each mycobacterial strain and enhance host defensive mechanisms [16, 64–70]. However, little is known about the potential drug candidate(s) that can activate innate host defense against both Mtb and NTM infections. Here, we show that DMI offers a therapeutic promise for HDT against multiple mycobacteria including Mtb, BCG, Mav, and even to MDR-Mtb. Importantly, DMI acts as a potential HDT agent through diverse mechanisms involving the maintenance of inflammatory homeostasis, enhancement of autophagy and phagosomal maturation, and suppression of STAT3 signaling.
The natural form of itaconate is produced by IRG1, which is expressed in murine and human macrophages, and Irg1 expression is induced in host cells by Mtb infection via the Mtb ESX-1 secretion system, and the host STING, type I IFN, and TLR2-dependent signaling pathways [17, 71]. Michelucci et al. reported that IRG1 and its product itaconic acid suppress mycobacterial growth by blockade of bacterial isocitrate lyase [17]. IRG1 and its product itaconate in myeloid cells are required to control neutrophil infiltration and suppress pathological inflammation, ROS generation, and tissue injury in a mouse model of Mtb infection [19]. However, whether cell-permeable derivatives of itaconate suppress in vitro and in vivo growth of multiple mycobacteria is largely unclear. Our data is important to show that DMI has an in vivo antimicrobial activity to control bacterial growth in the lung tissues during a variety of infections caused by mycobacteria including Mtb, BCG, Mav, and even by MDR-Mtb. At least partly consistent with our findings, recent studies showed that either endogenous itaconate or itaconate derivatives potentiate intracellular killing of bacteria such as Salmonella Typhimurium [72, 73]. A recent study also showed that DMI administration ameliorates the cognitive deficits and proinflammatory responses in microglia caused by *Toxoplasma gondii* infection [74]. Additionally, our data partly correlated with the previous studies showing that DMI exerts anti-fungal effects in fungal keratitis [75]. Combined with our current findings, these data strongly provide a clue that DMI is being useful for the potential therapeutic modality against a variety of infection. Given that IRG1 is required to control excessive neutrophil infiltration in the lungs to ameliorate pathological inflammation and progression of Mtb infection [76, 77], further research should examine whether DMI-mediated antimicrobial defense is associated with attenuation of neutrophil recruitment in the lungs of mice infected with Mtb, BCG, Mav, or MDR-Mtb.
Although it reduced the intracellular survival of mycobacteria in macrophages, DMI did not exert a direct antimicrobial effect because a DMI concentration of 1000-fold the IC50 of INH is required to suppress bacterial growth under 7H9-OADC conditions. In addition, IC50 values of DMI showed significantly increased IC50 values for Mtb, BCG, and Mav, under 7H9-acetate conditions, the carbon-limiting culture conditions for activating bacterial isocitrate lyase, a target of itaconate [34], compared to those induced by INH for Mtb. Thus, antimicrobial effects of DMI are mainly mediated through multiple host-defense strategies, not by direct bactericidal activities, during mycobacterial infection. Firstly, DMI maintained the homeostasis of inflammation by reducing the level of proinflammatory (Il6 and Il1b) and anti-inflammatory (Il10) cytokines in vivo and in macrophages during infection. Accumulating evidence suggests that DMI and OI reduce the infection-induced inflammatory responses and the release of chemokines CXCL10 and CCL2 [78]. Importantly, DMI-mediated suppression of influenza virus-induced Cxcl10 does not depend on the expression of Irg1 in murine macrophages [78]. Furthermore, DMI treatment alleviates the generation of inflammatory cytokines/chemokines such as IL-1β, IL-6, and IL-8 in human corneal epithelial cells through Nrf2/heme oxygenase‐1 (HO‐1) [75]. Combined with the previous findings that DMI activates the Nrf2 protein [75, 79], DMI ameliorates excessive pathologic inflammation during mycobacterial infections presumably through the activation of Nrf2, and this warrants further examination in the context of Mtb and NTM infections. Inflammatory response and production of antimicrobial factors including ROS and antimicrobial peptides are important for host immune defense against intracellular mycobacteria [80, 81]. However, the unbalanced inflammatory response to chronic infections with intracellular pathogens causes damage to the host, rendering inflammatory homeostasis as a target for HDT against Mtb and NTM, particularly drug-resistant strains [82]. Therefore, new drugs that maintain the balance between inflammatory host defense and prevention of necrotic inflammation are urgently needed. Our data revealed that DMI is a promising anti-mycobacterial therapeutic target with a balanced activity through regulating both pro- and anti-inflammatory responses during infection.
In addition, DMI enhances the Csf2 mRNA level in the lung tissues from mice infected with Mtb and BCG, whereas it enhances Ifng mRNA expression in the lungs during BCG, but not Mtb, infection. In addition to IFN-γ, which is a well-known protective Th1 cytokine during Mtb infection [37, 38], emerging evidence suggests that GM-CSF is required for the restriction of Mtb infection in macrophages at least partly mediated through peroxisome proliferator-activated receptor-γ [36]. GM-CSF is mainly produced by iNKT cells and γδ T cells in early phase of infection [36] and functions as an antibacterial effector cytokine participating in IFN-γ-independent host protection against Mtb infection [35]. Thus, the DMI effects may contribute to improve host defense through keeping immune homeostasis and activating protective cytokine generation, during Mtb and NTM infections.
Autophagy, a cell-autonomous defense mechanism, is a possible target of HDT against mycobacterial infection [69, 70, 83]. Several autophagy-activating factors enhance innate host defense and lysosomal degradation of intracellular mycobacteria [67–70, 83]. DMI robustly activated autophagy, which partly affects intracellular mycobacterial survival in Atg7 cKO macrophages. These data suggest that DMI-mediated autophagy activation is necessary but not sufficient, to suppress intracellular mycobacterial survival. DMI further enhances the colocalization of Mtb-ERFP with autolysosomes, indicating that DMI-induced autophagy may promote phagosomal maturation against Mtb infection. These results are in partial agreement with a recent report that OI enhances autophagy in chondrocytes by suppressing PI3K/Akt/mTOR signaling [84]. However, OI also suppresses autophagy and ROS generation to exert an anti-fibrotic effect in renal tissue [85]. These data suggest that cell-permeable itaconate modulates host autophagy in a context-dependent manner.
Importantly, DMI significantly reduced the STAT3 phosphorylation levels in macrophages against Mtb, BCG, or Mav infection. STAT3 is a transcription factor activated by cytokines such as IL-6, IL-10, and growth factors, and is implicated in the activation of Th17 cell responses and autoimmune diseases as well as anti-inflammatory responses [44, 86, 87]. In mycobacterial infection, the role of STAT3 signaling in the immune response has been debated [88]. In human innate immune responses, STAT3 signaling and TLR4 pathway activation are important in the vitamin D-mediated antimicrobial pathways in macrophages [89], although the molecular mechanisms are unknown. However, the p-STAT3 inhibitor AG-490 protected against lung injury in a mouse model of type 2 diabetes-associated TB [47]. The inhibition of p-STAT3 by AG-490 improves mouse survival and histopathological findings and ameliorates inflammation, fibrosis, and Mtb growth [47]. Therefore, DMI-mediated STAT3 inhibition is likely responsible for the suppression of pathological inflammation during mycobacterial infection. Also, STAT3 signaling activation is associated with anti-apoptotic responses, favoring the intracellular survival of Mtb [48]. In addition, Mtb Rv2145c-mediated intracellular bacterial growth is dependent on STAT3-mediated IL-10 production [49], suggesting an immunosuppressive role for STAT3. Indeed, STAT3 signaling suppresses autophagy by multiple molecular mechanisms including transcriptional regulation of autophagy-related genes and IL-10-mediated inhibition of autophagy [45, 46]. Together with its suppressive functions upon autophagy pathway, the STAT3 pathway contributes to intracellular Mav survival in macrophages [90]. Further research is needed to determine whether DMI-mediated suppression of STAT3 signaling underlies the activation of autophagy induced by DMI treatment.
## Conclusions
In summary, the present study has demonstrated that DMI shows potent antimicrobial activities during Mtb, BCG, Mav, or even to MDR-Mtb infection. In addition, DMI functions as an important regulator of inflammatory homeostasis, activation of autophagy, and control of STAT3 signaling. DMI-induced autophagy partly contributes to improve host defenses against mycobacterial infections. Together, we propose DMI as a new promising candidate for HDT against both Mtb and NTM infections through orchestrating multiple innate immune strategies.
## Supplementary Information
Additional file 1: Figure S1. Direct effect of DMI on various mycobacteria under 7H9-OADC culture conditions. Figure S2. Direct effect of DMI on various mycobacteria under carbon-limiting conditions. Figure S3. The treatment with DMI regulates the expression level of proinflammatory cytokines in both Mtb- and Mav-infected murine macrophages.
## References
1. 1.WHO 2021. Global tuberculosis report 2021, WHO.
2. Espinal MA, Laszlo A, Simonsen L, Boulahbal F, Kim SJ, Reniero A. **Global trends in resistance to antituberculosis drugs. World Health Organization-international union against tuberculosis and lung disease working group on anti-tuberculosis drug resistance surveillance**. *N Engl J Med* (2001.0) **344** 1294-1303. DOI: 10.1056/NEJM200104263441706
3. Singh R, Dwivedi SP, Gaharwar US, Meena R, Rajamani P, Prasad T. **Recent updates on drug resistance in**. *J Appl Microbiol* (2020.0) **128** 1547-1567. DOI: 10.1111/jam.14478
4. Nathanson E, Nunn P, Uplekar M, Floyd K, Jaramillo E, Lonnroth K. **MDR tuberculosis—critical steps for prevention and control**. *N Engl J Med* (2010.0) **363** 1050-1058. DOI: 10.1056/NEJMra0908076
5. Sulis G, Pai M. **Isoniazid-resistant tuberculosis: a problem we can no longer ignore**. *PLoS Med* (2020.0) **17** e1003023. DOI: 10.1371/journal.pmed.1003023
6. Chai J, Han X, Mei Q, Liu T, Walline JH, Xu J. **Clinical characteristics and mortality of non-tuberculous mycobacterial infection in immunocompromised vs. immunocompetent hosts**. *Front Med* (2022.0) **9** 884446. DOI: 10.3389/fmed.2022.884446
7. Gopalaswamy R, Shanmugam S, Mondal R, Subbian S. **Of tuberculosis and non-tuberculous mycobacterial infections—a comparative analysis of epidemiology, diagnosis and treatment**. *J Biomed Sci* (2020.0) **27** 74. DOI: 10.1186/s12929-020-00667-6
8. Shamaei M, Mirsaeidi M. **Nontuberculous mycobacteria, macrophages, and host innate immune response**. *Infect Immun* (2021.0) **89** e0081220. DOI: 10.1128/IAI.00812-20
9. Dahl VN, Molhave M, Floe A, van Ingen PJ, Schon PT, Lillebaek PT. **Global trends of pulmonary infections with nontuberculous mycobacteria: a systematic review**. *Int J Infect Dis* (2022.0). DOI: 10.1016/j.ijid.2022.10.013
10. Tissot A, Thomas MF, Corris PA, Brodlie M. **Nontuberculous mycobacteria infection and lung transplantation in cystic fibrosis: a worldwide survey of clinical practice**. *BMC Pulm Med* (2018.0) **18** 86. DOI: 10.1186/s12890-018-0635-3
11. Gill LI, Dominic C, Tiberi S. **Atypical mycobacterial infections—management and when to treat**. *Curr Opin Pulm Med* (2021.0) **27** 216-223. DOI: 10.1097/MCP.0000000000000764
12. Abate G, Stapleton JT, Rouphael N, Creech B, Stout JE, El Sahly HM. **Variability in the management of adults with pulmonary nontuberculous mycobacterial disease**. *Clin Infect Dis* (2021.0) **72** 1127-1137. DOI: 10.1093/cid/ciaa252
13. Thornton CS, Mellett M, Jarand J, Barss L, Field SK, Fisher DA. **The respiratory microbiome and nontuberculous mycobacteria: an emerging concern in human health**. *Eur Respir Rev* (2021.0) **30** 200299. DOI: 10.1183/16000617.0299-2020
14. Llibre A, Dedicoat M, Burel JG, Demangel C, O'Shea MK, Mauro C. **Host immune-metabolic adaptations upon mycobacterial infections and associated co-morbidities**. *Front Immunol* (2021.0) **12** 747387. DOI: 10.3389/fimmu.2021.747387
15. Shi L, Jiang Q, Bushkin Y, Subbian S, Tyagi S. **Biphasic dynamics of macrophage immunometabolism during**. *MBio* (2019.0) **10** e02550-18. DOI: 10.1128/mBio.02550-18
16. Paik S, Jo EK. **An interplay between autophagy and immunometabolism for host defense against mycobacterial infection**. *Front Immunol* (2020.0) **11** 603951. DOI: 10.3389/fimmu.2020.603951
17. Michelucci A, Cordes T, Ghelfi J, Pailot A, Reiling N, Goldmann O. **Immune-responsive gene 1 protein links metabolism to immunity by catalyzing itaconic acid production**. *Proc Natl Acad Sci USA* (2013.0) **110** 7820-7825. DOI: 10.1073/pnas.1218599110
18. Kwai BXC, Collins AJ, Middleditch MJ, Sperry J, Bashiri G, Leung IKH. **Itaconate is a covalent inhibitor of the**. *RSC Med Chem* (2021.0) **12** 57-61. DOI: 10.1039/d0md00301h
19. Nair S, Huynh JP, Lampropoulou V, Loginicheva E, Esaulova E, Gounder AP. **Irg1 expression in myeloid cells prevents immunopathology during**. *J Exp Med* (2018.0) **215** 1035-1045. DOI: 10.1084/jem.20180118
20. Gidon A, Louet C, Rost LM, Bruheim P, Flo TH. **The tumor necrosis factor alpha and interleukin 6 auto-paracrine signaling loop controls**. *MBio* (2021.0) **12** e0212121. DOI: 10.1128/mBio.02121-21
21. Zhao C, Jiang P, He Z, Yuan X, Guo J, Li Y. **Dimethyl itaconate protects against lippolysacchride-induced mastitis in mice by activating MAPKs and Nrf2 and inhibiting NF-kappaB signaling pathways**. *Microb Pathog* (2019.0) **133** 103541. DOI: 10.1016/j.micpath.2019.05.024
22. Kuo PC, Weng WT, Scofield BA, Paraiso HC, Brown DA, Wang PY. **Dimethyl itaconate, an itaconate derivative, exhibits immunomodulatory effects on neuroinflammation in experimental autoimmune encephalomyelitis**. *J Neuroinflamm* (2020.0) **17** 138. DOI: 10.1186/s12974-020-01768-7
23. Zhang S, Jiao Y, Li C, Liang X, Jia H, Nie Z. **Dimethyl itaconate alleviates the inflammatory responses of macrophages in sepsis**. *Inflammation* (2021.0) **44** 549-557. DOI: 10.1007/s10753-020-01352-4
24. Bambouskova M, Gorvel L, Lampropoulou V, Sergushichev A, Loginicheva E, Johnson K. **Electrophilic properties of itaconate and derivatives regulate the IkappaBzeta-ATF3 inflammatory axis**. *Nature* (2018.0) **556** 501-504. DOI: 10.1038/s41586-018-0052-z
25. Swain A, Bambouskova M, Kim H, Andhey PS, Duncan D, Auclair K. **Comparative evaluation of itaconate and its derivatives reveals divergent inflammasome and type I interferon regulation in macrophages**. *Nat Metab* (2020.0) **2** 594-602. DOI: 10.1038/s42255-020-0210-0
26. ElAzzouny M, Tom CT, Evans CR, Olson LL, Tanga MJ, Gallagher KA. **Dimethyl itaconate is not metabolized into itaconate intracellularly**. *J Biol Chem* (2017.0) **292** 4766-4769. DOI: 10.1074/jbc.C117.775270
27. Yang CS, Kim JJ, Lee HM, Jin HS, Lee SH, Park JH. **The AMPK-PPARGC1A pathway is required for antimicrobial host defense through activation of autophagy**. *Autophagy* (2014.0) **10** 785-802. DOI: 10.4161/auto.28072
28. Kim JK, Kim YS, Lee HM, Jin HS, Neupane C, Kim S. **GABAergic signaling linked to autophagy enhances host protection against intracellular bacterial infections**. *Nat Commun* (2018.0) **9** 4184. DOI: 10.1038/s41467-018-06487-5
29. Woods GL, Brown-Elliott BA, Conville PS, Desmond EP, Hall GS, Lin G. *Susceptibility testing of mycobacteria, nocardiae, and other aerobic actinomycetes* (2011.0)
30. Munoz-Elias EJ, McKinney JD. *Nat Med* (2005.0) **11** 638-644. DOI: 10.1038/nm1252
31. McKinney JD, HonerzuBentrup K, Munoz-Elias EJ, Miczak A, Chen B, Chan WT. **Persistence of**. *Nature* (2000.0) **406** 735-738. DOI: 10.1038/35021074
32. Fahnoe KC, Flanagan ME, Gibson G, Shanmugasundaram V, Che Y, Tomaras AP. **Non-traditional antibacterial screening approaches for the identification of novel inhibitors of the glyoxylate shunt in gram-negative pathogens**. *PLoS ONE* (2012.0) **7** e51732. DOI: 10.1371/journal.pone.0051732
33. Ahn S, Jung J, Jang IA, Madsen EL, Park W. **Role of glyoxylate shunt in oxidative stress response**. *J Biol Chem* (2016.0) **291** 11928-11938. DOI: 10.1074/jbc.M115.708149
34. Ko EM, Kim JY, Lee S, Kim S, Hwang J, Oh JI. **Regulation of the**. *J Bacteriol* (2021.0) **203** e0040221. DOI: 10.1128/JB.00402-21
35. Van Dis E, Fox DM, Morrison HM, Fines DM, Babirye JP, McCann LH. **IFN-gamma-independent control of**. *PLoS Pathog* (2022.0) **18** e1010721. DOI: 10.1371/journal.ppat.1010721
36. Rothchild AC, Stowell B, Goyal G, Nunes-Alves C, Yang Q, Papavinasasundaram K. **Role of granulocyte-macrophage colony-stimulating factor production by T cells during**. *MBio* (2017.0) **8** e01514-17. DOI: 10.1128/mBio.01514-17
37. MacMicking JD, Taylor GA, McKinney JD. **Immune control of tuberculosis by IFN-gamma-inducible LRG-47**. *Science* (2003.0) **302** 654-659. DOI: 10.1126/science.1088063
38. Fabri M, Stenger S, Shin DM, Yuk JM, Liu PT, Realegeno S. **Vitamin D is required for IFN-gamma-mediated antimicrobial activity of human macrophages**. *Sci Transl Med* (2011.0) **3** 104ra2. DOI: 10.1126/scitranslmed.3003045
39. Yang S, Zhang X, Zhang H, Lin X, Chen X, Zhang Y. **Dimethyl itaconate inhibits LPS induced microglia inflammation and inflammasomemediated pyroptosis via inducing autophagy and regulating the Nrf2/HO1 signaling pathway**. *Mol Med Rep* (2021.0) **24** 1-14. DOI: 10.3892/mmr.2021.12311
40. Manna S, Howitz WJ, Oldenhuis NJ, Eldredge AC, Shen J, Nihesh FN. **Immunomodulation of the NLRP3 inflammasome through structure-based activator design and functional regulation via lysosomal rupture**. *ACS Cent Sci* (2018.0) **4** 982-995. DOI: 10.1021/acscentsci.8b00218
41. Liu K, Kong L, Graham DB, Carey KL, Xavier RJ. **SAC1 regulates autophagosomal phosphatidylinositol-4-phosphate for xenophagy-directed bacterial clearance**. *Cell Rep* (2021.0) **36** 109434. DOI: 10.1016/j.celrep.2021.109434
42. Lerner TR, de Souza C-W, Repnik U, Russell MR, Borel S, Diedrich CR. **Lymphatic endothelial cells are a replicative niche for**. *J Clin Invest* (2016.0) **126** 1093-1108. DOI: 10.1172/JCI83379
43. Lerner TR, Queval CJ, Fearns A, Repnik U, Griffiths G, Gutierrez MG. **Phthiocerol dimycocerosates promote access to the cytosol and intracellular burden of**. *BMC Biol* (2018.0) **16** 1. DOI: 10.1186/s12915-017-0471-6
44. Gao Y, Zhao H, Wang P, Wang J, Zou L. **The roles of SOCS3 and STAT3 in bacterial infection and inflammatory diseases**. *Scand J Immunol* (2018.0) **88** e12727. DOI: 10.1111/sji.12727
45. You L, Wang Z, Li H, Shou J, Jing Z, Xie J. **The role of STAT3 in autophagy**. *Autophagy* (2015.0) **11** 729-739. DOI: 10.1080/15548627.2015.1017192
46. Shi J, Wang H, Guan H, Shi S, Li Y, Wu X. **IL10 inhibits starvation-induced autophagy in hypertrophic scar fibroblasts via cross talk between the IL10-IL10R-STAT3 and IL10-AKT-mTOR pathways**. *Cell Death Dis* (2016.0) **7** e2133. DOI: 10.1038/cddis.2016.44
47. Wang X, Lin Y, Liang Y, Ye Y, Wang D, Tai A. **Phosphorylated STAT3 suppresses microRNA-19b/1281 to aggravate lung injury in mice with type 2 diabetes mellitus-associated pulmonary tuberculosis**. *J Cell Mol Med* (2020.0) **24** 13763-13774. DOI: 10.1111/jcmm.15954
48. Fu B, Xue W, Zhang H, Zhang R, Feldman K, Zhao Q. **MicroRNA-325-3p facilitates immune escape of**. *MBio* (2020.0) **11** e00557-20. DOI: 10.1128/mBio.00557-20
49. Park HS, Back YW, Jang IT, Lee KI, Son YJ, Choi HG. *Front Immunol* (2021.0) **12** 666293. DOI: 10.3389/fimmu.2021.666293
50. Cambier CJ, Falkow S, Ramakrishnan L. **Host evasion and exploitation schemes of**. *Cell* (2014.0) **159** 1497-1509. DOI: 10.1016/j.cell.2014.11.024
51. Goldberg MF, Saini NK, Porcelli SA. **Evasion of innate and adaptive immunity by**. *Microbiol Spectr* (2014.0) **2** 747-772. DOI: 10.1128/microbiolspec.MGM2-0005-2013
52. Ernst JD. **Mechanisms of**. *Cell Host Microbe* (2018.0) **24** 34-42. DOI: 10.1016/j.chom.2018.06.004
53. Mehra A, Zahra A, Thompson V, Sirisaengtaksin N, Wells A, Porto M. *PLoS Pathog* (2013.0) **9** e1003734. DOI: 10.1371/journal.ppat.1003734
54. Shimada K, Takimoto H, Yano I, Kumazawa Y. **Involvement of mannose receptor in glycopeptidolipid-mediated inhibition of phagosome-lysosome fusion**. *Microbiol Immunol* (2006.0) **50** 243-251. DOI: 10.1111/j.1348-0421.2006.tb03782.x
55. Velmurugan K, Chen B, Miller JL, Azogue S, Gurses S, Hsu T. *PLoS Pathog* (2007.0) **3** e110. DOI: 10.1371/journal.ppat.0030110
56. Stutz MD, Allison CC, Ojaimi S, Preston SP, Doerflinger M, Arandjelovic P. **Macrophage and neutrophil death programs differentially confer resistance to tuberculosis**. *Immunity* (2021.0) **54** 1758-1771.e7. DOI: 10.1016/j.immuni.2021.06.009
57. Ouimet M, Koster S, Sakowski E, Ramkhelawon B, van Solingen C, Oldebeken S. *Nat Immunol* (2016.0) **17** 677-686. DOI: 10.1038/ni.3434
58. Voskuil MI, Bartek IL, Visconti K, Schoolnik GK. **The response of**. *Front Microbiol* (2011.0) **2** 105. DOI: 10.3389/fmicb.2011.00105
59. Colangeli R, Haq A, Arcus VL, Summers E, Magliozzo RS, McBride A. **The multifunctional histone-like protein Lsr2 protects mycobacteria against reactive oxygen intermediates**. *Proc Natl Acad Sci USA* (2009.0) **106** 4414-4418. DOI: 10.1073/pnas.0810126106
60. Mayer-Barber KD, Andrade BB, Barber DL, Hieny S, Feng CG, Caspar P. **Innate and adaptive interferons suppress IL-1alpha and IL-1beta production by distinct pulmonary myeloid subsets during**. *Immunity* (2011.0) **35** 1023-1034. DOI: 10.1016/j.immuni.2011.12.002
61. Roca FJ, Ramakrishnan L. **TNF dually mediates resistance and susceptibility to mycobacteria via mitochondrial reactive oxygen species**. *Cell* (2013.0) **153** 521-534. DOI: 10.1016/j.cell.2013.03.022
62. Bouzeyen R, Haoues M, Barbouche MR, Singh R, Essafi M. **FOXO3 transcription factor regulates IL-10 expression in mycobacteria-infected macrophages, tuning their polarization and the subsequent adaptive immune response**. *Front Immunol* (2019.0) **10** 2922. DOI: 10.3389/fimmu.2019.02922
63. Redpath S, Ghazal P, Gascoigne NR. **Hijacking and exploitation of IL-10 by intracellular pathogens**. *Trends Microbiol* (2001.0) **9** 86-92. DOI: 10.1016/S0966-842X(00)01919-3
64. Tiberi S, du Plessis N, Walzl G, Vjecha MJ, Rao M, Ntoumi F. **Tuberculosis: progress and advances in development of new drugs, treatment regimens, and host-directed therapies**. *Lancet Infect Dis* (2018.0) **18** e183-e198. DOI: 10.1016/S1473-3099(18)30110-5
65. Kilinc G, Saris A, Ottenhoff THM, Haks MC. **Host-directed therapy to combat mycobacterial infections**. *Immunol Rev* (2021.0) **301** 62-83. DOI: 10.1111/imr.12951
66. Kaufmann SHE, Dorhoi A, Hotchkiss RS, Bartenschlager R. **Host-directed therapies for bacterial and viral infections**. *Nat Rev Drug Discov* (2018.0) **17** 35-56. DOI: 10.1038/nrd.2017.162
67. Periyasamy KM, Ranganathan UD, Tripathy SP, Bethunaickan R. **Vitamin D—a host directed autophagy mediated therapy for tuberculosis**. *Mol Immunol* (2020.0) **127** 238-244. DOI: 10.1016/j.molimm.2020.08.007
68. Ouyang Q, Zhang K, Lin D, Feng CG, Cai Y, Chen X. **Bazedoxifene suppresses intracellular**. *mSphere* (2020.0) **5** e00124-20. DOI: 10.1128/mSphere.00124-20
69. Silwal P, Kim IS, Jo EK. **Autophagy and host defense in nontuberculous mycobacterial infection**. *Front Immunol* (2021.0) **12** 728742. DOI: 10.3389/fimmu.2021.728742
70. Silwal P, Paik S, Kim JK, Yoshimori T, Jo EK. **Regulatory mechanisms of autophagy-targeted antimicrobial therapeutics against mycobacterial infection**. *Front Cell Infect Microbiol* (2021.0) **11** 633360. DOI: 10.3389/fcimb.2021.633360
71. Bomfim CCB, Fisher L, Amaral EP, Mittereder L, McCann K, Correa AAS. *Front Cell Infect Microbiol* (2022.0) **12** 862582. DOI: 10.3389/fcimb.2022.862582
72. Schuster EM, Epple MW, Glaser KM, Mihlan M, Lucht K, Zimmermann JA. **TFEB induces mitochondrial itaconate synthesis to suppress bacterial growth in macrophages**. *Nat Metab* (2022.0) **4** 856-866. DOI: 10.1038/s42255-022-00605-w
73. Zhang Z, Chen C, Yang F, Zeng YX, Sun P, Liu P. **Itaconate is a lysosomal inducer that promotes antibacterial innate immunity**. *Mol Cell* (2022.0) **82** 2844-2857.e10. DOI: 10.1016/j.molcel.2022.05.009
74. He Y, Xu D, Yan Z, Wu Y, Zhang Y, Tian X. **A metabolite attenuates neuroinflammation, synaptic loss and cognitive deficits induced by chronic infection of**. *Front Immunol* (2022.0) **13** 1043572. DOI: 10.3389/fimmu.2022.1043572
75. Gu L, Lin J, Wang Q, Li C, Peng X, Fan Y. **Dimethyl itaconate protects against fungal keratitis by activating the Nrf2/HO-1 signaling pathway**. *Immunol Cell Biol* (2020.0) **98** 229-241. DOI: 10.1111/imcb.12316
76. Nandi B, Behar SM. **Regulation of neutrophils by interferon-gamma limits lung inflammation during tuberculosis infection**. *J Exp Med* (2011.0) **208** 2251-2262. DOI: 10.1084/jem.20110919
77. Kimmey JM, Huynh JP, Weiss LA, Park S, Kambal A, Debnath J. **Unique role for ATG5 in neutrophil-mediated immunopathology during**. *Nature* (2015.0) **528** 565-569. DOI: 10.1038/nature16451
78. Sohail A, Iqbal AA, Sahini N, Chen F, Tantawy M, Waqas SFH. **Itaconate and derivatives reduce interferon responses and inflammation in influenza A virus infection**. *PLoS Pathog* (2022.0) **18** e1010219. DOI: 10.1371/journal.ppat.1010219
79. Mills EL, Ryan DG, Prag HA, Dikovskaya D, Menon D, Zaslona Z. **Itaconate is an anti-inflammatory metabolite that activates Nrf2 via alkylation of KEAP1**. *Nature* (2018.0) **556** 113-117. DOI: 10.1038/nature25986
80. Weiss G, Schaible UE. **Macrophage defense mechanisms against intracellular bacteria**. *Immunol Rev* (2015.0) **264** 182-203. DOI: 10.1111/imr.12266
81. Krug S, Parveen S, Bishai WR. **Host-directed therapies: modulating inflammation to treat tuberculosis**. *Front Immunol* (2021.0) **12** 660916. DOI: 10.3389/fimmu.2021.660916
82. Mohan M, Bhattacharya D. **Host-directed therapy: a new arsenal to come**. *Comb Chem High Throughput Screen* (2021.0) **24** 59-70. DOI: 10.2174/1386207323999200728115857
83. Paik S, Kim JK, Chung C, Jo EK. **Autophagy: a new strategy for host-directed therapy of tuberculosis**. *Virulence* (2019.0) **10** 448-459. DOI: 10.1080/21505594.2018.1536598
84. Pan X, Shan H, Bai J, Gao T, Chen B, Shen Z. **Four-octyl itaconate improves osteoarthritis by enhancing autophagy in chondrocytes via PI3K/AKT/mTOR signalling pathway inhibition**. *Commun Biol* (2022.0) **5** 641. DOI: 10.1038/s42003-022-03592-6
85. Tian F, Wang Z, He J, Zhang Z, Tan N. **4-Octyl itaconate protects against renal fibrosis via inhibiting TGF-beta/Smad pathway, autophagy and reducing generation of reactive oxygen species**. *Eur J Pharmacol* (2020.0) **873** 172989. DOI: 10.1016/j.ejphar.2020.172989
86. Zhou L, Ivanov II, Spolski R, Min R, Shenderov K, Egawa T. **IL-6 programs T(H)-17 cell differentiation by promoting sequential engagement of the IL-21 and IL-23 pathways**. *Nat Immunol* (2007.0) **8** 967-974. DOI: 10.1038/ni1488
87. Liu X, Lee YS, Yu CR, Egwuagu CE. **Loss of STAT3 in CD4+ T cells prevents development of experimental autoimmune diseases**. *J Immunol* (2008.0) **180** 6070-6076. DOI: 10.4049/jimmunol.180.9.6070
88. Rottenberg ME, Carow B. **SOCS3 and STAT3, major controllers of the outcome of infection with**. *Semin Immunol* (2014.0) **26** 518-532. DOI: 10.1016/j.smim.2014.10.004
89. Liang S, Huang G, Wu T, Peng Y, Liu X, Ji X. **MIR337-3p enhances mycobacterial pathogenicity involving TLR4/MYD88 and STAT3 signals, impairing VDR antimicrobial response and fast-acting immunity**. *Front Immunol* (2021.0) **12** 739219. DOI: 10.3389/fimmu.2021.739219
90. Wang Y, Chen C, Xu XD, Li H, Cheng MH, Liu J. **Levels of miR-125a-5p are altered in**. *Microbes Infect* (2020.0) **22** 31-39. DOI: 10.1016/j.micinf.2019.07.002
|
---
title: 'Eating speed and abdominal adiposity in middle-aged adults: a cross-sectional
study in Vietnam'
authors:
- Dong Van Hoang
- Ami Fukunaga
- Chau Que Nguyen
- Thuy Thi Phuong Pham
- Rachana Manandhar Shrestha
- Danh Cong Phan
- Huy Xuan Le
- Hung Thai Do
- Masahiko Hachiya
- Tetsuya Mizoue
- Yosuke Inoue
journal: BMC Public Health
year: 2023
pmcid: PMC9993665
doi: 10.1186/s12889-023-15328-0
license: CC BY 4.0
---
# Eating speed and abdominal adiposity in middle-aged adults: a cross-sectional study in Vietnam
## Abstract
### Background
Several studies have associated fast eating speed with the risk of general obesity, but there are inadequate data on the association between eating speed and abdominal adiposity which may pose a higher threat to health than general obesity. The present study aimed to investigate the association between eating speed and abdominal obesity in a Vietnamese population.
### Methods
Between June 2019 and June 2020, the baseline survey of an ongoing prospective cohort study on the determinants of cardiovascular disease in Vietnamese adults was conducted. A total of 3,000 people aged 40–60 years old (1,160 men and 1,840 women) were recruited from eight communes in the rural district of Cam Lam, Khanh Hoa province, in Central Vietnam. Self-reported eating speed was assessed on a 5-point Likert scale, and responses were collapsed into the following three categories: slow, normal, and fast. Abdominal obesity was defined as a waist-to-height ratio of ≥ 0.5. Poisson regression with a robust variance estimator was used to assess the association between eating speed and abdominal obesity.
### Results
Compared with slow eating speed, the adjusted prevalence ratio ($95\%$ confidence interval) for abdominal obesity was 1.14 (1.05, 1.25)1.14 (1.05, 1.25) for normal eating speed and 1.30 (1.19, 1.41) for fast eating speed (P for trend < 0.001).
### Conclusion
A faster eating speed was associated with a higher prevalence of abdominal obesity in a middle-aged population in rural Vietnam.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-023-15328-0.
## Background
Obesity continues to be a major public health concern. Approximately 1.9 billion adults worldwide were estimated to be overweight in 2016, with 650 million being affected by obesity, the prevalence of which has tripled over the past four decades [1]. Obesity is an important risk factor for various chronic diseases, [1] including diabetes, cardiovascular disease, [2] and certain types of cancer [3]. A chronic excess of energy intake over expenditure is the fundamental cause of obesity, [1] and this positive energy imbalance usually results from the combination of overeating and physical inactivity, both of which are modifiable in terms of obesity prevention [4, 5] *Epidemiological data* show that fast eating speed may be a modifiable risk factor for obesity. A meta-analysis of 23 cross-sectional and prospective studies reported fast eating speed was associated with both a higher body mass index [BMI], which is the most common index of overall adiposity, and a greater risk of general obesity defined by BMI [6]. Specifically, the pooled odds ratio for general obesity (BMI ≥ 25 kg/m2) was 2.15 ($95\%$ confidence interval [CI]: 1.84, 2.51) comparing the fastest vs. slowest eating. This suggests that controlling eating speed can be a possible approach for regulating body weight and preventing obesity.
Although abdominal obesity may pose a higher threat to health than general obesity, particularly in Asian populations, [7–10] there remains some issues pertaining to the association between eating speed and abdominal obesity have not yet been investigated. Of the three common measurements of abdominal obesity, i.e., waist circumference (WC), waist-to-hip ratio and waist-to-height ratio (WHtR), the latter appears to be the best predictor for cardiometabolic risk; a WHtR of ≥ 0.5 has been widely used to define abdominal obesity, for both sex [11–13]. However, the current data on the relationship between eating speed and WHtR are derived from children populations [14–16]. In adults, there is evidence for the association between eating speed and WC, but no evidence for the association between eating speed and WHtR [17, 18]. In addition, the majority of the previous studies on the association between eating speed and adiposity are from high-income countries, while little is known about this issue in low- and middle-income countries, which are currently undergoing a rapid nutrition transition [19] and experiencing a drastic increase in obesity prevalence [20]. The identification of dietary behaviors associated with abdominal adiposity may facilitate efforts to reduce the disease burden, particularly in low-resource settings.
Therefore, the present study aimed to examine the association between eating speed and abdominal obesity (defined as WHtR ≥ 0.5) in a Vietnamese middle-aged population. We hypothesized that a faster eating speed would be associated with a higher prevalence of abdominal adiposity.
## Study setting
The present data were a part of the Khanh Hoa Cardiovascular Study which is an ongoing cohort study on the determinants of cardiovascular disease among the Vietnamese population. The study sites consist of eight communes in the rural district of Cam Lam, Khanh Hoa province, in Central Vietnam, which are deemed average in terms of their affluence in rural Vietnam. The participants were recruited among commune residents who were between the ages of 40 and 59 years at the time of recruitment. All eligible residents, who were listed by local commune health center staff members, were invited until we reached a sample size of 3000 participants (consent rate: 75–$87\%$).
The baseline survey, which collected information on lifestyle parameters, anthropometry, and biochemical measurements, was conducted between June 2019 and June 2020 among the 3000 participants. Face-to-face interviews and anthropometric measurements were carried out at selected commune health centers, and biochemical measurements were performed at the Pasteur Institute in Nha Trang, Khanh Hoa. It should be mentioned that the sample size was not calculated specifically to examine the association between eating speed and abdominal obesity.
## Anthropometric measurements
WC was measured to the nearest 0.1 cm (at the umbilical level) using a measuring tape, with the participant in the standing position. The WHtR was obtained by dividing the WC (cm) by the height (cm). Body height and weight were measured to the nearest 0.1 cm and 0.1 kg, respectively, using a digital scale (Tanita, HD-661, Tokyo, Japan) and a portable stadiometer (Charder, HM200P, Tokyo, Japan). All measurements were performed by trained staff members, and the same measurement protocol was used throughout the study period.
## Adiposity
The main outcome was abdominal obesity, which was defined as a WHtR of ≥ 0.50 [11–13]. For the purpose of comparison, we also considered two secondary outcomes: high waist circumference (WC of ≥ 90 cm in men or ≥ 80 cm in women) and general obesity (defined as BMI [calculated as weight in kilograms divided by height in meters squared] ≥ 25 kg/m2) according to the criteria of the World Health Organization for the Asian population [21].
## Eating speed
Eating speed was assessed with the following question: “how would you describe your speed of eating?”. The five response options were “very slow,” “relatively slow,” “normal,” “relatively quick,” and “very quick.” Due to the very low frequency of the first and last responses ($$n = 39$$, and $$n = 100$$, respectively), we regrouped the responses into three new categories: slow (very slow and relatively slow), normal, and fast (relatively quick and very quick) eating speeds.
## Covariates
We collected information pertaining to the following socio-demographic covariates via a questionnaire: age (years), sex (men or women), marital status (married or not married), education level (less than primary school, primary school, secondary school, or high school or higher), job category (government employees, non-government employees, self-employed, farmers or fishermen, housewives, others, or unemployed), and household income (low, middle, or high). The household income per month (in Vietnamese dong; 23,475 dong were equivalent to 1 United States dollar as of June 1, 2019) was estimated by the household representative and categorized into the following levels: ≤1,000,000; 1,000,001 to ≤ 2,000,000; 2,000,001 to ≤ 3,000,000; 3,000,001 to ≤ 4,000,000; 4,000,001 to ≤ 6,000,000; 6,000,001 to ≤ 8,000,000; 8,000,001 to ≤ 12,000,000; 12,000,001 to ≤ 16,000,000; 16,000,001 to ≤ 20,000,000; >20,000,000; or do not know). Each response was assigned the midpoint of the range as a proxy score. The values were divided by the square root of the number of household members to obtain the equivalized income, which was then categorized into tertiles.
Other covariates included smoking (never, former, or current smoker); alcohol consumption (non-drinker or drinker consuming < 1, 1–1.9, or ≥ 2 standard drinks/day); sleeping hours (< 6; 6–6.9, 7–7.9, 8–8.9, or ≥ 9 h/day); addition of sugar to beverages (yes or no); consumption of soft drinks (yes or no), fruits/vegetables (< 1, 1–1.99, 2–2.99, 3–2.99, 4–4.99, or ≥ 5 servings/day), rice (< 3, 3–5, 6–8, or ≥ 8 bowls/day), and meat (< 100, 100–199, 200–299, or ≥ 300 g/day); and a medical history (yes or no) of cancer, cardiovascular disease, or antidiabetic medication use (yes or no). Physical activity (total metabolic equivalent task) was assessed using the Global Physical Activity Questionnaire, [22] and scores were categorized into tertiles (low, middle, or high).
## Statistical analysis
Participant characteristics are presented as mean (standard deviation [SD]) or median (interquartile range [IQR]) for continuous variables, and percentage for categorical variables. A Poisson regression model with a robust variance estimator was used to estimate the prevalence ratio (PR) and $95\%$ CI of abdominal obesity in relation to eating speed. To account for missing data on household income (i.e., “do not know”; $$n = 33$$), we used multiple imputation to create 20 datasets [23] and combined them according to Rubin’s rule [24]. The main analyses consisted of two models. Model 1: unadjusted; Model 2: adjusted for age, sex, commune, education, marital status, occupation, household income, smoking, alcohol, physical activity, sleeping hours, adding sugar to beverages, medical history of cancer or diseases of the circulatory system, and using antidiabetic medication. Subsequently, the analyses were stratified by sex to determine if the relationship between eating speed and abdominal obesity would be different between men and women. We also stratified the analyses by smoking status, and alcohol consumption (among men only [$$n = 1160$$], since almost all female participants did not smoke tobacco [$99.3\%$], or drink alcohol [$97.3\%$]). The trend association between eating speed and abdominal obesity was assessed by assigning an ordinal number (1 to 3) to the eating speed category (slow, normal, and fast), which was then treated as a continuous variable in the regression models. We also assessed the association between eating speed and the WHtR which was treated as a continuous variable in linear regression models.
To test the robustness of the study findings, we conducted a series of sensitivity analyses. First, we excluded those with missing information on household income ($$n = 33$$). Second, we excluded those with medical history of cancer, cardiovascular diseases, or antidiabetic medication use ($$n = 154$$) to rule out potential effect of these factors. Finally, we examined the association of eating speed with high waist circumference, and general obesity.
Statistical significance was set at $P \leq 0.05.$ All statistical analyses were performed using RStudio (version 3.2.4, RStudio Team, Boston, USA) [25].
## Results
Among 3,000 participants, 1,073 ($35.8\%$) and 502 ($16.7\%$) reported that they were fast eaters and slow eaters, respectively (Table 1). Participants who were fast eaters tended to be men, overweight, governmental employees, and ex- or current smokers. They were also more likely to drink a higher amount of alcohol, add sugar to beverages, and eat more rice and meat than participants who were slow eaters.
Table 1Basic characteristics of study participants, 2019–2020CharacteristicsAll participants($$n = 3000$$)Eating speedSlow($$n = 502$$)Normal($$n = 1425$$)Fast($$n = 1073$$)Age, mean [standard deviation]48.7 [5.5]49.3 [5.8]48.8 [5.5]48.3 [5.4]Sex (men)1160 (38.7)154 (30.7)535 (37.5)471 (43.9)Body mass index18.5-139 (4.6)40 (8.0)69 (4.8)30 (2.8)18.5–24.92083 (69.4)364 (72.5)1008 (70.7)711 (66.3)≥ 25778 (25.9)98 (19.5)348 (24.4)332 (30.9)EducationPrimary school352 (11.7)60 (12.0)169 (11.9)123 (11.5)Secondary school863 (28.8)126 (25.1)416 (29.2)321 (29.9)High school1068 (35.6)184 (36.7)495 (34.7)389 (36.3)Tertiary study717 (23.9)132 (26.3)345 (24.2)240 (22.4)Married2691 (89.7)427 (85.1)1278 (89.7)986 (91.9)OccupationGovernment employee295 (9.8)43 (8.6)137 (9.6)115 (10.7)Non-government employee483 (16.1)80 (15.9)212 (14.9)191 (17.8)Self-employed595 (19.8)103 (20.5)263 (18.5)229 (21.3)Farmer/fisherman870 (29.0)141 (28.1)415 (29.1)314 (29.3)Others757 (25.2)135 (26.9)398 (27.9)224 (20.9)Household incomeLow tertile1087 (36.2)181 (36.1)543 (38.1)363 (33.8)Middle tertile930 (31.0)154 (30.7)439 (30.8)337 (31.4)High tertile983 (32.8)167 (33.3)443 (31.1)373 (34.8)SmokingNon-smoker2036 (67.9)365 (72.7)1000 (70.2)671 (62.5)Ex-smoker350 (11.7)57 (11.4)140 (9.8)153 (14.3)Current smoker614 (20.5)80 (15.9)285 (20.0)249 (23.2)Alcohol consumptionNon-drinker2114 (70.5)381 (75.9)1039 (72.9)694 (64.7)Drinker consuming< 1 standard drink/day416 (13.9)59 (11.8)187 (13.1)170 (15.8)1-1.9 standard drink/day201 (6.7)25 (5.0)99 (6.9)77 (7.2)≥ 2 standard drink/day269 (9.0)37 (7.4)100 (7.0)132 (12.3)Physical activity (MET-h/week)Low1030 (34.3)142 (28.3)565 (39.6)323 (30.1)Middle975 (32.5)170 (33.9)463 (32.5)342 (31.9)High995 (33.2)190 (37.8)397 (27.9)408 (38.0)Sleeping hours per day< 6485 (16.2)77 (15.3)227 (15.9)181 (16.9)6-6.9669 (22.3)123 (24.5)318 (22.3)228 (21.2)7-7.9915 (30.5)147 (29.3)441 (30.9)327 (30.5)8-8.9613 (20.4)112 (22.3)294 (20.6)207 (19.3)≥ 9318 (10.6)43 (8.6)145 (10.3)130 (12.1)Adding sugar to beverages (yes)1088 (36.3)177 (35.3)469 (32.9)442 (41.2)Consumption of soft drink (yes)454 (15.1)85 (16.9)198 (13.9)171 (15.9)Fruit/vegetable [serving/day] †2.0 [1.9]2.0 [1.8]1.7 [1.4]2.0 [1.8]Rice consumption [bowl/day] †4.0 [3.0]4.0 [4.0]4.0 [3.0]4.0 [3.0]Meat consumption [gram/day] †82.9 [100.0]85.0 [100.0]80.0 [94.3]85.7 [122.9]Using antidiabetic medication (yes)93 (3.1)13 (2.6)47 (3.3)33 (3.1)History of diseases of the circulatory system (yes)51 (1.7)9 (1.8)27 (1.9)15 (1.4)History of cancer (yes)18 (0.6)2 (0.4)8 (0.6)8 (0.7)Figures are n (%), unless otherwise stated; † values are median [interquartile range]; MET: metabolic equivalent task Using the WHtR, $63.6\%$ of participants were identified as having an abdominal obesity. High waist circumference and general obesity were identified in $39.0\%$ and $25.9\%$ of the participants, respectively. A significant association was found between eating speed and abdominal obesity (Table 2). The crude PR ($95\%$ CI) for abdominal obesity associated with normal and fast eating speeds were 1.14 (1.05, 1.25) and 1.27 (1.16, 1.38), respectively (P for trend < 0.001), compared with slow eating speed. The associations remained significant after an adjustment for all confounders: the adjusted PR ($95\%$ CI) were 1.14 (1.05, 1.25) and 1.30 (1.19, 1.41) for normal and fast eating speeds, respectively (P for trend < 0.001).
Table 2Association between eating speed and abdominal obesity among 3000 participants of the baseline survey of the Khanh Hoa Cardiovascular Study in Vietnam (2019–2020)Eating speedAbdominal obesity† n (%)Prevalence ratio ($95\%$ confidence interval)Model 1Model 2 Overall Slow275 (54.8)1.00 (ref)1.00 (ref)Normal889 (62.4)1.14 (1.05, 1.25)1.14 (1.05, 1.25)Fast744 (69.3)1.27 (1.16, 1.38)1.30 (1.19, 1.41) P trend < 0.001 < 0.001 Men Slow71 (10.8)1.00 (ref)1.00 (ref)Normal287 (43.8)1.16 (0.96, 1.40)1.18 (0.99, 1.41)Fast297 (45.3)1.37 (1.14, 1.64)1.41 (1.18, 1.68) P trend 0.007 0.009 Women Slow204 (16.3)1.00 (ref)1.00 (ref)Normal602 (48.0)1.15 (1.04, 1.27)1.16 (1.05, 1.27)Fast447 (35.7)1.27 (1.15, 1.40)1.26 (1.15, 1.39) P trend 0.005 0.008 † waist-to-heigh ratio ≥ 0.5; ref: reference; Model 1: unadjusted; Model 2: adjusted for age, commune, education, marital status, occupation, household income, smoking, alcohol, physical activity, sleeping hours, adding sugar to beverages, medical history of cancer or diseases of the circulatory system, and using antidiabetic medication When we stratified the analysis by sex (Table 2), smoking status (Table S1), and alcohol consumption (Table S2), we did not observe pronounced differences in the associations between eating speed and abdominal obesity among the subgroups. Those who self-reported to eat fast tended to be abdominally obese than those who self-reported to eat slowly.
The sensitivity analysis after excluding of participants with missing information on household income resulted in similar results (e.g., the adjusted PR [$95\%$ CI] for abdominal obesity was 1.29 [1.19, 1.41], comparing fast versus slow eating speeds [Table 3]). Similarly, the exclusion of history of cancer, cardiovascular diseases, or antidiabetic medication use also did not materially change the associations between eating speed and abdominal obesity (Table 4). Eating speed was also associated with the continuous values of WHtR, e.g., adjusted coefficient for fast vs. slow eating speed was 0.017 ($95\%$ CI: 0.012, 0.023) (Table S3).
Table 3Association between eating speed and abdominal obesity, excluding participants with missing information on household income ($$n = 33$$)Eating speedAbdominal obesity† n (%)Prevalence ratio ($95\%$ confidence interval)Model 1Model 2Slow271 (55.0)1.00 (ref)1.00 (ref)Normal880 (62.4)1.14 (1.04, 1.24)1.14 (1.04, 1.24)Fast739 (69.5)1.26 (1.16, 1.38)1.29 (1.19, 1.41) P trend < 0.001 < 0.001 †waist-to-heigh ratio ≥ 0.5; ref: reference; Model 1: unadjusted; Model 2: adjusted for age, sex, commune, education, marital status, occupation, household income, smoking, alcohol, physical activity, sleeping hours, adding sugar to beverages, medical history of cancer or diseases of the circulatory system, and using antidiabetic medication Table 4Association between eating speed and abdominal obesity, excluding participants with medical history of cancer or diseases of the circulatory system, or using antidiabetic medication ($$n = 153$$)Eating speedAbdominal obesity† n (%)Prevalence ratio ($95\%$ confidence interval)Model 1Model 2Slow262 (54.7)1.00 (ref)1.00 (ref)Normal825 (61.2)1.12 (1.02, 1.23)1.13 (1.03, 1.23)Fast702 (68.8)1.26 (1.15, 1.38)1.29 (1.18, 1.41) P trend < 0.001 < 0.001 †waist-to-heigh ratio ≥ 0.5; ref: reference; Model 1: unadjusted; Model 2: adjusted for age, sex, commune, education, marital status, occupation, household income, smoking, alcohol, physical activity, sleeping hours, and adding sugar to beverages A similar pattern of associations was also observed for high waist circumference (Table S4) and general obesity defined with BMI (Table S5). For example, the adjusted prevalence ratios ($95\%$ CI) of high waist circumference and general obesity were 1.41 (1.23, 1.61; P for trend < 0.001) and 1.51 (1.24, 1.84; P for trend < 0.001), respectively, for the comparison between fast and slow eating speeds.
## Discussion
In the present study, we found that fast eating speed was significantly associated with a higher prevalence of abdominal obesity, as defined according to the WHtR, among a rural middle-aged population in Vietnam. To the best of our knowledge, no prior studies have investigated the relationship between eating speed and WHtR in adults.
Our findings are supported by existing epidemiological data on the association of fast eating speed with high WC [17, 18] and visceral fat accumulation [26, 27]. For example, in a meta-analysis of 11 studies on metabolic syndrome, [18] fast eating speed was associated with high WC (odds ratio: 1.54; $95\%$ CI: 1.37, 1.73), which is a component of metabolic syndrome [28]. In a Japanese cross-sectional study, [26] fast eating speed was associated with a higher odds of an increased visceral fat area (i.e., ≥ 100 cm2, as determined via computed tomography scans) (odds ratio: 1.99; $95\%$ CI: 1.40, 2.90). Our results provide additional evidence to support the assertion that fast eating speed is associated with abdominal obesity, which may pose a greater health risk than general obesity [7–10].
Although the mechanism by which fast eating speed affects abdominal adiposity remains unclear, it is possible that fast eating speed may lead to excessive caloric intake, which in turn results in abdominal obesity. The over-intake of calories may be related to a delay in the onset of satiation and the feeling of fullness. Fast eating speed may reduce the secretion of peptide YY [29] (a postprandial hormone that induces satiety), [30] thereby delaying the sensation of fullness, which results in the overconsumption of food. Studies have shown that fast eating speed can lead to increased energy intake [31] which is a major driver of obesity [4]. For example, in a Singaporean cohort study, [32] fast eaters consumed 105 kcal/day more than slow eaters. In the present study, fast eaters appeared to consume more rice and meat than slow eaters (Table 1). Furthermore, fast eating speed results in a shorter oral processing time, which may also contribute to the delayed onset of satiation and increased food intake [33, 34] In addition to the overconsumption of food, heavy congestion of dietary fat also may partly explain. During the postprandial stage, an excessive intake of dietary fat leads to a congestion of free fatty acids in the intestinal lamina propria, which is where free fatty acids are directly transported to the abdominal visceral adipocytes [35]. The heavier congestion results in the delivery of more free fatty acids and subsequent accumulation in the abdominal visceral adipocytes [36]. This may partly explain our observed association between fast eating speed and abdominal adiposity.
In the present study, fast eating speed was associated with both abdominal and general adiposity; however, the former association may be more informative for public health strategies aimed at mitigating obesity-related health risks. Compared with general obesity, abdominal obesity is a stronger predictor of cardiovascular diseases, [8] cancer, [9] and mortality [10]. The detrimental impact of abdominal obesity on health risk can be even significant among normal-weight people [7]. Given the high global prevalence of individuals with normal-weight abdominal obesity (e.g., $19.9\%$ in Japanese adults [37] and $29.5\%$ in South African adults [38]), the identification of preventable risk factors of abdominal fat accumulation is crucial to establish appropriate strategies for mitigating obesity-related health risks. The present finding suggests that preventive measures for abdominal adiposity should account for the role of eating speed.
The results of the present study should be interpreted with several considerations. First, the eating speed was not objectively measured, but self-reported by participants, which might have affected the accuracy of estimates. Nevertheless, this asssessment method has been frequently utilized in previous studies on this topic [6]. Second, our brief dietary questionnaire did not allow us to calculate the total energy intake, which did not allow us to estimate the possible role of caloric intake in the association between eating speed and abdominal obesity. It is possible that those who eat fast tend to consume more calories or that those who eat a lot need to eat fast. Third, given the cross-sectional design of this study, we were unable to infer a causal relationship between fast eating speed and abdominal obesity. Finally, since our study population comprised middle-aged rural residents in a Central province of Vietnam, the generalization of the study findings to other population groups should be made with caution.
## Conclusion
The present study found that a faster eating speed was associated with a higher prevalence of abdominal obesity among a middle-aged population in rural Vietnam.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1
## References
1. 1.World Health Organization. Obesity and overweight. World health organization. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. Published June 9, 2021. Accessed October 20, 2021.
2. Piché ME, Tchernof A, Després JP. **Obesity phenotypes, diabetes, and cardiovascular diseases**. *Circ Res* (2020.0) **126** 1477-500. DOI: 10.1161/CIRCRESAHA.120.316101
3. Sung H, Siegel RL, Torre LA. **Global patterns in excess body weight and the associated cancer burden**. *CA Cancer J Clin* (2019.0) **69** 88-112. DOI: 10.3322/caac.21499
4. Elagizi A, Kachur S, Carbone S, Lavie CJ, Blair SN. **A review of obesity, physical activity, and cardiovascular disease**. *Curr Obes Rep* (2020.0) **9** 571-81. DOI: 10.1007/s13679-020-00403-z
5. Haczeyni F, Bell-Anderson KS, Farrell GC. **Causes and mechanisms of adipocyte enlargement and adipose expansion**. *Obes Rev* (2018.0) **19** 406-20. DOI: 10.1111/obr.12646
6. Ohkuma T, Hirakawa Y, Nakamura U, Kiyohara Y, Kitazono T, Ninomiya T. **Association between eating rate and obesity: a systematic review and meta-analysis**. *Int J Obes 2005* (2015.0) **39** 1589-96. DOI: 10.1038/ijo.2015.96
7. Sahakyan KR, Somers VK, Rodriguez-Escudero JP. **Normal-weight central obesity: implications for total and cardiovascular mortality**. *Ann Intern Med* (2015.0) **163** 827-35. DOI: 10.7326/M14-2525
8. Ragino YI, Stakhneva EM, Polonskaya YV, Kashtanova EV. **The role of secretory activity molecules of visceral adipocytes in abdominal obesity in the development of cardiovascular disease: a review**. *Biomolecules* (2020.0) **10** 374. DOI: 10.3390/biom10030374
9. Barberio AM, Alareeki A, Viner B. **Central body fatness is a stronger predictor of cancer risk than overall body size**. *Nat Commun* (2019.0) **10** 383. DOI: 10.1038/s41467-018-08159-w
10. Jayedi A, Soltani S, Zargar MS, Khan TA, Shab-Bidar S. **Central fatness and risk of all cause mortality: systematic review and dose-response meta-analysis of 72 prospective cohort studies**. *BMJ* (2020.0) **370** m3324. DOI: 10.1136/bmj.m3324
11. Ashwell M, Gunn P, Gibson S. **Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis**. *Obes Rev Off J Int Assoc Study Obes* (2012.0) **13** 275-86. DOI: 10.1111/j.1467-789X.2011.00952.x
12. Yoo EG. **Waist-to-height ratio as a screening tool for obesity and cardiometabolic risk**. *Korean J Pediatr* (2016.0) **59** 425. DOI: 10.3345/kjp.2016.59.11.425
13. Ashwell M, Gibson S. **Waist-to-height ratio as an indicator of ‘early health risk’: simpler and more predictive than using a ‘matrix’ based on BMI and waist circumference**. *BMJ Open* (2016.0) **6** e010159. DOI: 10.1136/bmjopen-2015-010159
14. 14.Li X, Wang J, Zhang Y. Self-Reported Eating Speed Is Associated With Overweight Among Chinese School-Children: A Cross-Sectional Survey. Published online 2021.
15. Zeng X, Cai L, Ma J, Ma Y, Jing J, Chen Y. **Eating fast is positively associated with general and abdominal obesity among chinese children: a national survey**. *Sci Rep* (2018.0) **8** 1-8. DOI: 10.1038/s41598-018-32498-9
16. Ochiai H, Shirasawa T, Nanri H. **Eating quickly is associated with waist-to-height ratio among japanese adolescents: a cross-sectional survey**. *Arch Public Health* (2016.0) **74** 1-7. DOI: 10.1186/s13690-016-0130-3
17. Wuren, Endoh K, Kuriki K, Shizuoka-Sakuragaoka J-MICC. **Eating rate as risk for body mass index and waist circumference obesity with appropriate confounding factors: a cross-sectional analysis of the Shizuoka-Sakuragaoka J-MICC Study**. *Asia Pac J Clin Nutr* (2019.0) **28** 79-91. DOI: 10.6133/apjcn.201903_28(1).0012
18. 18.Yuan S, Liu Y ming, Liang W et al. Association between eating speed and metabolic syndrome: a systematic review and meta-analysis. Front Nutr. 2021;8:764. doi:10.3389/fnut.2021.700936.
19. Harris J, Nguyen PH, Tran LM, Huynh PN. **Nutrition transition in Vietnam: changing food supply, food prices, household expenditure, diet and nutrition outcomes**. *Food Secur* (2020.0) **12** 1141-55. DOI: 10.1007/s12571-020-01096-x
20. Nguyen TT, Hoang MV. **Non-communicable diseases, food and nutrition in Vietnam from 1975 to 2015: the burden and national response**. *Asia Pac J Clin Nutr* (2018.0) **27** 19-28. DOI: 10.6133/apjcn.032017.13
21. 21.World Health Organization. The Asia-Pacific perspective: redefining obesity and its treatment. https://iris.wpro.who.int/handle/10665.1/5379. Published online 2000. Accessed October 20, 2021.
22. Bull FC, Maslin TS, Armstrong T. **Global physical activity questionnaire (GPAQ): nine country reliability and validity study**. *J Phys Act Health* (2009.0) **6** 790-804. DOI: 10.1123/jpah.6.6.790
23. Raghunathan TE, Lepkowski JM, Van Hoewyk J, Solenberger P. **A multivariate technique for multiply imputing missing values using a sequence of regression models**. *Surv Methodol* (2001.0) **27** 85-96
24. 24.Rubin DB. Multiple Imputation for Nonresponse in Surveys, New York: JohnWiley & Sons, 1987. 6th ed. Willey & sons; 1987.
25. 25.RStudio Team. RStudio: Integrated development environment for R. RStudio Integr Dev Environ R. Published online 2019. doi:10.1002/jwmg.232.
26. Mochizuki K, Yamada M, Miyauchi R. **Self-reported faster eating is positively associated with accumulation of visceral fat in middle-aged apparently healthy japanese men**. *Eur J Nutr* (2014.0) **53** 1187-94. DOI: 10.1007/s00394-013-0619-2
27. Iwasaki T, Hirose A, Azuma T. **Self-reported behavior of eating quickly is correlated with visceral fat area in japanese non-obese adults**. *Asia Pac J Clin Nutr* (2019.0) **28** 92-8. DOI: 10.6133/apjcn.201903_28(1).0013
28. 28.Alberti KGMM, Eckel RH, Grundy SM et al. Harmonizing the Metabolic Syndrome: A Joint Interim Statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International. Vol 120.; 2009. doi:10.1161/CIRCULATIONAHA.109.192644.
29. Kokkinos A, le Roux CW, Alexiadou K. **Eating slowly increases the postprandial response of the anorexigenic gut hormones, peptide YY and glucagon-like peptide-1**. *J Clin Endocrinol Metab* (2010.0) **95** 333-7. DOI: 10.1210/jc.2009-1018
30. le Roux CW, Bloom SR. **Peptide YY, appetite and food intake**. *Proc Nutr Soc* (2005.0) **64** 213-6. DOI: 10.1079/PNS2005427
31. Robinson E, Almiron-Roig E, Rutters F. **A systematic review and meta-analysis examining the effect of eating rate on energy intake and hunger**. *Am J Clin Nutr* (2014.0) **100** 123-51. DOI: 10.3945/ajcn.113.081745
32. Teo PS, van Dam RM, Whitton C, Tan LWL, Forde CG. **Association between self-reported eating rate, energy intake, and cardiovascular risk factors in a multi-ethnic asian population**. *Nutrients* (2020.0) **12** 1080. DOI: 10.3390/nu12041080
33. Zijlstra N, de Wijk RA, Mars M, Stafleu A, de Graaf C. **Effect of bite size and oral processing time of a semisolid food on satiation**. *Am J Clin Nutr* (2009.0) **90** 269-75. DOI: 10.3945/ajcn.2009.27694
34. Krop EM, Hetherington MM, Nekitsing C, Miquel S, Postelnicu L, Sarkar A. **Influence of oral processing on appetite and food intake – a systematic review and meta-analysis**. *Appetite* (2018.0) **125** 253-69. DOI: 10.1016/j.appet.2018.01.018
35. 35.Bayly GR. Lipids and disorders of lipoprotein metabolism. Clinical Biochemistry: Metabolic and Clinical Aspects.Elsevier; 2014:pp. 702–736. doi:10.1016/B978-0-7020-5140-1.00037-7.
36. Nauli AM, Matin S. **Why do men accumulate abdominal visceral fat?**. *Front Physiol* (2019.0) **10** 1486. DOI: 10.3389/fphys.2019.01486
37. Shirasawa T, Ochiai H, Yoshimoto T. **Associations between normal weight central obesity and cardiovascular disease risk factors in japanese middle-aged adults: a cross-sectional study**. *J Health Popul Nutr* (2019.0) **38** 46. DOI: 10.1186/s41043-019-0201-5
38. Owolabi EO, Ter Goon D, Adeniyi OV. **Central obesity and normal-weight central obesity among adults attending healthcare facilities in Buffalo City Metropolitan Municipality, South Africa: a cross-sectional study**. *J Health Popul Nutr* (2017.0) **36** 54. DOI: 10.1186/s41043-017-0133-x
|
---
title: MiR-146b-5p/SEMA3G regulates epithelial-mesenchymal transition in clear cell
renal cell carcinoma
authors:
- Mengxi Tang
- Tao Xiong
journal: Cell Division
year: 2023
pmcid: PMC9993666
doi: 10.1186/s13008-023-00083-w
license: CC BY 4.0
---
# MiR-146b-5p/SEMA3G regulates epithelial-mesenchymal transition in clear cell renal cell carcinoma
## Abstract
### Objective
The primary purpose was to unveil how the miR-146b-5p/SEMA3G axis works in clear cell renal cell carcinoma (ccRCC).
### Methods
ccRCC dataset was acquired from TCGA database, and target miRNA to be studied was further analyzed using survival analysis. We performed miRNA target gene prediction through the database, and those predicted miRNAs were intersected with differential mRNAs. After calculating the correlation between miRNAs and mRNAs, we completed the GSEA pathway enrichment analysis on mRNAs. MiRNA and mRNA expression was examined by qRT-PCR. Western blot was introduced to detect SEMA3G, MMP2, MMP9 expression, epithelial-mesenchymal transition (EMT) marker proteins, and Notch/TGF-β signaling pathway-related proteins. Targeted relationship between miRNA and mRNA was validated using a dual-luciferase test. Transwell assay was employed to assess cell migration and invasion. Wound healing assay was adopted for evaluation of migration ability. The effect of different treatments on cell morphology was observed by a microscope.
### Results
In ccRCC cells, miR-146b-5p was remarkably overexpressed, yet SEMA3G was markedly less expressed. MiR-146b-5p was capable of stimulating ccRCC cell invasion, migration and EMT, and promoting the transformation of ccRCC cell morphology to mesenchymal state. SEMA3G was targeted and inhibited via miR-146b-5p. MiR-146b-5p facilitated ccRCC cell migration, invasion, morphology transforming to mesenchymal state and EMT process by targeting SEMA3G and regulating Notch and TGF-β signaling pathways.
### Conclusion
MiR-146b-5p regulated Notch and TGF-β signaling pathway by suppressing SEMA3G expression, thus promoting the growth of ccRCC cells, which provides a possible target for ccRCC therapy and prognosis prediction.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13008-023-00083-w.
## Introduction
Clear cell renal cell carcinoma (ccRCC) is a predominant histology of RCC, occupying $75\%$ of RCC patients [1]. Distant metastases have been observed in 25–$30\%$ of new cases with ccRCC each year [2]. Changes in the microenvironment that entails epithelial-mesenchymal transition (EMT), such as inflammation, angiogenesis, cancerous stroma, and vascular intercalation, control the progression of cancer metastasis [3]. EMT has also been linked to the pathogenesis of ccRCC [4, 5]. Therefore, studying the underlying molecular mechanisms of ccRCC metastasis and EMT is beneficial for the precise treatment of ccRCC. MicroRNAs (miRNAs) act as regulators in metastasis and EMT phenotypes of cancer cells [6, 7], so we invesgated miRNAs in this study.
As one of the subtypes of non-coding RNAs, miRNAs can specifically recognize and bind to 3ʹ-untranslated region (3ʹ-UTR) of downstream target mRNAs, facilitating mRNA degradation and impeding target gene translation [8]. MiR-146b-5p can play a pro-cancer or anti-cancer role in cancer progression. Shi et al. [ 9] showed that miR-146b-5p could promote colorectal cancer development through targeting TRAF6. Ren et al. [ 10] disclosed that miR-146b-5p is a cholangiocarcinoma inhibitor through targeting TRAF6 to repress proliferation and stimulate apoptosis of cholangiocarcinoma cells. Notably, Zhang et al. [ 11] proved that LINC01535/miR-146b-5p/TRIM2 axis could regulate ccRCC progression via the PI3K/Akt signaling pathway. At present, limited research has focused on molecular modulatory mechanism of miR-146b-5p in ccRCC, and the correction between miR-146b-5p and ccRCC still has great research. Here, miR-146b-5p was taken as the target gene to study its regulatory mechanism in ccRCC development and the downstream regulatory gene SEMA3 was found.
Class 3 semaphorins (SEMA3) is a secreted glycoproteins, approximately 100 kDa consisting of seven isoforms (SEMA3A-G), which is linked to the development of nervous system and axon guidance [12]. The role of SEMA3 in multiple effectors signaling has been found to regulate the formation of dermal lymphatic networks negatively [13]. Regarding SEMA3G, research discovered that PPAR-γ could enhance the migration of endothelial cell by promoting SEMA3G expression [14]. In addition, in a Nrp2/PlexinD1-dependent manner, SEMA3G stimulates distribution of lymphatic endothelial cells away from arteries and generates a branching network during arterial lymphatic development [15]. At present, it has been predicted that SEMA3G can be used as a prognostic indicator in ccRCC [16, 17], but there is no relevant report on the mechanism of SEMA3G in ccRCC. Our investigation found that miR-146b-5p inhibited the expression of SEMA3 expression and affected EMT in ccRCC, and it has been reported that EMT is associated with the Notch and TGF-β signaling pathways [18], so we added contents related to the Notch and TGF-β signaling in our study.
Notch receptor comprises an intracellular domain and an extracellular domain with nuclear localization motifs. Evidence suggests that Notch activity dysregulation is linked to cancer development like breast cancer [19], ovarian cancer [20], prostate cancer [21], and colorectal cancer [22]. TGF-β signaling is the most well-studied mechanism for inducing EMT, and it works via a variety of intracellular messengers. TGF-β superfamily ligands, which contain 3 TGF isoforms (TGF1, 2, and 3) and 6 BMP isoforms, generally activate signaling (BMP2 to BMP7). TGF-β signaling pathway has a connection with the development of cancers, including esophageal squamous cell carcinoma [23], liver cancer [24], prostate cancer [21], breast cancer [25], and small cell lung cancer [26]. We evaluated the impacts of miR-146b-5p and SEMA3G on the Notch and TGF-β signaling pathways in ccRCC.
This research aimed to clarify how miR-146b-5p and SEMA3G affected the modulation of Notch and TGF-β signaling, thus identifying their roles in ccRCC development. We experimentally discovered that miR-146b-5p enhanced cell growth in ccRCC cells by down-regulating SEMA3G expression and regulating the Notch and TGF-β signaling pathways.
## Bioinformatics
Expression data of ccRCC mRNAs (normal: 72, tumor: 539) and mature miRNAs (normal: 71, tumor: 545) were retrieved from TCGA database (https://portal.gdc.cancer.gov/). The R package “edgeR” (log|FC|> 1.5, FDR < 0.05) was employed to compare the expression of miRNA and mRNA in the normal and tumor groups (log|FC|> 1.5, FDR < 0.05). And we utilized R package “survival” to examine association between miR-146b-5p and the prognoses of ccRCC patients. To determine miRNA downstream regulatory target genes, we used the miRDB (http://mirdb.org/), TargetScan (http://www.targetscan.org/vert_72/), starBase (http://starbase.sysu.edu.cn/) and mirDIP (http://ophid.utoronto.ca/mirDIP/index.jsp#r) databases. Differential mRNAs with targeted binding sites to target miRNAs were screened, which were intersected with differentially down-regulated mRNAs. Then target genes were finally determined using correlation analysis. GSEA software was applied to perform a KEGG analysis.
## Cell culture
BeNa Culture Collection (BNCC, China) provided human normal renal cell lines HK-2 (BNCC339833), ccRCC cell lines 786-O (BNCC338472), 769-P (BNCC341606), Caki-2 (BNCC340136) and renal carcinoma cell line A498 (BNCC338630). Cells were cultivated in a $5\%$ CO2 incubator at 37 ℃. The mediums used for each cell line were as follows: HK-2, A498, 769-P cell lines: RPMI-1640 medium (BNCC341471, BNCC, China) containing $10\%$ fetal bovine serum (FBS); 786-O cell line: DMEM medium (BNCC351841, BNCC, China) containing $10\%$ FBS; Caki-2 cell line: McCoy’s 5a culture medium (BNCC341856, BNCC, China) containing $10\%$ FBS.
## Cell transfection
MiR-146b-5p mimic (miR-mimic) and mimic NC (miR-NC), miR-146b-5p inhibitor (miR-inhibitor) and inhibitor NC (inhibitor-NC), pcDNA3.1-SEMA3G plasmid encoding SEMA3G (oe-SEMA3G), and blank pcDNA3.1 plasmid (oe-NC) vectors were all acquired from RiboBio (China). 786-O cell line was transfected with miR-inhibitor/inhibitor-NC and miR-mimic/miR-NC or pcDNA3.1-SEMA3G/blank pcDNA3.1 plasmid was transfected into Caki-2 cell line by Lipofectamine 2000 kit (Invitrogen, USA). Transfection efficiency was detected after 24 h of transfection. Transfection sequences are shown in Table 1. Additionally, to evaluate whether the transfection efficiency changed with transfection time, we further explored the transfection efficiency after 48 h and 72 h.Table 1Transfection sequences of genesGeneSequencemiRNA-146b-5p mimic5ʹ-UGAGAACUGAAUUCCAUAGGCU-3ʹmimic NC5ʹ-UUCUCCGAACGUGUCACGUTT-3ʹmiRNA-146b-5p inhibitor5ʹ-AGCCUAUGGAAUUCAGUUCUCA-3ʹinhibitor NC5ʹ-CAGUACUUUUGUGUAGUACAA-3ʹoe-SEMA3G5ʹ-ATCTGTCTCCATGCTTTGGAAT-3ʹoe-NC5ʹ-ATCTGTCTCCATGCTTTGGAAT-3ʹ
## Real-time PCR detection
TRIzol reagent (Life Technologies, USA) was the tool for extracting total RNA from cells, and RNA concentration was assayed on NanoDrop 2000 system (Thermo Fisher Scientific, USA). Hairpin-it miRNAs qRT-PCR kit (GenePharm, China) was used to reverse-transcribe miRNAs into cDNA, and PrimeScript RT Master Mix (Takara, Japan) was applied to reverse-transcribe mRNA into cDNA. MiRNA and mRNA expression were evaluated using miScript SYBR Green PCR Kit (Qiagen, Germany) and SYBR® Premix Ex Taq TM II (Takara, Japan). The endogenous control for miR-146b-5p was U6, and that for SEMA3G and other mRNAs was GAPDH. Relative expression of miR-146b-5p and mRNAs was compared using the 2−ΔΔCt value. Table 2 lists the primer sequences. Table 2Primer sequences used for qRT-PCRGeneSequencemiRNA-146b-5pForward primer5ʹ-GGGCGGTGAGAACTGAATT-3ʹReverse primer5ʹ-CAGTGCGTGTCGTGGAGT-3ʹU6Forward primer5ʹ-CTCGCTTCGGCAGCACA-3ʹReverse primer5ʹ-AACGCTTCACGAATTTGCGT-3ʹSEMA3GForward primer5ʹ-GGGTCTGTGCTCAAAGTCATCG-3ʹReverse primer5ʹ-AAGTCCCACTGCCTCTTCTTCC-3ʹGAPDHForward primer5ʹ-AGAAGGCTGGGGCTCATTTG-3ʹReverse primer5ʹ-AGGGGCCATCCACAGTCTTC-3ʹNOTCH1Forward primer5ʹ-GCAGAGGCGTGGCAGACTAT-3ʹReverse primer5ʹ-CAGTAGAAGGAGGCCACACG-3ʹSnail1Forward primer5ʹ-CTCGGACCTTCTCCCGAATG-3ʹReverse primer5ʹ-AAAGTCCTGTGGGGCTGATG-3ʹE-CadherinForward primer5ʹ-AAGTTGAGCCCCAAGGTGAT-3ʹReverse primer5ʹ-CTGGAAGGAGCGGTTCTTTTT-3ʹN-cadherinForward primer5ʹ-CACCGACGTAGACAGGATCG-3ʹReverse primer5ʹ-CGTCTAGCCGTCTGATTCCC-3ʹVimentinForward primer5ʹ-TCCTGCCAATGTTGCTCACT-3ʹReverse primer5ʹ-CCAGCCACTGTTGTCAGAGT-3ʹSlugForward primer5ʹ-CTCCTCATCTTTGGGGCGAG-3ʹReverse primer5ʹ-TCCTTGAAGCAACCAGGGTC-3ʹ
## Western blot
RIPA lysis buffer (Thermo Fisher Scientific, USA) was employed to extract proteins, and protein quantification was determined by the BCA Kit (Beyotime, China). After proteins (30 µg per lane) were separated on $12\%$ SDS-PAGE gels, they were transferred onto PVDF (Millipore, USA) membranes. Membranes were blocked for 2 h with $5\%$ nonfat dried milk and then maintained with primary antibodies at 4 ℃ overnight. Membranes were then rinsed three times in PBST buffer for 30 min and probed with secondary antibody at room temperature for 2 h. Protein signals were detected with an enhanced chemiluminescence kit (Thermo Fisher Scientific, USA). Primary antibodies employed in this research were: rabbit anti-SEMA3G (ab197108, 1:500), rabbit anti-MMP2 (ab92536, 1:2000), rabbit anti-MMP9 (ab76003, 1:2000), rabbit anti-Snail1 (ab216347, 1:1000), rabbit anti-Snail2 (Slug, ab63568, 1:500), rabbit anti-E-cadherin (ab40772, 1:25,000), rabbit anti-N-cadherin (ab18203, 1:10,000), rabbit anti-Vimentin (ab92547, 1:3000), rabbit anti-Notch1 (ab52627, 1:1500), rabbit anti-Hes1 (ab108937, 1:1000), rabbit anti-Hes5 (ab194111, 1:1000), rabbit anti-TGFβR1 (ab235178, 1:1000), rabbit anti-TGFβR2 (ab186838, 1:1000), rabbit anti-Smad3 (ab40854, 1:5000), rabbit anti-p-Smad3 (ab52903, 1:2000) and rabbit anti-GAPDH (ab181602, 1:10,000). Secondary antibody was goat-anti-rabbit IgG (ab205718, 1:10,000). All of them were procured from Abcam (UK).
## Transwell assay
ccRCC cells (1 × 105 cells) were plated into the upper chamber of the device coated with Matrigel (BD Biosciences, USA). Next, 700 μL of DMEM containing $20\%$ FBS was filled into the lower chamber. The cells were kept routine conditions for 24 h. The non-invaded cells were removed from the filter membrane, and invaded ones were immobilized with $4\%$ paraformaldehyde for 20 min and dyed with $0.1\%$ crystal violet for 30 min. Photographs were taken under a microscope and cells passing through the membrane were counted. Matrigel was not applied to the upper chamber in the migration assay, and other steps were the same with the invasion assay.
## Wound healing experiment
Cells at the logarithmic growth phase (2 × 105 cells/well) were plated with 6-well plates, with a volume of 2 mL per well. When the cells filled the wells, the cell layer in each well was marked with a “ + ” with a sterilized 200 μL pipette tip. When scratching, pipette tip was kept perpendicular to the bottom of the plate to ensure the scratch straight and uniform. Then cells were rinsed 4 times with PBS to wash off scratched cells. The remained cells were kept in serum-free DMEM. The wound healing condition was observed and photoed at 0 h and 24 h of incubation.
## Dual-luciferase assay
Binding of miR-146b-5p to the 3ʹ-UTR of SEMA3G was identified via dual-luciferase assay. Firstly, pmirGLO-SEMA3G-3ʹ-UTR-WT and pmirGLO-SEMA3G-3ʹ-UTR-MUT luciferase reporter vectors (Promega, USA) were established. The ccRCC cell line Caki-2 was plated in 96-well plates (2 × 105 cells/well) and co-transfected with 3 µg of miR-mimic/miR NC and SEMA3G-WT/SEMA3G-MUT plasmids. Luciferase activity was measured after 48 h of culture on Dual-Luciferase Reporter System (Promega, USA).
## Analysis of statistics
All data were processed using GraphPad Prism 6.0 (La Jolla, CA) and presented as mean ± standard deviation. Two-group comparing was done using t-test. One-way ANOVA analysis of variance was used for comparison of more than three groups. All experiments were performed in 3 biological replicates. * $P \leq 0.05$ indicates statistical differencee, **$P \leq 0.01$ indicates significant difference, ***$P \leq 0.001$ indicates extremely significant difference.
## MiR-146b-5p is significantly highly expressed in ccRCC cells, and correlates with poor prognosis
Firstly, TCGA database (https://portal.gdc.cancer.gov/) was utilized to predict miR-146b-5p expression. Compared to normal renal tissues, miR-146b-5p was strikingly and strongly expressed in ccRCC tissues (Fig. 1A). Next, we used the R package “survival” to measure correlation between miR-146b-5p expression and data from 10-year follow-up of the ccRCC patients. We further performed Kaplan–Meier (K–M) survival curves to analyze the results, which showed that patients with higher expression of miR-146b-5p presented prominently shorter overall survival than those with lower miR-146b-5p expression (Fig. 1B). Based on the results of the above bioinformatics analyses, we further analyzed miR-146b-5p expression at cellular level. The results of qRT-PCR revealed that miR-146b-5p expression in ccRCC cells was much higher than in normal human renal cells. ( Fig. 1C). Then, miR-146b-5p mimic or miR-146b-5p inhibitor were transfected into ccRCC cells to reveal the potential role of miR-146b-5p in ccRCC. Since miR-146b-5p expression was highest in 786-O cell line and lowest in Caki-2 cell line, the 786-O was used for knockdown treatment, and the Caki-2 was used for overexpression treatment, and transfection efficiency was examined by qRT-PCR. We evaluated transfection efficiency of miR-146b-5p mimic in Caki-2 cells or of miR-146b-5p inhibitor in 786-O cells at 24, 48, and 72 h, and the results demonstrated that the transfection efficiency at 24 h reached a good level. Therefore, the subsequent transfection time was set as 24 h (Additional file 1: Fig. S1). MiR-146b-5p expression was reduced in 786-O cells with miR-146b-5p inhibitor (Fig. 1D), which was increased in Caki-2 cells with miR-146b-5p mimic (Fig. 1E). These findings indicated that miR-146b-5p was significant high-expressed in ccRCC cells and correlated with poor prognosis. Fig. 1MiR-146b-5p is highly expressed in ccRCC cells. A: Expression of miR-146b-5p, blue box plot indicates normal samples, yellow box plot indicates tumor samples; B: Overall survival curve of miR-146b-5p expression level on patients, red represents high miR-146b-5p expression, blue represents low miR-146b-5p expression; C: Expression of miR-146b-5p in normal cell line HK-2 and ccRCC cell lines 786-O, A498, 769-P and Caki-2; D: Transfection efficiency of knockdown miR-146b-5p in 786-O cells; E: Transfection efficiency of overexpression miR-146b-5p in Caki-2 cells; All the above experiments were performed with 3 biological replicates; ** $P \leq 0.01$ indicates siginficant difference, ***$P \leq 0.001$ indicates extremely significant difference.
## MiR-146b-5p enhances migration, invasion and EMT of ccRCC cells
It has been validated that abnormal expression of miR-146b-5p is implicated in progression of malignancies [27–29]. Next, we explored the modulatory role of miR-146b-5p in migration, invasion and EMT of ccRCC cells. We used transwell test and wound healing experiment to assess cell migration and invasion. The findings illustrated that knocking down miR-146b-5p markedly decreased ccRCC cell migration and invasion, whereas overexpressing miR-146b-5p boosted ccRCC cell migration and invasion considerably (Fig. 2A, B). MMP2 and MMP9 are strongly expressed in varying malignant tumor tissues and are strongly associated with tumor cell invasion and metastasis [30]. Therefore, we analyzed the expression of MMP2 and MMP9 in ccRCC cells via western blot assay, which showed that miR-146b-5p knockdown evidently suppressed expression of MMP2 and MMP9 in ccRCC cells. However, overexpression of miR-146b-5p evidently enhanced the levels of MMP2 and MMP9 (Fig. 2C), which confirmed the experimental results in Fig. 2A, B. Many mechanistic investigations have discovered that elevated miR-146b-5p expression can trigger EMT and play a tumor-promoting or tumor-suppressing role [31, 32]. Therefore, we looked in whether miR-146b-5p could influence EMT progression of ccRCC cells. Firstly, we analyzed the cell morphology of ccRCC cells under the microscope, which presented that miR-146b-5p mimic treatment enhanced Caki-2 cell adhesion and polarity, reduced pseudopodia formation. While the changes after miR-146b-5p inhibitor treatment in 786-O cells were opposite (Fig. 2D). In addition, Snail1 and Snail2 are important transcription factors in EMT and are closely related to Notch signaling pathway [33–35]. Therefore, when detecting mRNA and protein expression levels of EMT markers using qRT-PCR and western blot, the expression levels of Snail1, Snail2 (Slug) and Notch1 were also detected. qRT-PCR and western blot unraveled that knocking down miR-146b-5p elevated the expression of EMT marker protein E-cadherin but reduced the expression of Notch1, Snail1, Snail2, N-cadherin and Vimentin in ccRCC cells, suggesting that EMT process of ccRCC cells was hindered. Increased miR-146b-5p had the opposite results, indicating that overexpressed miR-146b-5p promoted EMT process of ccRCC cells (Fig. 2E, F). Taken together, the above results suggested that miR-146b-5p could facilitate migration, invasion, and EMT of ccRCC cells. Fig. 2MiR-146b-5p promotes migration, invasion and EMT of ccRCC cells. A, B: Transwell assay and wound healing experiment were employed to determine the migratory and invasive abilities of ccRCC cells 786–O and Caki-2 in different treatment groups (100 ×); C: Western blot assay was employed to measure the expression levels of MMP2 and MMP9 in 786–O and Caki-2cells in different treatment groups; D: The morphology of 786–O and Caki-2 cells in each transfection group was observed under microscope; E: qRT-PCR evaluated the mRNA levels of Notch1, Snail1, Snail2, E-cadherin, N-cadherin and Vimentin in 786–O and Caki-2 cells; F: The protein expression of Notch1, E-cadherin, N-cadherin, Vimentin, Snail1, Snail2, and GAPDH in ccRCC cells 786–O and Caki-2 in different treatment groups; All the above experiments were performed with 3 biological replicates; * $P \leq 0.05$ indicates statistical difference, **$P \leq 0.01$ indicates significant difference, ***$P \leq 0.001$ indicates extremely siginficant difference.
## MiR-146b-5p targetes and down regulates SEMA3G expression
To unravel the modulatory mechanism of miR-146b-5p downstream in ccRCC cells, we performed the differential analysis of mRNAs in TCGA-KIRC database. We intersected differentially down-regulated mRNAs with miR-146b-5p target genes predicted through TargetScan, miRDB, mirDIP, and starBase databases, and obtained 6 differential mRNAs which had targeted binding sites with miR-146b-5p (Fig. 3A). Next, we performed Pearson correlation analysis on the 6 predicted differential mRNAs and miR-146b-5p. Pearson correlation analysis revealed that miR-146b-5p had the strongest negative connection with SEMA3G (Fig. 3B, C), and the role of SEMA3G in the malignant progression of ccRCC had not been reported yet. Therefore, SEMA3G was chosen as the research subject. Next, we conducted bioinformatics analysis on SEMA3G, and data from TCGA-KIRC database (normal: 72, tumor: 539) showed that the expression level of SEMA3G in ccRCC tissues was remarkably lowerthan that in normal tissues (Fig. 3D). Subsequently, bioinformatics R package “survival” was applied to analyze correlation between SEMA3G expression and the prognosis of ccRCC patients. K-M analysis demonstrated that patients with low SEMA3G level had a much shorter survival than those with high level (Fig. 3E). Next, the above bioinformatics analysis results were verified by cell functional experiments. qRT-PCR exhibited that the mRNA level of SEMA3G was substantially reduced in ccRCC cells (Fig. 3F), in line with the above bioinformatics analysis results. Whereafter, we predicted a binding site for miR-146b-5p with SEMA3G through bioinformatics analysis (Fig. 3G). Dual-luciferase assay further confirmed the targeted relationship between miR-146b-5p and SEMA3G. Overexpression of miR-146b-5p inhibited wild-type SEMA3G 3’-UTR luciferase activity, but did not affect mutant SEMA3G 3’-UTR luciferase activity, which indicated that miR-146b-5p could target and bind to SEMA3G 3’-UTR (Fig. 3H). Finally, qRT-PCR analyzed the regulatory relationship between miR-146b-5p and SEMA3G. Experimental findings illustrated that overexpression of miR-146b-5p could repress SEMA3 expression (Fig. 3I). Accordingly, we concluded that SEMA3G was a target of miR-146b-5p and downregulated by miR-146b-5p. Fig. 3MiR-146b-5p targets the expression of SEMA3G A: Venn diagram of miR-146b-5p predicted target mRNA and down-regulated differential mRNAs. Green indicates TargetScan database, orange indicates down-regulated differential mRNAs in TCGA-KIRC dataset, yellow indicates starBase database, pink indicates mirDIP database, blue indicates miRDB database; B: Pearson correlation analysis plot of miR-146b-5p and candidate genes; C: Pearson correlation analysis plot of miR-146b-5p and SEMA3G; D: Expression of SEMA3G in TCGA-KIRC database, blue box plot indicates normal samples, yellow box plot indicates tumor samples; E: Expression level of SEMA3G on overall survival curve of patients. Red indicates high expression of SEMA3G. Blue represents low SEMA3G expression; F: SEMA3G mRNA expression level in normal cell line HK-2 and ccRCC cell lines 786–O, A498, 769-P and Caki-2; G: Schematic diagram of SEMA3G-WT and SEMA3G-MUT binding to miR-146b-5p sequence predicted by starBase; H: Dual-luciferase assay was used to detect luciferase activity in different treatment groups in ccRCC cell line Caki-2; I: qRT-PCR was performed to detect the regulatory relationship between miR-146b-5p and SEMA3G in Caki-2 cells; All the above experiments were performed with 3 biological replicates; *$P \leq 0.05$ indicates statistical difference, **$P \leq 0.01$ indicates significant difference, ***$P \leq 0.001$ indicates extremely significant difference.
## MiR-146b-5p targets SEMA3G to modulate Notch and TGF-β signaling, thereby affecting migration, invasion and EMT of ccRCC cells
In the above studies, we have proved stimulative impacts of miR-146b-5p on ccRCC cell migration and invasion. Here, we studied effects of miR-146b-5p/SEMA3G on functions of ccRCC cells. Firstly, we conducted GSEA for SEMA3G and found that SEMA3G was mainly concentrated in the Notch and TGF-β signaling (Fig. 4A). To investigate whether miR-146b-5p/SEMA3G modulates ccRCC cell phenotypes through the Notch and TGF-β signaling, we constructed a SEMA3G overexpression cell line (miR-NC + oe-SEMA3G) and a simultaneous overexpression cell line of miR-146b-5p and SEMA3G (miR-mimic + oe-SEMA3G) using Caki-2 cell line. As evaluated by qRT-PCR and western blot, mRNA and protein expression levels of SEMA3G were elevated in miR-NC + oe-SEMA3G group. Compared to miR-NC + oe-SEMA3G group, SEMA3G mRNA and protein levels were repressed in cells after overexpressing both miR-146b-5p and SEMA3G. (Fig. 4B, C). MiR-146b-5p attenuated SEMA3G expression, and that these cells could be utilized for subsequent assays. Afterward, we performed transwell assays and wound healing experiment, and discovered that cancer cell migration and invasion were significantly reduced following SEMA3G overexpression. However, when miR-146b-5p and SEMA3G were overexpressed at the same time, migratory and invasive abilities of cancer cells were considerably increased in contrast to those with SEMA3G overexpression alone (Fig. 4D, E). Western blot measured the expression of MMP2 and MMP9. According to the results, the expression levels of MMP2 and MMP9 were significantly decreased in miR-NC + oe-SEMA3 group compared to miR-NC + oe-NC group, while those in miR-mimic + oe-SEMA3G group returned to the levels in miR-NC + oe-NC group (Fig. 4F). As for the results of cell morphology analysis, in comparison with miR-NC + oe-NC group, miR-NC + oe-SEMA3 group revealed loss of intercellular adhesion, loss of cell polarity and increased pseudopodia formation, while the cell morphology of miR-mimic + oe-SEMA3G group was similar to that of miR-NC + oe-NC group (Fig. 4G). mRNA expression levels of Notch1, Snail1, Snail2 and EMT markers E-cadherin, N-cadherin and Vimentin were evaluated by qRT-PCR (Fig. 4H). Notch and TGF-β signaling pathway-related proteins Notch 1, Hes1, Hes5, TGFβR1, TGFβR2, Smad3, and p-Smad3 in cells of different treatment groups were then determined using western blot (Fig. 4 I). EMT process and the activities of Notch and TGF-β signaling pathways were inhibited in ccRCC cells during SEMA3G overexpression, but after simultaneous increasing miR-146b-5p and SEMA3G, EMT process and activities of the Notch and TGFβ signaling pathways in ccRCC cells were notably increased compared with those when SEMA3G was overexpressed alone. All in all, the results above demonstrated that miR-146b-5p facilitated migration, invasion, and EMT of tumor cells by down-regulating SEMA3G via the Notch and TGF-β signaling pathways. Fig. 4MiR-146b-5p targets SEMA3G to regulate Notch and TGF-β signaling pathways to affect the migration, invasion and EMT of ccRCC cells. A: GSEA pathway enrichment analysis results of SEMA3G; B, C: The mRNA and protein expression of SEMA3G in different treatment groups of ccRCC cells Caki-2; D, E: Transwell assays and wound healing experiment were performed to inspect the migratory and invasive abilities of ccRCC cells Caki-2 in different treatment groups (100 ×); F: Western blot was performed to inspect the expression levels of MMP2 and MMP9 in Caki-2 cells; G: The morphology of Caki-2 cells in each transfection group was observed under microscope; H: qRT-PCR evaluated the mRNA levels of Notch1, Snail1, Snail2, E-cadherin, N-cadherin and Vimentin in Caki-2 cells; I: The expression of EMT marker proteins, Notch, and TGF-β signaling pathway marker proteins in different treatment groups of ccRCC cells Caki-2; All the above experiments were performed with 3 biological replicates; *$P \leq 0.05$ indicates statistical difference, **$P \leq 0.01$ indicates significant difference, ***$P \leq 0.001$ indicates extremely significant difference. One-way ANOVA analysis of variance was used for comparison of three groups.
## Discussion
MiRNAs are novel and prospective biomarkers for diagnosing and treating human malignancies. MiRNAs’ role and regulation mechanism in human cancers has been a research hotspot in recent years. MiR-146b-5p dysregulation is unveiled in many cancers. For example, miR-146b-5p controls colorectal cancer cell proliferation, invasion, and metabolism targeting PDHB [29]. Through targeting ZNRF3, miR-146b-5p facilitates thyroid cancer metastasis and triggers EMT [32]. The results of bioinformatics analysis in the current study showed that miR-146b-5p expression was considerably up-regulated in ccRCC cells, which was confirmed by cell biological assays and consistent with the study of Zhang et al [11]. Herein, cell functional experiments unraveled that miR-146b-5p enhanced ccRCC cell migration, invasion, and EMT, which also proved correlation between miR-146b-5p and EMT and agreed with previous study [32].
In this study, a bioinformatics investigation revealed SEMA3G as a new putative target gene downstream of miR-146b-5p. Dual luciferase assay was adopted to validate binding relationship between miR-146b-5p and SEMA3G. There are few reports on the role of SEMA3G as an oncogene, and SEMA3G prevents tumor cell migration and invasion in gliomas [36]. Li et al. [ 37] disclosed that low level of SEMA3G is a protective factor in ccRCC. According to the results of bioinformatics analysis as well as the cell experiments in the current study, SEMA3G was downregulated in ccRCC cells, which agreed with Li et al. Rescue assay has illustrated that miR-146b-5p accelerated ccRCC cell migration, invasion and EMT by inhibiting SEMA3G expression.
In addition, this study also discovered that SEMA3G was enriched in Notch and TGF-β signaling pathways, thereby regulating cellular EMT progression. Some studies have confirmed the close association of these two signaling pathways with EMT. Previous studies have found that Notch signaling can regulate SNAI1 expression directly [38, 39] or indirectly through inducing hypoxia-inducible factor 1α (HIF-1α). Interaction of Snail2 with *Notch is* crucial for Notch-mediated E-cadherin inhibition and β-catenin activation in mouse mammary epithelial to mesenchymal (NMuMG) cells [40, 41]. Notch overexpression in endothelial cells gives rise to the loss of vascular endothelial-cadherin, leading to endothelial transition (EndMT) [38]. Inhibited Notch1 decreased cell invasiveness and partially reversed EMT in lung adenocarcinoma cells [42]. We measured the expression of Notch1 and Snail1 in ccRCC cells and found that miR-146b-5p/SEMA3G axis could facilitate the expression of Notch1 and Snail1. Moreover, detection of EMT-related markers confirmed that miR-146b-5p/SEMA3G axis fostered EMT, which confirmed the correlation between Notch1, Snail1 and EMT, echoing previous studies. TGF-β signaling is of essence for maintaining immune responses and tissue homeostasis [43, 44]. It plays a role in multiple cellular functions associated with the environment, such as differentiation, growth arrest, proliferation, EMT and apoptosis [45–47]. Currently, most cancer and fibrotic EMT are regulated by TGFβ1 [48], while TGFβ2 primarily controls EndMT in the heart development [49], and TGFβ3 mediates EMT in development [50]. Many studies has reported the involvement of signaling pathways such as Notch [51, 52] and TGF-β [53, 54] in the biological and pathological processes of ccRCC, but there are few reports on the involvement of signaling pathways such as Notch and TGF-β regulated by miRNAs in ccRCC development. Zhang et al. [ 55] found that miR-154 can regulate Wnt/β-catenin and Notch activities in RCC. Aberrant activation of Notch signaling pathway is closely implicated in cell proliferation, metastasis and EMT [56]. Jingushi et al. [ 57] disclosed that miR-629 can promote the TGF-β/Smad signaling and accelerate ccRCC migration and invasion via TRIM33. Based on these, we looked deeply into modulatory role of miRNA-146b-5p on SEMA3G and even signaling pathways like Notch and TGF-β in ccRCC. As our study revealed, overexpression of SEMA3G reduced the activity of Notch/TGF-β signaling pathway. And it decreased migration, invasion, and EMT of ccRCC cells compared to controls, but overexpressed of miR-146b-5p reverted these effects. Therefore, we suggested that miR-146b-5p fostered migration, invasion, and EMT of ccRCC cells by inhibiting SEMA3G expression from inactivating signaling pathways such as Notch/TGF-β. These findings provided a molecular mechanism of SEMA3G on ccRCC metastasis and EMT, which supported theoretical establishment of SEMA3G as a modulator and facilitates precision treatment of ccRCC.
In conclusion, miR-146b-5p facilitated migration, invasion and EMT of ccRCC cells by downregulating SEMA3G expression and activating signaling pathways such as Notch/TGF-β. For the first time, functions of miR-146b-5p/SEMA3G axis in ccRCC migration, invasion, and EMT are shown in this work. These findings are conducive to the completion of modulatory network downstream of miR-146b-5p in ccRCC progression. We provided a new entry point to find new targeted therapeutic pathways and therapeutic ideas for ccRCC, which increased our knowledge of SEMA3G regulatory involvement in signaling networks, as well as its role in cancer development. As a matter of course, this study has some potential limitations, mainly lies in the lack of clinical examination of miR-146b-5p/SEMA3G expression, and lack of relevant functional verification from the perspective of in vivo experiments. It is believed that more progress will be made in the treatment of ccRCC with further research.
## Supplementary Information
Additional file 1. Figure S1 Correlation between transfection efficiency and transfection time. A-B: Transfection efficiency of miR-146b-5p mimic in Caki-2 cells or of miR-146b-5p inhibitor in 786-O cells at 24 h, 48 h and 72 h; All the above experiments were performed with 3 biological replicates; ** $P \leq 0.01$ indicates significant difference, ***$P \leq 0.001$ indicates extremely significant difference.
## References
1. Zbar B, Klausner R, Linehan WM. **Studying cancer families to identify kidney cancer genes**. *Annu Rev Med* (2003) **54** 217-233. DOI: 10.1146/annurev.med.54.101601.152514
2. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. **Global cancer statistics, 2012**. *CA Cancer J Clin.* (2015) **65** 87-108. DOI: 10.3322/caac.21262
3. Gupta GP, Massague J. **Cancer metastasis: building a framework**. *Cell* (2006) **127** 679-695. DOI: 10.1016/j.cell.2006.11.001
4. Gasinska A, Jaszczynski J, Adamczyk A, Janecka-Widla A, Wilk W, Cichocka A, Stelmach A. **Biomarkers of epithelial-mesenchymal transition in localized, surgically treated clear-cell renal cell carcinoma**. *Folia Histochem Cytobiol* (2018) **56** 195-206. DOI: 10.5603/FHC.a2018.0023
5. Wang L, Yang G, Zhao D, Wang J, Bai Y, Peng Q, Wang H, Fang R, Chen G, Wang Z, Wang K, Li G, Yang Y, Wang Z, Guo P, Peng L, Hou D, Xu W. **CD103-positive CSC exosome promotes EMT of clear cell renal cell carcinoma: role of remote MiR-19b-3p**. *Mol Cancer* (2019) **18** 86. DOI: 10.1186/s12943-019-0997-z
6. Zhu Z, Huang J, Li X, Xing J, Chen Q, Liu R, Hua F, Qiu Z, Song Y, Bai C, Mo YY, Zhang Z. **Gut microbiota regulate tumor metastasis via circRNA/miRNA networks**. *Gut Microbes* (2020) **12** 1788891. DOI: 10.1080/19490976.2020.1788891
7. Pan G, Liu Y, Shang L, Zhou F, Yang S. **EMT-associated microRNAs and their roles in cancer stemness and drug resistance**. *Cancer Commun (Lond)* (2021) **41** 199-217. DOI: 10.1002/cac2.12138
8. Yamakuchi M, Ferlito M, Lowenstein CJ. **miR-34a repression of SIRT1 regulates apoptosis**. *Proc Natl Acad Sci U S A* (2008) **105** 13421-13426. DOI: 10.1073/pnas.0801613105
9. Shi L, Su Y, Zheng Z, Qi J, Wang W, Wang C. **miR-146b-5p promotes colorectal cancer progression by targeting TRAF6**. *Exp Ther Med* (2022) **23** 231. DOI: 10.3892/etm.2022.11155
10. Ren Y, Wang X, Ji T, Cai X. **MicroRNA-146b-5p suppresses cholangiocarcinoma cells by targeting TRAF6 and modulating p53 translocation**. *Acta Histochem* (2021) **123** 151793. DOI: 10.1016/j.acthis.2021.151793
11. Zhang Z, Fu X, Gao Y, Nie Z. **LINC01535 Attenuates ccRCC progression through regulation of the miR-146b-5p/TRIM2 axis and inactivation of the PI3K/Akt pathway**. *J Oncol* (2022) **2022** 2153337. PMID: 35342411
12. Luo Y, Raible D, Raper JA. **Collapsin: a protein in brain that induces the collapse and paralysis of neuronal growth cones**. *Cell* (1993) **75** 217-227. DOI: 10.1016/0092-8674(93)80064-L
13. Uchida Y, James JM, Suto F, Mukouyama YS. **Class 3 semaphorins negatively regulate dermal lymphatic network formation**. *Biol Open* (2015) **4** 1194-1205. DOI: 10.1242/bio.012302
14. Liu W, Li J, Liu M, Zhang H, Wang N. **PPAR-gamma promotes endothelial cell migration by inducing the expression of Sema3g**. *J Cell Biochem* (2015) **116** 514-523. DOI: 10.1002/jcb.24994
15. Liu X, Uemura A, Fukushima Y, Yoshida Y, Hirashima M. **Semaphorin 3G provides a repulsive guidance cue to lymphatic endothelial cells via Neuropilin-2/PlexinD1**. *Cell Rep* (2016) **17** 2299-2311. DOI: 10.1016/j.celrep.2016.11.008
16. Wan B, Liu B, Huang Y, Yu G, Lv C. **Prognostic value of immune-related genes in clear cell renal cell carcinoma**. *Aging (Albany NY)* (2019) **11** 11474-11489. DOI: 10.18632/aging.102548
17. Gao X, Yang J, Chen Y. **Identification of a four immune-related genes signature based on an immunogenomic landscape analysis of clear cell renal cell carcinoma**. *J Cell Physiol* (2020) **235** 9834-9850. DOI: 10.1002/jcp.29796
18. Lin YT, Wu KJ. **Epigenetic regulation of epithelial-mesenchymal transition: focusing on hypoxia and TGF-beta signaling**. *J Biomed Sci* (2020) **27** 39. DOI: 10.1186/s12929-020-00632-3
19. Krishna BM, Jana S, Singhal J, Horne D, Awasthi S, Salgia R, Singhal SS. **Notch signaling in breast cancer: From pathway analysis to therapy**. *Cancer Lett* (2019) **461** 123-131. DOI: 10.1016/j.canlet.2019.07.012
20. Li Y, Gu Y, Tang N, Liu Y, Zhao Z. **miR-22-Notch signaling pathway is involved in the regulation of the apoptosis and autophagy in human ovarian cancer cells**. *Biol Pharm Bull* (2018) **41** 1237-1242. DOI: 10.1248/bpb.b18-00084
21. Su Q, Xin L. **Notch signaling in prostate cancer: refining a therapeutic opportunity**. *Histol Histopathol* (2016) **31** 149-157. PMID: 26521657
22. Vinson KE, George DC, Fender AW, Bertrand FE, Sigounas G. **The notch pathway in colorectal cancer**. *Int J Cancer* (2016) **138** 1835-1842. DOI: 10.1002/ijc.29800
23. Ren C, Zhou Z, Wang X, Hua X, Zou M, Zhang X. **SHCBP1 promotes the progression of Esophageal squamous cell carcinoma via the TGFbeta pathway**. *Appl Immunohistochem Mol Morphol* (2021) **29** 136-143. DOI: 10.1097/PAI.0000000000000858
24. Li Y, Yang J, Zhang X, Liu H, Guo J. **KDM6A suppresses hepatocellular carcinoma cell proliferation by negatively regulating the TGF-beta/SMAD signaling pathway**. *Exp Ther Med* (2020) **20** 2774-2782. PMID: 32765772
25. Chod J, Zavadova E, Halaska MJ, Strnad P, Fucikova T, Rob L. **Preoperative transforming growth factor-beta 1 (TGF-beta 1) plasma levels in operable breast cancer patients**. *Eur J Gynaecol Oncol* (2008) **29** 613-616. PMID: 19115689
26. Fischer JR, Darjes H, Lahm H, Schindel M, Drings P, Krammer PH. **Constitutive secretion of bioactive transforming growth factor beta 1 by small cell lung cancer cell lines**. *Eur J Cancer* (1994) **30A** 2125-2129. DOI: 10.1016/0959-8049(94)00364-B
27. Jia M, Shi Y, Li Z, Lu X, Wang J. **MicroRNA-146b-5p as an oncomiR promotes papillary thyroid carcinoma development by targeting CCDC6**. *Cancer Lett* (2019) **443** 145-156. DOI: 10.1016/j.canlet.2018.11.026
28. Li Y, Zhang H, Dong Y, Fan Y, Li Y, Zhao C, Wang C, Liu J, Li X, Dong M, Liu H, Chen J. **MiR-146b-5p functions as a suppressor miRNA and prognosis predictor in non-small cell lung cancer**. *J Cancer* (2017) **8** 1704-1716. DOI: 10.7150/jca.16961
29. Zhu Y, Wu G, Yan W, Zhan H, Sun P. **miR-146b-5p regulates cell growth, invasion, and metabolism by targeting PDHB in colorectal cancer**. *Am J Cancer Res* (2017) **7** 1136-1150. PMID: 28560062
30. Liu W, Fu X, Li R. **CNN1 regulates the DKK1/Wnt/beta-catenin/c-myc signaling pathway by activating TIMP2 to inhibit the invasion, migration and EMT of lung squamous cell carcinoma cells**. *Exp Ther Med* (2021) **22** 855. DOI: 10.3892/etm.2021.10287
31. Yu S, Li L, Tian W, Nie D, Mu W, Qiu F, Liu Y, Liu X, Wang X, Du Z, Chu WF, Yang B. **PEP06 polypeptide 30 exerts antitumour effect in colorectal carcinoma via inhibiting epithelial-mesenchymal transition**. *Br J Pharmacol* (2018) **175** 3111-3130. DOI: 10.1111/bph.14352
32. Deng X, Wu B, Xiao K, Kang J, Xie J, Zhang X, Fan Y. **MiR-146b-5p promotes metastasis and induces epithelial-mesenchymal transition in thyroid cancer by targeting ZNRF3**. *Cell Physiol Biochem* (2015) **35** 71-82. DOI: 10.1159/000369676
33. Tang Y, Tang Y, Cheng YS. **miR-34a inhibits pancreatic cancer progression through Snail1-mediated epithelial-mesenchymal transition and the Notch signaling pathway**. *Sci Rep* (2017) **7** 38232. DOI: 10.1038/srep38232
34. Hu Y, Nie Q, Dai M, Chen F, Wu H. **Histone deacetylases inhibit the snail2-mediated EMT during metastasis of hepatocellular carcinoma cells**. *Front Cell Dev Biol* (2020) **8** 752. DOI: 10.3389/fcell.2020.00752
35. Yang G, Zhao Z, Zhang X, Wu A, Huang Y, Miao Y, Yang M. **Effect of berberine on the renal tubular epithelial-to-mesenchymal transition by inhibition of the Notch/snail pathway in diabetic nephropathy model KKAy mice**. *Drug Des Devel Ther* (2017) **11** 1065-1079. DOI: 10.2147/DDDT.S124971
36. Zhou X, Ma L, Li J, Gu J, Shi Q, Yu R. **Effects of SEMA3G on migration and invasion of glioma cells**. *Oncol Rep* (2012) **28** 269-275. PMID: 22562223
37. Zhang QJ, He Y, Li Y, Shen H, Lin L, Zhu M, Wang Z, Luo X, Hill JA, Cao D, Luo RL, Zou R, McAnally J, Liao J, Bajona P, Zang QS, Yu Y, Liu ZP. **Matricellular Protein Cilp1 promotes myocardial fibrosis in response to myocardial infarction**. *Circ Res* (2021) **129** 1021-1035. DOI: 10.1161/CIRCRESAHA.121.319482
38. Timmerman LA, Grego-Bessa J, Raya A, Bertran E, Perez-Pomares JM, Diez J, Aranda S, Palomo S, McCormick F, Izpisua-Belmonte JC, de la Pompa JL. **Notch promotes epithelial-mesenchymal transition during cardiac development and oncogenic transformation**. *Genes Dev* (2004) **18** 99-115. DOI: 10.1101/gad.276304
39. Leong KG, Niessen K, Kulic I, Raouf A, Eaves C, Pollet I, Karsan A. **Jagged1-mediated Notch activation induces epithelial-to-mesenchymal transition through Slug-induced repression of E-cadherin**. *J Exp Med* (2007) **204** 2935-2948. DOI: 10.1084/jem.20071082
40. Yu L, Hebert MC, Zhang YE. **TGF-beta receptor-activated p38 MAP kinase mediates Smad-independent TGF-beta responses**. *EMBO J* (2002) **21** 3749-3759. DOI: 10.1093/emboj/cdf366
41. Yue J, Mulder KM. **Activation of the mitogen-activated protein kinase pathway by transforming growth factor-beta**. *Methods Mol Biol* (2000) **142** 125-131. PMID: 10806618
42. Xie M, Zhang L, He CS, Xu F, Liu JL, Hu ZH, Zhao LP, Tian Y. **Activation of Notch-1 enhances epithelial-mesenchymal transition in gefitinib-acquired resistant lung cancer cells**. *J Cell Biochem* (2012) **113** 1501-1513. PMID: 22173954
43. Massague J. **TGF-beta signal transduction**. *Annu Rev Biochem* (1998) **67** 753-791. DOI: 10.1146/annurev.biochem.67.1.753
44. David CJ, Massague J. **Contextual determinants of TGFbeta action in development, immunity and cancer**. *Nat Rev Mol Cell Biol* (2018) **19** 419-435. DOI: 10.1038/s41580-018-0007-0
45. Massague J. **TGFbeta in Cancer**. *Cell* (2008) **134** 215-230. DOI: 10.1016/j.cell.2008.07.001
46. Massague J. **TGFbeta signalling in context**. *Nat Rev Mol Cell Biol* (2012) **13** 616-630. DOI: 10.1038/nrm3434
47. Mu Y, Gudey SK, Landstrom M. **Non-Smad signaling pathways**. *Cell Tissue Res* (2012) **347** 11-20. DOI: 10.1007/s00441-011-1201-y
48. Akhurst RJ, Derynck R. **TGF-beta signaling in cancer–a double-edged sword**. *Trends Cell Biol* (2001) **11** S44-51. DOI: 10.1016/S0962-8924(01)02130-4
49. Camenisch TD, Molin DG, Person A, Runyan RB, Gittenberger-de Groot AC, McDonald JA, Klewer SE. **Temporal and distinct TGFbeta ligand requirements during mouse and avian endocardial cushion morphogenesis**. *Dev Biol* (2002) **248** 170-181. DOI: 10.1006/dbio.2002.0731
50. Nawshad A, Hay ED. **TGFbeta3 signaling activates transcription of the LEF1 gene to induce epithelial mesenchymal transformation during mouse palate development**. *J Cell Biol* (2003) **163** 1291-1301. DOI: 10.1083/jcb.200306024
51. Bhagat TD, Zou Y, Huang S, Park J, Palmer MB, Hu C, Li W, Shenoy N, Giricz O, Choudhary G, Yu Y, Ko YA, Izquierdo MC, Park AS, Vallumsetla N, Laurence R, Lopez R, Suzuki M, Pullman J, Kaner J, Gartrell B, Hakimi AA, Greally JM, Patel B, Benhadji K, Pradhan K, Verma A, Susztak K. **Notch pathway is activated via genetic and epigenetic alterations and is a therapeutic target in clear cell renal cancer**. *J Biol Chem* (2017) **292** 837-846. DOI: 10.1074/jbc.M116.745208
52. Feng C, Xiong Z, Jiang H, Ding Q, Fang Z, Hui W. **Genetic alteration in notch pathway is associated with better prognosis in renal cell carcinoma**. *BioFactors* (2016) **42** 41-48. PMID: 26662507
53. Bostrom AK, Lindgren D, Johansson ME, Axelson H. **Effects of TGF-beta signaling in clear cell renal cell carcinoma cells**. *Biochem Biophys Res Commun* (2013) **435** 126-133. DOI: 10.1016/j.bbrc.2013.04.054
54. Sjolund J, Bostrom AK, Lindgren D, Manna S, Moustakas A, Ljungberg B, Johansson M, Fredlund E, Axelson H. **The notch and TGF-beta signaling pathways contribute to the aggressiveness of clear cell renal cell carcinoma**. *PLoS ONE* (2011) **6** e23057. DOI: 10.1371/journal.pone.0023057
55. Zhang R, Zhang W, Xu B, Lv C, Hou J, Zhang G. **Long intergenic non-coding RNA 1939 eliminates proliferation and migration of human renal cell carcinoma (RCC) cells by down-regulation of miR-154**. *Artif Cells Nanomed Biotechnol* (2020) **48** 695-702. DOI: 10.1080/21691401.2020.1725024
56. Zhou B, Lin W, Long Y, Yang Y, Zhang H, Wu K, Chu Q. **Notch signaling pathway: architecture, disease, and therapeutics**. *Signal Transduct Target Ther* (2022) **7** 95. DOI: 10.1038/s41392-022-00934-y
57. Jingushi K, Ueda Y, Kitae K, Hase H, Egawa H, Ohshio I, Kawakami R, Kashiwagi Y, Tsukada Y, Kobayashi T, Nakata W, Fujita K, Uemura M, Nonomura N, Tsujikawa K. **miR-629 targets TRIM33 to promote TGFβ/Smad signaling and metastatic phenotypes in ccRCC**. *Mol Cancer Res* (2015) **13** 565-574. DOI: 10.1158/1541-7786.MCR-14-0300
|
---
title: 'Association of maternal nationality with preterm birth and low birth weight
rates: analysis of nationwide data in Japan from 2016 to 2020'
authors:
- Tasuku Okui
- Yoko Sato
- Seiichi Morokuma
- Naoki Nakashima
journal: Maternal Health, Neonatology and Perinatology
year: 2023
pmcid: PMC9993667
doi: 10.1186/s40748-023-00149-1
license: CC BY 4.0
---
# Association of maternal nationality with preterm birth and low birth weight rates: analysis of nationwide data in Japan from 2016 to 2020
## Abstract
### Background
The rate of low birth weight or preterm birth is known to vary according to the birth place of mothers. However, in Japan, studies that investigated the association between maternal nationalities and adverse birth outcomes are few. In this study, we investigated the association between maternal nationalities and adverse birth outcomes.
### Methods
We obtained live birth data from the Vital Statistics 2016–2020 of the Ministry of Health, Labour, and Welfare. We used data on maternal age, sex, parity, gestational age, birth weight, number of fetuses, household occupation, paternal nationality, and maternal nationality for each infant. We compared the rates of preterm birth and low birth weight at term among mothers whose nationalities were Japan, Korea, China, Philippines, Brazil, and other countries. Log binomial regression model was used to investigate the association between maternal nationality and the two birth outcomes using the other infants’ characteristics as covariates.
### Results
In the analysis, data on 4,290,917 singleton births were used. Mothers from Japan, Korea, China, the Philippines, Brazil, and other nations had preterm birth rates of $4.61\%$, $4.16\%$, $3.97\%$, $7.43\%$, $7.69\%$, and $5.61\%$, respectively. The low birth weight rate among Japanese mothers was $5.36\%$ and was the highest among the maternal nationalities. Regression analysis showed that the relative risk for preterm birth among Filipino, Brazilian, and mothers from other countries (1.520, 1.329, and 1.222, respectively) was statistically significantly higher compared with Japanese mothers. In contrast, the relative risk for Korean and Chinese mothers (0.870 and 0.899, respectively) was statistically significantly lower compared with Japanese mothers. Mothers from Korea, China, the Philippines, Brazil, and other nations had a relative risk for low birth weight that was statistically significantly lower than that of Japanese mothers (0.664, 0.447, 0.867, 0.692, and 0.887, respectively).
### Conclusions
Support for mothers from the Philippines, Brazil, and other countries are necessary to prevent preterm birth. A future study is necessary to investigate the differences in characteristics among mothers of different nationalities in order to uncover the reason for the high risk for low birth weight among Japanese mothers.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40748-023-00149-1.
## Background
The rates of low birth weight or preterm birth are representative indicators of adverse birth outcomes and had been positively related with neonatal or infant mortality [1, 2]. These rates largely vary among countries [3, 4] and among the birth place of mothers even within the same country [5, 6]. In Japan, the rate of low birth weight or preterm birth is known to vary according to the maternal social characteristics [7–9]. However, studies on the association between maternal nationalities and adverse birth outcomes are few in Japan.
Over the recent decades, the number of non-Japanese people in Japan had been increasing [10], along with the increasing number of births from non-Japanese women [11]. Although the total fertility rate is not high among non-Japanese women, the reported number of births from non-Japanese women in Japan was approximately 20,000 each year [11]. A study has investigated the differences in infant and fetal mortality rates according to the nationality of mothers living in Japan, and the rates among Filipino mothers were high [12]. However, the association of maternal nationalities with the rate of low birth weight or preterm birth has not been investigated. In other countries, many studies on foreign-born or immigrant mothers and adverse birth outcomes found that native-born mothers tended to have lower risk of adverse birth outcomes [5, 6, 13, 14], but the results varied depending on the country or ethnicity [15, 16]. Non-Japanese mothers are known to have some difficulties during the perinatal period in Japan [17]. Knowing the differences in adverse birth outcomes depending on maternal nationality in Japan would enable implementation of preventive measures among the high risk population.
In this study, we investigated the association between maternal nationalities and adverse birth outcomes using the Vital Statistics in Japan.
## Methods
We used live birth data from the Vital Statistics from 2016 to 2020. The individual-level data were obtained from the Ministry of Health, Labour, and Welfare in Japan. We used data on maternal age, infant’s sex, parity, gestational age, birth weight, number of fetuses, household occupation, paternal nationality, and maternal nationality for each infant. Household occupations were classified as farmer, self-employed, full-time worker 1, full-time worker 2, other occupations, and unemployed. Full-time worker 1 meant workers of a company with < 100 employees, and full-time worker 2 meant public servants and workers or board members of a company with ≥ 100 employees. Japan, Korea, China, Philippines, Brazil, Thailand, the United States, the United Kingdom, Peru, and other countries were available as nationalities of fathers and others in the data. Any combinations of maternal and paternal nationalities between couples were included in the analysis, and non-Japanese couples were also included in the analysis.
We used only singleton births, and illegitimate infants were not included, because paternal nationalities were included in the analysis. Maternal age was grouped into < 20 years, 20–24 years, 25–29 years, 30–34 years (reference), 35–39 years, and ≥ 40 years [18]. Parity was classified into primiparous and multiparous. Births with maternal nationalities from Thailand, the United States, the United Kingdom, and Peru were small in number and were included into the group of other countries in the analysis. Preterm birth was defined as gestational age of ≤ 36 weeks. Low birth weight was defined as < 2,500 g. In this study, the rate of low birth weight at term was calculated according to previous studies [19, 20].
For each maternal nationality, we counted the number of births and the rates of preterm birth and low birth weight by the infants’ characteristics. Log binomial regression model was used to investigate the association between the two birth outcomes and maternal nationality using the other infants’ characteristics (maternal age, infant’s sex, parity, household occupation, paternal nationality) as covariates. The traits of those other infants were included since they may have an impact on results and their distribution may vary based on the nationality of the mother. Log binomial regression model is a regression model used for binary outcome [21]. Relative risk (RR), its $95\%$ confidence interval (CI), and p value were calculated for each maternal nationality; a p value of < 0.05 was judged as statistically significant.
In this study, complete-case analysis was carried out. An imputation utilizing the hot-deck imputation approach was also utilized as a sensitivity analysis [22]. With this technique, a missing value from an observation was substituted with another observation’s value whose values for non-missing variables were comparable to those of the missing value observation. All statistical analyses were conducted using R4.1.3 (https://www.r-project.org/). The statistics shown in this study were processed by the authors using the Vital *Statistics data* obtained from the Ministry of Health, Labour, and Welfare, and these are different from the statistics published by the Ministry.
## Results
Figure 1 shows the flowchart of selection of the study population. After removing cases with missing data, the data of 4,290,917 births were used in the analysis. Fig. 1Flowchart of selection of the study population Table 1 shows the number of births by the infants’ characteristics and maternal nationality. The largest number of births was from Japanese mothers (4,182,823 births), followed by Chinese mothers (41,041 births).Table 1Number of births by the infants’ characteristics and maternal nationalityMaternal NationalityJapanKoreaChinaPhilippinesBrazilOther countriesTotal4,182,8239,66941,04112,3916,95638,037Maternal age group 19 years or less28,64823498896241 20–24 years330,3693471,7981,4519385,022 25–29 years1,079,7481,66811,6573,2801,85313,733 30–34 years1,538,3843,73217,4993,8282,10012,026 35–39 years967,5973,0458,5662,9391,5425,807 40 years or more238,0778541,4728054271,208Sex Male2,145,2824,93421,2486,3293,55219,548 Female2,037,5414,73519,7936,0623,40418,489Parity Primiparous1,949,3914,61021,6613,7492,37119,506 Multiparous2,233,4325,05919,3808,6424,58518,531Household occupation Farmer48,6994721116421255 Self-employed297,9581,5425,1481,4043443,500 Full-time worker 11,377,8773,71316,3724,6001,93713,884 Full-time worker 22,067,3843,48614,8554,2213,64013,722 Other occupations357,8207293,0641,3088083,957 Unemployed33,0851521,3916942062,719Paternal nationality Japan4,139,1456,45614,0467,5551,30610,428 Korea9,9452,907161257149 China5,8796626,318257166 Philippines1,4088124,1111644 Brazil2,0794312455,406364 Other countries24,36722847343021426,886Birth weight 1499 g or less23,7315223014269320 1500–2499 g312,6825071,3878854202,206 2500 g or more3,846,4109,11039,42411,3646,46735,511Gestational age 36 weeks or less192,7664021,6289215352,134 37 weeks or more3,990,0579,26739,41311,4706,42135,903 Table 2 shows the preterm birth rate by the infants’ characteristics and maternal nationality. The preterm birth rate for Chinese women ($3.97\%$) was the smallest among the maternal nationalities. The rates for Filipino and Brazilian mothers were particularly high ($7.43\%$ and $7.69\%$, respectively), compared with those for mothers from the other countries. In addition, the preterm birth rate tended to be high in older mothers and was larger in male infants than in female infants, regardless of maternal nationality. Table 2Preterm birth rate (%) by the infants’ characteristics and maternal nationalityMaternal NationalityJapanKoreaChinaPhilippinesBrazilOther countriesTotal4.614.163.977.437.695.61Maternal age group 19 years or less5.188.700.009.095.217.88 20–24 years4.062.882.454.554.904.94 25–29 years3.933.423.336.166.314.89 30–34 years4.403.753.827.638.055.79 35–39 years5.354.505.119.199.796.63 40 years or more6.776.566.0510.3111.019.52Sex Male5.194.484.368.288.566.24 Female3.993.823.546.556.794.95Parity Primiparous4.523.713.787.477.385.24 Multiparous4.694.574.177.427.856.00Household occupation Farmer4.754.268.066.100.004.71 Self-employed4.804.993.697.488.146.26 Full-time worker 14.724.344.067.577.805.56 Full-time worker 24.483.764.037.157.475.41 Other occupations4.663.023.467.728.176.07 Unemployed5.795.923.817.938.745.48Paternal nationality Japan4.614.384.217.165.365.20 Korea4.623.614.354.0014.296.04 China4.356.063.844.000.003.01 Philippines4.830.000.007.786.259.09 Brazil6.060.006.458.168.217.69 Other countries4.924.393.598.848.885.75 Table 3 shows the rate of low birth weight at term by the infants’ characteristics and maternal nationality. The low birth weight rate was the highest in Japanese mothers ($5.36\%$) and was the lowest in Chinese mothers ($1.90\%$).Table 3Low birth weight rate at term (%) by infant’s characteristics and maternal nationalityMaternal NationalityJapanKoreaChinaPhilippinesBrazilOther countriesTotal5.363.391.904.413.223.68Maternal age group 19 years or less5.749.520.006.251.105.86 20–24 years5.372.671.433.612.914.44 25–29 years5.193.661.684.783.463.73 30–34 years5.203.201.854.383.263.68 35–39 years5.543.272.284.163.452.90 40 years or more6.364.262.825.262.373.11Sex Male4.152.551.503.532.563.02 Female6.614.262.345.313.914.36Parity Primiparous6.013.582.116.344.104.50 Multiparous4.793.211.673.572.772.81Household occupation Farmer5.048.891.032.600.001.65 Self-employed5.264.031.614.462.224.08 Full-time worker 15.523.461.854.443.423.92 Full-time worker 25.273.042.024.083.303.54 Other occupations5.263.112.274.973.103.47 Unemployed6.472.801.725.482.663.11Paternal nationality Japan5.373.892.584.633.643.36 Korea4.452.460.008.330.004.29 China3.820.001.568.330.005.59 Philippines5.5212.500.004.120.0010.00 Brazil4.710.006.905.333.182.38 Other countries3.191.831.322.302.053.79
Table 4 shows the results of the regression analysis on the association of maternal nationality with preterm birth and low birth weight at term. Regression analysis showed that the RR for preterm birth among Filipino, Brazilian, and mothers from other countries (1.520, 1.329, and 1.222, respectively) was statistically significantly higher compared with Japanese mothers. In contrast, the relative risk for Korean and Chinese mothers (0.870 and 0.899, respectively) was statistically significantly lower compared with Japanese mothers. For low birth weight, the RR for Korean, Chinese, Filipino, Brazilian, and other countries’ mothers (0.664, 0.447, 0.867, 0.692, and 0.887, respectively) was statistically significantly lower compared with Japanese mothers. Table 4Regression analysis on the association of maternal nationality with preterm birth and low birth weight at termPreterm birthLow birth weight at termMaternal nationalityRR ($95\%$ CI)*p-valueRR ($95\%$ CI)*p-valueJapanReferenceReferenceKorea0.870 (0.788, 0.960)0.0060.664 (0.594, 0.742)< 0.001China0.899 (0.841, 0.960)0.0020.447 (0.410, 0.488)< 0.001Philippines1.520 (1.413, 1.637)< 0.0010.867 (0.788, 0.956)0.004Brazil1.329 (1.170, 1.511)< 0.0010.692 (0.580, 0.826)< 0.001Other countries1.222 (1.159, 1.287)< 0.0010.887 (0.833, 0.945)< 0.001RR Relative risk, CI Confidence interval*Maternal age, sex, parity, household occupation, and paternal nationality were adjusted The results of the regression analysis utilizing an imputation method on the relationship between mother nationality and preterm delivery and low birth weight at term are shown in the supplemental table. The results were consistent with the primary analysis.
## Discussion
This study using the Vital Statistics in Japan revealed an association between maternal nationality and adverse birth outcomes. Compared with Japanese women, Filipino, Brazilian, and mothers from other countries had higher risk of preterm birth. On the other hand, the risk for low birth weight at term was higher in Japanese women than in mothers from the other countries. There were possible reasons and implications of the results.
In a previous study, Filipinos in Japan were reported to have the highest infant and fetal mortality rates [12]. A similar high risk among mothers from the Philippines has been observed in Canada and Korea [5, 23]. In addition, in this study, a high risk for preterm birth was observed among mothers from Brazil and other countries. The particularly high proportion of caesarean sections affects the preterm birth rate in Brazil [24]. Additionally, the preterm birth rate was reported to be lower in Japan than the worldwide prevalence [25] and was higher in Brazil and Philippines than in Japan [26, 27]. Therefore, the difference in preterm birth rates among countries is possibly related with the difference depending on maternal nationalities in Japan. In addition, foreign mothers had been known to have difficulty in understanding health services in Japan or in communicating with others using Japanese language [17, 28]. Moreover, according to an ecological study in Japan, the percentage of non-Japanese nationalities was positively associated with the delay or lack of utilization prenatal care [29], which is known to be effective for reduction of preterm birth [30]. These factors may explain the high risk in non-Japanese mothers.
In contrast, Korean and Chinese mothers had lower risk, compared with Japanese women. Although the reason for the lower risk is uncertain, the reported preterm birth rates in Korea and China were not higher, compared with that in Japan [5, 31]. In addition, China and Korea are neighboring countries of Japan, and the population of Chinese and Koreans in Japan are higher, compared with that of Filipinos or Brazilians [10]. Therefore, people from these countries might have less psychological stress compared with those from the other countries. Another possible reason is the lower smoking prevalence among women in China and Korea than in Japan [32–34]; and a similar difference might exist among mothers in Japan.
Compared with mothers from other nationalities, Japanese women had higher risk of delivering infants of low birth weight. This result was contrary to previous reports that foreign-born or immigrant women tended have adverse birth outcomes in other countries [5, 6, 35]. The estimated worldwide prevalence of low birth weight is higher, compared with the rate in Japan [3], and non-Japanese mothers have some difficulties in childbirth in Japan. However, the relative risk for low birth weight at term was lower in non-Japanese mothers than in Japanese women. Japanese mothers’ physical traits, such as their low body mass index (BMI) and short stature, is one of the assumed causes, as evidenced by studies conducted in other nations [36–38]. Actually, a similar trend of Japanese mothers having higher risk, compared with white mothers or mothers from Korea, China, and the Philippines, was observed in the United States [36, 37]. In addition, the birth weight of infants was reported to be lower in Japanese than in other ethnicities [38]. Possible reasons for the relatively high SGA risk in Japanese mothers had been maternal height, pre-pregnancy weight, and gestational weight gain [36]. In Japan, a low birth weight rate had been known to have an increasing trend from the late twentieth century to the year 2010 [39, 40], and a decrease in birth weight was also observed [41]. Increase in the number of underweight Japanese women has been pointed out as a reason for these birth outcomes [40]. In Japan, a low pre-pregnancy BMI was found to be a major risk factor for low birth weight rate [42, 43]. According to the National Health and Nutrition Survey in 2019, the percentage of underweight (BMI < 18.5) women was $20.7\%$ among the 20–29 years age group and $16.4\%$ among the 30–39 years age group [44]; these values were higher compared with those reported in Korea and China [45–47]. Therefore, the high prevalence of underweight young women in Japan might be one of the reasons for the results of this study.
This study found that women whose nationality was from the Philippines, Brazil, and other countries had relatively high risk of preterm birth rate in Japan. As an implication, unawareness of the available health services in Japan could make consults for prenatal care difficult among non-Japanese women. In that case, more community intervention by public health nurses to connect medical institutions with local non-Japanese residents might be needed. In addition, an epidemiological study on the differences in health behaviors and statuses among maternal nationalities is necessary to uncover the reason for the high risk of low birth weight among Japanese women. Additionally, governments should be aware of the findings about low birth weight since they can make it known to the general population. This could help Japanese women who are expecting to ameliorate their lifestyles. Local governments can also take measures in order for non-Japanese mothers to participate prenatal care or consult a physician. Obstetricians are also advised to be aware of the results because they can speak with both Japanese and non-Japanese moms and warn them of the risks.
There were some limitations in this study. Some major characteristics of pregnant women, such as household income, education level, utilization of prenatal care, and BMI, could not be obtained, because the Vital *Statistics data* were used in this study. Investigating these factors in a future epidemiological study may help specify the reason for disparities. In addition, period of stay in Japan or Japanese language skill might need to be scrutinized.
## Conclusions
This study based on the Vital Statistics in Japan revealed an association between maternal nationality and adverse birth outcomes. The risk for preterm birth was significantly higher in Filipino, Brazilian, and mothers from other countries than in Japanese mothers but was significantly lower in Chinese and Korean mothers than in Japanese mothers. On the other hand, the risk for low birth weight in Japanese mothers was higher, compared with that in mothers from all the other countries.
## Supplementary Information
Additional file 1: Supplementary Table. The results of the regression analysis using an imputation method on the association of maternal nationality with preterm birth and low birth weight at term.
## References
1. Vilanova CS, Hirakata VN, de Souza Buriol VC, Nunes M, Goldani MZ, da Silva CH. **The relationship between the different low birth weight strata of newborns with infant mortality and the influence of the main health determinants in the extreme south of Brazil**. *Popul Health Metr* (2019.0) **17** 15. DOI: 10.1186/s12963-019-0195-7
2. García-Basteiro AL, Quintó L, Macete E, Bardají A, González R, Nhacolo A, Sigauque B, Sacoor C, Rupérez M, Sicuri E, Bassat Q, Sevene E, Menéndez C. **Infant mortality and morbidity associated with preterm and small-for-gestational-age births in Southern Mozambique: A retrospective cohort study**. *PLoS One* (2017.0) **12** e0172533. DOI: 10.1371/journal.pone.0172533
3. Blencowe H, Krasevec J, de Onis M, Black RE, An X, Stevens GA, Borghi E, Hayashi C, Estevez D, Cegolon L, Shiekh S, Ponce Hardy V, Lawn JE, Cousens S. **National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: a systematic analysis**. *Lancet Glob Health* (2019.0) **7** e849-e860. DOI: 10.1016/S2214-109X(18)30565-5
4. Chawanpaiboon S, Vogel JP, Moller AB, Lumbiganon P, Petzold M, Hogan D, Landoulsi S, Jampathong N, Kongwattanakul K, Laopaiboon M, Lewis C, Rattanakanokchai S, Teng DN, Thinkhamrop J, Watananirun K, Zhang J, Zhou W, Gülmezoglu AM. **Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis**. *Lancet Glob Health* (2019.0) **7** e37-e46. DOI: 10.1016/S2214-109X(18)30451-0
5. Song IG, Kim MS, Shin SH, Kim EK, Kim HS, Choi S, Kwon S, Park SM. **Birth outcomes of immigrant women married to native men in the Republic of Korea: a population register-based study**. *BMJ Open* (2017.0) **7** e017720. DOI: 10.1136/bmjopen-2017-017720
6. Urquia ML, Qiao Y, Ray JG, Liu C, Hjern A. **Birth outcomes of foreign-born, native-born, and mixed couples in Sweden**. *Paediatr Perinat Epidemiol* (2015.0) **29** 123-130. DOI: 10.1111/ppe.12179
7. Okui T, Nakashima N. **Analysis of Differences in Preterm Birth Rates According to Household Occupation in Japan From 2007 to 2019**. *J Prev Med Public Health* (2022.0) **55** 371-378. DOI: 10.3961/jpmph.22.178
8. Fujiwara T, Ito J, Kawachi I. **Income inequality, parental socioeconomic status, and birth outcomes in Japan**. *Am J Epidemiol* (2013.0) **177** 1042-1052. DOI: 10.1093/aje/kws355
9. Babazono A, Tsuda T, Amamoto E, Mino Y, Babazono T, Kishi Y, Sigemi J, Ogawa T, Aoyama H. **Risk Factors for Low Birth Weight Infants in Japan**. *J Epidemiol* (1994.0) **4** 91-98. DOI: 10.2188/jea.4.91
10. 10.Ministry of Justice. Foreign national residents by nationality. Available from: https://www.e-stat.go.jp/stat-search/files?page=1&toukei=00250012&tstat=000001018034. [Cited 07 12 2022].
11. 11.Yamauchi M. Recent fertility pattern among the immigrant population in Japan. J Popul Probl. 2010;66:41:59. Japanese.
12. Korekawa Y. **An impact of settlements of foreigners on mortality trend; analysis by changing of the foreign population classified by statuses of residence**. *J Population Stud* (2011.0) **47** 23
13. Acevedo-Garcia D, Soobader MJ, Berkman LF. **The differential effect of foreign-born status on low birth weight by race/ethnicity and education**. *Pediatrics* (2005.0) **115** e20-30. DOI: 10.1542/peds.2004-1306
14. Vik ES, Aasheim V, Schytt E, Small R, Moster D, Nilsen RM. **Stillbirth in relation to maternal country of birth and other migration related factors: a population-based study in Norway**. *BMC Pregnancy Childbirth* (2019.0) **19** 5. DOI: 10.1186/s12884-018-2140-3.PMID:30611227;PMCID:PMC6321699
15. Singh GK, Yu SM. **Adverse pregnancy outcomes: differences between US- and foreign-born women in major US racial and ethnic groups**. *Am J Public Health* (1996.0) **86** 837-843. DOI: 10.2105/ajph.86.6.837.PMID:8659659;PMCID:PMC1380404
16. Flores ME, Simonsen SE, Manuck TA, Dyer JM, Turok DK. **The "Latina epidemiologic paradox": contrasting patterns of adverse birth outcomes in U.S.-born and foreign-born Latinas**. *Womens Health Issues* (2012.0) **22** e501-7. DOI: 10.1016/j.whi.2012.07.005
17. Kita S, Minatani M, Hikita N, Matsuzaki M, Shiraishi M, Haruna M. **A Systematic Review of the Physical, Mental, Social, and Economic Problems of Immigrant Women in the Perinatal Period in Japan**. *J Immigr Minor Health* (2015.0) **17** 1863-1881. DOI: 10.1007/s10903-015-0192-2
18. Kim YN, Choi DW, Kim DS, Park EC, Kwon JY. **Maternal age and risk of early neonatal mortality: a national cohort study**. *Sci Rep* (2021.0) **11** 814. DOI: 10.1038/s41598-021-80968-4.PMID:33436971;PMCID:PMC7804272
19. 19.Yorifuji T, Kashima S, Doi H. Outdoor air pollution and term low birth weight in Japan. Environ Int. 2015;74:106–11. 10.1016/j.envint.2014.09.003. Epub 2014 Oct 22. Erratum in: Environ Int. 2015;78:95–6.
20. Auger N, Luo ZC, Platt RW, Daniel M. **Do mother's education and foreign born status interact to influence birth outcomes? Clarifying the epidemiological paradox and the healthy migrant effect**. *J Epidemiol Community Health* (2008.0) **62** 402-409. DOI: 10.1136/jech.2007.064535.PMID:18413452.yorifujioutdoorairpollution
21. Chen W, Shi J, Qian L, Azen SP. **Comparison of robustness to outliers between robust poisson models and log-binomial models when estimating relative risks for common binary outcomes: a simulation study**. *BMC Med Res Methodol* (2014.0) **26** 82. DOI: 10.1186/1471-2288-14-82
22. Kowarik A, Templ M. **Imputation with the R Package VIM**. *J Stat Softw* (2016.0) **74** 1-16. DOI: 10.18637/jss.v074.i07
23. Bartsch E, Park AL, Jairam J, Ray JG. **Concomitant preterm birth and severe small-for-gestational age birth weight among infants of immigrant mothers in Ontario originating from the Philippines and East Asia: a population-based study**. *BMJ Open* (2017.0) **7** e015386. DOI: 10.1136/bmjopen-2016-015386
24. Barros FC, RabelloNeto DL, Villar J, Kennedy SH, Silveira MF, Diaz-Rossello JL, Victora CG. **Caesarean sections and the prevalence of preterm and early-term births in Brazil: secondary analyses of national birth registration**. *BMJ Open* (2018.0) **8** e021538. DOI: 10.1136/bmjopen-2018-021538
25. Walani SR. **Global burden of preterm birth**. *Int J Gynaecol Obstet* (2020.0) **150** 31-33. DOI: 10.1002/ijgo.13195
26. Ferreira ÉK, de Almeida MF, Alencar GP, da Silva ZP. **Live births of immigrant mothers in Brazil: A population-based study**. *J Migr Health* (2022.0) **20** 100108. DOI: 10.1016/j.jmh.2022.100108
27. 27.World Health Organization. Born too soon: the global action report on preterm birth: World Health Organization; 2012. Available from: https://apps.who.int/iris/handle/10665/44864. [Cited 07 12 2022].
28. 28.Hashimoto H, Ito K, Yamaji Y, Sasaki Y, Murashima S, Yanagisawa S. Difficulties of pregnancy, delivery, and child raising for immigrant women in Japan and their strategies for overcoming them. Kokusai Hoken Iryo (J Int Health). 2011;26(4):281–93. Japanese.
29. Osawa E, Kodama T. **Regional socio-environmental characteristics associated with inadequate prenatal care during pregnancy: an ecological study of 47 prefectures in Japan**. *BMC Pregnancy Childbirth* (2021.0) **21** 619. DOI: 10.1186/s12884-021-04100-0
30. Vintzileos AM, Ananth CV, Smulian JC, Scorza WE, Knuppel RA. **The impact of prenatal care in the United States on preterm births in the presence and absence of antenatal high-risk conditions**. *Am J Obstet Gynecol* (2002.0) **187** 1254-1257. DOI: 10.1067/mob.2002.127140
31. Song Q, Chen J, Zhou Y, Li Z, Li H, Liu J. **Preterm delivery rate in China: a systematic review and meta-analysis**. *BMC Pregnancy Childbirth* (2022.0) **22** 383. DOI: 10.1186/s12884-022-04713-z.PMID:35501738;PMCID:PMC9063297
32. Chang Y, Kang HY, Lim D, Cho HJ, Khang YH. **Long-term trends in smoking prevalence and its socioeconomic inequalities in Korea, 1992–2016**. *Int J Equity Health* (2019.0) **18** 148. DOI: 10.1186/s12939-019-1051-x
33. Reitsma MB, Flor LS, Mullany EC, Gupta V, Hay SI, Gakidou E. **Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and initiation among young people in 204 countries and territories, 1990–2019**. *Lancet Public Health* (2021.0) **6** e472-e481. DOI: 10.1016/S2468-2667(21)00102-X
34. Tomioka K, Kurumatani N, Saeki K. **The Association Between Education and Smoking Prevalence, Independent of Occupation: A Nationally Representative Survey in Japan**. *J Epidemiol* (2020.0) **30** 136-142. DOI: 10.2188/jea.JE20180195
35. Behboudi-Gandevani S, Bidhendi-Yarandi R, Panahi MH, Mardani A, Paal P, Prinds C, Vaismoradi M. **Adverse Pregnancy Outcomes and International Immigration Status: A Systematic Review and Meta-analysis**. *Ann Glob Health* (2022.0) **88** 44. DOI: 10.5334/aogh.3591
36. Morisaki N, Kawachi I, Oken E, Fujiwara T. **Parental Characteristics can Explain Why Japanese Women Give Birth to the Smallest Infants in the United States**. *Paediatr Perinat Epidemiol* (2016.0) **30** 473-8. DOI: 10.1111/ppe.12308
37. Yusuf KK, Dongarwar D, Alagili DE, Maiyegun SO, Salihu HM. **Temporal trends and risk of small for gestational age (SGA) infants among Asian American mothers by ethnicity**. *Ann Epidemiol* (2021.0) **63** 79-85. DOI: 10.1016/j.annepidem.2021.07.004
38. Morisaki N, Kawachi I, Oken E, Fujiwara T. **Social and anthropometric factors explaining racial/ethnical differences in birth weight in the United States**. *Sci Rep* (2017.0) **21** 46657. DOI: 10.1038/srep46657.PMID:28429791;PMCID:PMC5399358
39. 39.Mine T, Tsuboi S, Fukushima, F. Twenty-year trends of low birth weight in japan: a joinpoint regression analysis of data from 2000 to 2019. Front Reprod Health. 2021;3:772575. 10.3389/frph.2021.772575.
40. Takemoto Y, Ota E, Yoneoka D, Mori R, Takeda S. **Japanese secular trends in birthweight and the prevalence of low birthweight infants during the last three decades: A population-based study**. *Sci Rep* (2016.0) **9** 31396. DOI: 10.1038/srep31396
41. Kato N, Sauvaget C, Yoshida H, Yokoyama T, Yoshiike N. **Factors associated with birthweight decline in Japan (1980–2004)**. *BMC Pregnancy Childbirth* (2021.0) **21** 337. DOI: 10.1186/s12884-021-03819-0
42. Murai U, Nomura K, Kido M, Takeuchi T, Sugimoto M, Rahman M. **Pre-pregnancy body mass index as a predictor of low birth weight infants in Japan**. *Asia Pac J Clin Nutr* (2017.0) **26** 434-437. DOI: 10.6133/apjcn.032016.11
43. Suzuki K, Nomura K, Takenoshita S, Ando K, Kido M. **Combination of parity and pre-pregnancy BMI and low birth weight infants among Japanese women of reproductive age**. *Ind Health* (2016.0) **54** 515-520. DOI: 10.2486/indhealth.2016-0088
44. 44.Ministry of Health, Labour, and Welfare. National health and nutrition survey. Available from: https://www.e-stat.go.jp/stat-search/files?page=1&toukei=00450171&tstat=000001041744. [Cited 07 12 2022].
45. Shin HY, Kang HT. **Recent trends in the prevalence of underweight, overweight, and obesity in Korean adults: The Korean National Health and Nutrition Examination Survey from 1998 to 2014**. *J Epidemiol* (2017.0) **27** 413-419. DOI: 10.1016/j.je.2016.08.014
46. He Y, Pan A, Yang Y, Wang Y, Xu J, Zhang Y, Liu D, Wang Q, Shen H, Zhang Y, Yan D, Peng Z, Hu FB, Ma X. **Prevalence of Underweight, Overweight, and Obesity Among Reproductive-Age Women and Adolescent Girls in Rural China**. *Am J Public Health* (2016.0) **106** 2103-2110. DOI: 10.2105/AJPH.2016.303499
47. Wang M, Xu PS, Liu W, Zhang C, Zhang X, Wang L, Liu J, Zhu Z, Hu J, Luo PX, Wang PW. **Prevalence and changes of BMI categories in China and related chronic diseases: Cross-sectional National Health Service Surveys (NHSSs) from 2013 to 2018**. *EClinicalMedicine* (2020.0) **11** 100521. DOI: 10.1016/j.eclinm.2020.100521
|
---
title: Implementation of an antimicrobial stewardship program in the Vascular Surgery
ward of a university tertiary care hospital in Pavia, Northern Italy
authors:
- Marco Vecchia
- Marta Colaneri
- Paolo Sacchi
- Lea Nadia Marvulli
- Andrea Salvaderi
- Jessica Lanza
- Stefano Boschini
- Franco Ragni
- Piero Marone
- Sara Cutti
- Alba Muzzi
- Carlo Marena
- Monica Calvi
- Luigia Scudeller
- Enrico Maria Marone
- Raffaele Bruno
journal: BMC Infectious Diseases
year: 2023
pmcid: PMC9993681
doi: 10.1186/s12879-023-08061-x
license: CC BY 4.0
---
# Implementation of an antimicrobial stewardship program in the Vascular Surgery ward of a university tertiary care hospital in Pavia, Northern Italy
## Abstract
### Purpose
The commitment of multidisciplinary teams in antimicrobial stewardship programs (ASPs) is often inadequately considered, especially in surgical wards. We wanted to evaluate clinical, microbiological, and pharmacological outcomes before and after the implementation of an ASP in the Vascular Surgery ward of Fondazione IRCCS Policlinico San Matteo, a tertiary care hospital in Pavia, Italy.
### Methods
This was a quasi-experimental quality-improvement study. The antimicrobial stewardship activity was conducted twice a week for 12 months and consisted of both prospective audit and feedback of all the ongoing antimicrobial prescriptions by the infectious diseases’ consultants and educational meetings for the healthcare workers of the Vascular Surgery ward. For comparison between the study periods, Student t test (Mann–Whitney test for skewed distributions) was used for quantitative variables (ANOVA or Kruskall-Wallis for > 2 groups respectively), and Pearson’s chi-squared test (Fisher exact test where appropriate) for categorical variables. 2-tailed tests were used. P-value significance cut-off was 0.05.
### Results
During the 12-month intervention period, among a total number of 698 patients, 186 prescriptions were revised, mostly leading to de-escalating an ongoing antimicrobial therapy (39, $20.97\%$). A statistically significant reduction in isolates of carbapenem-resistant *Pseudomonas aeruginosa* (p-value 0.003) and the absence of Clostridioides difficile infections were reported. No statistically significant changes were observed in terms of length of stay and all-cause in-hospital mortality. A significant decrease in the administration of carbapenems (p-value 0.01), daptomycin (p-value < 0.01) and linezolid (p-value 0.43) was registered. A significant reduction in antimicrobial costs was also observed.
### Conclusions
The implementation of a 12-month ASP brought significant clinical and economic results, highlighting the benefits of a multidisciplinary teamwork.
## Background
It has currently become a commonly accepted wisdom that antimicrobial resistance (AMR) is a major threat to global health [1], and infections caused by multidrug resistant organisms (MDRO) are undoubtedly associated with increased morbidity, mortality and healthcare costs [2, 3]. Hence, since the causal role of antimicrobial use in the development of antimicrobial resistance is not questionable [4], antimicrobial stewardship programs (ASPs), aimed at optimizing antimicrobial consumption, are increasingly in demand.
However, a key point which appears to be as essential as rarely stressed, is the extent of multidisciplinary teams in achieving these interventions [5]. This means that it is not the infectious diseases (ID) specialist who merely imparts an over specific expertise; rather, the multidisciplinary stewardship team would include other specialists, who may take the occasion to become more aware of the AMR threat and consequently keen to improve the management of antimicrobial drugs in their wards of competence.
This topic applies not only to clinical wards, but also and especially to surgical ones, as for some of the most common surgical conditions, an infectious aetiology can be recognized [6]. Particularly in the Vascular Surgery ward, patients with chronic peripheral artery disease (PAD), often associated with insufficient glycaemic control and neuropathy are at increased risk of acquiring MDRO infections, thus frequently needing multiple antibiotic treatments [7]; moreover, surgical procedures expose them to surgical site infections.
With this in mind, we hereby sought to firstly evaluate how the implementation of an ASP based on a multidisciplinary team impacted on the occurrence of MDRO infections in the Vascular Surgery ward of our tertiary care hospital in Pavia, Northern Italy. Secondly, we evaluated how this ASP affected some hospital indicators of healthcare quality, such as the number of admissions in the Vascular Surgery ward for at least 48 h, the mean length of stay (LOS), the all-cause in-hospital mortality rate, the antimicrobial consumption, and the costs. Specifically, we selected readily recognisable measures, that are not only straightforward to evaluate before, during and after the intervention, but more importantly, potentially feasible for all surgical and non-surgical wards.
## Study design, settings, and duration
This quasi-experimental quality-improvement study was applied in the Vascular Surgery ward of Fondazione IRCCS Policlinico San Matteo, a tertiary care hospital in Pavia, Italy.
The study was conceived and designed according to the SQUIRE 2.0 guidelines [8], with the aim to evaluate clinical, microbiological, and pharmacological outcomes by comparing a 12-month baseline period prior to the implementation of the ASP (Period A) to a 12-month period following its start (Period B). Specifically, Period A covered from the 1st July 2017 to the 30th June 2018, while Period B ranged from the 1st July 2018 to the 30th June 2019.
Data related to Period A for comparison were retrospectively collected from the hospital digital data warehouse, clinical records, and discharge letters. The monthly antibiotic and antifungal consumptions were inferred from the dispensation from the hospital pharmacy to the Vascular Surgery ward and converted in Defined Daily Dose (DDDs) per 100 patient days (PDs), according to the WHO definition.
## Intervention
Our antimicrobial stewardship intervention was based on the presence of at least two ID consultants in the Vascular Surgery ward twice a week (on Monday and Thursday afternoons) for about two hours per day and was continued for a 12-month period (Period B). It included two types of enabling elements:Prospective audit and feedback. For every patient hospitalised for at least 48 h and receiving at least one antibiotic and/or antifungal drug for therapeutic purposes during the intervention activity, a revision of the antimicrobial prescription was conducted through an active discussion among the two ID consultants and a resident or a senior surgeon, resulting in a written consultation included in the medical record of the patient. Each evaluation considered the clinical picture, blood tests, radiological exams and microbiological results. Antimicrobials prescribed for surgical prophylaxis were not revised. Decisions on the prescriptions were coded as follows: Antimicrobials not recommended; Stop antimicrobials; De-escalate antimicrobials (by switching from parenteral to oral, narrowing the spectrum of activity or reducing the number of drugs administered); Change antimicrobials; Change dosage of antimicrobials; Continue antimicrobials; Start antimicrobials; Escalate antimicrobials (by switching from oral to parenteral, broadening the spectrum of activity, increasing the number of drugs administered).Educational meetings about antimicrobial stewardship and infection control. During Period B, monthly meetings were organized by the ID consultants to increase knowledge about AMR, hospital-acquired infections and infection control. Particularly, the consultants showed both medical and non-medical healthcare workers staff (i.e., nurses and auxiliary staff), the basic principles of patient contact isolation, cohorting, hand hygiene and the use of personal protection equipment (PPE) in case of patients either colonized or infected with MDRO or Clostridioides difficile.
## Measures
The primary outcome of the study was to evaluate the impact of the implementation of an ASP in the Vascular Surgery ward on the occurrence of MDRO infections.
Secondary outcomes included changes before, during and after the implementation of the ASP in terms of number of admissions in the Vascular Surgery ward for at least 48 h, length of stay (LOS), all-cause in-hospital mortality rate, antimicrobial consumption, and costs.
## Definitions
Definitions used in the paper and codes for the types of infections diagnosed in the Vascular Surgery ward during the study period are summarized in Table 1.Table 1Summary of definitions usedDefinitionReferences and guidelinesAcute bacterial skin and skin structure infection (ABSSSI) and/or Surgical site infection (SSI)ABSSSI: A bacterial infection of the skin with a lesion size area of at least 75 cm2 (lesion size measured by the area of redness, oedema and induration)SSI: An infection related to a surgical procedure that occurs near the surgical site within 30 days following surgery (or up to 90 days following surgery where an implant is involved)FDA [9]CDC [10]BacteraemiaThe presence of viable bacteria in the blood documented by a positive blood culture resultCDC [11]Defined daily dose (DDD)The assumed average maintenance dose per day for a drug used for its main indication in adults. For antimicrobial consumption in hospital, the indicator DDD/100 patients-days is generally usedWHO [12]Diabetic foot infection (DFI)Foot wound infection in diabetic patients defined by the presence of at least two classic findings of inflammation or purulent secretionsIDSA [13]Hospital-acquired pneumonia (HAP)Pneumonia acquired ≥ 48 h after hospital admissionIDSA [14]Intra-abdominal infections (IAIs)Infections that develop within the abdominal cavity, usually classified into uncomplicated and complicatedWSES [15]Multidrug-resistant organisms (MDRO)Microorganisms, predominantly bacteria, that are resistant to one or more classes of antimicrobial agentsCDC [16]No infection/ColonisationAbsence of local or systemic signs and symptoms of infection, even in presence of bacterial isolationSepsisLife-threatening organ dysfunction caused by a dysregulated host response to infectionSinger et al., 2016 [17]*Septic arthritis* and/or Spinal infectionsSeptic arthritis: An acute inflammation involving one or more joints on an infectious basis confirmed by a positive culture of synovial fluidSpinal infections: Include vertebral, discitis and spondylodiscitisColston at Atkins, 2018 [18]EANM/ESNR/ESCMID [19]Urinary tract infections (UTIs)Infections involving any part of the urinary system. They include both lower (cystitis) and upper (pyelonephritis) UTIs and are classified into uncomplicated and complicatedEAU [20]
## Statistical analysis
Descriptive statistics were produced for all variables. Mean and standard deviation (SD) are presented for normally distributed variables, and median and interquartile range (IQR) for non-normally distributed variables, numbers, and percentages for categorical variables. Whenever relevant, $95\%$ confidence intervals ($95\%$ CI) were calculated. Shapiro Wilk’s and Kolmogorov–Smirnov test, as well as visual methods, were applied to test for normality.
For comparison between the study periods, Student t test (Mann–Whitney test for skewed distributions) was used for quantitative variables (ANOVA or Kruskall-Wallis for > 2 groups respectively), and Pearson’s chi-squared test (Fisher exact test where appropriate) for categorical variables. In all cases, 2-tailed tests were used. P-value significance cut-off was 0.05.
Stata computer software version 15.0 (Stata Corporation, 4905 Lakeway Drive, College Station, Texas 77845, USA) was used for statistical analysis.
## Results
Between the 1st July 2018 and the 30th June 2019, among a total number of 698 patients admitted in the Vascular Surgery ward for at least 48 h, 108 ($15.67\%$) patients received at least one evaluation from the ID consultants, for a maximum of six different evaluations for a single patient. The median age was 71 (IQR 63–78) and 79 patients ($73.15\%$) were males. The most prevalent underlying disease was of cardiovascular aetiology, followed by diabetes and metabolic comorbidities (Table 2).Table 2Characteristics of patients evaluated in the Vascular Surgery wardVariablesOverall ($$n = 108$$)Age (y), median (IQR)71 (63–78)Gender (male), n (%)79 (73.15)Comorbidities Cardiovascular, n (%)102 (94.44) Diabetes, n (%)48 (44.44) Haematologic, n (%)5 (4.63) HCV, n (%)4 (3.70) HIV, n (%)1 (0.93) Immunologic, n (%)4 (3.70) Metabolic, n (%)59 (54.63) Neurologic, n (%)4 (3.70) Oncologic, n (%)3 (2.78) Osteoarticular, n (%)3 (2.78) Respiratory, n (%)15 (13.89) Urologic, n (%)17 (15.74)Type of infection evaluated ABSSSI and/or surgical site infection, n (%)52 (48.15) Bacteremia, n (%)2 (1.85) Diabetic foot infection, n (%)10 (9.26) Hospital-acquired pneumonia, n (%)15 (13.89) Intra-abdominal infection, n (%)7 (6.48) No infection/colonization, n (%)13 (12.04) Sepsis, n (%)1 (0.93) *Septic arthritis* and/or osteomyelitis, n (%)6 (5.55) Urinary tract infection, n (%)2 (1.85) Overall, 186 prescriptions were revised. The most prevalent infectious condition evaluated during the intervention period was ABSSSI and/or surgical site infection, accounting for 52 patients ($48.15\%$), followed by HAP (15 patients, $13.89\%$) (Table 2).
Regarding the outcomes of the prescriptions’ revisions, those bringing to a de-escalation of an ongoing antimicrobial therapy were the most prevalent (39 revisions, $20.97\%$), followed by decisions to start an antimicrobial therapy (34 revisions, $18.28\%$) and to escalate (32 revisions, $17.20\%$). Antimicrobial treatments were left unmodified in 31 revisions ($16.67\%$) and were discontinued or not recommended in 22 and 16 revisions, respectively ($11.83\%$ and $8.60\%$). Finally, antimicrobial choices and dosages were modified in 9 and 3 revisions ($4.84\%$ and $1.61\%$, respectively) (Fig. 1). As a result, antimicrobials were discontinued, not prescribed or de-escalated in 77 revisions ($41.4\%$).Fig. 1Outcomes of the antimicrobial prescriptions’ revisions
## Occurrence of MRDO isolates
Comparing Period A and Period B, we reported an overall decrease of the total occurrence of MDRO isolates, although it did not reach a statistically significance. Among carbapenem-resistant pathogens, only *Pseudomonas aeruginosa* isolates showed a statistically significant decrease (p-value 0.003, Table 3). Occurrence of ESBL-producing enteric Gram-negative bacteria also decreased but not significantly. Remarkably, during the intervention period no Clostridioides difficile infections were reported. Table 3Summary of the study outcomesPeriod A (1st July 2017–30th June 2018)Period B (1st July 2018–30th June 2019)p-valuePatients admitted for at least 48 h, n7096980.67Occurrence of MDRO infections Total MDRO isolates, n53390.22 Carbapenem-resistant Acinetobacter baumannii, n010.25 Carbapenem-resistant Klebsiella pneumoniae, n760.42 Carbapenem-resistant Pseudomonas aeruginosa, n71 < 0.01 Clostridioides difficile, n200.10 ESBL-producing enteric Gram-negative bacteria, n18140.59 Methicillin-resistant Staphylococcus aureus, n19160.37 Vancomycin-resistant enterococci, n210.60Length of in-hospital stay, mean9.469.80.60All-cause in-hospital mortality, n (%)19 (2.68)15 (2.15)0.50Antibiotic consumption Carbapenems, DDDs*100 PDs, mean4.531.510.01 Daptomycin, DDDs*100 PDs, mean2.640.05 < 0.01 Clindamycin, DDDs*100 PDs, mean0.333.34 < 0.01Antibiotic costs, euros54,876.4421,777.260.03ESBL extended-spectrum beta-lactamase*DDDs: Defined Daily Dose
## Secondary outcomes
No statistically significant changes before and after the implementation of the ASP in the Vascular Surgery ward were observed in terms of amount of admittances, length of stay and all-cause in-hospital mortality (Table 3).
During the 12-month period following the start of the ASP, we noticed an improvement in the antimicrobial prescription appropriateness. More precisely, our intervention was particularly effective in reducing the administration of carbapenems, daptomycin and linezolid. The monthly mean DDDs*100 PDs of carbapenems decreased from 4.53 to 1.51 (p-value 0.01) and daptomycin DDDs*100 PDs from 2.64 to 0.05 (p-value < 0.01, see Table 3). Linezolid DDDs*100 PDs also diminished from 0.49 to 0.26 (p-value 0.43, data not shown).
Glycopeptides, specifically vancomycin, were used as anti-MRSA agents in place of daptomycin, so we noticed a non-statistically significant increase of administered DDDs*100 PDs in concomitance with our intervention (p-value 0.14, data not shown). Similarly, consumptions of both trimethoprim/sulfametoxazole (TMP/SMX) and fluoroquinolones increased non significantly: TMP/SMX DDDs increased from 3,953 to 7,191 (p-value 0.0584, data not shown) whereas fluoroquinolones DDDs increased from 9,1893 to 13,408 (p-value 0.2553, data not shown). Oral fosfomycin was introduced in the clinical practice of the Vascular Surgery ward to treat uncomplicated lower urinary tract infections, in place of beta-lactams. In particular, DDDs*100 PDs of fosfomycin increased from 0 to 0.65 (p-value 0.0258, data not shown).
Penicillins had a non-significant increase of administration, from 33,92 to 36.91 DDDs*100 PDs (p-value 0.9372, data not shown), as third generation cephalosporins, which moved from 3,52 to 4,29 DDDs*100 PDs (p-value 0.6649, data not shown). As regards antifungals, we noticed a non-significant decrease of DDDs*100 PDs from 0.23 to 0.15 (p-value 0.96, data not shown).
During the intervention period, a significant reduction in antimicrobial costs was observed. In particular, the total cost of antimicrobial drugs prescribed in the Vascular Surgery ward after the start of the ASP was equal to 21.777,26 €, with a net difference of 33.099,18 € (60,$31\%$; p value 0.03, see Table 3) compared to the previous 12 months (54.876,44 €).
## Discussion
After performing a 12-month period of antimicrobial stewardship intervention in the Vascular Surgery ward of our hospital, we have certainly achieved some positive results, both from a microbiological and economic perspective. On the one hand, in Period B we reported a minor occurrence of carbapenem-resistant *Pseudomonas aeruginosa* isolates and the absence of infections caused by Clostridioides difficile. On the other hand, we also observed a significant reduction in the consumption of some broad-spectrum and expensive antimicrobial drugs compared to Period A, with no adverse influence on LOS and mortality. These findings are in line with the avowed concept of the utility of ASPs in minimizing antimicrobial resistance and Clostridioides difficile diarrhoea [21]. Hence, since the strong correlation between the multidrug-resistant *Pseudomonas aeruginosa* occurrence and broad-spectrum antimicrobial consumption, a substantial decline in the prescription of these drugs, which is accomplished by ASPs, might effectively control the occurrence of such resistant microorganism [22].
Regarding antimicrobial costs, a recent narrative review thoroughly discussed the most recent evidence on the positive consequences of ASPs for healthcare systems, concluding that, despite ASPs would be cost-effectiveness, the majority of the available studies do not deal with cost–utility analyses, rendering an unambiguous evaluation difficult [23]. In our particular case, though, we have to consider that the model of stewardship intervention we implemented has not required any further costs. In fact, our ASP can be classified as a “handshake stewardship” strategy, as it is centered on the in-person approach to feedback and thus it is based on the commitment of the two ID consultants in terms of number of hours spent weekly in the surgical ward [24].
Along with considering the achieved results as a prompt to reflect on the magnitude and apparent simplicity of ASPs the strength of our work resides in laying the groundwork for a team effort. In fact, we believe that a successful ASP cannot be indeed achieved without the support and joint collaborative effort between different hospital units; this is favoured by the direct communication promoted by a model of stewardship as ours, which includes some of the core elements required for an ASP, namely multidisciplinary teamwork, accountability, enabling actions and education [25].
It could be argued that, despite our work has been conceived as a quality-improvement study, no changes have been observed in terms of LOS and all-cause in-hospital mortality, which are part of the so-called outcome measures. However, it has been recognised that these kinds of indicators may be misleading in evaluating the quality of hospital care; instead, clinical process measures, such as the formerly mentioned decline in occurrence of healthcare-associated infections, must be preferred, as they are a direct measure of performance based on adherence to established clinical standards [26]. In fact, in our study a solid effect on these measures has been reported, so we can assert with good reason that an improvement in the quality of care of the Vascular Surgery ward has been obtained thanks to the implementation of the ASP.
We should not neglect some of the limitations of our study. Firstly, the retrospective nature of the data collected for Period A may have influenced the outcome, since unrecognised confounders cannot be excluded. Secondly, at the time the study was conducted the Pharmacy Unit of our Hospital lacked a computerized drugs prescription and administration system. For this reason, the data of antimicrobial consumption were inferred from the amount dispensed from the Pharmacy Unit to the Vascular Surgery ward and thus may not accurately reflect the real usage trend. Thirdly, we did not analyse the extent of antimicrobial prescriptions following the patients’ discharge. These data are a significant marker for antimicrobial exposure and may impact both colonization and infections caused by MDRO.
## Conclusions
In conclusion, the implementation of a 12-month antimicrobial stewardship program brought significant clinical and economic results in the Vascular Surgery ward of our hospital, highlighting the benefits of a multidisciplinary teamwork and the importance of the commitment of each actor involved, both medical and non-medical.
We hope to further extend this model of antimicrobial stewardship program to a hospital level, possibly integrating it with modern technologies intended to help the prescription activity and the collection of data for additional evaluations.
## References
1. TalebiBezmin Abadi A, Rizvanov AA, Haertlé T, Blatt NL. **World Health Organization report: current crisis of antibiotic resistance**. *BioNanoScience* (2019.0) **9** 778-788. DOI: 10.1007/s12668-019-00658-4
2. Lambert ML, Suetens C, Savey A, Palomar M, Hiesmayr M, Morales I, Agodi A, Frank U, Mertens K, Schumacher M, Wolkewitz M. **Clinical outcomes of health-care-associated infections and antimicrobial resistance in patients admitted to European intensive-care units: a cohort study**. *Lancet Infect Dis* (2011.0) **11** 30-38. DOI: 10.1016/S1473-3099(10)70258-9
3. Martin-Loeches I, Torres A, Rinaudo M, Terraneo S, de Rosa F, Ramirez P, Diaz E, Fernández-Barat L, Li Bassi GL, Ferrer M. **Resistance patterns and outcomes in intensive care unit (ICU)-acquired pneumonia. Validation of European Centre for Disease Prevention and Control (ECDC) and the Centers for Disease Control and Prevention (CDC) classification of multidrug resistant organisms**. *J Infect* (2015.0) **70** 213-222. DOI: 10.1016/j.jinf.2014.10.004
4. Tammer I, Geginat G, Lange S, Kropf S, Lodes U, Schlüter D, Lippert H, Meyer F. **Antibiotic consumption and the development of antibiotic resistance in surgical units**. *Zentralbl Chir* (2016.0) **141** 53-61. DOI: 10.1055/s-0033-1351087
5. Manuel Vázquez A, Balibrea JM, Ramia JM. **Antimicrobial stewardship programs and surgery: What is our role?**. *Cir Esp* (2019.0) **97** 187-189. DOI: 10.1016/j.ciresp.2018.07.008
6. Leeds IL, Fabrizio A, Cosgrove SE, Wick EC. **Treating wisely: the surgeon’s role in antibiotic stewardship**. *Ann Surg* (2017.0) **265** 871-873. DOI: 10.1097/SLA.0000000000002034
7. Zamani N, Sharath SE, Barshes NR, Braun JD, Kougias P. **Long-term outcomes of lower extremity graft preservation using antibiotic beads in patients with early deep wound infections after major arterial reconstructions**. *J Vasc Surg* (2020.0) **71** 1315-1321. DOI: 10.1016/j.jvs.2019.06.192
8. Ogrinc G, Davies L, Goodman D, Batalden P, Davidoff F, Stevens D. **SQUIRE 2.0 (Standards for QUality Improvement Reporting Excellence): revised publication guidelines from a detailed consensus process**. *BMJ Qual Saf* (2016.0) **25** 986-992. DOI: 10.1136/bmjqs-2015-004411
9. 9.U.S. Department of Health and Human Services, Food and Drug Administration, and Center for Drug Evaluation and Research, ‘Acute Bacterial Skin and Skin Structure Infections: Developing drugs for treatment’, 2013. p. 18. https://www.fda.gov/files/drugs/published/Acute-Bacterial-Skin-and-Skin-Structure-Infections---Developing-Drugs-for-Treatment.pdf. Accessed 03 Apr 2022.
10. Borchardt RA, Tzizik D. **Update on surgical site infections: the new CDC guidelines**. *JAAPA* (2018.0) **31** 52-54. DOI: 10.1097/01.JAA.0000531052.82007.42
11. Garner JS, Jarvis WR, Emori TG, Horan TC, Hughes JM. **CDC definitions for nosocomial infections, 1988**. *Am J Infect Control* (1988.0) **16** 128-140. DOI: 10.1016/0196-6553(88)90053-3
12. 12.WHO Collaborating Centre for Drug Statistics Methodology, ‘Guidelines for ATC classification and DDD assignment 2022’, 2022. p. 250,. https://www.whocc.no/atc_ddd_index_and_guidelines/guidelines/. Accessed 03 Apr 2022.
13. Lipsky BA, Berendt AR, Cornia PB, Pile JC, Peters EJ, Armstrong DG, Deery HG, Embil JM, Joseph WS, Karchmer AW, Pinzur MS, Senneville E. **Infectious Diseases Society of America, ‘2012 infectious diseases society of america clinical practice guideline for the diagnosis and treatment of diabetic foot infections’**. *Clin Infect Dis* (2012.0) **54** e132-e173. DOI: 10.1093/cid/cis346
14. Kalil AC, Metersky ML, Klompas M, Muscedere J, Sweeney DA, Palmer LB, Napolitano LM, O'Grady NP, Bartlett JG, Carratalà J, El Solh AA, Ewig S, Fey PD, File TM, Restrepo MI, Roberts JA, Waterer GW, Cruse P, Knight SL, Brozek JL. **Management of adults with hospital-acquired and ventilator-associated pneumonia: 2016 clinical practice guidelines by the infectious diseases society of America and the American Thoracic Society**. *Clin Infect Dis* (2016.0) **63** e61-e111. DOI: 10.1093/cid/ciw353
15. Sartelli M, Chichom-Mefire A, Labricciosa FM, Hardcastle T, Abu-Zidan FM, Adesunkanmi AK, Ansaloni L, Bala M, Balogh ZJ, Beltrán MA, Ben-Ishay O, Biffl WL, Birindelli A, Cainzos MA, Catalini G, Ceresoli M, Che Jusoh A, Chiara O, Coccolini F, Coimbra R, Cortese F, Demetrashvili Z, Di Saverio S, Diaz JJ, Egiev VN, Ferrada P, Fraga GP, Ghnnam WM, Lee JG, Gomes CA, Hecker A, Herzog T, Kim JI, Inaba K, Isik A, Karamarkovic A, Kashuk J, Khokha V, Kirkpatrick AW, Kluger Y, Koike K, Kong VY, Leppaniemi A, Machain GM, Maier RV, Marwah S, McFarlane ME, Montori G, Moore EE, Negoi I, Olaoye I, Omari AH, Ordonez CA, Pereira BM, Pereira Júnior GA, Pupelis G, Reis T, Sakakhushev B, Sato N, Segovia Lohse HA, Shelat VG, Søreide K, Uhl W, Ulrych J, Van Goor H, Velmahos GC, Yuan KC, Wani I, Weber DG, Zachariah SK, Catena F. **The management of intra-abdominal infections from a global perspective: 2017 WSES guidelines for management of intra-abdominal infections**. *World J Emerg Surg* (2017.0) **12** 29. DOI: 10.1186/s13017-017-0141-6
16. Siegel JD, Rhinehart E, Jackson M, Chiarello L. **Healthcare infection control practices advisory committee, ‘management of multidrug-resistant organisms in healthcare settings, 2006’**. *Am J Infect Control* (2007.0) **35** 165-193. DOI: 10.1016/j.ajic.2007.10.006
17. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, Hotchkiss RS, Levy MM, Marshall JC, Martin GS, Opal SM, Rubenfeld GD, van der Poll T, Vincent JL, Angus DC. **The third international consensus definitions for sepsis and septic shock (Sepsis-3)**. *JAMA* (2016.0) **315** 801-810. DOI: 10.1001/jama.2016.0287
18. Colston J, Atkins B. **Bone and joint infection**. *Clin Med (Lond)* (2018.0) **18** 150. DOI: 10.7861/clinmedicine.18-2-150
19. Lazzeri E, Bozzao A, Cataldo MA, Petrosillo N, Manfrè L, Trampuz A, Signore A, Muto M. **Joint EANM/ESNR and ESCMID-endorsed consensus document for the diagnosis of spine infection (spondylodiscitis) in adults**. *Eur J Nucl Med Mol Imaging* (2019.0) **46** 2464-2487. DOI: 10.1007/s00259-019-04393-6
20. 20.Bonkat G, Bartoletti R, Bruyère F, Cai T, Geerlings SE, Köves B, chubert S, Pilatz A, Veeratterapillay R, Wagenlehner F, ‘EAU Guidelines on Urological Infections’, 2022. https://d56bochluxqnz.cloudfront.net/documents/full-guideline/EAU-Guidelines-on-Urological-Infections-2022.pdf. Accessed 03 Apr 2022.
21. Davey P, Brown E, Fenelon L, Finch R, Gould I, Hartman G, Holmes A, Ramsay C, Taylor E, Wilcox M, Wiffen P. **Interventions to improve antibiotic prescribing practices for hospital inpatients**. *Cochrane Database Syst Rev* (2017.0) **2** 2017. DOI: 10.1002/14651858.CD003543.pub4
22. Liu L, Liu B, Li Y, Zhang W. **Successful control of resistance in**. *Infect Drug Resist* (2018.0) **11** 637-646. DOI: 10.2147/IDR.S163853
23. Standiford HC, Chan S, Tripoli M, Weekes E, Forrest GN. **Antimicrobial stewardship at a large tertiary care academic medical center: cost analysis before, during, and after a 7-year program**. *Infect Control Hosp Epidemiol* (2012.0) **33** 338-345. DOI: 10.1086/664909
24. Hurst AL, Child J, Pearce K, Palmer C, Todd JK, Parker SK. **Handshake stewardship: a highly effective rounding-based antimicrobial optimization service**. *Pediatr Infect Dis J* (2016.0) **35** 1104-1110. DOI: 10.1097/INF.0000000000001245
25. 25.Centers for Disease Control and PreventionCore elements of hospital antibiotic stewardship programs2019AtlantaUS Department of Health and Human Services, CDC. *Core elements of hospital antibiotic stewardship programs* (2019.0)
26. Lilford R, Mohammed MA, Spiegelhalter D, Thomson R. **Use and misuse of process and outcome data in managing performance of acute medical care: avoiding institutional stigma**. *Lancet* (2004.0) **363** 1147-1154. DOI: 10.1016/S0140-6736(04)15901-1
|
---
title: Association between thyroid hormones and diabetic kidney disease in Chinese
adults
authors:
- Meng-chao Liu
- Jia-lin Li
- Yue-fen Wang
- Yuan Meng
- Zhen Cai
- Cun Shen
- Meng-di Wang
- Wen-jing Zhao
- Wen-quan Niu
journal: BMC Endocrine Disorders
year: 2023
pmcid: PMC9993682
doi: 10.1186/s12902-023-01299-1
license: CC BY 4.0
---
# Association between thyroid hormones and diabetic kidney disease in Chinese adults
## Abstract
### Objective
We aimed to explore the association between thyroid hormones and different stages of diabetic kidney disease (DKD) in Chinese adults.
### Methods
This is a retrospective study involving 2,832 participants. DKD was diagnosed and classified according to the Kidney Disease: Improving Global Outcomes (KDIGO) categories. Effect sizes are expressed as odds ratio (OR) with $95\%$ confidence interval (CI).
### Results
After propensity score matching (PSM) on age, gender, hypertension, hemoglobin A1c(HbA1c), total cholesterol (TC), serum triglyceride (TG) and duration of diabetes, per 0.2 pg/mL increment in serum free triiodothyronine (FT3) was significantly associated with $13\%$, $22\%$ and $37\%$ reduced risk of moderate-risk (OR, $95\%$ CI, P: 0.87, 0.70–0.87, < 0.001), high-risk (0.78, 0.70–0.87, < 0.001) and very-high-risk (0.63, 0.55–0.72, < 0.001) DKD stages relative to the low-risk DKD stage, respectively. After PSM analyses, serum FT4 and TSH showed no statistical significance in risk estimates for all DKD stages. To facilitate clinical application, a nomogram prediction model was established for the moderate-risk, high-risk and very-high-risk DKD stages, with decent accuracy.
### Conclusion
Our results indicate that high concentrations of serum FT3 were associated with the significantly reduced risk of having moderate-risk to very-high-risk DKD stages.
## Introduction
Diabetic kidney disease (DKD) has become the leading cause of kidney failure around the world, accounting for approximately $50\%$ of cases in developed countries [1, 2]. Recently, besides classic manifestations, some patients with DKD present with novel phenotypes, such as nonalbuminuric renal impairment and progressive renal decline, which necessitate an integrated consideration of proteinuria levels and glomerular filtration rate (GFR) in patients with DKD [3, 4]. In 2012, the Kidney Disease: Improving Global Outcomes (KDIGO) addressed a new classification of chronic kidney disease (CKD), viz. “ risk categories” [5], a combination of GFR and albuminuria [6], that has been applied widely to classify DKD in routine clinical practice [7]. This classification was once again underscored by the KDIGO Diabetes Work Group in 2020 [8]. Despite the use of existing lifestyle and pharmacological therapies, patients with diabetes mellitus are still at high risk for developing DKD [5]. Therefore, it is of clinical importance to identify which biomarkers can accurately predict the progression of DKD.
As one of the most important endocrine hormones in the human body, thyroid hormones have become a hot point of DKD-related research in recent years. A large number of studies have confirmed that hypothyroidism or subclinical hypothyroidism in patients with DKD has significantly increased in recent years [9–11], and diabetic patients with hypothyroidism have a higher risk of DKD and DR [10]. In a study of 146 patients diagnosed with diabetic nephropathy (DN) by renal biopsy, patients with high thyroid-stimulating hormone (TSH) or low free triiodothyronine (FT3) were found to have more severe proteinuria, renal insufficiency and glomerulonpathy [12]. Additionally, in an attempt to distinguish the effect of thyroid hormones on other kidney diseases, Bando and colleagues conduced a meta-analysis and found that patients with advanced DKD had a significantly higher prevalence of non-autoimmune primary hypothyroidism than patients with other types of renal insufficiency [13]. However, there is no consensus on the relationship between each thyroid hormone and DKD progression, and the association between thyroid hormones and DKD using the KDIGO risk categories is rarely reported in the medical literature.
To fill this gap in knowledge, we conducted a retrospective study to explore the association between thyroid hormones and different DKD stages in Chinese adults.
## Study participants
Participants aged 18 to 80 were chronologically and retrospectively recruited from Beijing Hospital of Traditional Chinese Medicine affiliated to Capital Medical University from January, 2011 to January 2021. Initially, a total of 16,589 patients with type 2 diabetes mellitus were enrolled in this study. After screening, 13,757 patients were excluded for the following reasons: (i) receiving renal replacement therapies; (ii) having a definite diagnosis of nondiabetic kidney disease; (iii) having diabetic acute complications, such as diabetic ketoacidosis or hyperosmolar hyperglycemic coma; (iv) having chronic diseases that may affect metabolic function, including hypothalamic disease, adrenal disease, any thyroid medication (levothyroxine or antithyroid medication), or a history of thyroid diseases prior to diabetes mellitus; (v) having severe respiratory, digestive, or hematological diseases, or current acute or severe infections, autoimmune diseases, or malignancies; (vi) lacking necessary information, such as serum creatinine and urinary albumin-to-creatinine ratio (UACR). Finally, total 2,832 eligible patients were enrolled in the present study, including 1,710 males and 1,122 females.
## Diagnosis of DKD
According to the 2012 KDIGO clinical practice guidelines, DKD was defined as urinary albumin/creatinine ratio (UACR) ≥ 30 mg/g, or an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 in the absence of signs or symptoms of other primary causes of kidney damage [6]. The eGFR was estimated based on the 2009 CKD Epidemiology Collaboration (CKD-EPI) equation [14].
## Stages of DKD
The DKD patients were divided into four stages according to the KDIGO risk categories, that is, low risk, moderate risk, high risk and very-high risk [5]. The KDIGO risk categories have two components: persistent albuminuria categories and GFR categories. The GFR categories include G1 (GFR in ml/min/1.73m2: ≥ 90), G2 (60–89), G3a (45–59), G3b (30–44), G4 (15–29) and G5 (≤ 15 or treatment by dialysis). The persistent albuminuria categories include A1 (ACR in mg/g: ≤30), A2 (30–299) and A3 (≥ 300). The G3a and G3b categories were combined into G3 due to limited sample sizes.
## Clinical and biochemical indices
Data analyzed in this study were applied and abstracted from the scientific research sharing platform (Yidu Cloud Research Collaboration Platform) of Beijing Hospital of Traditional Chinese Medicine affiliated to Capital Medical University, and all study participants were given standardized questionnaires for demographics and medical histories. Diabetes mellitus was defined as fasting plasma glucose ≥ 7.0 mmol/L, or 2-h plasma glucose ≥ 11.1mmol/l during an OGTT, or hemoglobin A1c (HbA1c) ≥ $6.5\%$, or random plasma sugar ≥ 11.1mmol/l, or the acts of taking hypoglycemic drugs or receiving parenteral insulin therapy [15]. Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg or receiving antihypertensive medication [16]. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Laboratory tests were performed for serum samples obtained by venipuncture after fasting for 8 h in patients prior to clinical treatment. Concentrations of serum thyroid hormone were measured by chemiluminescence, including triiodothyronine (T3), thyroxine (T4), FT3, free thyroxine (FT4) and TSH. The corresponding reference ranges used for these hormones were 0.60–1.81 ng/ml, 4.50–10.90 ug/dL, 2.30–4.20 pg/mL and 0.89–1.76 ng/dL, and 0.51–6.27 uIU/mL. Creatinine concentrations were determined by the enzymatic method, and urine microalbumin was determined by the immunoturbidimetric method. Serum triglycerides (TG), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), and low density lipoprotein cholesterol (LDL-C) concentrations were measured with an automated biochemical analyzer. HbA1c was determined by high performance liquid chromatography. All tests were measured twice before reporting and performed by trained staff at the laboratory of the Beijing Hospital of Traditional Chinese Medicine, Capital Medical University.
## Statistical analysis
Statistical analysis was completed with STATA version 16 (StataCorp, College Station, TX, USA). Normally distributed continuous variables are expressed as mean (standard deviation), skewed continuous variables as median (interquartile range), and categorical variables as count (percent). Between-group differences were assessed with χ2 test for categorical variables and with Wilcoxon rank sum test for continuous variables. The association between thyroid hormones and DKD stages before and after adjustment for confounders was examined using logistic regression analysis, with effect sizes expressed as odds ratio (OR) and $95\%$ confidence interval ($95\%$ CI). Potential bias in group-based equivalents was controlled using propensity score matching (PSM). Calibration was assessed with Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and − 2 log likelihood ratio tests. Discrimination was judged by net reclassification improvement (NRI) and integrated differential improvement (IDI). The association between thyroid hormones, UACR, and eGFR was evaluated with Spearman correlation analysis. Nomogram was made using the “RMS” package in the R programming environment (version 3.5.2). P values of less than 0.05 were considered statistically significant. Study power was estimated by adopting the PS Power and Sample Size Calculations software (version 3.0).
## Characteristics of subjects
Table 1 shows the baseline characteristics of study participants. Patients in low-risk DKD stage were used as a reference group (controls), and they were younger than patients in moderate to very high risk stages. Sex differed significantly between patients in lower-risk and high-risk stages. Compared with controls, duration of diabetes and percentages of diabetic retinopathy and hypertension were higher in patients in high-risk stages. For thyroid hormones, FT3 concentrations decreased gradually with the increasing severity of DKD. No significance was noted for T3, T4 and TSH between patients in moderate-risk stage and controls.
Table 1Baseline characteristics of patients stratified by KDIGO categoriesCharacteristicsPatients with diabetes mellitusTotal($$n = 2832$$)Low risk($$n = 736$$)Moderate risk($$n = 501$$)High risk($$n = 578$$)Very high risk($$n = 1017$$)Age (years)61 (53–69)58 (51–66)62 (55–70)**61 (53–69)**62 (54–70)**Female, n (%)1122 (39.62)340 (46.20)208 (41.50)200 (34.60)**374 (36.80)**Duration of diabetes (yrs)10 (6–20)7 (3–12)10 (5–16)**10 (5–18)**14.5 (9–20)**Diabetic retinopathy, n (%)990 (34.96)122 (16.60)125 (25.00)**217 (37.50)**526 (51.70)**Hypertension, n (%)2045 (72.20)256 (64.60)350 (71.30)*483 (83.60)**956 (94.00)**SBP (mmHg)140 (129–155)130 (120–140)135 (125–148)**140 (130–155)**150 (139–165)**DBP (mmHg)80 (74–90)80 (71–85)80 (72–88)80 (75–90)**80 (75–90)**BMI (kg/m2)26.00 (23.40–28.40)26.20 (24.20–29.20)26.05 (23.90–29.00)25.30 (22.75–28.20)25.70 (23.10–28.10)HbA1c (%)7.1 (6.2–8.6), 0.247.5 (6.5–9.1)7.6 (6.6–9.3)7.3 (6.4-9.0)6.6 (5.9–7.7)**TG (mmol/L)1.67 (1.19–2.42), 0.861.54 (1.11–2.18)1.56 (1.14–2.28)1.77 (1.25–2.56)**1.72 (1.24–2.52)**TC (mmol/L)4.77 (3.97–5.73), 0.364.64 (3.93–5.38)4.57 (3.77–5.34)4.90 (4.10–6.15)**4.88 (3.99–6.12)**LDL-C (mmol/L)2.75 (2.15–3.46), 0.422.66 (2.12–3.26)2.61 (2.03–3.29)2.87 (2.22–3.71)**2.85 (2.19–3.69)**HDL-C (mmol/L)1.17 (0.99–1.38), 0.311.19 (1.01–1.37)1.12 (0.97–1.32)**1.19 (0.99–1.42)1.16 (0.97–1.39)eGFR (ml/min/1.73 m²)73.6 (36.9–99.4), 0.57100.2 (91.4-108.9)91.8 (73.6-103.6)**82.6 (64.4–100.0)**25.1 (12.2–39.7)**UACR (mg/g)309.06 (22.96-2182.22), 3.409.24 (4.96–15.91)75.83 (39.50-148.20)**957.54 (399.50-2444.80)**2251.14 (869.76-4418.82)**sALB (mg/L)37.9 (33.3–41.8), 0.2141.2 (38.2–44.2)40.2 (37.5–43.5)**36.0 (30.4–40.4)**34.3 (28.8–38.5)**T3 (ng/mL)0.90 (0.77–1.04), 0.340.98 (0.85–1.09)0.94 (0.81–1.08)0.91 (0.79–1.07)**0.83 (0.70–0.96)**T4 (µg/dL)8.00 (6.70–9.20), 0.258.27 (7.10–9.20)8.20 (7.00-9.30)7.79 (6.60–8.98)**7.90 (6.50–9.20)*FT3 (pg/mL)2.73 (2.43–3.02), 0.182.95 (2.71–3.20)2.86 (2.62–3.07)**2.76 (2.50–3.05)**2.45 (2.19–2.70)**FT4 (ng/dL)1.15 (1.02–1.28), 0.201.18 (1.06–1.31)1.22 (1.07–1.34)*1.17 (1.03–1.30)1.10 (0.98–1.23)**TSH (µIU/mL)1.91 (1.25–3.32), 2.541.70 (1.10–2.65)1.61 (1.09–2.53)2.01 (1.24–3.24)**2.24 (1.46–4.11)**Abbreviations:yrs, years; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; HbA1c, hemoglobin A1c; TG, serum triglyceride; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; UACR, urinary albumin-to-creatinine ratio; sALB, serum albumin; T3, triiodothyronine; T4, total thyroxine; FT3, free triiodothyronine; FT4, free thyroxine; TSH, thyroid-stimulating hormone. Continuous variables are expressed as median (interquartile range), and categorical variables as count (percent). Between-group comparison was done using Wilcoxon rank sum test or 2 test, where appropriate. * $P \leq 0.05$; **$P \leq 0.01.$ In total patients, coefficient of variation (CV) was provided after median (interquartile range)
## Correlation analysis
Since free thyroid hormones are the physiologically active forms of thyroid hormones, only FT3, FT4, and TSH were examined in subsequent analyses. Table 2 shows the correlations of serum FT3, FT4 and TSH across persistent albuminuria categories and GFR categories.
Table 2Correlations between serum thyroid Hormones and persistent albuminuria categories and GFR categoriesFT3FT4TSHPersistent albuminuria categories-0.290-0.1120.210P < 0.001P < 0.001P < 0.001GFR categories-0.490-0.1780.197P < 0.001P < 0.001P < 0.001Abbreviations: FT3, free triiodothyronine; FT4, free thyroxine; TSH, thyroid-stimulating hormone. The correlation between thyroid hormones, UACR, and eGFR was evaluated using Spearman correlation analysis
## Thyroid hormones and DKD stages
Table 3 shows the association between thyroid hormones and DKD stages before and after PSM analyses by balancing age, gender, hypertension, HbA1c, TC, TG and duration of diabetes. Multiple comparisons were adjusted by Bonferroni correction method, with P values less than $\frac{0.05}{9}$ indicating statistical significance. Before PSM analyses, per 0.2 pg/mL increment in serum FT3 was significantly associated with $13\%$, $22\%$ and $37\%$ reduced risk of moderate-risk (OR, $95\%$ CI, P: 0.87, 0.70–0.87, < 0.001), high-risk (0.78, 0.70–0.87, < 0.001) and very-high-risk (0.63, 0.55–0.72, < 0.001) DKD stages relative to the low-risk DKD stage, respectively. After PSM analyses, serum FT4 and TSH showed no statistical significance in risk estimates for all DKD stages.
Table 3Effect-size estimates of serum thyroid hormones with various stages of DKDSignificant risk factorsLow riskModerate riskHigh riskVery high riskFT3 (+ 0.2 pg/mL)Reference0.87, 0.81 to 0.93, < 0.0010.79, 0.74 to 0.85, < 0.0010.56, 0.52 to 0.61, < 0.001FT4 (+ 0.2 ng/dL)Reference1.20, 1.06 to 1.38, 0.0051.04, 0.92 to 1.18, 0.4910.82, 0.73 to 0.92, 0.001TSH (+ 0.5 µIU/mL)Reference1.00, 0.99 to 1.00, 0.3941.00, 0.99 to 1.00, 0.3781.00, 1.00 to 1.01, 0.344 After balancing age, gender, hypertension, HbA1c, TC, TG and duration of diabetes FT3 (+ 0.2 g/mL)Reference0.87, 0.70 to 0.87, 0.0060.78, 0.70 to 0.87, < 0.0010.63, 0.55 to 0.72, < 0.001FT4 (+ 0.2 ng/dL)Reference1.24, 1.04 to 1.50, 0.0151.09, 0.93 to 1.30, 0.2770.93, 0.78 to 1.11, 0.418TSH (+ 0.5 µIU/mL)Reference0.99, 0.97 to 1.02, 0.3520.99, 0.96 to 1.01, 0.2640.99, 0.98 to 1.01, 0.329Abbreviations: FT3, free triiodothyronine; FT4, free thyroxine; TSH, thyroid-stimulating hormone. Data are expressed as odds ratio, $95\%$ confidence interval, P value The power to reject null hypotheses with OR equal to 1 was over $80\%$ for above significant comparisons.
## Prediction accuracy assessment
Table 4 shows an evaluation on the prediction accuracy after adding FT3 to the basic model that included age, gender, hypertension, HbA1c, TC, TG and duration of diabetes. The predicted probabilities of FT3 additions reflected the actual observed risk. In terms of calibration, reduction in AIC and BIC statistics was greater than 10 after the addition of FT3 to the basic model, for each stage. Moreover, the likelihood ratio test showed that the difference was statistically significant with FT3 for all stages.
Table 4Prediction accuracy for DKD gained by adding thyroid hormones to the basic model for different stagesStatisticsModerate riskHigh riskVery high riskBasic modelBasic model + FT3Basic modelBasic model + FT3Basic model + FT3Basic model Calibration AIC768.23672.5762.08658.71834.96612.96BIC803.02710.54797.75697.86874.19656.11LR test (χ2)12.229.46127.32LR test P value0.0005< 0.001< 0.001 Discrimination NRI (P value)< 0.0010.003< 0.001IDI (P value)< 0.001< 0.001< 0.001Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criterion; LR, likelihood ratio; NRI net reclassification improvement; IDI, integrated discrimination improvement; FT3, free triiodothyronine
## Nomogram prediction model
To facilitate clinical application, a nomogram prediction model was established for the moderate-risk, high-risk and very-high-risk DKD stages, as illustrated in Fig. 1. The predictive accuracy and discriminative capability of FT3 for each DKD stage were assessed by C-index (C-index: 0.673, $P \leq 0.001$ for moderate-risk stage; C-index: 0.810, $P \leq 0.001$ for high-risk stage; and C-index: 0.907, $P \leq 0.001$ for the very-high-risk stage), indicating significant improvement in model performance.
Fig. 1Nomogram prediction models for the moderate-risk (panel A), high-risk (panel B), and very-high-risk stages (panel C)Abbreviations: SBP, systolic blood pressure; HbA1c, hemoglobin A1c; FT3, free triiodothyronine; TC, total cholesterol For example, on the basis of nomogram models, considering a patient with type 2 diabetes who has a 10-year history of diabetes but has not yet been evaluated for renal impairment, has no diabetic retinopathy, and has SBP at 140 mmHg, TC at 4.5 mmol/L, and FT3 at 4.2 pg/mL, the odds of having moderate-risk DKD was estimated to be $78\%$, high-risk DKD to be $84\%$ and very-high-risk DKD to be $60\%$.
## Discussion
The aim of this retrospective study was to explore the association between thyroid hormones and different DKD stages in Chinese adults. Our key findings are that high concentrations of serum FT3 were associated with the significantly reduced risk of having moderate-risk to very-high-risk DKD stages, which supported the hypothesis that serum FT3 may serve as a promising biomarker to predict DKD progression.
Our findings of serum FT3 and DKD progression are biologically plausible. Thyroid hormones can affect renal development, glomerular and tubular function and renal hemodynamics, and activate the renin-angiotensin-aldosterone system [17, 18], and they may affect renal functions through cardiovascular and systemic hemodynamics in addition to acting directly on the kidney [19]. However, the kidney regulates thyroid hormone metabolism and elimination by promoting the removal of iodine through glomerular filtration [17], and elevated serum concentrations of inorganic iodide and thyroid iodine in patients with kidney disease can prolong the Wolff-Chaikoff effect and promote hypothyroidism [20]. Additionally, thyroxine binds tightly to protein and can be lost in urine of patients with kidney disease, which is commonly seen in patients with DKD. Furthermore, as effective regulators of glucose metabolism, thyroid hormones can also act in the development of diabetes mellitus, enhance the expressions of GK and Mafa in the pancreas, facilitate the rapid maturation and renewal of β-cells, and strengthen the expression and secretion of insulin in the pancreas [21, 22], which may affect the development of DKD. In particular, FT3 may be related to DKD through several mechanisms. Aggravated inflammation may lead to a decrease in FT3, and inflammatory cytokines such as tumor necrosis factor (TNF)-α and interleukin (IL)-1 can inhibit the expression of type 1 5’-deiodase and reduce the transformation of T4-to-T3 [29]. It is thus reasonable to infer that unfavorable changes in serum thyroid hormones, notably FT3, may worsen renal function, which then indicate the development and progression of DKD.
In recent years, some studies have explored the relationship between thyroid hormones and DKD, yet the results of these studies are not often reproducible. In a cross-sectional study involving 862 diabetic patients, FT3 in the normal range was negatively correlated with DKD in patients with type 2 diabetes mellitus and there was no correlation between FT4, TSH and DKD, consistent with the findings of the present study; however, the relationship between FT3 and different stages of DKD was not examined [23]. Another cross-sectional study involving 1,071 patients with type 2 diabetes mellitus revealed that after adjusting for covariates, serum FT3 and FT4 were negatively correlated with DKD and serum TSH was positively correlated with DKD [24]. In addition, the relationship between serum TSH and DKD also showed a positive correlation [25]. Other studies also have shown that low concentrations of normal FT3 were associated with a higher incidence of microalbuminuria [27]. In this present study, we focused on northern Chinese adults and employed the propensity score matching method to reduce possible selection bias. Our findings indicated that serum FT4 was negatively correlated with persistent albuminuria catagories and GFR categories and TSH was positively correlated with these categories as well, yet this result was not statistically significant after balancing confounders. These controversial findings may have thus resulted from the confounding factors of the study participants.
Moreover, we also found that FT3 decreased with the increase in DKD risk category, consistent with the results of several studies [23, 26]. We also constructed nomogram prediction models for DKD in different stages in an attempt to apply our findings to routine clinical practice and facilitate clinical decision making.
This study has some strengths. Following the recommendations of the 2020 KDIGO Diabetes Working Group, we first applied the KDIGO risk categories, combining eGFR and UACR to examine the association between thyroid hormone and DKD risk. Currently, few studies existed on the relationship between thyroid hormones and DKD even though thyroid hormone testing is commonly used and easy to obtain, and treatment methods for thyroid dysfunction are readily available. In addition, our study has a relatively large sample, and the collected data was relatively standardized, complete, and reliable.
However, our study has several limitations that need to be addressed. First, this is a retrospective analysis, and so we were only able to evaluate association, not causation. Further prospective and longitudinal studies are needed to determine the correlation between thyroid hormones and DKD more directly. Second, information on drug regimens, such as hypoglycemic drugs or antihypertensive drugs was not available for us, which left their potential contribution to the association between thyroid hormones and DKD an open question. Third, this study only involved Chinese adults, and so extrapolation to other ethnic groups or races may be limited. Fourth, study participants were patients from a single hospital. Although the study was strictly screened, there may still have been some unknown confounding factors that may have led to bias.
Despite these limitations, our findings indicate that high concentrations of serum FT3 were associated with the significantly reduced risk of having moderate-risk to very-high-risk DKD stages. We hope that this study can provide some clinical evidence for the association between thyroid hormones and DKD, and importantly lay a foundation for further research on the potential therapeutic effects of thyroid hormones on DKD.
## References
1. 1.de Boer IH, Rue TC, Hall YN et al. Temporal trends in the prevalence of diabetic kidney disease in the United States. JAMA. 2011 Jun 22;305(24):2532-9. 10.1001/jama.2011.861. PMID: 21693741; PMCID: PMC3731378.
2. 2.Tuttle KR, Bakris GL, Bilous RW et al. Diabetic kidney disease: a report from an ADA Consensus Conference. Diabetes Care. 2014;37(10):2864–2883. doi:10.2337/dc14-1296
3. 3.Kramer HJ, Nguyen QD, Curhan G et al. Renal insufficiency in the absence of albuminuria and retinopathy among adults with type 2 diabetes mellitus. JAMA. 2003 Jun 25;289(24):3273-7. 10.1001/jama.289.24.3273. PMID: 12824208.
4. 4.Dai Q, Chen N, Zeng L, Lin XJ, Jiang FX, Zhuang XJ, Lu ZY. Clinical features of and risk factors for normoalbuminuric diabetic kidney disease in hospitalized patients with type 2 diabetes mellitus: a retrospective cross-sectional study. BMC Endocr Disord. 2021 May 22;21(1):104. 10.1186/s12902-021-00769-8. PMID: 34022855; PMCID: PMC8141213.
5. 5.Goldenberg RM, Berall M, Chan CTM, et al. Managing the course of kidney disease in adults with type 2 diabetes: from the Old to the New. Can J Diabetes. 2018 Jun;42(3):325–34. 10.1016/j.jcjd.2017.06.008. Epub 2017 Aug 16. PMID: 28822777.
6. **Improving global outcomes (KDIGO) CKD Work Group KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease**. *Kidney Int Suppl* (2013.0) **3** 1-150
7. 7.Furuichi K, Shimizu M, Hara A et al. Diabetic Nephropathy: A Comparison of the Clinical and Pathological Features between the CKD Risk Classification and the Classification of Diabetic Nephropathy 2014 in Japan. Intern Med. 2018 Dec 1;57(23):3345–3350. 10.2169/internalmedicine.1132-18. Epub 2018 Aug 10. PMID: 30101924; PMCID: PMC6306527.
8. 8.Kidney Disease: Improving Global Outcomes (KDIGO) Diabetes Work Group. KDIGO 2020 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease. Kidney Int. 2020 Oct;98(4S):S1-S115. 10.1016/j.kint.2020.06.019. PMID: 32998798.
9. 9.Xie J, Wang X, Zhang Y et al. The longitudinal effect of subclinical hypothyroidism on urine microalbumin-to-urine creatinine ratio in patients with type 2 diabetes mellitus. BMC Endocr Disord. 2019 Aug 5;19(1):84. 10.1186/s12902-019-0405-0. PMID: 31382952; PMCID: PMC6683563.
10. 10.Chen HS, Wu TE, Jap TS et al. Subclinical hypothyroidism is a risk factor for nephropathy and cardiovascular diseases in Type 2 diabetic patients. Diabet Med. 2007 Dec;24(12):1336-44. 10.1111/j.1464-5491.2007.02270.x. Epub 2007 Oct 17. Erratum in: Diabet Med. 2008 Feb;25(2):244. PMID: 17941864.
11. Furukawa S, Yamamoto S, Todo Y. **Association between subclinical hypothyroidism and diabetic nephropathy in patients with type 2 diabetes mellitus**. *Endocr J* (2014.0) **61** 1011-8. DOI: 10.1507/endocrj.ej14-0206
12. 12.Han Q, Zhang J, Wang Y et al. Thyroid hormones and diabetic nephropathy: An essential relationship to recognize. Nephrology (Carlton). 2019 Feb;24(2):160–169. 10.1111/nep.13388. PMID: 29660205.
13. 13.Bando Y, Ushiogi Y, Okafuji K, et al. Non-autoimmune primary hypothyroidism in diabetic and non-diabetic chronic renal dysfunction. Exp Clin Endocrinol Diabetes. 2002 Nov;110(8):408–15. 10.1055/s-2002.
14. 14.Palacio-Lacambra ME, Comas-Reixach I, Blanco-Grau A, et al. Comparison of the Cockcroft-Gault, MDRD and CKD-EPI equations for estimating ganciclovir clearance. Br J Clin Pharmacol. 2018 Sep;84(9):2120–8. 10.1111/bcp.13647. Epub 2018 Jul 8. PMID: 29791023; PMCID: PMC6089827.
15. 15.American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2013 Jan;36(1):67–74. 10.2337/dc13-S067. PMID: 23264425; PMCID: PMC3537273.
16. 16.Liu LS, Writing Group of 2010 Chinese Guidelines for the Management of Hypertension. [2010 chinese guidelines for the management of hypertension]. Zhonghua xin xue guan bing za zhi. 2011 Jul;39(7):579–615. Chinese. PMID: 22088239.
17. 17.Iglesias P, Bajo MA, Selgas R et al. Thyroid dysfunction and kidney disease: An update. Rev Endocr Metab Disord. 2017 Mar;18(1):131–144. 10.1007/s11154-016-9395-7. PMID: 27864708.
18. 18.Kobori H, Ichihara A, Miyashita Y, et al. Mechanism of hyperthyroidism-induced renal hypertrophy in rats. J Endocrinol. 1998 Oct;159(1):9–14. 10.1677/joe.0.1590009. PMID: 9795336; PMCID: PMC2574502.
19. 19.Mariani LH, Berns JS. The renal manifestations of thyroid disease. J Am Soc Nephrol. 2012 Jan;23(1):22 – 6. 10.1681/ASN.2010070766. Epub 2011 Oct 21. PMID: 22021708.
20. 20.Miki H, Oshimo K, Inoue H et al. Thyroid carcinoma in patients with secondary hyperparathyroidism. J Surg Oncol. 1992 Mar;49(3):168 – 71. 10.1002/jso.2930490308. PMID: 1548891.
21. 21.Gauthier BR, Sola-García A, Cáliz-Molina M, et al. Thyroid hormones in diabetes, cancer, and aging. Aging Cell. 2020 Nov;19(11):e13260. 10.1111/acel.13260. Epub 2020 Oct 13. PMID: 33048427; PMCID: PMC7681062.
22. 22.Benedetti V, Lavecchia AM, Locatelli M et al. Alteration of thyroid hormone signaling triggers the diabetes-induced pathological growth, remodeling, and dedifferentiation of podocytes. JCI Insight. 2019 Sep 19;4(18):e130249. 10.1172/jci.insight.130249. PMID: 31534055; PMCID: PMC6795387.
23. 23.Zou J, Tian F, Zhang Y et al. Association between Thyroid Hormone Levels and Diabetic Kidney Disease in Euthyroid Patients with Type 2 Diabetes. Sci Rep. 2018 Mar16;8(1):4728. 10.1038/s41598-018-22904-7. PMID: 29549262; PMCID: PMC5856822.
24. 24.Wang J, Li H, Tan M et al. Association between thyroid function and diabetic nephropathy in euthyroid subjects with type 2 diabetes mellitus: a cross-sectional study in China. Oncotarget. 2019 Jan 4;10(2):88–97. 10.18632/oncotarget.26265. PMID: 30719205; PMCID: PMC6349442.
25. 25.Qi Q, Zhang QM, Li CJ et al. Association of Thyroid-Stimulating Hormone Levels with Microvascular Complications in Type 2 Diabetes Patients. Med Sci Monit. 2017 Jun4;23:2715–2720. 10.12659/msm.902006. PMID: 28578377; PMCID: PMC5467710.
26. 26.Fei X, Xing M, Wo M, et al. Thyroid stimulating hormone and free triiodothyronine are valuable predictors for diabetic nephropathy in patient with type 2 diabetes mellitus. Ann Transl Med. 2018 Aug;6(15):305. 10.21037/atm.2018.07.07. PMID: 30211193; PMCID: PMC6123216.
27. 27.De Groot LJ. Dangerous dogmas in medicine: the nonthyroidal illness syndrome. J Clin Endocrinol Metab. 1999 Jan;84(1):151 – 64. 10.1210/jcem.84.1.5364. PMID: 9920076.
28. 28.Wiederkehr MR, Kalogiros J, Krapf R. Correction of metabolic acidosis improves thyroid and growth hormone axes in haemodialysis patients. Nephrol Dial Transplant. 2004 May;19(5):1190–7. 10.1093/ndt/gfh096. Epub 2004 Feb 19. PMID: 14993483.
29. 29.Zoccali C, Tripepi G, Cutrupi S, et al. Low triiodothyronine: a new facet of inflammation in end-stage renal disease. J Am Soc Nephrol. 2005 Sep;16(9):2789–95. 10.1681/ASN.2005040356. Epub 2005 Jul 20. PMID: 16033857.
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---
title: 'The long-term and short-term effects of ambient air pollutants on sleep characteristics
in the Chinese population: big data analysis from real world by sleep records of
consumer wearable devices'
authors:
- Peining Zhou
- Jing Ma
- Xueying Li
- Yixue Zhao
- Kunyao Yu
- Rui Su
- Rui Zhou
- Hui Wang
- Guangfa Wang
journal: BMC Medicine
year: 2023
pmcid: PMC9993685
doi: 10.1186/s12916-023-02801-1
license: CC BY 4.0
---
# The long-term and short-term effects of ambient air pollutants on sleep characteristics in the Chinese population: big data analysis from real world by sleep records of consumer wearable devices
## Abstract
Several studies on long-term air pollution exposure and sleep have reported inconsistent results. Large-scale studies on short-term air pollution exposures and sleep have not been conducted. We investigated the associations of long- and short-term exposure to ambient air pollutants with sleep in a Chinese population based on over 1 million nights of sleep data from consumer wearable devices. Air pollution data including particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) were collected from the Ministry of Ecology and Environment. Short-term exposure was defined as a moving average of the exposure level for different lag days from Lag0 to Lag0-6. A 365-day moving average of air pollution was regarded as long-term exposure. Sleep data were recorded using wearable devices from 2017 to 2019. The mixed-effects model was used to evaluate the associations. We observed that sleep parameters were associated with long-term exposure to all air pollutants. Higher levels of air pollutant concentrations were associated with longer total sleep and light sleep duration, shorter deep sleep duration, and decreases in wake after sleep onset (WASO), with stronger associations of exposures to NO2 and CO [a 1-interquartile range (IQR) increased NO2 (10.3 μg/m3) was associated with 8.7 min ($95\%$ CI: 8.08 to 9.32) longer sleep duration, a 1-IQR increased CO (0.3 mg/m3) was associated with 5.0 min ($95\%$ CI: − 5.13 to − 4.89) shorter deep sleep duration, 7.7 min ($95\%$ CI: 7.46 to 7.85) longer light sleep duration, and $0.5\%$ ($95\%$ CI: − 0.5 to − $0.4\%$) lower proportion of WASO duration to total sleep]. The cumulative effect of short-term exposure on Lag0-6 is similar to long-term exposure but relatively less. Subgroup analyses indicated generally greater effects on individuals who were female, younger (< 45 years), slept longer (≥ 7 h), and during cold seasons, but the pattern of effects was mixed. We supplemented two additional types of stratified analyses to reduce repeated measures of outcomes and exposures while accounting for individual variation. The results were consistent with the overall results, proving the robustness of the overall results. In summary, both short- and long-term exposure to air pollution affect sleep, and the effects are comparable. Although people tend to have prolonged total sleep duration with increasing air pollutant concentrations, their sleep quality might remain poor because of the reduction in deep sleep.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12916-023-02801-1.
## Introduction
Sleep is an important factor affecting health, similar to exercise and diet [1]. High-quality sleep is crucial for maintaining health and quality of life. Sleep disturbances have risen to be one of the major public health concerns.
Sleep disturbances are associated with numerous health problems such as cardiovascular events, diabetes, mental disorders, and cancer [2]. Previous publications [3, 4] have demonstrated the relationship between sleep duration and mortality using a U- shaped curve, whereby both short (< 7 h) and long (> 9 h) sleep duration could increase mortality risk, particularly in Asian populations [4]. Furthermore, sleep stability is potentially modifiable risk factors for cardiometabolic diseases. Decreased inter-daily stability increases hypertension prevalence and blood pressure [5]. Increased night-to-night sleep variability has been associated with an increased risk of adiposity, metabolic syndrome, and type 2 diabetes [6].
Numerous factors influence sleep quality, such as age, sex, physical activity, psychological or physiological conditions, and environmental factors [7]. Air pollution, another major public health concern, has been reported to affect sleep and has similar consequences to other diseases, such as cardiopulmonary health [8], diabetes [9], and cancer [10]. In particular, emerging research has recently focused on the effects of outdoor air pollution on sleep, as $91\%$ of the worldwide population lives in places where the World Health Organization (WHO) ambient air quality guideline levels are not met [11].
Nevertheless, the relationship between ambient air pollution and sleep quality remains ambiguous and inconsistent. Many studies using questionnaires have revealed that poor air quality is associated with poor sleep quality [12]. Increased particulate matter with a diameter of 2.5 μm or less (PM2.5), particulate matter with a diameter of 10 μm or less (PM10), and nitrogen dioxide (NO2) concentrations are correlated with a reduction in daily sleep hours among college freshmen [13]. However, other studies have reported that air pollution deterioration is associated with increased sleep duration [14, 15] and wake times during sleep [16]. Long-term exposure to black carbon may induce shorter sleep duration in men and those with low socioeconomic status but longer sleep duration in blacks [17].
These discrepancies may result from different populations, study designs, pollutants, and, more importantly, methodologies of sleep evaluation. Almost no large-sample studies have employed objective sleep-scoring systems. Instead, most researchers have used a self-reported questionnaire or the Pittsburgh Sleep Quality questionnaire. The questionnaire tools will introduce bias due to their limitations and the participants’ cognition. With technological innovation, wearable devices, such as bracelets or watches, have owned the function to record and monitor wake or sleep in different stages [18–20], thus providing an excellent and convenient methodology for sleep evaluation. We analyzed a total of 1,245,817 nights of sleep records from a type of consumer bracelet in China between 2017 and 2019 and controlled several common influencing factors of sleep and air quality to clarify the long- and short-term effects of ambient air pollution on sleep.
## Study population
A retrospective analysis was performed using data from consumer bracelets (Zepp Health Corp.) in China between 2017 and 2019. They were collected in an anonymous and aggregated dataset without personal identifiers such as names, email addresses, and cell phone numbers. Random strings were used to identify the sleep records for each night. The study was approved by the IRB of the Peking University First Hospital [2020-635].
In the real world, users often wear bracelets intermittently and irregularly, particularly during sleep. Only few people can wear bracelets continuously over a long period as the air quality fluctuates. For the study population, most people resided in a relatively fixed community, and only a small portion migrated or traveled frequently. Therefore, considering the privacy policy, we took each night’s record as a research object and used the air quality data collected at the sleep tracking site for lag analysis. We analyzed 1,245,817 nights of sleep data from 7682 participants for 3 years.
## Covariates
Several factors could influence sleep and were controlled in the statistical analysis, including registered sex, age, body mass index (BMI), city development level, altitude, season, and the type of night in which sleep records were tracked. The cities (five tiers) were classified based on development level according to business resource concentration, pivot function, the activity of urban residents, lifestyle diversity, and future plasticity, which has been widely quoted in China [21]. The sleep tracking seasons were divided into quarters in this study because of the large latitude span in China. Generally, in most parts of China, the first quarter (January to March) includes part of winter and early spring, the second quarter (April to June) includes spring and early summer, the third quarter (July to September) includes summer and early autumn, and the fourth quarter (October to December) includes the majority of autumn and winter. Additionally, we defined two types of night recordings: weeknight (Sunday to Thursday, the last night of the legal holidays) and night of rest (Friday, Saturday, the day before the legal holidays to the penultimate night).
## Data cleaning
We eliminated unreasonable or extreme values according to the following criteria to obtain eligible records from the raw data: registered age < 14 years, registered BMI < 15 kg/m2 or ≥ 45 kg/m2, total sleep duration ≤ 180 min or ≥ 720 min, mean heart rate of 24 h, or mean heart rate during sleep > 120 bpm.
## Sleep parameters
Sleep parameters recorded by the bracelets included total sleep duration (minutes of sleep per night for each participant), deep sleep duration, light sleep duration, times of wake after sleep onset (WASO), and duration of WASO. We used several ratios in the analysis to reduce the influence of total sleep duration on sleep parameters, such as deep sleep duration/total sleep duration, deep sleep duration/light sleep duration, times of WASO per hour of sleep, and durations of WASO per hour of sleep.
The sleep parameters recorded by the bracelets are summarized in Table 2. Of the overall nights studied, the mean total sleep duration was 419.7 ± 87.3 min, ranging from 180 to 720 min. The deep sleep duration was 108.49 ± 49.77 min, and the light sleep duration was 311.20 ± 80.44 min, respectively. The average proportion of deep sleep in total sleep duration was $25.9\%$ ± $10.9\%$, and the ratio of deep to light sleep duration was 0.38 ± 0.24. The average times of WASO per night of sleep were 0.90 ± 1.1. The duration of WASO was 9.27 ± 19.69 min, which accounted for $2.3\%$ ± $5.09\%$ of total sleep. Table 2Summary of parameters of the study population during the recording periodSleep parametersMean (SD)Median (IQR)MinMaxTotal sleep duration, min419.69 (87.29)423 [366,476]180720Deep sleep duration, min108.49 (49.77)104 [72,139]8505Light sleep duration, min311.20 (80.44)311 [257,364]37680Deep sleep duration/light sleep duration, %0.38 (0.24)0.34 (0.22,0.49)0.019.22Deep sleep duration/total sleep duration, %25.94 (10.87)25.14 (18.12, 32.87)1.3791.65Times of WASO0.90 (1.10)1 [0, 1]017Times of WASO per hour of sleep0.13 (0.16)0.12 (0, 0.20)03.31Duration of WASO, min9.27 (19.69)0 [0, 7]0456Duration of WASO/total sleep duration, %2.28 (5.09)0 (0, 1.73)086.51Abbreviations: IQR Interquartile range, SD Standard deviation, WASO Wake after sleep onset
## Ambient air pollution data
The origin data of the main pollutants were collected from the National Urban Air Quality Real-time Publishing Platform (http://106.37.208.233:20035), linked to the open website of the Ministry of Ecology and Environment of the People’s Republic of China (https://www.mee.gov.cn/hjzl/). This website has been closed recently, and the corresponding data have been updated to a new website (http://air.cnemc.cn:18007/). These data were collected and reported every hour. This study measured the effect of short-term exposure with different lag days from Lag0 (record day) to Lag0-6. For instance, Lag0–6 represents the 7-day moving average of air pollutant concentrations between the record day and the 6th day before the record. Lag0-364 calculated a total of 365-day moving averages of air pollutant concentrations between the record day and the 364th day before the record, representing long-term exposure. The period of long-term exposure data for all participants ranged from 3 years (2016–2018). The data for PM2.5, PM10, NO2, sulfur dioxide (SO2), and carbon monoxide (CO) were calculated from the mean estimated 24 h concentrations, and ozone (O3) was calculated from the maximum 8-h mean values. In addition, we matched the participants’ residential cities with the air pollution exposure data of the corresponding cities on the above website.
## Statistical analysis
We assessed normality and described distributions as mean, standard deviation (SD), minimum, and maximum for continuous variables or proportions for categorical variables. Mixed-effects model analysis was performed to investigate the associations of sleep parameters with ambient air pollution on both short- and long-term exposures because it allows the analysis of data from multiple measurements in one participant. Considering the high or moderate correlations among air pollutants (Supplementary Table S1), only single-pollutant models were used in our study to avoid collinearity.
The effect estimates were expressed as the change in sleep parameters per 1-IQR increase in each air-pollutant concentration with a random effect for each participant and fixed linear effects for air pollution and other covariates. Air pollutants were entered separately into single-pollutant models. The mixed-effects model was constructed using Eq.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Y}_{ij}={\beta}_0+{\beta}_{0\textrm{j}}+{\beta}_1{X}_{0 ij}\kern0.5em +{\beta}_2{X}_{1\textrm{ij}}\kern0.5em +{\beta}_3{X}_{2\textrm{ij}}\kern0.5em +{\beta}_4{X}_{3\textrm{ij}}+\dots \dots {\beta}_N{X}_{nij}+{\varepsilon}_{ij};$$\end{document}Yij=β0+β0j+β1X0ij+β2X1ij+β3X2ij+β4X3ij+⋯⋯βNXnij+εij; where Yij represents the sleep parameters, β0 is the fixed-effect intercept term, β0j is the random-effect intercept term, X0ij represents each air pollutant concentration, β1 is the regression coefficient for air pollutants, β2…βN are the regression coefficients for the covariates in the model, j represents the study participant, i identifies the sleep record, and εij is the residual error term. The results were presented as regression coefficients and $95\%$ confidence intervals (CI). Additionally, the model was adjusted for other covariates, as noted previously. Subgroup analyses were conducted according to sex, age, season, and sleep duration. A cross-product term was added to the mixed-effects model to assess the significance of the interaction.
Given the bias caused by repeated measures of pollutant exposures and sleep parameters under the existing data structure, we further designed two stratified analyses to reduce repeated measures and consider individual variation, while still using the mixed-effects model. Figure 1 shows the two methods of stratified analysis. On the one hand, the sleep data for each participant were arranged in ascending chronological order. Then, starting from the first data of each subject, a piece of record was extracted every 7 days and 365 days intervals to analyze the impact of short- and long-term exposure on sleep. Alternatively, we regarded each continuous sleep record of each subject as a dataset and averaged the sleep parameters of each dataset for the analysis of long- and short-term effects. For the analysis of long-term effects, we averaged the sleep parameters of each subject’s first dataset and calculated air pollutant exposure based on the time of the first record in the dataset. Ultimately, only one piece of data was collected for each participant. For the analysis of short-term effects, we averaged the sleep parameters of the first 7 days of each dataset for each subject if the consecutive days of the dataset were ≥ 7 days. The time interval between the first record of each dataset and the last record of the previous valid dataset exceeded 7 days. If the consecutive days of the dataset were fewer than 7 days, the average of all sleep parameters in the dataset was calculated. Through these two stratified analyses, we sufficiently reduced the repeated measures of outcomes and exposures and took individual variation into account by calculating the mean value of sleep parameters, thus further verifying the stability of the overall data results. Fig. 1Two methods for stratified analyses Analyses were conducted using the SPSS statistical software version 27 and R software version 3.6.2 with a p-value < 0.05 considered statistically significant for a two-tailed test.
## Characteristics of the study population
The characteristics of the study population are shown in Table 1. Our study comprised 1,245,817 accumulated sleep records over 3 years from 1005 nights of sleep tracking among 7682 participants, $70.6\%$ of which were weeknights. There were relatively even between seasons in this analysis across the 3 years, although the highest proportion of records came from autumn ($27.8\%$). Over half of the study population resided in first-tier or super-first-tier cities and came from low-elevation regions. The mean age of the participants was 47.7 ± 13.8 years old. Individuals aged 18 to 64 years provided the most nights of the population. Males accounted for $74.1\%$ of this analysis. The mean BMI of the study population was 24.4 ± 3.2 kg/m2, predominantly in the BMI normal group ($43.5\%$).Table 1Characteristics of research dataItemsNumber of participantsAll participants7682Gender of participants, no. (%) Male5689 ($74.1\%$) Female1993 ($25.9\%$)Age of participants, mean (SD), years47.7 (13.8)Age groups of participants, no. (%) < 18 years45 ($0.6\%$) 18–44 years4018 ($52.3\%$) 45–64 years2636 ($34.3\%$) ≥ 65983 ($12.8\%$)BMI of participants, mean (SD), kg/m224.4 (3.2)BMI groups of participants, no. (%) Low weight (< 18.5)243 ($3.2\%$) Normal (18.5–23.9)3340 ($43.5\%$) Overweight (24–27.9)2993 ($39.0\%$) Obesity (≥ 28)1106 ($14.4\%$)City groups of participants, no. (%) Super first-tier cities2426 ($31.6\%$) First-tier cities1811 ($23.6\%$) Second-tier cities1488 ($19.4\%$) Third-tier cities960 ($12.5\%$) Fourth-tier cities690 ($9.0\%$) Fifth-tier cities307 ($4.0\%$)Altitude groups of participants, no. (%) ≥ 1000 m389 ($5.1\%$) < 1000 m7293 ($94.9\%$)Smoking, no. (%) Never5338 ($69.5\%$) Occasional929 ($12.1\%$) Regular345 ($4.5\%$) Daily1070 ($13.9\%$)Drinking, no. (%) Never2300 ($29.9\%$) Occasional4419 ($57.5\%$) Regular774 ($10.1\%$) Daily189 ($2.5\%$)Total nights of sleep tracking, no.1005Accumulative sleep records, nights1,245,817Type of the night sleep records tracked in, no. (%) Weeknights879,289 ($70.6\%$) Nights of rest day366,528 ($29.4\%$)Seasons sleep records tracked in, no. (%) Spring (March to May)326,332 ($26.2\%$) Summer (June to August)284,066 ($22.8\%$) Autumn (September to November)346,791 ($27.8\%$) Winter (December to February)288,628 ($23.2\%$)Abbreviations: IQR Interquartile range, SD Standard deviation Figure 2 a and b illustrate the temporal distribution of the overall data. The period of the data was from April 2017 to December 2019, with the largest amount derived from 2019. Specifically, the data volume peaked in March 2019, including 58,012 data from 3026 participants. Figure 2c shows the distribution of the number of participants in the continuous records of different lengths. The number of consecutive record days ranged from 2 to 113 days. In terms of the overall trend, the longer the consecutive days, the fewer the participants. Fig. 2a–c Characteristics of data distribution. a, b The temporal distribution of participants and sleep records respectively. c The distribution of the number of participants in the continuous records of different lengths
## Ambient air pollutant concentrations
Table 3 shows the distribution of short- and long-term air pollutant levels in this study. The mean long-term concentrations of PM2.5, PM10, NO2, O3, SO2, and CO were 45.3 ± 13.7μg/m3, 77.5 ± 25.9μg/m3, 39.8 ± 9.0μg/m3, 61.0 ± 9.1μg/m3, 13.0 ± 7.7μg/m3, and 0.9 ± 0.2μg/m3 respectively. These values were higher than the WHO air quality guidelines [11]. Even the annual minimum of PM2.5 and PM10 exceeded the WHO standard (5 μg/m3 for PM2.5 and 15 μg/m3 for PM10). Figure 3 presents the spatial distribution of long-term air pollutant concentrations in participants’ residences. It was found that the participants mostly lived in developed regions where air pollution was severe, and the population density was high. Table 3Distribution of air pollutant concentrations in the studyShort-term exposureLong-term exposureLag 0Lag 0-1Lag 0-2Lag 0-3Lag 0-4Lag 0-5Lag 0-6PM2.5 Mean (SD)42.4 (33.6)42.3 (31.1)42.3 (29.2)42.3 (27.8)42.2 (26.7)42.2 (25.8)42.2 (25.2)45.3 (13.7) Median (IQR)33.0 (21.0, 52.0)34.0 (22.5, 52.0)34.7 (23.5, 52.0)35.0 (24.3, 51.7)35.4 (24.8, 51.6)35.7 (25.3, 51.5)36.0 (25.7, 51.3)43.1 (36.3, 53.3) Min000000010.6 Max771.0613.0463.0370.0327.8302.0280.0115.3PM10 Mean (SD)73.2 (53.0)73.2 (48.8)73.2 (46.0)73.1 (44.0)73.0 (42.5)73.0 (41.3)73.0 (40.4)77.5 (25.9) Median (IQR)60.0 (40.0, 91.0)61 (41.5, 90.5)61.3 (42.7, 90.3)62.0 (43.5, 90.0)62.6 (44.2, 89.6)63.0 (44.7, 89.8)63.5 (45.1, 89.6)73.3 (57.5, 88.7) Min000000025.1 Max1908.01132.5792.3676.5819.4710.8637.9193.0NO2 Mean (SD)38.4 (18.4)38.4 (17.2)38.4 (16.4)38.4 (15.9)38.3 (15.4)38.3 (15.1)38.3 (14.8)39.8 (9.0) Median (IQR)35.0 (25.0, 49.0)35.5 (26.0, 48.0)36.0 (26.7, 47.7)36.0 (27.0, 47.8)36.2 (27.2, 47.7)36.4 (27.3, 47.7)36.4 (27.5, 47.6)41.8 (34.8, 45.1) Min00000007.6 Max181.0161.0158.3147.0141.6136.2130.163.0O3 Mean (SD)60.3 (32.0)60.2(30.4)60.2(29.4)60.2(28.7)60.2(28.2)60.3 (27.7)60.3(27.4)61.0 (9.1) Median (IQR)56.2(36.1, 80.4)56.5(36.9, 79.5)56.8 (37.5,79.3)56.9 (38.0, 79.2)57.3(38.3, 78.9)57.4 (38.6,78.9)57.6 (38.8, 78.7)60.1(54.6, 67.7) Min11.92.52.82.82.83.133.2 Max233.6232.0224.3214.2210.5200.6193.6107.0SO2 Mean (SD)11.2 (9.2)11.2 (8.7)11.2 (8.5)11.2 (8.3)11.2 (8.2)11.2 (8.2)11.2 (8.1)13.0 (7.7) Median (IQR)9.0 (6.0, 13.0)35.5 (26.0, 48.0)9.0 (6.3, 13.0)9.0 (6.5, 13.0)9.0 (6.4, 13.0)9.0 (6.5, 13.0)9.0 (6.6, 13.0)11.4 (8.0, 15.1) Min00000002.6 Max342.0161.0225.7215.0207.8202.8192.1115.4CO Mean (SD)0.8 (0.4)0.8 (0.4)0.8 (0.3)0.8 (0.3)0.8 (0.3)0.8 (0.3)0.8 (0.3)0.9 (0.2) Median (IQR)0.8 (0.6, 1.0)0.8 (0.6, 1.0)0.8 (0.6, 1.0)0.8 (0.6, 1.0)0.8 (0.6, 1.0)0.8 (0.6, 1.0)0.8 (0.6, 1.0)0.8 (0.7, 1.0) Min00000000.3 Max6.45.65.75.44.84.74.32.4Abbreviations: CO Carbon monoxide, IQR Interquartile range, NO2 Nitrogen dioxide, O3 Ozone, PM2.5 Particulate matter with aerodynamic diameter ≤ 2.5 μm, PM10 Particulate matter with aerodynamic diameter ≤ 10 μm, SD Standard deviation, SO2 Sulfur dioxideFig. 3a–f Spatial distribution of long-term air pollutant concentration in participants’ residences. The red dots represent participants’ residential cities, and the color depth represents the concentration of each air pollutant. The World Health Organization air quality guidelines 2021 for PM2.5, PM10, NO2, O3, SO2, and CO concentrations were 5 μg/m3 (annual), 15 μg/m3 (annual), 10 μg/m3 (annual), 100 μg/m3(8-h average), 40 μg/m3 (24-h average), and 4 mg/m3 (24-h average), respectively The 7-day moving average levels of PM2.5, PM10, NO2, O3, SO2, and CO were 42.2 ± 25.2μg/m3, 73.0 ± 40.4μg/m3, 38.3 ± 14.8μg/m3, 60.3 ± 27.4μg/m3, 11.2 ± 8.1μg/m3, and 0.8 ± 0.3μg/m3 respectively. The trend for each pollutant varied in designative cumulative lag days. The moving averages of PM2.5, PM10, and NO2 gradually increased as the recording day approached (Lag0). The moving average of O3 concentration was lowest in Lag0-3 and had two peaks on Lag0 and Lag0-6, showing a U-shaped curve. The moving average of SO2 levels peaked at Lag0 and Lag0-3 and gradually decreased during the other periods. The moving averages of CO were essentially the same for cumulative lag days. In addition, a previous study [22] illustrated that the closer the cumulative days are to the record day, the larger the variations are for the values. Similar patterns were observed in our study.
## Overall data analysis
The long-term effects of ambient air pollutants on sleep parameters Figure 4 demonstrates the association between long-term exposure to air pollutants and sleep parameters. The adjusted mixed-effect models showed that a higher concentration of each air pollutant was associated with longer total sleep and light sleep durations, whereas with reduced deep sleep duration and proportion. Nitrogen dioxide had the greatest impact on the total sleep duration. Every 1-IQR increase in NO2 exposure prolonged the total sleep duration by 8.7 (8.08 to 9.32) minutes. Carbon monoxide was most closely related to both deep and light sleep duration, with each 1-IQR increase in CO shortening deep sleep duration by 5.0 (− 5.13 to − 4.89) minutes and prolonging light sleep duration by 7.7 (7.46 to 7.85) minutes. Statistically, elevated concentrations of each air pollutant significantly reduced the times of WASO per hour of sleep and the proportion of WASO duration, except for ozone, which was in contrast to a previous publication [16]. Although the regression coefficients were relatively low, they may still be meaningful because WASO rarely occurs during sleep.2.The short-term effects of ambient air pollutants on sleep parametersFig. 4a–g Associations between sleep parameters and long-term exposures to ambient air pollutants. Data are β ($95\%$ CI). β indicates partial regression coefficient. Estimates were associated with per 1-interquartile range increase in concentration of each pollutant. Adjusted for age, sex, BMI, city development level, altitude, season, and the type of night. * $p \leq 0.05.$ CI, confidence intervals; CO, carbon monoxide; NO2, nitrogen dioxide; O3, ozone; PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 μm; PM10, particulate matter with aerodynamic diameter ≤ 10 μm; SO2, sulfur dioxide; WASO, wake after sleep onset Figure 5 presents the associations between ambient air pollutant levels and sleep parameters on cumulative 0–6 days (from Lag0 to Lag0–6), adjusting for confounders. We observed that although the effect of the same air pollutant on sleep parameters on different cumulative days was inconsistent, the effect of each air pollutant on sleep parameters had a certain degree of similarity. Specifically, the majority of air pollutants had the greatest impact on sleep parameters at Lag0-6, including generally positive associations with total sleep and light sleep duration, and negative associations with both deep sleep and WASO, except for ozone, which had a negative association with total sleep duration and no significant association with light sleep duration. Conversely, some air pollutant (PM2.5, NO2, SO2, CO) levels were positively associated with deep sleep duration in Lag0-5, while their impacts were significantly smaller than those in Lag0-6. In summary, the cumulative effects from Lag0 to Lag0-5 were generally unset and insignificant. In contrast, the cumulative effects at Lag0-6 tended to become significant and comparable to the long-term effects but relatively less.3.Subgroup analyses of the associations between long-term exposure to ambient air pollutants and sleep parametersFig. 5a–g Associations between sleep parameters and short-term exposure to ambient air pollutants. Data are β ($95\%$ CI). β indicates partial regression coefficient. Estimates were associated with per 1-interquartile range increase in concentration of each pollutant. Adjusted for age, sex, BMI, city development level, altitude, season, and the type of night. * $p \leq 0.05.$ CI, confidence intervals; CO, carbon monoxide; NO2, nitrogen dioxide; O3, ozone; PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 μm; PM10, particulate matter with aerodynamic diameter ≤ 10 μm; SO2, sulfur dioxide; WASO, wake after sleep onset Since the short-term effects were unset and insignificant, we only estimated the association between long-term air pollution and sleep parameters classified according to age, sex, sleep duration, and season (Supplementary Table S2, S3, S4, S5).
First, the majority of ambient air pollutants in different subgroups still had a significant impact on sleep, which is generally consistent with the impact on the overall population. Second, most air pollutants’ effects on sleep parameters were significantly different between subgroups. When classified by sex (Supplementary Table S2), the associations were more apparent in the female group. In terms of age (Supplementary Table S3), the effects on total sleep, deep sleep, and light sleep durations were greater in the younger age group (age < 45 years), whereas the effect on WASO was more pronounced in older people (age ≥ 45 years). For those with longer sleep (≥ 7 h), the impacts on deep and light sleep durations were more remarkable. Nevertheless, the impact on deep sleep proportion (the ratio of deep sleep to total sleep and light sleep) was stronger in the shorter sleep duration group (< 7 h) (Supplementary Table S4). Considering the seasons (Supplementary Table S5), the effects of air pollutants on total sleep and light sleep durations were more significant in the cold seasons. The effects on WASO were more pronounced in the warm season. Finally, an interesting phenomenon is that the effects of ozone on some sleep parameters in the subgroups were inconsistent with the overall effects. The significance of its effect in different subpopulations showed partly opposite trends to those of other pollutants; for instance, ozone exposure prolonged deep sleep duration in females, younger individuals, and those in cold seasons. Additionally, the effect was greater in males.
## Stratified analyses
We conducted stratified analyses, as described above, to reduce repeated measures of outcomes and exposures meanwhile accounting for individual variation.
First, we used the method for sampling at time intervals of 7 days and 365 days. After data screening, 11,413 records remained for the analysis of long-term effects, and 181,392 records were for the analysis of short-term effects. The results were generally similar to the overall results (Fig. 6, Supplementary Figure S1). The long-term effects of CO on deep and light sleep duration remained strongest, with each 1-IQR increase in CO shortening 4.8 (− 6.05 to − 3.59) minutes of deep sleep and prolonging 6.4 (4.42 to 8.41) minutes of light sleep. Although the effects of individual air pollutants on some sleep parameters lost statistical significance, they remained consistent with the trends in the overall results. For instance, all pollutants had the trend of prolonging the total sleep duration and shortening the duration and proportion of WASO.Fig. 6a–g *Stratified analysis* 1—Effect of long-term pollutant exposure on sleep parameters. Use the method of extracting records every 365 days intervals. Data are β ($95\%$ CI). β indicates partial regression coefficient. Estimates were associated with per 1-interquartile range increase in concentration of each pollutant. Adjusted for age, sex, BMI, city development level, altitude, season, and the type of night. * $p \leq 0.05.$ CI, confidence intervals; CO, carbon monoxide; NO2, nitrogen dioxide; O3, ozone; PM2.5, particulate matter with aerodynamic diameter ≤ 2.5μm; PM10, particulate matter with aerodynamic diameter ≤ 10 μm; SO2, sulfur dioxide; WASO, wake after sleep onset Second, we adopted the method for calculating the averages of the outcome parameters. In total, 7682 and 114,194 records were utilized for long- and short-term effect analyses. The results were also consistent with the overall results, even though the associations were relatively fewer (Fig. 7, Supplementary Figure S2). For example, each 1-IQR increase in CO exposure was associated with 4.1 (− 5.28 to − 3.01) minutes shorter deep sleep and 5.7 (3.88 to 7.48) minutes longer light sleep. In summary, stratified analyses proved the robustness of the overall results from multiple perspectives. Fig. 7a–g *Stratified analysis* 2—Effect of long-term pollutant exposure on sleep parameters. Use the method of calculating the average value of the first data set of each subject. Data are β ($95\%$ CI). β indicates partial regression coefficient. Estimates were associated with per 1-interquartile range increase in concentration of each pollutant. Adjusted for age, sex, BMI, city development level, altitude, season, and the type of night. * $p \leq 0.05.$ CI, confidence intervals; CO, carbon monoxide; NO2, nitrogen dioxide; O3, ozone; PM2.5, particulate matter with aerodynamic diameter ≤ 2.5μm; PM10, particulate matter with aerodynamic diameter ≤ 10 μm; SO2, sulfur dioxide; WASO, wake after sleep onset
## Discussion
In this real-world big data analysis of sleep records from consumer wearable devices in the Chinese population, greater exposure to both long- and short-term ambient air pollution was associated with longer total sleep duration and reduction in deep sleep and awake time during sleep.
To our knowledge, this is the first study to illuminate the associations between ambient air pollution and sleep characteristics through big data analysis of users of a popular sleep tracker on this platform for 3 years and over 1 million nights. Additionally, this study not only focused on the effects of long-term air pollution exposure but also examined the relationship between short-term air pollution exposure and sleep outcomes, which remains limited in the current literature. Furthermore, this study is one of the few studies to evaluate the general effect of ambient air pollution on sleep, including inhalable particulate matter (PM2.5, PM10), nitrogen dioxide, sulfur dioxide, carbon monoxide, and ozone.
Recently, an increasing number of studies have focused on the effects of air pollution exposure on sleep health, demonstrating an overall adverse effect of various air pollutants on sleep across the lifespan [23]. However, these findings largely depend on self-report questionnaires [12–14, 24–26], which have been proven to vary widely from objective sleep trackers. As an emerging technical device, bracelets are portable, commercially available, and feasible to detect sleep and have therefore become increasingly popular among the general population in recent years, presenting researchers with an opportunity to analyze the big data captured by these devices and explore the effects of long-term and short-term air pollution exposure on population sleep health.
## Long-term exposures and sleep characteristics
In our study, long-term exposure to each air pollutant was positively associated with total sleep duration and negatively associated with deep sleep duration. Ours is the first large-scale study to demonstrate the association of deep sleep with ambient air pollution exposure, revealing that although people tend to have prolonged total sleep duration with increasing air pollutant concentrations, their sleep quality might remain poor due to the reduction of deep sleep. Deep sleep is a homeostatic process that reflects the restorative role of sleep [27]. Increasing evidence supports the crucial role of deep sleep in modulating a multitude of physiological processes, including memory consolidation [28], energy conservation [29], clearance of metabolites [30], and immunity [31]. The relationship between air pollutant concentration and total sleep duration remains controversial. Our result is in line with findings from a small prospective cohort study in the USA that recruited 98 participants with previous a diagnosis of episodic migraines and demonstrated that greater ozone exposure resulted in approximately 7 min longer sleep duration at night [32]. However, the study population was not representative, and the duration of air pollution exposure was insufficient. Contrarily, other studies have demonstrated negative associations between higher long-term air pollutant levels and sleep duration, but in specific subpopulations such as female teachers [33], preschoolers [34], and college freshmen [13], most of which were assessed by self-reported questionnaires. Furthermore, several studies [35–40] examined the specific effects of ambient air pollutant exposure on sleep-disordered breathing (SDB), which is generally measured by the apnea-hypopnea index (AHI) and oxygen desaturation index (ODI). They reported a positive association between SDB and air pollution. A significant deficit in deep sleep has also been observed in patients with SDB [41]. These studies might suggest a mechanism for the negative association between air pollutants and deep sleep duration.
Another novel point of our study lies in the investigation of the associations between both arousal time and arousal frequency and ambient air pollution, showing that elevated concentrations of air pollutants reduced times of WASO (wake after sleep onset) and duration of WASO, whereas ozone had no significant effect on WASO. A limited number of previous studies have examined the association between air pollution and WASO, but in specific populations with small sample scales and shorter observation times. A study of 98 participants with episodic migraine exploring the association between air pollution exposure and WASO over an average of 45 days reported modest positive associations between ozone and WASO. In contrast, lower SO2 and CO were associated with high WASO [32]. Another study, contrary to our conclusion, reported that PM2.5 levels in metal fumes were positively associated with wake times during sleep, as measured by actigraphy, among 16 welding workers in China [16]. Our results are based on big-data analysis of long-term exposure duration. Therefore, we hypothesized that even if elevated concentrations of ambient air pollutants increase total sleep and reduce WASO, the proportion of deep sleep decreases, thus leading to low sleep efficiency and poor sleep quality. This hypothesis might be confirmed from the other aspect. In 39,259 Chinese rural residents, poor sleep quality, evaluated by the Pittsburgh Sleep Quality Index (PSQI), was associated with an increase in long-term exposure to PM2.5, PM10, and NO2 [12]. In 59,574 children from northeastern China, sleep disorders were associated with increased pollutants [26].
## Short-term exposures and sleep characteristics
In the present study, the effects of short-term exposure on sleep characteristics were also investigated. We found that the effects of short-term exposure to all air pollutants were most pronounced at Lag0-6 and partially resemble the long-term effects, including longer total sleep and light sleep duration, shorter deep sleep duration, and WASO. A study from China, including 12,000 freshmen, observed a positive association between weekly PM2.5 exposure and sleep duration in self-reported questionnaires [14], which was in line with our study. From Lag0 to Lag0-5, their impacts were somewhat unset and insignificant. To the best of our knowledge, this is the first study to observe associations between multiple short-term air pollutant exposures and sleep parameters, suggesting that we ought to emphasize the negative impact of short-term air pollutant exposure on sleep in the meantime, as merely a 1-week exposure has the potential to evolve toward a similar long-term exposure.
The majority of epidemiological studies have explored the association between long-term exposure and sleep; however, few studies have highlighted the association between short-term exposure and sleep. Therefore, this study is also novel because we not only evaluated the effects of both long- and short-term exposures but also inquired into their intrinsic relationship. In 4312 adults from Northern Taiwan urban areas, Shen and colleagues [35] examined the associations between daily, weekly mean, and annual PM2.5 exposure and SDB. The study found that both long- and short-term exposure increases in PM2.5 levels were associated with SDB, and the effect of long-term PM2.5 exposure was more significant. In other studies examining the effects of long- and short-term air pollution exposure on blood pressure [42], cardiovascular diseases [43], and psychiatric disorders [44], consistency between long- and short-term effects was also observed to some extent among which was more pronounced in the long-term. The stronger effect of long-term exposure can be explained by the cumulative damage of short-term exposure.
## Subgroup analyses of long-term effects
The results of subgroup analyses are similar to the analysis results of the total population, which further proves the credibility of our conclusion that the influence of ambient air pollution on sleep is consistent in different seasons and populations with different genders, ages, and sleep durations.
Furthermore, when classified by sex, the relationship between long-term exposure to air pollution and sleep was generally greater in females, which is in line with previous studies [45–47]. On the one hand, long-term exposure to ambient air pollution was identified as a risk factor for mental disorders, such as depression [48]. Sleep disorder is frequently regarded as a symptom of a sub-health psychological state. Correspondingly, women are relatively more emotional and, thus, more susceptible to the effects of air pollution on sleep. On the other hand, a recent study reported that compared to male patients with OSA, stronger effects of air pollution on SDB parameters were observed in female patients [46]. This evidence not only supports our results but more significantly suggests that we should pay particular attention to the impact of air pollution on women’s sleep.
Age effect has also been investigated in our study. More significant associations between long-term air pollutant exposure and sleep parameters were observed in the younger population. Previous Chinese studies focusing on the association between long-term exposure to air pollution and sleep quality [12] or diabetes [9] also reported stronger effects in the younger subgroup. One plausible explanation for this discrepancy might be the activity pattern. Young people have more work and entertainment activities and are exposed to more air pollutants while aging individuals are less exposed to ambient air pollutants due to physical limitations. Nevertheless, the effect of air pollution on WASO is more pronounced in older adults. Increasing involuntary awakening during sleep is one of the hallmarks of human sleep alterations with age. Thus, fragile regulation of sleep/wakefulness and sleep homeostasis in older people might be more vulnerable to air pollution [49].
Another innovation of our study lies in stratification according to sleep duration. This is the first study to focus on the impact of air pollution on populations with different sleep durations. Both short (< 7 h) and long sleep durations (> 9 h) seem to be detrimental to health. Because few people in our study slept for more than 9 h, we classified sleep duration by 7 h as the threshold. Subgroup analyses showed that the impact of air pollution on sleep was greater in those who slept for more than 7 h. Participants who sleep shorter than 7 h might frequently suffer from other factors that more significantly affect sleep duration, such as various activities, working pressure, insomnia, and other mental and somatic disorders, thus obscuring the impact of air pollution on sleep. But the impact on deep sleep proportion was stronger in the shorter sleep duration group, which might be due to that shorter sleep durations influenced the results of such ratios rather than a direct effect of air pollutants.
Of note, the effects of ozone on sleep indicators are not entirely consistent with the overall effect, and the significance of its effect in different subgroups has a partially opposite trend to that of other air pollutants. Previous studies investigating the effects of air pollution on human health, such as arterial pressure [50], blood lipids [51], and circulating inflammatory markers [52], similarly found discrepant characteristics in the effects of ozone. However, to our knowledge, there is limited data to explain this phenomenon. We inferred that these complicated associations are affected by distinct biological mechanisms of diverse air pollutants [23]. In addition, the negative correlation between ozone concentration and other air pollutants might also play a role. Therefore, further studies are warranted.
## Strengths and limitations
The strengths of our analysis include the big data used to perform the analyses of sleep data from a large and representative population over 3 years. The effects of multiple common ambient air pollutants on sleep were comprehensively studied, and several factors strongly influencing sleep were controlled. We also investigated the effects of short- and long-term air pollution exposure in the same sample compared their similarities and differences and analyzed their intrinsic associations. Moreover, the association between deep sleep and air pollutant exposure was innovatively highlighted. Last but not least, we conducted multifaceted subgroup analyses to demonstrate the credibility of our results and compare discrepancies in the effects of air pollutants on sleep according to population characteristics, sleep duration, and seasonal conditions.
Several limitations should be acknowledged. First, although wearable sleep-tracking devices have been proven to be reasonably sensitive and can identify the sleep cycle with a certain degree of accuracy [53, 54], some recent studies have reported that they tend to underestimate sleep disruptions and overestimate total sleep times compared to polysomnography (PSG) [54, 55]. Second, we did not test the accuracy of this sleep-tracking device by using PSG or medical actigraphy. Third, because of the intermittent wearing of bracelets in the real world and the privacy policies of producers, we cannot continuously and regularly analyze the sleep of individuals for a sufficient duration. As a consequence, all sleep data were studied in units per night rather than conventionally per person. However, big data analysis and the mixed-effects model incorporate overall data, which can reduce confounding factors to some extent. The corresponding sleep research applied a similar analysis method [56]. Fourth, exposure levels were assigned using data from the nearest air monitor rather than personal air pollution exposure data, which may have misclassified some participants by randomly underestimating exposure in some and over-estimating exposure in others, and also overlooked the indoor air pollutant exposures. Fifth, although we adjusted for several confounders, there is still a possibility that unmeasured factors, such as temperature, humidity, traffic noise, and light, partly contributed to the associations. It should be clarified that we have obtained temperature and humidity data from multiple air monitoring stations across the country. However, because our study covered a wide geographic range of provinces and cities in China, the temperature and humidity data from decentralized monitoring stations cannot accurately reflect the actual exposure of the participants. Therefore, we did not adopt the temperature and humidity data in the study but took the season as a covariate, which is closely related to both temperature and humidity. Sixth, we did not consider multi-pollutant models because of strong correlations between the studied air pollutants. Seventh, we could not collect all the information of participants due to the limitations of the device app. Important information on comorbidities is missing. It cannot be excluded that the findings are related to subjects with diseases and maybe healthy persons show no alterations. Finally, repeated measures of outcomes and exposures might lead to potential bias, we thus supplemented the stratified analyses to demonstrate the robustness of the overall results.
## Conclusions
We analyzed sleep data from over 1 million nights captured by a consumer wearable sleep-tracking device over 3 years in the Chinese population. Our findings show that both short- and long-term exposure to ambient air pollution is associated with sleep characteristics, among which the cumulative effects of 1-week exposure tended to be comparable to those of long-term exposure. Generally, although people tend to have prolonged total sleep duration with increasing air pollutant concentrations, their sleep quality may remain poor due to the reduction of deep sleep. Subgroup analyses indicated greater effects on the individuals who were female, younger (< 45 years), slept longer (≥ 7 h), and in cold seasons, but the pattern of effects was mixed. More evidence should confirm these associations and clarify the biological mechanisms. In addition, researchers and sleep-tracking developers could collaborate on more stable sleep-tracking and accurate algorithms to facilitate large-scale studies for objective sleep evaluation.
## Supplementary Information
Additional file 1: Supplementary Table S1. Pearson’s correlation coefficients for long-term exposures to ambient air pollutants. Supplementary Table S2. Adjusted subgroup analysis of the associations between sleep parameters and long-term exposures to ambient air pollutants by sex. Supplementary Table S3. Adjusted subgroup analysis of the associations between sleep parameters and long-term exposures to ambient air pollutants by age. Supplementary Table S4. Adjusted subgroup analysis of the associations between sleep parameters and long-term exposures to ambient air pollutants by sleep duration. Supplementary Table S5. Adjusted subgroup analysis of the associations between sleep parameters and long-term exposures to ambient air pollutants by season. Supplementary Table S6. Associations between sleep parameters and long-term exposures to ambient air pollutants when adjusting for year, month, and day of week. Supplementary Table S7. Associations between sleep parameters and short-term exposure to ambient air pollutants when adjusting for year, month, and day of week. Supplementary Figure S1. ( a-g) *Stratified analysis* 1—Effect of short-term pollutant exposure on sleep parameters. Supplementary Figure S2. ( a-g) *Stratified analysis* 2—Effect of short-term pollutant exposure on sleep parameters.
## References
1. Mukherjee S, Patel SR, Kales SN, Ayas NT, Strohl KP, Gozal D. **An official American Thoracic Society statement: the importance of healthy sleep. Recommendations and future priorities**. *Am J Respir Crit Care Med.* (2015.0) **191** 1450-1458. DOI: 10.1164/rccm.201504-0767ST
2. Mogavero MP, DelRosso LM, Fanfulla F, Bruni O, Ferri R. **Sleep disorders and cancer: state of the art and future perspectives**. *Sleep Med Rev.* (2021.0) **56** 101409. DOI: 10.1016/j.smrv.2020.101409
3. Cappuccio FP, Cooper D, D’Elia L, Strazzullo P, Miller MA. **Sleep duration predicts cardiovascular outcomes: a systematic review and meta-analysis of prospective studies**. *Eur Heart J.* (2011.0) **32** 1484-1492. DOI: 10.1093/eurheartj/ehr007
4. Krittanawong C, Tunhasiriwet A, Wang Z, Zhang H, Farrell AM, Chirapongsathorn S. **Association between short and long sleep durations and cardiovascular outcomes: a systematic review and meta-analysis**. *Eur Heart J Acute Cardiovasc Care.* (2019.0) **8** 762-770. DOI: 10.1177/2048872617741733
5. Abbott SM, Weng J, Reid KJ, Daviglus ML, Gallo LC, Loredo JS. **Sleep timing, stability, and BP in the Sueño ancillary study of the Hispanic community health study/study of Latinos**. *Chest.* (2019.0) **155** 60-68. DOI: 10.1016/j.chest.2018.09.018
6. Zuraikat FM, Makarem N, Redline S, Aggarwal B, Jelic S, St-Onge M-P. **Sleep regularity and cardiometabolic heath: is variability in sleep patterns a risk factor for excess adiposity and glycemic dysregulation?**. *Curr Diab Rep.* (2020.0) **20** 38. DOI: 10.1007/s11892-020-01324-w
7. Billings ME, Hale L, Johnson DA. **Physical and social environment relationship with sleep health and disorders**. *Chest.* (2020.0) **157** 1304-1312. DOI: 10.1016/j.chest.2019.12.002
8. Newman JD, Bhatt DL, Rajagopalan S, Balmes JR, Brauer M, Breysse PN. **Cardiopulmonary impact of particulate air pollution in high-risk populations: JACC state-of-the-art review**. *J Am Coll Cardiol.* (2020.0) **76** 2878-2894. DOI: 10.1016/j.jacc.2020.10.020
9. Yang B-Y, Qian ZM, Li S, Chen G, Bloom MS, Elliott M. **Ambient air pollution in relation to diabetes and glucose-homoeostasis markers in China: a cross-sectional study with findings from the 33 Communities Chinese Health Study**. *Lancet Planet Health.* (2018.0) **2** e64-e73. DOI: 10.1016/S2542-5196(18)30001-9
10. Turner MC, Andersen ZJ, Baccarelli A, Diver WR, Gapstur SM, Pope CA. **Outdoor air pollution and cancer: an overview of the current evidence and public health recommendations**. *CA Cancer J Clin.* (2020.0) **70** 460-479. DOI: 10.3322/caac.21632
11. Ye J, Liu H, Zhang G, Li P, Wang Z, Huang S. **The treg/th17 imbalance in patients with obstructive sleep apnoea syndrome**. *Mediators Inflamm.* (2012.0) **2012** 815308. DOI: 10.1155/2012/815308
12. Chen G, Xiang H, Mao Z, Huo W, Guo Y, Wang C. **Is long-term exposure to air pollution associated with poor sleep quality in rural China?**. *Environ Int.* (2019.0) **133** 105205. DOI: 10.1016/j.envint.2019.105205
13. Yu H, Chen P, Paige Gordon S, Yu M, Wang Y. **The association between air pollution and sleep duration: a cohort study of freshmen at a university in Beijing, China**. *Int J Environ Res Public Health.* (2019.0) **16** 3362. DOI: 10.3390/ijerph16183362
14. An R, Yu H. **Impact of ambient fine particulate matter air pollution on health behaviors: a longitudinal study of university students in Beijing, China**. *Public Health* (2018.0) **159** 107-115. DOI: 10.1016/j.puhe.2018.02.007
15. An R, Shen J, Ying B, Tainio M, Andersen ZJ, de Nazelle A. **Impact of ambient air pollution on physical activity and sedentary behavior in China: a systematic review**. *Environ Res.* (2019.0) **176** 108545. DOI: 10.1016/j.envres.2019.108545
16. Chuang H-C, Su T-Y, Chuang K-J, Hsiao T-C, Lin H-L, Hsu Y-T. **Pulmonary exposure to metal fume particulate matter cause sleep disturbances in shipyard welders**. *Environ Pollut.* (2018.0) **232** 523-532. DOI: 10.1016/j.envpol.2017.09.082
17. Fang SC, Schwartz J, Yang M, Yaggi HK, Bliwise DL, Araujo AB. **Traffic-related air pollution and sleep in the Boston Area Community Health Survey**. *J Expo Sci Environ Epidemiol.* (2015.0) **25** 451-456. DOI: 10.1038/jes.2014.47
18. Roberts DM, Schade MM, Mathew GM, Gartenberg D, Buxton OM. **Detecting sleep using heart rate and motion data from multisensor consumer-grade wearables, relative to wrist actigraphy and polysomnography**. *Sleep.* (2020.0) **43** 1-19. DOI: 10.1093/sleep/zsaa045
19. Scott H, Lack L, Lovato N. **A systematic review of the accuracy of sleep wearable devices for estimating sleep onset**. *Sleep Med Rev.* (2020.0) **49** 101227. DOI: 10.1016/j.smrv.2019.101227
20. Depner CM, Cheng PC, Devine JK, Khosla S, de Zambotti M, Robillard R. **Wearable technologies for developing sleep and circadian biomarkers: a summary of workshop discussions**. *Sleep.* (2020.0) **43** 1-13. DOI: 10.1093/sleep/zsz254
21. 21.Huang X. 2019 Official list of China’s new first tier cities https://www.yicai.com/news/100200192.html?_b1577986606=t2019
22. Baek J, Kash BA, Xu X, Benden M, Roberts J, Carrillo G. **Association between ambient air pollution and hospital length of stay among children with asthma in South Texas**. *Int J Environ Res Public Health.* (2020.0) **17** 3812. DOI: 10.3390/ijerph17113812
23. Liu J, Wu T, Liu Q, Wu S, Chen J-C. **Air pollution exposure and adverse sleep health across the life course: a systematic review**. *Environ Pollut.* (2020.0) **262** 114263. DOI: 10.1016/j.envpol.2020.114263
24. Abou-Khadra MK. **Association between PM**. *Sleep Breath.* (2013.0) **17** 653-657. DOI: 10.1007/s11325-012-0738-7
25. Kheirandish-Gozal L, Ghalebandi M, Salehi M, Salarifar MH, Gozal D. **Neighbourhood air quality and snoring in school-aged children**. *Eur Respir J.* (2014.0) **43** 824-832. DOI: 10.1183/09031936.00113113
26. Lawrence WR, Yang M, Zhang C, Liu R-Q, Lin S, Wang S-Q. **Association between long-term exposure to air pollution and sleep disorder in Chinese children: the Seven Northeastern Cities study**. *Sleep.* (2018.0) **41** 1-10. DOI: 10.1093/sleep/zsy122
27. Ren R, Covassin N, Zhang Y, Lei F, Yang L, Zhou J. **Interaction between slow wave sleep and obstructive sleep apnea in prevalent hypertension**. *Hypertension.* (2020.0) **75** 516-523. DOI: 10.1161/HYPERTENSIONAHA.119.13720
28. Diekelmann S, Born J. **The memory function of sleep**. *Nat Rev Neurosci.* (2010.0) **11** 114-126. DOI: 10.1038/nrn2762
29. Léger D, Debellemaniere E, Rabat A, Bayon V, Benchenane K, Chennaoui M. **Slow-wave sleep: from the cell to the clinic**. *Sleep Med Rev.* (2018.0) **41** 113-132. DOI: 10.1016/j.smrv.2018.01.008
30. Ju Y-ES, Ooms SJ, Sutphen C, Macauley SL, Zangrilli MA, Jerome G. **Slow wave sleep disruption increases cerebrospinal fluid amyloid-β levels**. *Brain.* (2017.0) **140** 2104-2111. DOI: 10.1093/brain/awx148
31. Raison CL, Rye DB, Woolwine BJ, Vogt GJ, Bautista BM, Spivey JR. **Chronic interferon-alpha administration disrupts sleep continuity and depth in patients with hepatitis C: association with fatigue, motor slowing, and increased evening cortisol**. *Biol Psychiatry.* (2010.0) **68** 942-949. DOI: 10.1016/j.biopsych.2010.04.019
32. Li W, Bertisch SM, Mostofsky E, Vgontzas A, Mittleman MA. **Associations of daily weather and ambient air pollution with objectively assessed sleep duration and fragmentation: a prospective cohort study**. *Sleep Med.* (2020.0) **75** 181-187. DOI: 10.1016/j.sleep.2020.06.029
33. Zhong C, Longcore T, Benbow J, Chung NT, Chau K, Wang SS. **Environmental influences on sleep in the California Teachers Study Cohort**. *Am J Epidemiol.* (2022.0) **191** 1532-1539. DOI: 10.1093/aje/kwab246
34. Bose S, Ross KR, Rosa MJ, Chiu Y-HM, Just A, Kloog I. **Prenatal particulate air pollution exposure and sleep disruption in preschoolers: Windows of susceptibility**. *Environ Int.* (2019.0) **124** 329-335. DOI: 10.1016/j.envint.2019.01.012
35. Shen Y-L, Liu W-T, Lee K-Y, Chuang H-C, Chen H-W, Chuang K-J. **Association of PM with sleep-disordered breathing from a population-based study in Northern Taiwan urban areas**. *Environ Pollut.* (2018.0) **233** 109-113. DOI: 10.1016/j.envpol.2017.10.052
36. Billings ME, Gold D, Szpiro A, Aaron CP, Jorgensen N, Gassett A. **The association of ambient air pollution with sleep apnea: the multi-ethnic study of atherosclerosis**. *Ann Am Thorac Soc.* (2019.0) **16** 363-370. PMID: 30571166
37. Cheng W-J, Liang S-J, Huang C-S, Lin C-L, Pien L-C, Hang L-W. **Air pollutants are associated with obstructive sleep apnea severity in non-rapid eye movement sleep**. *J Clin Sleep Med.* (2019.0) **15** 831-837. DOI: 10.5664/jcsm.7830
38. Cassol CM, Martinez D, da Silva FABS, Fischer MK, Lenz MCS, Bós ÂJG. **Is sleep apnea a winter disease?: meteorologic and sleep laboratory evidence collected over 1 decade**. *Chest.* (2012.0) **142** 1499-1507. DOI: 10.1378/chest.11-0493
39. Yıldız Gülhan P, Güleç Balbay E, Elverişli MF, Erçelik M, Arbak P. **Do the levels of particulate matters less than 10 μm and seasons affect sleep?**. *Aging Male.* (2020.0) **23** 36-41. DOI: 10.1080/13685538.2019.1655637
40. Weinreich G, Wessendorf TE, Pundt N, Weinmayr G, Hennig F, Moebus S. **Association of short-term ozone and temperature with sleep disordered breathing**. *Eur Respir J.* (2015.0) **46** 1361-1369. DOI: 10.1183/13993003.02255-2014
41. Appleton SL, Vakulin A, D’Rozario A, Vincent AD, Teare A, Martin SA. **Quantitative electroencephalography measures in rapid eye movement and nonrapid eye movement sleep are associated with apnea-hypopnea index and nocturnal hypoxemia in men**. *Sleep.* (2019.0) **42** 1-9. DOI: 10.1093/sleep/zsz092
42. Cai Y, Zhang B, Ke W, Feng B, Lin H, Xiao J. **Associations of short-term and long-term exposure to ambient air pollutants with hypertension: a systematic review and meta-analysis**. *Hypertension.* (2016.0) **68** 62-70. DOI: 10.1161/HYPERTENSIONAHA.116.07218
43. Qiu X, Wei Y, Wang Y, Di Q, Sofer T, Awad YA. **Inverse probability weighted distributed lag effects of short-term exposure to PM and ozone on CVD hospitalizations in New England Medicare participants - Exploring the causal effects**. *Environ Res.* (2020.0) **182** 109095. DOI: 10.1016/j.envres.2019.109095
44. Braithwaite I, Zhang S, Kirkbride JB, Osborn DPJ, Hayes JF. **Air pollution (particulate matter) exposure and associations with depression, anxiety, bipolar, psychosis and suicide risk: a systematic review and meta-analysis**. *Environ Health Perspect.* (2019.0) **127** 126002. DOI: 10.1289/EHP4595
45. Li L, Zhang W, Xie L, Jia S, Feng T, Yu H. **Effects of atmospheric particulate matter pollution on sleep disorders and sleep duration: a cross-sectional study in the UK biobank**. *Sleep Med.* (2020.0) **74** 152-164. DOI: 10.1016/j.sleep.2020.07.032
46. Wang J, Wang W, Zhang W, Wang J, Huang Y, Hu Z. **Co-exposure to multiple air pollutants and sleep disordered breathing in patients with or without obstructive sleep apnea: A cross-sectional study**. *Environ Res.* (2022.0) **212** 113155. DOI: 10.1016/j.envres.2022.113155
47. Li D, Ji A, Lin Z, Liu J, Tan C, Huang X. **Short-term ambient air pollution exposure and adult primary insomnia outpatient visits in Chongqing, China: a time-series analysis**. *Environ Res.* (2022.0) **212** 113188. DOI: 10.1016/j.envres.2022.113188
48. Wei F, Yu Z, Zhang X, Wu M, Wang J, Shui L. **Long-term exposure to ambient air pollution and incidence of depression: a population-based cohort study in China**. *Sci Total Environ.* (2022.0) **804** 149986. DOI: 10.1016/j.scitotenv.2021.149986
49. Cajochen C, Münch M, Knoblauch V, Blatter K, Wirz-Justice A. **Age-related changes in the circadian and homeostatic regulation of human sleep**. *Chronobiol Int.* (2006.0) **23** 461-474. DOI: 10.1080/07420520500545813
50. Ljungman PLS, Li W, Rice MB, Wilker EH, Schwartz J, Gold DR. **Long- and short-term air pollution exposure and measures of arterial stiffness in the Framingham Heart Study**. *Environ Int.* (2018.0) **121** 139-147. DOI: 10.1016/j.envint.2018.08.060
51. Yang B-Y, Bloom MS, Markevych I, Qian ZM, Vaughn MG, Cummings-Vaughn LA. **Exposure to ambient air pollution and blood lipids in adults: the 33 Communities Chinese Health Study**. *Environ Int.* (2018.0) **119** 485-492. DOI: 10.1016/j.envint.2018.07.016
52. Lee H, Myung W, Jeong B-H, Choi H, Jhun BW, Kim H. **Short- and long-term exposure to ambient air pollution and circulating biomarkers of inflammation in non-smokers: a hospital-based cohort study in South Korea**. *Environ Int.* (2018.0) **119** 264-273. DOI: 10.1016/j.envint.2018.06.041
53. Kolla BP, Mansukhani S, Mansukhani MP. **Consumer sleep tracking devices: a review of mechanisms, validity and utility**. *Expert Rev Med Devices.* (2016.0) **13** 497-506. DOI: 10.1586/17434440.2016.1171708
54. Berryhill S, Morton CJ, Dean A, Berryhill A, Provencio-Dean N, Patel SI. **Effect of wearables on sleep in healthy individuals: a randomized crossover trial and validation study**. *J Clin Sleep Med.* (2020.0) **16** 775-783. DOI: 10.5664/jcsm.8356
55. Meltzer LJ, Hiruma LS, Avis K, Montgomery-Downs H, Valentin J. **Comparison of a commercial accelerometer with polysomnography and actigraphy in children and adolescents**. *Sleep.* (2015.0) **38** 1323-1330. DOI: 10.5665/sleep.4918
56. Robbins R, Affouf M, Seixas A, Beaugris L, Avirappattu G, Jean-Louis G. **Four-year trends in sleep duration and quality: a longitudinal study using data from a commercially available sleep tracker**. *J Med Internet Res.* (2020.0) **22** e14735. DOI: 10.2196/14735
|
---
title: 'The relationship between self-assessment living standard and mental health
among the older in rural China: the mediating role of sleep quality'
authors:
- Beibei Zhang
- Xianwen Wang
- Song Liu
- Min Zhang
- Xinran He
- Weizheng Zhu
- Hong Ding
journal: BMC Public Health
year: 2023
pmcid: PMC9993696
doi: 10.1186/s12889-023-15157-1
license: CC BY 4.0
---
# The relationship between self-assessment living standard and mental health among the older in rural China: the mediating role of sleep quality
## Abstract
### Background and objective
Mental health imbalance are the main cause of anxiety, depression and happiness reduction in the older. Self-assessment living standard and sleep quality are both influencing factors of mental health. Meantime, self-assessment living standard has an impact on sleep quality. But there’s no research on the relationship between the three, we conducted this study to explore the relationship between self-assessment living standard and mental health and the mediating role of sleep quality among the older in rural areas of China.
### Methods
Using typical field sampling method, M County, Anhui Province was selected as the investigation site, and a total of 1223 respondents were selected. With the help of questionnaires enclosing respondents’ sociodemographics information, 12 Items General Health Questionnaire (GHQ-12) and Pittsburgh Sleep Quality Scale (PSQI), face-to-face interviews were used to collect data. Bootstrap test was used for data analysis.
### Results
The results showed that the age of the respondents ranged from 60 to 99 years, with an average age of (66.53 ± 6.77) years, the proportion of the older with a tendency to mental health problems was $24.7\%$. Most of the older people’s self-assessment living standard was normal (average score was 2.89 ± 0.726), accounting for $59.3\%$ of the total. The average sleep quality score was (6.97 ± 4.066), and $2.5\%$ of the respondents reported serious sleep problems. older with low self- assessment living standards were more likely to report a higher propensity for psychological problems (β = 0.420, $P \leq 0.001$) and poorer sleep quality (β = 0.608, $P \leq 0.001$) than older with high self- assessment living standards. Mental health of the older may be related to sleep quality (β = 0.117, $P \leq 0.001$). In addition, the effect of self- assessment living standard on mental health was significantly mediated by sleep quality (β = 0.071, $P \leq 0.001$).
### Conclusion
Mental health is associated with self-assessment living standard, with this association mediated by sleep quality. A reasonable mechanism needs to be established to improve self-assessment living standard and sleep quality.
## Introduction
According to the results of China’s seventh census, the number of Chinese aged 60 and above has reached 264 million, accounting for $18.70\%$ of the total population. The population aged 65 and above is about 190 million, accounting for $13.50\%$ [1]. With the deepening of the aging degree in China, the psychological problems of the older have become increasingly prominent due to the changes in social and family structure, physical aging, the influence of diseases, and inadequate support system [2]. The data showed that the proportion of the older with mental problems in various regions of China ranged from 2.26 to $69.68\%$ [3], Among them, the proportion of the older in rural areas with mental health problems is 1.76 times that of the older in urban areas [4], meanwhile, the life security, physical function and social support of the older in rural areas are significantly lower than those in urban areas [5]. According to the China Statistical Yearbook, the per capita disposable income of rural residents was 18,931 yuan in 2021, while that of urban residents was 42,412 yuan. Urban residents’ per capita disposable income was 2.5 times that of rural residents [6]. Although the government provides certain security policies for the elderly, the national standard of 55 yuan per person per month for elderly people over 60 without employee pension insurance in China. Most rural residents do not enjoy the fair treatment of social endowment insurance. There are differences in pension funds paid and received by rural and urban residents. The pension funds paid by rural residents account for a larger proportion of their income, but the insurance funds received by rural residents are lower than those of urban residents [7].
WHO defines health as a state of complete physical, mental and social adaptation, not just no disease or infirmity [8]. Psychological imbalance not only has a direct impact on physical health, but also has an indirect impact on social support and quality of life, bringing a huge burden of disease [9]. Studies have shown that age, economic status, physical status, social support, social role and lifestyle change are the main factors affecting the mental health of the older [10]. Research shows that rural residents in health spending suffered greater economic burden, its proportion is 2.4 times that of urban residents [11], at the same time, another study found, catastrophic health expenditure of Chinese rural areas was obviously higher than that of urban areas [12], studies have shown that China’s rural older people’s quality of life in rural areas and the low state of the economy [13]. Jiang Haochen’s research proved that compared with the older with a more affluent living standard, the older with a poorer living standard reported worse mental health [1], and reveal the self- assessment living standards may have an important impact on the mental health of the older population. Research pointed out that in addition to the objective measurement of economic level, people’s subjective economic pressure measurement on the personal happiness and satisfaction occupy more important position [14], of the living standards of their subjective evaluation mainly from compare yourself with other living conditions, especially in rural areas of China the older by education degree is generally low, less mental recreation makes it easier to compare oneself with others. If one continues to believe that his or her standard of living is below the average or reference level, this long-term gap will indirectly affect his or her mental health. At present, China has achieved comprehensive poverty alleviation, and absolute poverty, which is measured by meeting basic survival needs, has been eliminated. However, the identification of relative poverty, which reflects the gap between individual economy, living conditions and local average living standards, has received little attention [15]. In addition, relevant studies on the living standards of the older in rural *China focus* more on objective evaluation of living standards, and less on subjective evaluation [16–18].
Self-assessment living standard is a subjective evaluation of their own living standard, which reflects the satisfaction and expectation of the older to their living conditions. Evidence shows that the worse self-assessment living standard contributes to poor sleep health. For example, due to the rapid growth of social economy, rising price level, a large number of young and middle-aged people go out to work, the rural older people often feel helpless and declined the standard of living, think about things at night, and then suffer from poor sleep quality [19]. In turn, poor sleep quality will affect the older’s daily activities, social interactions and attitudes toward life, resulting in lower life satisfaction and worse self-assessment living standards [19].
Sleep quality usually declines gradually with age [20], and some older people may suffer from sleep disorders. The incidence of sleep disorders among the older over 60 years old in *China is* $30\%$ ~ $40\%$ [21]. Sleep disorders are mainly manifested as difficulty in falling asleep and maintaining sleep, which leads to sleep deficiency and fatigue, and patients find it difficult to recover from sleep [22]. A large number of studies have shown that poor sleep quality will not only increase the occurrence of chronic diseases, but also increase the risk of death [23–25]. According to the view of chronobiology theory, the biorhythmic system is closely related to many diseases, and regular sleep contributes to the stability of human psychological functions [26]. If the body’s sleep time changes and the body’s functions are disordered, it will affect the disorder of emotional function and lead to a decline in psychological conditions. Studies have shown that there is a significant correlation between sleep quality and mental health, and sleep disorders (such as insomnia, narcolepsy, sleep apnea and circadian complaints) have a high comorbidity rate with depression and anxiety [27, 28], optimizing sleep quality can promote mental health [29]. Meanwhile, the older with poor sleep quality are more likely to suffer from hypertension, depression and other diseases [25]. At present, there are many studies on the influencing factors of sleep disorders in the older in China. For example, moderate exercise is beneficial to improve the sleep quality of the older [30]. Interpersonal relationship can affect the sleep quality of the older by affecting their mood [31]. However, the potential causes of sleep quality among the older in China have not been fully appreciated.
Given that sleep quality is one of the important predictors of mental health [32]. Improving self-assessment living standards in older may reduce mental illness by improving sleep quality. However, little is known about the mechanisms that link self-assessment living standard and mental health prospectively. There are no studies that have tested whether poor sleep quality mediates the relation between self-assessment living standard and mental health [33–35]. In the context of the rapid development of aging society has become the basic national conditions of China, the mediation of the impact of self-assessment of living standards on mental health has become an issue that needs to be studied in the prevention of psychological abnormalities in the older. Therefore, we conducted a cross-sectional study to uncover the relationship between self-assessment living standards and mental health among older people in rural China, and to consider the role of sleep quality in this study. This study can provide a theoretical basis for improving the sleep quality and mental health of rural elderly.
## Study design and data collection
From July to September 2021, we conducted a cross-sectional survey in M County, Anhui Province, central China. M county is a pilot county of compact county medical community. The local county and village medical and health service system is sound and relevant departments have strong coordination, providing good external conditions for the research work.
Two towns in M County, Anhui Province, China were randomly selected, and 5 villages were randomly selected in each town. M *County is* a typical rural area in central China. Its level of economic development and per capita income are below the average level of China. In 2021, the per capita disposable income of permanent residents in M County was 24,344 yuan, and the per capita disposable income of permanent rural residents was 17,221 yuan [36]. The annual per capita disposable income of Chinese residents was 35,128 yuan [37], the economic level of the older in M County was significantly lower than the national average.
The older ≥ 60 years old in the villages were investigated. The selection criteria of the research objects were as follows: [1] subjects aged 60 years and above (according to Article 2 of the Law on the Protection of the Rights and Interests of the older, the age of the older is 60 years old); [2] Subjects who have lived there for at least 1 year at the time of investigation. Exclusion criteria included sensory or cognitive impairment, contraindications to physical activity, a medical diagnosis of a primary sleep disorder (for example, sleep apnea or primary insomnia). Before the investigation, all subjects were told the purpose and procedure of the study orally. The investigators were all postgraduates from Anhui Medical University who had received unified training and doctors from local township health centers. Each subject was visited and interviewed face-to-face. A total of 1223 older people were surveyed, of whom 1188 completed the survey, with an effective response rate of $97.14\%$ ($\frac{1188}{1223}$).
## Measurement of self-assessment living standard
This study used self-assessment living standard to measure the living standard of the older. The respondents were asked “*What is* your living standard in the local area?“, the answers were divided into five levels: “very good”, “good”, “average”, “poor” and “very poor”, with a value of 1, 2, 3, 4 and 5 respectively. The higher the score was, the lower the self-assessment living standard.
## Measurement of sleep quality
The Pittsburgh Sleep Quality Scale (PSQI) was used in this study. PSQI was developed by Buysse et al. [ 38] for self-assessment of sleep in the past 1 month. The scale consists of 7 dimensions, including subjective sleep quality, sleep time, sleep time, sleep efficiency, sleep disorders, sleep drugs, and daytime dysfunction. Each dimension is 0 ~ 3 points, and the cumulative score is the total score. The lower the score, the better the sleep, the cumulative score of 7 or more indicating sleep disturbance [39]. The Cronbachα coefficient of the scale was 0.77, the half-fold reliability was 0.83, and the structural validity was 0.63–0.91, indicating that the scale had good reliability and validity and was widely used [38, 40].
## Measurement of mental health
Mental health was measured using the 12 Items General Health Questionnaire (GHQ-12), a self-assessment screening tool that has been successfully applied to the Chinese sample. There are 12 items in the questionnaire, and the answers to each item are divided into four options. The first two items are counted as 0 points, and the last two items are counted as 1 point. The total score ranges from 0 to 12 points. The higher the GHQ-12 score, the higher the risk of developing psychological disorders [41]. The Cronbach’s alpha coefficient of GHQ-12 was 0.793.
## Statistical analysis
First, we used the Chi-square test to examine differences in mental health among older adults with different living standards and quality of sleep. Rates and percentages are used to describe the demographic characteristics of different groups of subjects.
Next, Pearson correlation analysis was used to test the correlation between variables. In order to further explore the specific role path of sleep quality in the mediating effect of self-assessment living standard on mental health, this study adopted the mediating effect test method proposed by Hayes [42], taking self-assessment living standard as independent variable, mental health as dependent variable and sleep quality as intermediary variable to test the significance of the mediating effect. Mediation test Model 4, developed by Hayes based on the SPSS macro program PROCESS, uses the non-parametric percentage Bootstrap method with bias correction to extract an estimated $95\%$ confidence interval repeatedly for 5000 times. When the confidence interval of each path coefficient does not include 0, it indicates that the mediation effect is significant. According to the test results, the mediation effect path analysis model is drawn, as shown in Fig. 1.
## Characteristics of participants
Table 1 describes the general demographic characteristics of the respondents. The study involved 1,188 participants, all participants are between the ages of 60–99 (mean age = 66.53 years, SD = 6.577). The average of self-assessment living standard is (2.89 ± 0.726), and the sleep quality was (6.97 ± 4.066), mental health is (2.08 ± 1.90). Of these participants, 895 reported good mental health and 293 reported poor mental health. There are statistically significant differences between the two groups in basic demographic characteristics such as gender, education level, living status, working status, chronic diseases and hospitalization, and sleep quality. Among the 895 subjects with good mental health, $50.72\%$ ($\frac{454}{895}$) are male, $36.65\%$ ($\frac{328}{895}$) are aged between 60 and 69, $21.90\%$ ($\frac{196}{895}$) lived with their spouse, and $85.70\%$ ($\frac{767}{895}$) had not seen a doctor in the last two weeks. $60.56\%$ ($\frac{542}{895}$) had not been hospitalized in the past one year.
Table 1General characteristics of the respondents and Chi-square test results of influencing factors of mental health in rural older people ($$n = 1188$$)Total ($$n = 1188$$)Mental healthχ2 p-value Good ($$n = 895$$)Poor ($$n = 293$$) Gender 5.2690.022 Male580(49.3)454(50.73)126(43.00) Female608(50.7)441(49.27)167(57.00) Age(years) 0.0270.986 60–69435(36.6)328(36.65)107(36.52) 70–79558(47.0)421(47.04)137(46.76) ≥ 80195(16.4)146(16.31)49(16.72) *Married status* 1.6130.656 Married841(70.8)642(71.73)199(67.92) Divorced8(0.7)6 (0.67)2(0.68) Widowed289(24.3)21(2.3)79(26.96) Others50(4.2)37(4.13)13(4.44) Education level 11.561< 0.001 Primary and below974(81.7)712(79.55)259(88.40) Junior and above217(18.3)183(20.45)34(11.60) *Employment status* 34.228< 0.001 Normal work265(22.3)197(22.01)68(23.21) Half work380(32.0)309(34.53)71(24.23) Housework316(26.6)250(27.93)66(22.53) Don’t work224(18.9)137(15.31)87(29.69) Other3(0.3)2(0.22)1(0.34) Living style 7.8680.020 Living alone250(21.0)485(54.19)145(49.49) Living with spouse630(53.0)196(21.90)54(18.43) Other308(25.9)214(23.91)94(32.08) Living standard 27.189< 0.001 Good282(23.7)239(26.70)44(15.02) Common705(59.3)529(59.11)176(60.07) Bad308(25.9)128(14.30)73(24.91) Chronic diseases 7.6880.006 Yes915(77.0)672(75.08)243(82.94) No273(23.0)223(24.92)50(17.06) Physical discomfort 9.2650.002 (Within two weeks) Yes996(83.8)767(85.70)229(78.16) No192(16.2)128(14.30)6(2.05) Hospitalization 12.490< 0.001 (*Within a* year) Yes685(57.7)524(58.55)143(48.81) No503(42.3)353(39.44)150(51.19) Sleep quality 29.578< 0.001 Good637(53.61)579(64.69)58(19.80) Bad551(46.38)316(35.30)235(80.2)
## The relationship between living standard, sleep quality and mental health
Pearson correlation analysis is conducted on the data of self- assessment living standard, sleep quality and mental health scales, and the results are shown in Table 2. The score of mental health status is significantly positively correlated with the score of self- assessment living standard and sleep quality.
In Table 3, bootstrap test analysis results showed that self-assessment living standard had a significant direct impact on mental health (β = 0.420, $95\%$CI 0.273–0.567). older people with higher self-assessment living standards are likely to report higher levels of mental health. Meanwhile, self-assessment living standard is significantly associated with sleep quality: higher self-assessment living standard is associated with better sleep quality compared with lower self-assessment living standard (β = 0.608, $95\%$CI 0.282–0.933). There is also a link between sleep quality and mental health. Higher sleep quality scores are associated with higher mental health level (β = 0.117, $95\%$CI 0.091–0.142). Based on the results, a path map of self-assessment living standards, sleep quality and mental health is drawn, as shown in Fig. 1.
Table 2Correlation analysis of subjective evaluation of living standard, sleep quality and mental healthSelf-assessment of living standardsSleep qualityMental healthSelf-assessment of living standards1Sleep quality0.107***1Mental health0.178***0.265***1for: ***means P＜0.001 Table 3Bootstrap test of self- assessment living standard, sleep quality and mental healthDependent variablePredictive variableStandardized regression coefficientSET$95\%$CIR²FPLLCIULCISleep qualityLiving standard0.6080.1663.6620.2820.9330.01213.4110.000Mental healthSleep quality0.1170.0138.8070.0910.1420.09560.4630.000Living standard0.4200.0776.3770.2730.5670.34040.670.000 Fig. 1Mediating model of self-assessment living standard, sleep quality and mental health
## Mediating effect analysis of self-assessment living standard, sleep quality and mental health
Sleep quality is a potential mediator in the association between self-assessment living standards and mental health (β = 0.071, $95\%$CI 0.021–0.099). Bootstrap test results showed that the $95\%$CI of direct and indirect effects of self-assessment living standard on mental health score did not include 0. The results indicate that sleep quality plays a partial mediating role in the relationship between the self-assessment living standard and mental health of the older in rural areas, and the partial mediating effect value is 0.071, accounting for $14.46\%$ of the total effect. The specific results are shown in Table 4.
Table 4The mediating effect of sleep quality on self-assessment living standard and mental healthEffectBoot SE t P $95\%$CIThe total effectX->M->Y0.4910.0776.3770.0000.3390.642Direct effectX->Y0.4200.0755.6040.0000.2730.567Indirect effectM->Y0.0710.0230.0290.119
## Discussion
This study proved the relationship among self-assessment living standard, mental health and sleep quality among the older in rural areas of Anhui Province. The results showed that self-assessment living standard was closely related to mental health, and the older with low self-assessment living standard had a higher risk of psychological problems. However, this correlation occurs through both direct and indirect effects. Sleep quality played a significant partially mediating role between self-assessment living standard and mental health.
## Rural older self-assessment living standard, sleep quality and mental health status
Among the 1,188 respondents, 702($59.34\%$) thought their living standard was average, 202($16.92\%$) thought their living standard was poor or even very poor, and 284($23.74\%$) thought their living standard was good, among which the proportion of self-assessment was average or poor was significantly higher than the research results of Jiang Haochen [43]. The reason may be that the regional distribution of the survey objects and the total number of samples are different. Meanwhile, the economic status and medical level of rural areas are lower than the national level [43]. Some studies have classified the lifestyle of the older in China into four types: survival type, healthy type, risk type and mixed type, with $45\%$, $25\%$, $13\%$ and $17\%$ respectively. The life style of the older in rural Areas of *China is* mainly subsistence lifestyle [44]. They control the living cost and have few social participation behaviors, mostly watching TV and listening to radio, and less intake of fresh fruits and fish in daily life, which may be an important reason for their low self-assessment of living standard [45].
The results of this study show that $24.7\%$ of the rural older have a tendency to have mental health problems, which is similar to $18.5\%$~$24.47\%$ of the general older population [46–48]. Compared with the urban older, the rural older in China have less financial resources, social support, family companionship, etc., and relatively overlapping living environment, which may have a negative impact on their mental health [49]. Gender, education level and working state have statistical significance to mental health difference, which is consistent with the research conclusions of Liang Xiaoli; Zhang Pei [50, 51].
Compared with men, women are more sensitive to emotions and more prone to mental problems [52]. The older with high education level have higher cognitive ability and health awareness, and can enrich themselves by reading books, reading newspapers and participating in social activities, so as to better cope with difficulties. On the other hand, the older with a low education level, limited by their cognitive level, have a poor ability to judge things and accept new things, and are prone to suffer from inferiority complex, loneliness and other psychological problems. Their enjoyment of life is relatively limited, which is more likely to cause psychological problems [53]. Older who are able to work regularly tend to report better mental health, possibly because working in rural areas is the norm. Older people who are unable to work normally always experience feelings of guilt, particularly if they are not able to work at all, they will see themselves as a burden on their families [54].
In addition, $48.4\%$ of participants with PSQI scores above 7, and $2.5\%$ of participants reported serious sleep problems, which was higher than the results of Ding Kunxiang’s study [55]. This may be due to the different time points we surveyed and changes in the social environment in rural areas [56].
## The effect of self-assessment living standard on mental health
Previous studies have observed a correlation between self-assessment living standards and mental health [57–59]. Previous studies have revealed the impact of poverty on the mental health of the older. For example, one research [2012] found that poverty was significantly correlated with cognitive impairment and depression in the older in India [60]. A study [2017] on the older in rural China shows that the mental health status of the older in poor families is worse [61]. A scholar. [ 2021] conducted a study on the older aged 65 and above in China, which verified that the older with lower self-assessment living standards had more severe negative psychological emotions [62]. The negative impact of lower living standards on mental health may come from the negative impact of less economic foundation and resources on individuals’ physical health and social behavior, or the poverty-related living environment may lead to more stress and negative emotions, thus affecting mental health [1]. In addition, social comparison theory believes that social comparison is intra-group and inter-group comparison, and the latter has a more obvious impact on individual psychological development [63]. However, these studies use different participant groups (e.g., young adults) or analytical methods (e.g., traditional regression and correlation). Although those studies differed from ours in terms of specific details, the results regarding the negative relationship between self-assessment living standard and mental health were consistent, which confirms the results of our study.
## The impact of self-assessment living standards on sleep quality
This study found that self-assessment living standard was associated with an increased likelihood of high sleep quality among the older in rural areas in Anhui province. A study conducted in Yunnan Province, China, showed that older people in rural areas with lower family property have a higher likelihood of sleep disorders [64]. It may be related to their sensitive emotions. The older with lower living standards are more likely to have negative thoughts and to have random thoughts before going to sleep, which affects their sleep. Low level of self-reported life means not only a single economic sources, less material resources, and poor living environment, also means that more stressful life events and negative mood [65], which will result in its sleep problems obviously increased, low level of self-reported life will bring such as difficulty falling asleep, wake up, wake up at night and having nightmares and other sleep problems.
## The mediating role of sleep quality in self-assessment living standard and mental health
This study found that sleep quality was the mediating variable between the self-assessment living standard and mental health of the rural older, playing a partial mediating role, accounting for $14.46\%$ of the total effect. Specifically, older who reported low self-assessment living standards are more likely to suffer from poor sleep quality, which in turn led to worse mental health over time. According to the theory of chronobiology [66], the onset of mental diseases is closely related to the biorhythmic system, and the elderly who report their poor living standards are prone to cranky thoughts at night, resulting in the disorder of the sleep system, destroying the normal regulatory mechanism of the human body, and increasing the risk of psychological problems. Earlier studies have also confirmed this conclusion. In a study of German communities and students found that when individuals’ sleep quality and mental health are not healthy, measures to improve sleep can better promote the improvement of mental health [63]. Another study found that poor sleep quality is associated with increased incidence of violations, aggression, depression and anxiety [67]. One study in China [68] shows that when the proportion of children going out is high, the negative missing time effect is dominant, which is not conducive to the improvement of parents’ health. Possible explanations for this result is that although China has comprehensive poverty alleviation, rural residents general living standards improve gradually, but the income of the rural older people in China still is generally low, cultural life still relatively monotonous and boring [69], coupled with the decline in physiological function, relative lack of medical resources, children migrant workers and other factors, It will have a negative impact on their economic status and living standards for a long time. At the same time, they are easy to fall into sleep difficulties, easy to wake up, nightmares and other sleep disorders, leading to their inability to relieve mental stress through sleep, resulting in psychological problems. When the quality of sleep is poor in the older, it will also affect their self-rated living standards [70]. Poor sleep quality will affect the older’s daily activities, social interactions and attitudes toward life, resulting in lower life satisfaction and worse self-assessment living standards [71]. Therefore, China can help prevent sleep and psychological problems in the older by strengthening the training of Primary healthcare workers in this therapy.
## Advantages and limitations
Advantages: First, the effective response rate of this study is $99.00\%$ ($\frac{1188}{1223}$), as we all know, studies with higher effective response rates were more reliable. Secondly, we used internationally recognized measurement questionnaires to make objective measurements of the study subjects. In addition, this is the first study to examine the relationship between the three variables and the mediating role of sleep quality in the older population in Anhui Province.
However, this study also has the following limitations: First, self-assessment living standards, sleep quality, and mental health were measured through questionnaires, which means that self-reported biases may affect the results. At the same time, because the measurement of the self-assessment living standard of the older is single, the reliability of the answer will be reduced, which may impact the research results. Second, since this study is a cross-sectional study, although there is a correlation between self-assessment living standards, sleep quality and mental health, it is difficult to determine the causal association. Finally, the investigation objects of this study only cover rural areas of Anhui Province, and the extensibility of the results of this study is limited by factors such as economic development and cultural background.
## Conclusion
Our research shows that self-assessment low living standards and poor sleep quality can exacerbate psychological problems. In addition, sleep quality mediates the relationship between self-assessment living standards and mental health. Our results may help alleviate psychological problems and improve sleep quality of rural older, and provide information for clinical prevention of diseases. It is suggested that the government and society pay more attention to the health of the rural older, improve the rural older security system, and improve the level of security.
## References
1. Jiang H, Jimin G. **Empirical research on subjective relative poverty, Mental Health and Life satisfaction of the older**. *Popul Dev* (2021.0) **27** 24-35
2. Zhang D, Zhonghong Z. **Study on the current Situation and influencing factors for Mental Health Service demand in the Community older**. *J Qilu Nurs* (2021.0) **27** 17-20
3. Guo Lixin W, Jianyu Z. **Mental health status of middle-aged and older in China**. *Chin J Gerontol* (2015.0) **35** 782-3
4. Guo A, Lai DWL. **A comparative study on depressive symptoms of Urban and Rural older Population in China**. *J Shandong Normal Univ* (2011.0) **56** 106-10
5. 5.Yang JL, Zhenjie HY. Matching of home care service supply and demand, satisfaction degree and its impact on mental health of disabled older in rural areas: An analysis of survey data in Shandong and Zhejiang. Popul Develop. 2022;28(05):70–83.
6. 6.People’s Republic of China Statistics BureauChina Statistical yearbook [M]2021BeijingChina Statistics Press. *China Statistical yearbook [M]* (2021.0)
7. 7.Li B. Guiding Social Pension Security from “Fragmentation” to “systematization”. People’s Forum. 2018;No.608(27):58–59.
8. Rudnicka E, Napierała P, Podfigurna A, Męczekalski B, Smolarczyk R, Grymowicz M. **The World Organization (WHO) approach to healthy ageing**. *Maturitas* (2020.0) **139** 6-11. DOI: 10.1016/j.maturitas.2020.05.018
9. Wulsin L, Herman J, Thayer JF. **Stress, autonomic imbalance, and the prediction of metabolic risk: a model and a proposal for research**. *Neurosci Biobehav Rev* (2018.0) **86** 12-20. DOI: 10.1016/j.neubiorev.2017.12.010
10. Ohrnberger J, Fichera E, Sutton M. **The relationship between physical and mental health: a mediation analysis**. *Soc Sci Med* (2017.0) **195** 42-9. DOI: 10.1016/j.socscimed.2017.11.008
11. Chen S, Guo L, Wang Z, Mao W, Ge Y, Ying X. **Current situation and progress toward the 2030 health-related sustainable development goals in China: a systematic analysis**. *PLoS Med* (2019.0) **16** e1002975. DOI: 10.1371/journal.pmed.1002975
12. Ma X, Wang Z, Liu X. **Progress on catastrophic health expenditure in China: evidence from china family panel studies (CFPS) 2010 to 2016**. *Int J Environ Res Public Health* (2019.0) **16** 4775. DOI: 10.3390/ijerph16234775
13. Ran L, Jiang X, Li B. **Association among activities of daily living, instrumental activities of daily living and health-related quality of life in older Yi ethnic minority**. *BMC Geriatr* (2017.0) **17** 74. DOI: 10.1186/s12877-017-0455-y
14. Selenko E, Batinic B. **Beyond debt. A moderator analysis of the relationship between perceived financial strain and mental health**. *Soc Sci Med* (2011.0) **73** 1725-32. DOI: 10.1016/j.socscimed.2011.09.022
15. 15.Wu C, Ling W. Focus on poverty among the older in rural China. Social Stud. 2005;22(S1):1–8.
16. Gao Li L, Shuzhuo WZ. **Urban-rural differences in the Effect of Community Poverty on older Mental Health–A Study based on the 2014 chinese longitudinal aging Social Survey**. *Popul Dev* (2019.0) **25** 28-49
17. Ma C, Song Z, Zong Q. **Urban-rural inequality of opportunity in Health Care: evidence from China**. *Int J Environ Res Public Health* (2021.0) **18** 7792. DOI: 10.3390/ijerph18157792
18. Wang C, Lang J, Xuan L, Li X, Zhang L. **The effect of health literacy and self-management efficacy on the health-related quality of life of hypertensive patients in a western rural area of China: a cross-sectional study**. *Int J Equity Health* (2017.0) **16** 58. DOI: 10.1186/s12939-017-0551-9
19. 19.Haiyan L, Lining Y, Shaoquan Z, Junrong S, Baoshi Z, Jiang W. Study on quality of life and its influencing factors of Dong older people in rural areas of Guizhou. J Qiannan Med Coll Nationalities. 2022;35(03):188–92.
20. Tian Yuan L. **Epidemiological study of sleep disorder in the older**. *Chin J Epidemiol* (2017.0) **38** 988-92
21. Song Aiqing C. **Analysis of sleep quality and risk factors of older in community in Tangshan city**. *Nurs Res* (2011.0) **25** 298-9
22. Xie Z, Chen F, Li WA. **A review of sleep disorders and melatonin**. *Neurol Res* (2017.0) **39** 559-65. DOI: 10.1080/01616412.2017.1315864
23. Tao F, Cao Z, Jiang Y. **Associations of sleep duration and quality with incident cardiovascular disease, cancer, and mortality: a prospective cohort study of 407500 UK biobank participants**. *Sleep Med* (2021.0) **81** 401-9. DOI: 10.1016/j.sleep.2021.03.015
24. 24.Zhao J. Sleep deprivation is 180% more likely to kill people. Consumer Electron Mag. 2017;19(04):34–7.
25. Fu P, Zhou C, Meng Q. **Associations of Sleep Quality and Frailty among the older adults with chronic disease in China: the Mediation Effect of Psychological distress**. *Int J Environ Res Public Health* (2020.0) **17** 5240. DOI: 10.3390/ijerph17145240
26. Kirlioglu SS, Balcioglu YH. **Chronobiology Revisited in Psychiatric Disorders: from a translational perspective**. *Psychiatry Investig* (2020.0) **17** 725-43. DOI: 10.30773/pi.2020.0129
27. Janati Idrissi A, Lamkaddem A, Benouajjit A. **Sleep quality and mental health in the context of COVID-19 pandemic and lockdown in Morocco**. *Sleep Med* (2020.0) **74** 248-53. DOI: 10.1016/j.sleep.2020.07.045
28. Feng Mingyu Xu. **Han Hui. In western China rural older anxiety status and influence factors analysis**. *J health Educ China* (2022.0) **38** 173-6
29. Hosker DK, Elkins RM, Potter MP. **Promoting Mental Health and Wellness in Youth through Physical Activity, Nutrition, and sleep**. *Child Adolesc Psychiatr Clin N Am* (2019.0) **28** 171-93. DOI: 10.1016/j.chc.2018.11.010
30. Palma JA, Urrestarazu E, Iriarte J. **Sleep loss as risk factor for neurologic disorders: a review**. *Sleep Med* (2013.0) **14** 229-36. DOI: 10.1016/j.sleep.2012.11.019
31. Hartescu I, Morgan K, Stevinson CD. **Increased physical activity improves sleep and mood outcomes in inactive people with insomnia: a randomized controlled trial**. *Sleep Res* (2015.0) **24** 526-34. DOI: 10.1111/jsr.12297
32. Dorrance Hall E, Meng J, Reynolds RM. **Confidant network and Interpersonal Communication Associations with Depression in older adulthood**. *Health Commun* (2020.0) **35** 872-81. DOI: 10.1080/10410236.2019.1598616
33. 33.Xiantao Q, Jianping R, Mengyan H, Lixian R, Qingchun C, Jinjing W. Wang Wenting. Study on the implementation effect and influencing factors of traditional Chinese medicine health management service in community population with chronic neck and shoulder pain: based on goal achievement assessment method. Chin J Gen Pract. 2022;25(34):4292–7.
34. Rossier G, Bonnet C, Soura D, Corker AB. **Mental health and urban living in sub-saharan Africa: major depressive episodes among the urban poor in Ouagadougou, Burkina Faso**. *Popul Health Metr* (2016.0) **14** 18. DOI: 10.1186/s12963-016-0084-2
35. Myhrvold T, Småstuen MC. **The mental healthcare needs of undocumented migrants: an exploratory analysis of psychological distress and living conditions among undocumented migrants in Norway**. *J Clin Nurs* (2017.0) **26** 825-39. DOI: 10.1111/jocn.13670
36. 36.Shu-hua Liu. The analysis of the influence factors of self-evaluation of health older [D]. Jilin University; 2022. 001208.
37. 37.M County People’s Government. M County 2021 National Economic and Social Development Statistical Bulletin [R]. 2021.
38. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. **The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research**. *Psychiatry Res* (1989.0) **28** 193-213. DOI: 10.1016/0165-1781(89)90047-4
39. 39.National Bureau of Statistics. National Economic and Social Development Bulletin [R]. 2021.
40. 40.Grandner MA, Kripke DF, Yoon IY, et al. Criterion validity of the Pittsburgh Sleep Quality Index: investigation in a non-clinical sample. Sleep Biol Rhythms. 2006;4(2):129-39.
41. Manzar MD, BaHammam AS, Hameed UA. **Dimensionality of the Pittsburgh Sleep Quality Index: a systematic review**. *Health Qual Life Outcomes* (2018.0) **16** 89. DOI: 10.1186/s12955-018-0915-x
42. **New trends in mediating effect research: study design and data statistical methods**. *Chin Mental Health J* (2014.0) **28** 584-5
43. Md M. **Socioeconomic inequalities of undiagnosed diabetes in a resource-poor setting: insights from the cross-sectional Bangladesh Demographic and Health Survey 2011**. *Int J Environ Res Public Health* (2019.0) **16** 115-5. DOI: 10.3390/ijerph16010115
44. 44.Xu DT. Does rural basic medical insurance reduce catastrophic medical expenditure and poverty caused by illness?. Nanjing Agricultural University, 2019.
45. Zhang Yun LH. **Socio-Economic Status and patterns of Lifestyle of the older adults in China: convergence at lower levels while divergence at higher Levels**. *Popul Res* (2021.0) **45** 114-28
46. 46.Yu CF. Effects of different pension modes on the quality of life and burden level of the older and their satisfaction evaluation. Chin J Geriatric Healthcare Med. 2021;19(06):83–85.
47. **Life Quality and influencing factors of aged In-patients with chronic Diseases**. *Chin Primarv Health Care* (2014.0) **28** 83-4
48. Fang Jintao L, Wenxiu LX. **Anxious and depressive symptoms and their influencing factors among people in Community aged in Beijing Haidian District**. *China J Health Psychol* (2015.0) **23** 447-50
49. Wu Yiling L, Ruichun Z. **Mental health status and its influence factors among senior citizens in Jinhua,Zhejiang province**. *Chin Rural Health Service Adm* (2015.0) **35** 213-5
50. 50.Jiang W, Juanjuan S. Residential style, residential environment and mental health of the older in urban and rural areas: an analytical framework for the construction of an age-friendly community. Urban Problems. 2022;No318(01):65–74.
51. Liang Xiaoli Y, Zhou L. **Study on mental health status, influencing factors and countermeasures of the older in Sichuan Province from the perspective of active aging**. *Occuptional Health* (2021.0) **37** 1789-92
52. **Study on the influencing factors of mental health of the older in Xi ‘an [D]**. *Xi ‘an Univ Arch Technol* (2021.0) **37** 1789-92
53. 53.Zhu EDY, Zhang D. Subjective Social Class and the Sense of Gain of Chinese Residents: The Multiple Mediating Roles of Social Exclusion and Social Support. Chin J Clin Psychol. 2022;30(01):111–5.
54. 54.Liu XX, Yu G-LY-E, Li M-z. Research on improving mental health status of Shidu older from the perspective of positive psychology. Chongqing Med J. 2020;49(12):2009–2012.
55. 55.Yang TBX. Does adult children going out weaken family support for the older in rural areas? -- Analysis based on propensity score matching method. China Rural Observation. 2020;No154(04):50–69.
56. 56.Kunxiang D. Correlation analysis between sleep quality and cognitive function and dietary intake of rural older in Jiaozhou city. Qingdao University. 2021.
57. 57.Jiang WW, Nian S, Qiuju F, Qingxuan L, Wenzhu HX. Analysis of sleep quality and its influencing factors in the older in military rest homes in Hefei. China Med Rev. 2022;19(05):56–9.
58. Downward P, Rasciute S, Kumar H. **Health subjective financial situation and well-being: a longitudinal observational study**. *Health Qual Life Outcomes* (2020.0) **18** 203. DOI: 10.1186/s12955-020-01456-3
59. Wong ES, Hebert PL, Hernandez SE. **Association between local area unemployment rates and use of Veterans Affairs outpatient health services**. *Med Care* (2014.0) **52** 137-43. DOI: 10.1097/MLR.0000000000000079
60. Bhattacharjee B, Acharya T. **COVID-19 pandemic and its Effect on Mental Health in USA - A Review with some coping Strategies"**. *Psychiatr Q* (2020.0) **91** 1135-45. DOI: 10.1007/s11126-020-09836-0
61. Reddy NB, Pallavi M, Reddy NN. **Psychological morbidity Status among the Rural Geriatric Population of Tamil Nadu, India: A Cross - sectional study**. *Indian J Psychol Med* (2013.0) **34** 227-31. DOI: 10.4103/0253-7176.106016
62. 62.Xingxiang W, Chao C. Whether poverty affects mental health of middle-aged and older people in rural Areas–an empirical study based on CHARLS Data. Southern Economy. 2017;No.322(12):47–65.
63. Haushofer J, Fehr E. **On the psychology of poverty**. *Science* (2014.0) **344** 862-7. DOI: 10.1126/science.1232491
64. Wang Daoyang W, Wei Y. **Migration Children’s inferiority and perceived academic Self-efficacy: the moderating role of emotion regulation strategies**. *Stud Psychol Behav* (2019.0) **17** 48-55
65. Ma Guoyu C, Le Y. **Analysis of sleep disorders and socio-economic differences among rural older in Ninger County, Yunnan Province**. *Mod Prev Med* (2019.0) **46** 3724-7
66. Zaki NFW, Spence DW, BaHammam AS, Pandi-Perumal SR, Cardinali DP, Brown GM. **Chronobiological theories of mood disorder**. *Eur Arch Psychiatry Clin Neurosci* (2018.0) **268** 107-18. DOI: 10.1007/s00406-017-0835-5
67. Kim JJ, Oldham M, Fernando AT. *Compassion Mediates Poor Sleep Quality and Mental Health Outcomes Mindfulness* (2021.0) **12** 1252-61
68. Su Y, Wang SB, Zheng H. **The role of anxiety and depression in the relationship between physical activity and sleep quality: a serial multiple mediation model**. *Affect Disord* (2021.0) **290** 219-26. DOI: 10.1016/j.jad.2021.04.047
69. 69.Guanghui J, Ying W, Hao H. Re-examining the relationship between Adult Children Migration and Rural Left-behind parents’ health from the perspective of intergenerational support. Popul Econ. 2021;No.249(06):108–25.
70. Haiyan Y. **Investigation and reflection on the Current Situation of the spiritual and cultural life of the Rural older: a case study of Lu’an City**. *J Chongqing Univ Arts Sciences: Social Sci Ed* (2019.0) **38** 1-8
71. Zheng H, Peng ZHongXu, Junyun C. **Related factors affecting - year - old people sleep time and quality analysis**. *J Pract Med J* (2019.0) **4** 1096-9
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---
title: Acute stress reduces population-level metabolic and proteomic variation
authors:
- Katherine F. Steward
- Mohammed Refai
- William E. Dyer
- Valérie Copié
- Jennifer Lachowiec
- Brian Bothner
journal: BMC Bioinformatics
year: 2023
pmcid: PMC9993721
doi: 10.1186/s12859-023-05185-4
license: CC BY 4.0
---
# Acute stress reduces population-level metabolic and proteomic variation
## Abstract
### Background
Variation in omics data due to intrinsic biological stochasticity is often viewed as a challenging and undesirable feature of complex systems analyses. In fact, numerous statistical methods are utilized to minimize the variation among biological replicates.
### Results
We demonstrate that the common statistics relative standard deviation (RSD) and coefficient of variation (CV), which are often used for quality control or part of a larger pipeline in omics analyses, can also be used as a metric of a physiological stress response. Using an approach we term Replicate Variation Analysis (RVA), we demonstrate that acute physiological stress leads to feature-wide canalization of CV profiles of metabolomes and proteomes across biological replicates. Canalization is the repression of variation between replicates, which increases phenotypic similarity. Multiple in-house mass spectrometry omics datasets in addition to publicly available data were analyzed to assess changes in CV profiles in plants, animals, and microorganisms. In addition, proteomics data sets were evaluated utilizing RVA to identify functionality of reduced CV proteins.
### Conclusions
RVA provides a foundation for understanding omics level shifts that occur in response to cellular stress. This approach to data analysis helps characterize stress response and recovery, and could be deployed to detect populations under stress, monitor health status, and conduct environmental monitoring.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12859-023-05185-4.
## Background
Cellular stress response (CSR) is mediated through numerous molecular mechanisms to maintain homeostasis. For example, DNA damage repair, the unfolded protein response, mitochondrial stress signaling, and regulated cell death are all global stress pathways [1]. These programs are initiated by a diverse group of signaling molecules that includes metabolites and proteins. Metabolomics and proteomics methods are thus well suited for investigating CSR, as they capture global snapshots of an organism’s cellular state at a given time [2, 3]. This global phenotypic information, built from individual molecules helps explain not only stress, but also disease states, antibiotic or herbicide resistance, and evolutionary fitness [4] by characterizing phenotypic plasticity relative to baseline conditions [2, 5, 6]. Studies investigating CSR generally focus on a specific stressor, model system, or signaling pathway. In this study, we find that acute stress globally decreases molecular variability in plants, animals, and microorganisms and that such measures are useful for understanding CSR.
Standard omics workflows typically report variability among individuals and groups to quantify the reliability of an experiment. Multiple metrics are used including relative standard deviation (RSD) or Coefficient of Variation (CV), hierarchical clustering, principal component analysis (PCA), as well as multivariate statistical analyses [7]. CV is used in omics analyses to evaluate the repeatability of a biological assay or the precision of an experiment [8] and is reported as a ratio of the standard deviation to the mean. Variability in data is generally considered to be undesirable, and many methods have been employed to minimize intra-group variation among biological replicates [9–11]. Nonetheless, intrinsic phenotypic variability among individuals in a population has been exploited to provide population-level insights into the fields of ecology, evolution, and genetics [12]. A recent study demonstrated that while the sigma factor σV lysozyme stress response is heterogenous in a *Bacillus subtilis* population, changing the amount of stress can push the population towards a more homogenous lysozyme resistance, reducing the phenotypic variability [13]. Historically, going back to 1862, Yablokov et al. used standard deviation and CV metrics to report ranges of phenotypic states within a population of marine mammals and proposed that such data informs on how new taxa arise [14]. More recently, it was reported that the CV of metabolites decreased due to acute stress in animals and microorganisms [15].
We now expand this foundation by characterizing and comparing CV profiles of metabolome and proteome data from resting and stress-challenged organisms as a tool to describe CSR. Multiple publicly available and in-house omics datasets were analyzed to assess changes in CV profiles in plants, animals, and microorganisms, in response to acute stress. Changes in CV means and medians were determined, and CV distribution profiles were analyzed [16] to complete what we term Replicate Variation Analysis (RVA). RVA is described in the context of a standard metabolomics workflow, including multivariate clustering and PCA analyses of treatment groups. The outcome of our analyses is a correlation between acute stress and reduced variation in global metabolite and protein profiles. This finding holds for a variety of organisms inclusive of both bacterial and eukaryotic species, including plants.
## Results
This project began with our previous observation that CV distributions of metabolomes ($$n = 8$$) derived from urine were altered during hemorrhagic shock in S. scrofa [17]. The focus of that work was to identify relevant stress biomarkers. Our reanalysis of the data revealed that metabolic variation among individuals was significantly reduced during hemorrhagic shock. A 2D PCA score plot indicated that data variability is canalized under stress with a reduction in both PC1 and PC2 (Fig. 1A). Further analysis revealed that stress is associated with a reduced median CV value (from 59 to $46\%$ and a significantly reduced mean CV (62–$49\%$, Wilcoxon t-test < 0.001), and a CV distribution that is more narrow (Kolomogrov–Smirnov [K–S] test, $d = 0.287$, $p \leq 0.001$, Fig. 1B). To establish if decreased variation in metabolite abundance among biological replicates is a general outcome of acute stress, we analyzed another metabolomics project that we had recently published. Fig. 1Metabolic variation in response to hemorrhagic shock in a mammal. A Principal component analysis of control (red) and shocked (green) S. scrofa ($$n = 8$$). B Profile distribution plots of the CV of metabolite features from S. scrofa replicates from a control (black) and a shocked group (pink). The X axis shows the CV and the Y axis is the proportion of metabolites in the metabolome. Adapted from Heinemann et al. [ 2014] Our attention turned to a data set that investigated the metabolic impact of stress-inducing Bio Orthogonal Non-Canonical Amino Acid Tags (BONCAT) on the growth of *Escherichia coli* [18]. Batch cultures of E. coli were grown on minimal medium (Control) or with additions of methionine (MET), azidohomoalanine (AHA), or homopropargylglycine (HPG) ($$n = 5$$). Intracellular metabolite profiles were analyzed using both MS and NMR-based metabolomics techniques. 2D PCA analysis of the MS metabolomics data (Fig. 2A) revealed that the control cultures displayed greater variation among biological replicates than the stress treatment groups. When the same mass spectrometry data were analyzed using RVA, changes in the CV profiles between the control and amino acid tag additions were also observed (Fig. 2B). CV means and medians were decreased, and the distribution profiles narrowed with a sharper peak (Fig. 2C). The NMR data revealed a similar pattern in distribution between the control and HPG samples (K–S test, $d = 0.28$, $$p \leq 0.022$$) with a median decrease from $18\%$ (control) to $13\%$ (HPG) and a significant decrease in mean CV (control = $26\%$, HPG = $15\%$, Wilcoxon t-test, $$p \leq 0.0015$$; Fig. 2D). The RVA approach demonstrated that metabolomic dysregulation in HPG was greater than AHA, which was greater than MET, and all three treatments caused a decrease in variation relative to the control, a pattern mirrored in the NMR metabolomics data as well (Additional file 1: Fig. S1A). The RVA distribution profiles matched the differential abundance analysis of the original work, in which we showed that the HPG, AHA and MET additions resulted in significant perturbation to 19, 11, and $7\%$ of the metabolites, respectively. RVA thus has the potential to be used as a measure of stress, as it correlates to dysregulation of analytes. Fig. 2Metabolic variation in E. coli treated with non-canonical amino acids. A Principal component analysis of four different treatment groups from non-canonical amino acid treatment experiments on E. coli cell cultures with median displayed as a solid line (red = AHA treatment, green = control, blue = HPG and cyan = MET) (Steward et al. 2020). B Distribution plots of CV of mass spectrometry metabolite feature profiles for the non-canonical amino acid treated cultures of E. coli. C Table of CV statistics include the K–S d statistic for the different comparisons of the Control to the other groups, the CV mean and the CV median. D Profile distribution plots of the CV of NMR metabolite features from E. coli replicates from a control (black) and HPG treated (pink) We next analyzed data from physiological investigations of the weedy plant *Avena fatua* (wild oat). To examine the global impact of this acute stress, we inflicted a heat shock treatment (40 °C, 24 h) on inbred seedlings, followed by metabolomics analyses after increasing durations of recovery ($$n = 8$$). This study demonstrated that CV distribution profiles were markedly altered soon after heat shock (Fig. 3A). Median CV values were reduced from $67\%$ in untreated plants to $28\%$ in heat shocked plants. Mean CV values also showed a significant change between untreated and heat shock groups (control = $76\%$, heat shock = $37\%$, Wilcoxon T test $p \leq 0.001$). As documented for S. scrofa and E. coli above, CV distributions were also significantly canalized following heat shock (K–S test, $d = 0.46$, $p \leq 0.001$).Fig. 3Distribution of CV in A. fatua and temporal RVA analysis. A Distribution profile plot of metabolomic CV of A. fatua exposed to heat shock at 40 C (pink) and the control group (black). B Temporal CV profiles from heat stressed A. fatua. Time post-stress is from zero to 100 h of recovery. Table below: values of the K–S test. C Temporal CV profiles of methionine dependent cancer cell line supplemented with homocysteine (hcy) in the growth media, with timepoints collected after 2, 4, 8 and 12 h of acclimation. Table below: values of the K–S test The A. fatua data were also analyzed to assess the kinetics of recovery from stress and how this impacts CV distribution. Throughout the 100-h recovery period, CV distribution means increased from 37 to $76\%$ (Wilcoxon t-test $p \leq 0.001$) while K–S test d values decreased from 0.45 to 0.13, indicating less difference from the unstressed CV distribution (Fig. 3B). During recovery, the CV distribution widened and became less peaked with the metabolome approaching a distribution that resembled data from untreated plants. As seen in the E. coli data above, the temporal CV distribution profiles of heat shock and recovery in A. fatua suggest that a qualitative measure of stress can be assessed based on CV distributions of the population.
## Analysis of public omics data sets
To examine the generality of our approach and observations, a series of published data sets from other research groups was analyzed. A structured approach to finding and utilizing data from public repositories was used: we selected studies that involved acute stressors that would result in a stress response or acclimation. Experimental regimes that included significant cell or organism die-off were excluded so that CSR was not complicated by system or pathway shutdown that occurs during death. We then confirmed that post-processed data was supplied, to eliminate potential bias from processing through our in-house pipeline. A significant and unexpected limitation in identifying pertinent datasets was the lack of sufficient metadata and documentation so that data could be assigned to a specific experimental group and the origin of numerical values was clear.
## Metabolomics data
We employed our RVA approach on MS-based metabolomics data that tracked the metabolic adaptations of a methionine sensitive cancer cell line [19]. The original experiment involved replacing methionine in the growth medium with homocysteine, followed by an acclimatization period ($$n = 4$$). The cell lines stressed by the loss of methionine failed to thrive in its absence, but supplementation with homocysteine resulted in adaptations that enabled cell growth. RVA analyses of the metabolic mass spectral features demonstrated that the stress imparted by the absence of methionine resulted in significant canalization of CV profiles (K–S test, $d = 0.72$, $p \leq 0.001$) (Additional file 1: Fig. S1B). This pattern was also reflected in mean CV values, which decreased from 15 to $6\%$ (Wilcoxon t-test $p \leq 0.001$) for the control and stressed groups, respectively, and median CV values decreased from 15 to $4\%$.
This cancer cell dataset was of particular interest because it also included a temporal analysis of CSR. Adaptation to homocysteine was tracked over 12 h by periodic removal of metabolite samples from untreated and methionine-stressed cells. The CV profiles indicated that the peaked profile of early time points shifted to a wider distribution resembling that of the control group, and KS-test d statistic changed from 0.72 to 0.22 between the 2 h and 12 h time points, again reflecting a CV distribution that is trending towards the unstressed control (Fig. 3C).
The second external dataset came from a study in which Neocloeon triangulifer (mayfly adults) were fasted overnight and then subjected to heat stress or ambient temperature [20]. Metabolite samples ($$n = 6$$) were analyzed by LCMS. RVA analysis revealed subtle changes in CV values, which displayed a slight decrease in the mean from 23 to $20\%$, and median CV decrease ($18.1\%$ to $16.5\%$) from the ambient temperature insects as compared to the heat shocked group. Although mean and median changes were small, CV distributions tended towards a canalized profile in the heat exposed group (K–S test, $d = 0.078$, $$p \leq 0.037$$) (Additional file 1: Fig. S1C). The difference in CV profiles reflect the impact of acute thermal stress, even under a shared fasting condition.
The next three datasets had acute stress treatments through diet or environmental adjustment, types of stress not previously discussed. The third external data set originated from a metabolomics study that investigated the impact of diet on *Mus muscula* (house mouse) intestinal digesta composition. The treated group was fed a low protein, low fat chow to mimic malnourishment, and mass spectrometry metabolite data were collected from control and diet-restricted mice ($$n = 4$$) [21]. RVA analysis demonstrated a clear change in CV distribution profiles (KS-test, $d = 0.48$, $p \leq 0.001$), with a change in median CVs from 48 (control) to 21 (diet) and mean CVs (control = $48\%$, diet = $21\%$, Wilcoxson’s t-test < 0.001) (Additional file 1: Fig. S2A). The fourth and fifth datasets originated from a study in which *Haliotis discus* hannai (sea abalone) ($$n = 9$$) had been acclimated to either high or low temperature and then subjected to heat shock or no heat treatment, and mass spectrometry metabolite profiles were compared [22]. When analyzed using RVA, the CV distribution of heat-shocked, cold-acclimated abalone was significantly lower than control: control $29\%$, heat shock $24\%$ (Wilcoxon t-test, $p \leq 0.001$) and the median CV decreased from 25 (control) to 20 (heat shock) (KS-test, $d = 0.18$, $p \leq 0.001$) (Additional file 1: Fig. S2B). High temperature-acclimated abalone groups exhibited a significant change in CV distribution profiles in response to heat shock (KS-test, $d = 0.077$, $$p \leq 0.033$$), representing a more narrowed distribution for the heat shock group, though the CV means were similar (Additional file 1: Fig. S2B). Together, re-analysis of the mayfly and high temperature-acclimated abalone data highlight that RVA profiles can detect even small changes reflecting intra-group metabolome variation and CV distribution changes imparted by acute stress, even after a stress acclimation period.
## Proteomics data
We next set out to establish whether the canalization of variation following acute stress could be observed in proteomics data sets. We first looked at data from an in-house proteomic study investigating E. coli cell cultures grown under aerobic or anaerobic conditions ($$n = 4$$). 2D PCA score plots indicated less variation across both PC1 and PC2 in the anaerobic group (Fig. 4A), while RVA revealed a significant difference in CV distribution (K–S test, $d = 0.19$, $p \leq 0.001$) as well as a trending towards smaller CV mean ($7.7\%$ and $6.6\%$) and CV median ($6.7\%$ and $5.3\%$) for the anaerobic group (Fig. 4B). We followed this analysis by mining the Pride proteome archive database [23] to search for additional external examples, including an investigation of 48-h PEG-induced drought stress on *Triticum aestivum* L (bread wheat) ($$n = 3$$) [24]. Our RVA analysis demonstrated reduced CV distributions in drought-stressed proteome profiles (K–S test, $d = 0.47$, $p \leq 0.001$), with changes in mean and median CV values in the control group (mean = $42\%$, median = $29.8\%$) as compared to the stressed group (mean = $25\%$, median = $11.1\%$, Wilcoxon t-test, $p \leq 0.001$; Additional file 1: Fig. S3). Thus, both prokaryotic and eukaryotic proteome datasets provide evidence that a reduction in intra-group variation in response to acute stress applies to diverse classes of omics data. Fig. 4RVA of proteomics data and simulation analysis. A Principal component analysis of proteomic data from anaerobic and aerobic E. coli cultures, shown in green and red respectively. B CV distribution plots for anaerobic (pink) versus aerobic (black) E. coli cultures. C, D *Simulated data* with 3, 6, 10 or 20 replicates using 50, 500 or 5000 features. The standard deviation was modeled at 0.5 of the mean (C) and 0.23 of the mean (D) We also tested a second proteomics data set from an in-house project. The experiment challenged *Methanocaccous voltae* to grow on different sources of iron and sulfur [25]. A comparison of M. voltae grown on the canonical source of iron and sulfide (Fe(II)/HS−) versus pyrite (FeS2) revealed a decrease in the CV of protein abundances when the cells were required to mobilize Fe directly from the mineral pyrite. The CV distributions between the FeS2 and the Fe(II)/HS− proteomes were significantly different (KS test, $d = 0.27616$, $p \leq 0.001$) (Additional file 1: Fig. S4). The mean and median of the FeS2 cultures (mean = 18.5, median = 13.2) were also decreased compared to the Fe(II)/HS− cells (mean = 25.8, median = 25.5) as 881 of 1242 proteins had a smaller CV (Wilcoxsons t-test < 0.001). These data supported our model of reduced variation in response to acute stress because growth on pyrite represents an energetic challenge for methanogens [25, 26].
The deep coverage and compact proteome of M. voltae present an opportunity to gain insight to proteins and pathways responsible for the reduced CV under stress. Pathway analysis through the lens of RVA highlighted that in the pyrite condition, proteins associated with stress response as well as iron/sulfur trafficking and storage had a decreased CV. A deeper look into stress-related proteins with a lower CV in the pyrite cultures showed they were part of biological (CRISPR) and environmental stress (heat shock/universal stress response) pathways. There were 14 stress-related proteins in the sulfide cultures, that had a lower CV compared to the pyrite condition, which are primarily involved in DNA repair and unfolded protein response. The other proteins with a lower CV in the sulfide condition were broadly involved in transcription and translation. Together, these proteomics data suggest that cellular stress response becomes more uniform through canalization of the important pathways.
## Exceptions to the model
Through mining the Metabolomics *Workbench data* repository, we determined that not all datasets exhibit this relationship between variation and stress. Three of the metabolomics datasets examined did not display a significant change in CV distribution between control and treatment groups. After a thorough analysis of experimental design, the exceptions were classified into two categories. The first category was for metabolomics analyses conducted using targeted rather than global approaches. This was the case in an analysis of isotopically labeled carbon in 256 specific metabolites to evaluate heat shock in Caenorhabditis elegans (nematode) [27]. CV distributions of the data from heat shocked and control groups were not significantly different (K–S test, $d = 0.04$, $$p \leq 0.83$$). Indicating that CV canalization is not universally present across metabolic pathways. An NMR metabolomics study analyzing cadmium exposure in Danio rerio (zebrafish) embryos did not show a difference in CV distribution between control and treatment groups (K–S test, $d = 0.27$, $$p \leq 0.17$$) [28]. As with the nematode study, this was a targeted analysis in which only 33 zebrafish metabolites were measured. This raises the important point that not all metabolites or pathways will show canalization. We hypothesize that targeted analyses may miss canalization because not all pathways need to display the effect in order to change the overall CV of a given class of biomolecules.
Other exceptions to the stress-induced CV profile changes involved chronic rather than acute stress. A blood plasma metabolomics study of Chronic Fatigue Syndrome (CFS) in both male (control = 18, CFS = 22) and female human (control = 23, CFS = 21) patients revealed that the CV distribution significantly increased in patients suffering from chronic fatigue compared to healthy control subjects (males: KS-test, $d = 0.10$, $p \leq 0.001$; females: KS-test, $d = 0.10$, $p \leq 0.001$), and the mean values increased slightly as well (males: $32\%$ to $34\%$; females: $36\%$ to $39\%$). Given our observations that the period of stress and/or recovery time impacts CV distribution, it is possible that in contrast to acute stress, chronic stress may result in an opposite trend and a corresponding increase in CV distribution patterns.
## Simulations and mean–variance relationships
The fact that targeted or less than global data failed to display canalization was worth further investigation. NMR datasets typically report on tens to hundreds of metabolites, while mass spectrometry-based proteomics and metabolomics data set usually contain a thousand or more spectral features. We hypothesized that the number of features comprising the CV distribution may affect statistical power to discern differences between data sets. To test the impact of data characteristics on RVA, authentic CV profiles were simulated by varying feature number (50, 500, and 5000), replicate number (3, 6, 10, and 20), and the ratio of feature mean to standard deviation. These values were selected as they are reasonable representations of different omics experimental designs. To begin, a CV profile was simulated from individual feature means and standard deviations from the mayfly data [20]. The CV profile was then randomly sampled 1000 times varying the number of features and replicates. Calculating the correlation coefficient between the “known” and sampled CV distributions revealed that more replicates in the experiment and a smaller ratio of standard deviation to the mean improves accuracy (Fig. 4C,D). Unexpectedly, the number of features is not a predictor of accuracy of CV distribution calculations as we had hypothesized. The number of biological replicates and the variance of a specific feature, however, are primary considerations. The power of RVA to detect a canalization of CVs positively correlates with the number of biological replicates.
A final test was performed to determine if the canalization of CVs due to stress could be the result of a technical artifact. The most likely source for introduction of an error is in the measurement of feature intensity, as observed in RNA-seq studies [29]. It is common for instruments to more accurately record signals for high intensity features. If this was the case, there would be a negative relationship between the variances and means of features using the data presented here. Analysis of the relationship between the mean and variance [30] in the mayfly dataset revealed the opposite trend. There was a very strong positive linear relationship between mean and variance (Additional file 1: Fig. S5) in both control and stress conditions. Therefore, we conclude that the CV is an appropriate statistic for standardizing these data for comparisons.
## Discussion
The analysis presented here identifies a correlation between variability of biological replicates and cellular stress that can be quantified in omics data. By repurposing CV as a statistic of merit, a stressed phenotype (phenome) was identified. This suggests that our RVA method can help to characterize CSR and to assess the presence and recovery from stress in biological systems.
Reduced variation in a population may be an unappreciated property of the phenome. A metabolic bottleneck or convergence (i.e. a single optimum solution to resource use) [31–33] is one possible mechanism to explain this behavior. We also propose that the change could be less of an active CSR pathway initiation and more of a passive reaction where ancillary metabolic pathways are suppressed in the perturbed organism. in this scenario, lack of nutrients or presence of negative factors (stress) on the system activates CSR and a down-regulation of other pathways to mitigate the physiological effects of stress [34]. This is consistent with studies that show trade-offs in energy allocation to alleviate competing physiological tasks during food scarcity is associated with physiological variation [35]. The proteomics data presented here for M. voltae grown on pyrite2, a less bioavailable source of Fe and S [25], fits the convergence model. RVA reinforced the idea that cells grown on pyrite were under stress, because the CV profile was significantly smaller than cells grown on iron and sulfide. In this case, M. voltae appeared to access classic stress management pathways including heat shock response, unfolded protein response and universal stress response [36]. Importantly, proteins from all of these categories had smaller CVs in the stressed group.
The mechanisms that result in canalization remain unknown; however, the ability to observe and quantify a population-level response provides a valuable perspective on the phenome. Whether it is activating CSR, turning down auxiliary pathways or a combination of both, our analyses demonstrate that acute stress can lead to decreased variation in omics data. At a deeper level, changes in population CV could be due to a gradient of CSR or temporal variations in stress response at the level of individuals. Single cell analysis of Xenopus (clawed frog) oocytes investigated this idea, by studying activation of the MAPK cascade to progesterone [37]. Ferrel et al. determined that patterns of protein phosphorylation in the population exhibited a bimodal distribution, with individuals responding to stress not gradually, but as if a switch had been flipped [37]. Research along this line, using RVA, will help to answer an ongoing and fundamental question about CSR: does it function as a rheostat or a switch? Additionally, RVA provides a finite characterization that can help identify the physiological mediators responsible for the canalization of a stressed phenotype.
CV as a global bottom-up statistic holds much potential; however, it is not without limitations. As we have shown, not all data sets follow the trend outlined here. Commonalities of studies that did not have reduced variability in “stress” groups included the presence of chronic stress and the use of a targeted rather than nontargeted analytical approaches. Chronic stress on a system is a known cause of deleterious mutations that lead to homogeneity and can result in disease, cancer and even death [38]. Data that support a reduced CV are from systems under acute stress that did not cause overt cellular death, an immediate disease state, or permanently altered CSR. For the wild oat and cancer cell data, a temporal RVA analysis showed that dampened CSR occurs in parallel with increased CV profiles that trend towards controls. We hypothesize that stress pathways will have specific time dependencies, which could explain why a change in CV distribution is not observed in some experiments. Further investigation into temporal response and return to a non-stressed phenotype are exciting topics for future research.
The use of RVA along with the standard statistical workflow for omics adds a new dimension to the data, especially where standard models requiring homogeneity of variances are not appropriate. We believe RVA will prove to be an important metric for the rapidly expanding field of phenomics. Located at the intersection of metabolomics, proteomics, and genomics, phenomics is at the forefront of human health and agricultural research. RVA helps describe the phenome of a population and is straightforward to generate. Upon further development, RVA could potentially be used as a predictive tool to pinpoint early changes in metabolite or protein levels that are indicative of stress or future disease. RVA also has implications at the juncture of stress response and resistance. It has been shown that repeated exposure to acute stress can result in long term phenotypic changes, as observed in antibiotic-resistant E. coli populations, herbicide-resistant weedy species[39], and prolonged stress adaptation in Drosophila melanogaster [39–41]. The nuances of the relationship between intra-population variability (the variome) and stress response are a gap in knowledge and a promising area for additional study.
## Methods
For previously published data, experimental details can be found in the respective publications. The *Sus scrofa* study analyzed machine learning techniques to identify biomarkers of hemorrhagic shock. Changes in CV were noted in this paper, but not further analyzed [17]. The effect of Bio Orthogonal Non-Canonical Amino Acids on E. coli was evaluated at the metabolite level, analyzing the addition of either AHA, HPG or Methionine [18]. Methionine sensitive cancer cells were subjected to methionine starvation with homocysteine replacement in the media, with the metabolite changes tracked over time [19]. The next study focused on heat shock treatment on mayflies to analyze stress tolerance, using GC–MS for metabolomics analysis [20]. A mouse model used to evaluate malnutrition was the next study, analyzing MS based metabolome changes [21]. The last two examples used in the metabolomics section came from a study on heat stress in abalone, studying metabolome effects of heat stress after a high or low temperature acclimation [22]. The proteomics data set utilized here analyzed drought stress on bread wheat [24].
## Metabolomics analysis of heat shocked Avena fatua
Avena fatua plants were grown from seeds as described in Burns et al. [ 42]. After three weeks of growth, plants ($$n = 8$$) were placed in a temperature-controlled chamber for 24 h at 40 C. Shoots were harvested at 0, 6, 24, 48, and 100 h after heat shock, immediately placed in liquid nitrogen, and stored at − 80 °C for metabolite extraction. Frozen tissue was ground for 1 min in liquid N2 with a mortar and pestle. The powdered tissue (approximately 150 mg per sample) was suspended in methanol (MeOH) at 70 °C for 15 min. Samples were vortexed for 1 min and then centrifuged (25,000 g, 10 min, 4 °C) to remove cellular debris from the soluble fraction. To precipitate proteins from the soluble metabolite fraction, ice cold acetone was added at a ratio of 4:1 acetone: extract and stored at − 20 °C overnight, followed by centrifugation (25,000 g) at 4 °C for 10 min. The resulting supernatant fraction was dried and stored at − 80 °C. Prior to analyses by LC–MS, samples were resuspended in 40 μL of $50\%$ HPLC grade water / $50\%$ MeOH. MS-based analysis of polar metabolites was accomplished using an Agilent 1290 ultra-high performance liquid chromatography (UPLC) system coupled to an Agilent 6538 Accurate-Mass quadrupole Time of Flight (TOF) mass spectrometer, using a HILIC column (Cogent diamond hydride HILIC 2.2 µM, 120 A, 150 mm × 2.1 mm Microsolv, Leland, NC) for metabolite separation. The gradient for separation started with a hold of solvent B ($0.1\%$ formic acid in acetonitrile) for 2 min at $50\%$, followed by a gradient ramp of 50–$100\%$ B over fourteen minutes. Then an isocratic hold at $100\%$ solvent B for one minute, with a return to initial conditions. Mass analysis was conducted in positive mode with a capillary voltage of 3500 V, dry gas temperature of 350 °C at a flow of 8 L/min and the nebulizer was set at 60 psi, injecting 2 µL sample volumes, with blanks run intermittently between samples. Data acquisition parameters were as follows: 50–1000 mass range at 1 Hz scan rate with a resolution of 18,000. Accuracy based on calibration standards was approximately 5 ppm.
## Proteomic analysis of Escherichia coli grown under aerobic or anaerobic conditions
Proteomics analysis of aerobic versus nonaerobic E. coli cultures was carried out on MG1655 (K12) in LB media at 37 C. Four replicate cultures were started with a 5 μL inoculation from an overnight culture and grown under an atmosphere of nitrogen or ambient air until harvest at mid-log phase (0.4 OD for the aerobic samples and 0.3 OD for anaerobic samples). Cells were pelleted using centrifugation and proteins extracted immediately. The cell pellets were resuspended in 0.1 M Tris–HCL pH 7.5 buffer with 8 M urea and subjected to three freeze/thaw cycles in liquid Nitrogen, followed by ultrasonication for 5 min (Biologix -Model 13,000). Samples were centrifuged and the resulting supernatant was removed and proteins precipitated from it using ice cold acetone and stored at – 20 C for 1 h. The precipitated proteins were centrifuged, the supernatant was removed and the protein pellet was resuspended in 0.1 M Tris–HCL pH 6.8, 5 um EDTA, 50 mM N-ethylmaleimide in 6 M urea. This sample was transferred to a 3 K MWCO Nanosep centrifuge device and a modified FASP digestion was carried out. The sample was reduced with an excess of DTT and alkylated using 50 mM Iodoacetamide. The samples were washed four times with 50 mM ammonium bicarbonate pH 7.8 and then digested using sequencing grade Trypsin at a 20:1 protein: protease ration for 18 h. Samples were run on a Dionex Ultimate 3000 Nano UHPLC equipped with an Acclaim PepMap 100 C18 trap column (100 μm × 2 cm) and an Acclaim PepMap RSLC C18 (75 μm × 50 cm, C18 2 μM 100A) for separation. Mobile phase A was $0.1\%$ formic acid in HPLC grade water and B was $\frac{80}{20}$ acetonitrile: water. Peptides were separated at 0.6 nL/min. using a linear solvent gradient from 3–$30\%$ B over 120 min. The LC system was coupled with a Bruker maXis Impact with captive spray ESI mass spectrometer was used for data collection of spectra from 150 to 1750 m/Z at a maximum rate of 2 Hz for precursor and fragment spectra with adaptive acquisition for highly abundant ions. Data dependent MS/MS was used to collect sequence information on the 5 most abundant ion per full scan. Data analysis was done using MaxQuant (v1.6.4.0) and Perseus (v1.6.4.10).
## Mining of public data
Data was obtained from the Metabolomics Workbench [43] and the PRIDE proteomics repository [23]. The archives were searched for data sets that matched “stress” in the keyword search. If the summary described an omics data set that evaluated a stress or perturbation and a control group, both with at least three biological replicates, the uploaded data set was evaluated. If the data provided was in a raw format (e.g. “sample.d” datafile) the set was discarded in order to avoid potential bias from our in-house processing pipeline. If the data was in a final, processed tabular format and experimental conditions were clearly described, the data was used. Reasons for not using a data set included lack of clearly defined experimental and control groups, undecipherable sample codes, or incomplete data inclusion. Data sets that met the criteria of containing stress and control groups with at least three biological replicates, were evaluated by replicate variation analysis (Additional file 2: Table S3).
## Statistical analysis
CV statistics were calculated using the standard deviation and the mean of individual metabolites or proteins in a group. The standard deviation was taken as a ratio to the mean and reported as a percentage. This was done for every detected metabolite feature or protein to obtain the distribution of the omic population. Statistical analysis was carried out in R [44] and distribution plots were made using ggplot2 [45] and ggridges [46], PCA plots, histograms of CV, distribution plots, and distribution statistics of mean and median were all calculated and plotted. A two sample Kolmogorov–Smirnoff (KS) test was utilized to analyze for the empirical distribution functions of the control and the treatment groups. The two sample KS test describes the differences between shape and location of the two distributions being tested using the d statistic with a calculated p value. A larger d statistic indicates a larger change between the two distributions being compared [16].
## Simulated data analysis
The process of simulating these CV distributions requires two levels of simulations—first, a simulation of the population level CV distribution and second, simulations of the individual replicates sampled from these CV distributions. Therefore, the “true” CV distributions across the population level were simulated first. For this, both the means and standard deviations were simulated for each omics feature. The Mayfly treatment dataset presented in Fig. 2 was used to parameterize simulations. The means were drawn from a normal distribution with a [1] mean equal to the log(mean) of the Mayfly dataset to disallow negative values and [2] a standard deviation equal to the standard deviation of the log(mean) of the dataset. Each mean also required a corresponding simulated standard deviation. Within the Mayfly treatment dataset, the standard deviation varies from 0.02 to 1.65x of its corresponding mean, with a mean standard deviation fold-change of 0.23. Therefore, we tested both 0.23-fold and 0.5-fold of the mean and 0.1 as the standard deviation to randomly assign each mean a corresponding standard deviation. Finally, the CV was calculated for each mean-standard deviation pair to create the “true” CV distribution. Forty distributions were simulated.
Random sampling from each of the CV distributions was simulated as follows: For each mean and standard deviation pair, varied numbers of replicates were drawn, and the CV was computed. The Spearman’s correlation between the CV for these simulated samples and the “true” CV simulated in the first step was determined. The process was repeated 1000 times for each replicate and feature number combination.
## Supplementary Information
Additional file 1. Figure S1. a Distribution plots of CV of NMR metabolite feature profiles for the non-canonical amino acid treated cultures of E. coli. b CV profiles of metabolites in methionine dependent cancer cells with methionine (MET) or homocysteine (Hcy). c CV profiles of metabolites from replicates of mayflies that were exposed to heat stress (pink) and the analogous control group (black). Figure S2. Undernourished mouse model studies and Temperature Acclimated Abalone. a Distribution plots of CV metabolite features from control mice (black) and malnourished mice (pink). b Distribution plots of CV metabolite features from replicates of cold (left panel) or high temperature (right panel) acclimated *Haliotis discus* hannai that were exposed to heat stress (pink) and the analogous control group (black). Figure S3. Distribution profile plots of proteomic data collected on wheatleaf (black) and wheatleaf that has been exposed to drought stress using PEG (pink). Figure S4. Distribution profile plots of proteomic data collected on M. voltae grown under canonical sulfide (Fe(II)/HS−) (black) and M. voltae that has been exposed to mineral stress through growth on pyrite (FeS2) (pink). Mean from 25.8 to $18.5\%$ Median (shown) from 21.5 to $13.2\%$. Figure S5. Relationship between feature mean and variance for control replicates from the mayfly data (a) and the heat stressed mayfly replicates (b).Additional file 2. Table S1. Protein CVs. Table S2. Data set details. Table S3. Metabolomics workbench search.
## References
1. Galluzzi L, Bravo-San Pedro JM, Kepp O, Kroemer G. **Regulated cell death and adaptive stress responses**. *Cell Mol Life Sci* (2016.0) **73** 2405-2410. DOI: 10.1007/s00018-016-2209-y
2. Johnson CH, Ivanisevic J, Siuzdak G. **Metabolomics: Beyond biomarkers and towards mechanisms**. *Nat Rev Mol Cell Biol* (2016.0) **17** 451-459. DOI: 10.1038/nrm.2016.25
3. Schuhmacher R, Krska R, Weckwerth W, Goodacre R. **Metabolomics and metabolite profiling**. *Anal Bioanal Chem* (2013.0) **405** 5003-5004. DOI: 10.1007/s00216-013-6939-5
4. Bush WS, Oetjens MT, Crawford DC. **Unravelling the human genome-phenome relationship using phenome-wide association studies**. *Nat Rev Genet* (2016.0) **17** 129-145. DOI: 10.1038/nrg.2015.36
5. Healy TM, Schulte PM. **Phenotypic plasticity and divergence in gene expression**. *Mol Ecol* (2015.0) **24** 3220-3222. DOI: 10.1111/mec.13246
6. Houle D, Govindaraju DR, Omholt S. **Phenomics: the next challenge**. *Nat Rev Genet* (2010.0) **11** 855-866. DOI: 10.1038/nrg2897
7. Want EJ. **Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry**. *Anal Chem* (2006.0) **78** 743-752. DOI: 10.1021/ac051312t
8. 8.Brown CE, Brown CE. Coefficient of variation. In: Applied multivariate statistics in geohydrology and related sciences. Berlin: Springer; 1998, pp. 155–157.
9. Bessette PH, Aslund F, Beckwith J, Georgiou G, Blanquet S. **Efficient folding of proteins with multiple disulfide bonds in the**. *Proc Natl Acad Sci* (1999.0) **96** 13703-13708. DOI: 10.1073/pnas.96.24.13703
10. De Livera AM, Olshansky G, Simpson JA, Creek DJ. **NormalizeMets: assessing, selecting and implementing statistical methods for normalizing metabolomics data**. *Metabolomics* (2018.0) **14** 54. DOI: 10.1007/s11306-018-1347-7
11. De Livera AM. **Statistical methods for handling unwanted variation in metabolomics data**. *Anal Chem* (2015.0) **87** 3606-3615. DOI: 10.1021/ac502439y
12. Jimenez-Gomez JM, Corwin JA, Joseph B, Maloof JN, Kliebenstein DJ. **Genomic analysis of QTLs and genes altering natural variation in stochastic noise**. *PLoS Genet* (2011.0) **7** e1002295. DOI: 10.1371/journal.pgen.1002295
13. Schwall CP. **Tunable phenotypic variability through an autoregulatory alternative sigma factor circuit**. *Mol Syst Biol* (2021.0). DOI: 10.15252/MSB.20209832
14. Olson EC, Yablokov AV. **Variability in mammals**. *J Mammal* (1967.0) **48** 500. DOI: 10.2307/1377806
15. Tokmina-Lukaszewska M, Movahed N, Lusczek E, Mulier K, Beilman G, Bothner B. **Transformation of UPLC-MS data overcomes extreme variability in urine concentration and metabolite fold change**. *Curr Metab* (2014.0) **2** 78-87. DOI: 10.2174/2213235X0202141127102223
16. Massey FJ. **The Kolmogorov–Smirnov test for goodness of fit**. *J Am Stat Assoc* (1951.0) **46** 68-78. DOI: 10.1080/01621459.1951.10500769
17. Heinemann J, Mazurie A, Tokmina-Lukaszewska M, Beilman GJ, Bothner B. **Application of support vector machines to metabolomics experiments with limited replicates**. *Metabolomics* (2014.0) **10** 1121-1128. DOI: 10.1007/s11306-014-0651-0
18. Steward KF. **Metabolic implications of using bioorthogonal non-canonical amino acid tagging (BONCAT) for tracking protein synthesis**. *Front Microbiol* (2020.0) **11** 197. DOI: 10.3389/fmicb.2020.00197
19. Borrego SL. **Metabolic changes associated with methionine stress sensitivity in MDA-MB-468 breast cancer cells**. *Cancer Metab* (2016.0) **4** 9. DOI: 10.1186/s40170-016-0148-6
20. Chou H, Pathmasiri W, Deese-Spruill J, Sumner S, Buchwalter DB. **Metabolomics reveal physiological changes in mayfly larvae (**. *J Insect Physiol* (2017.0) **101** 107-112. DOI: 10.1016/j.jinsphys.2017.07.008
21. Brown EM. **Diet and specific microbial exposure trigger features of environmental enteropathy in a novel murine model**. *Nat Commun* (2015.0). DOI: 10.1038/ncomms8806
22. Xu F, Gao T, Liu X. **Metabolomics adaptation of Juvenile Pacific Abalone**. *Sci Rep* (2020.0) **10** 1-11. DOI: 10.1038/s41598-020-63122-4
23. 23.PRIDE—Proteomics Identification Database. https://www.ebi.ac.uk/pride/archive/. Accessed 9 Oct 2020.
24. Zhang M. **Phosphoproteome analysis reveals new drought response and defense mechanisms of seedling leaves in bread wheat (**. *J Proteomics* (2014.0) **109** 290-308. DOI: 10.1016/j.jprot.2014.07.010
25. Payne D. **Examining pathways of iron and sulfur acquisition, trafficking, deployment, and storage in mineral-grown methanogen cells**. *J Bacteriol* (2021.0). DOI: 10.1128/JB.00146-21
26. Payne D, Spietz RL, Boyd ES. **Reductive dissolution of pyrite by methanogenic archaea**. *ISME J* (2021.0) **15** 3498-3507. DOI: 10.1038/S41396-021-01028-3
27. 27.Human Metabolome Database. https://hmdb.ca/. Accessed 9 Oct 2020.
28. Green AJ. **Cadmium exposure increases the risk of juvenile obesity: a human and zebrafish comparative study**. *Int J Obes* (2018.0) **42** 1285-1295. DOI: 10.1038/s41366-018-0036-y
29. Brennecke P. **Accounting for technical noise in single-cell RNA-seq experiments**. *Nat Methods* (2013.0) **10** 1093-1098. DOI: 10.1038/NMETH.2645
30. Geiler-Samerotte K, Bauer C, Li S, Ziv N, Gresham D, Siegal M. **The details in the distributions: why and how to study phenotypic variability**. *Curr Opin Biotechnol* (2013.0) **24** 752-759. DOI: 10.1016/J.COPBIO.2013.03.010
31. Pan FTC, Applebaum SL, Manahan DT. **Differing thermal sensitivities of physiological processes alter ATP allocation**. *J Exp Biol* (2021.0) **224** jeb233379. DOI: 10.1242/jeb.233379
32. Banavar JR, Damuth J, Maritan A, Rinaldo A. **Supply-demand balance and metabolic scaling**. *Proc Natl Acad Sci U S A* (2002.0) **99** 10506-10509. DOI: 10.1073/pnas.162216899
33. Pollock C, Farrar J, Tomos D, Gallagher J, Lu C, Koroleva O. **Balancing supply and demand: the spatial regulation of carbon metabolism in grass and cereal leaves**. *J Exp Bot* (2003.0) **54** 489-494. DOI: 10.1093/jxb/erg037
34. Chen F, Evans A, Pham J, Plosky B. **Molecular cell editorial cellular stress responses: a balancing act**. *Mol Cell* (2010.0) **40** 175. DOI: 10.1016/j.molcel.2010.10.008
35. Urlacher SS. **Constraint and trade-offs regulate energy expenditure during childhood**. *Sci Adv* (2019.0). DOI: 10.1126/SCIADV.AAX1065
36. Macario AJL, Lange M, Ahring BK, De Macario EC. **Stress genes and proteins in the archaea**. *Microbiol Mol Biol Rev* (1999.0) **63** 923-967. DOI: 10.1128/mmbr.63.4.923-967.1999
37. Ferrell JE, Machleder EM. **The biochemical basis of an all-or-none cell fate switch in xenopus oocytes**. *Science* (1998.0) **280** 895-898. DOI: 10.1126/science.280.5365.895
38. Juster RP, McEwen BS, Lupien SJ. **Allostatic load biomarkers of chronic stress and impact on health and cognition**. *Neurosci Biobehav Rev* (2010.0) **35** 2-16. DOI: 10.1016/j.neubiorev.2009.10.002
39. Dyer WE. **Stress-induced evolution of herbicide resistance and related pleiotropic effects**. *Pest Manag Sci* (2018.0) **74** 1759-1768. DOI: 10.1002/ps.5043
40. Pickering AM, Vojtovich L, Tower J, Davies KJA. **Oxidative stress adaptation with acute, chronic, and repeated stress**. *Free Radic Biol Med* (2013.0) **55** 109-118. DOI: 10.1016/j.freeradbiomed.2012.11.001
41. Ahmed MN, Porse A, Sommer MOA, Høiby N, Ciofu O. **Evolution of antibiotic resistance in biofilm and planktonic pseudomonas aeruginosa populations exposed to subinhibitory levels of ciprofloxacin**. *Antimicrob Agents Chemother* (2018.0). DOI: 10.1128/AAC.00320-18
42. Burns EE, Keith BK, Bothner B, Dyer WE. **Constiutive redox and phosphoproteome changes in multiple herbicide resistant**. *J Plant Physiol* (2018.0) **220** 105-114. DOI: 10.1016/j.jplph.2017.11.004
43. 43.Metabolomics Workbench: NIH Data Repository: Overview. https://www.metabolomicsworkbench.org/data/index.php. Accessed 9 Oct 2020.
44. 44.RStudio | Open source & professional software for data science teams—RStudio. https://rstudio.com/. Accessed 9 Oct 2020.
45. 45.ggplot2 citation info. https://cran.r-project.org/web/packages/ggplot2/citation.html. Accessed 16 Oct 2020.
46. 46.Wilke CO. Ridgeline Plots in ‘ggplot2’ [R package ggridges version 0.5.2], 2020. [Online]. Available: https://cran.r-project.org/package=ggridges. Accessed 16 Oct 2020.
|
---
title: 'The good, the bad, and the blameless in parenting: a thematic analysis of
discussions of childhood obesity on an internet forum'
authors:
- Terhi Koivumäki
- Piia Jallinoja
journal: BMC Public Health
year: 2023
pmcid: PMC9993749
doi: 10.1186/s12889-023-15314-6
license: CC BY 4.0
---
# The good, the bad, and the blameless in parenting: a thematic analysis of discussions of childhood obesity on an internet forum
## Abstract
### Background
Childhood obesity is affecting an increasing percentage of families globally. For families, obesity is often a tense issue, not least because of the negative stigma and cultural perceptions associated with it. Discussions around childhood obesity do not take place only at home or in healthcare, but increasingly on social media, such as Internet discussion forums. Our aim was to analyse how childhood obesity is discussed on a Finnish online discussion forum by parents of children with obesity and other commenters.
### Method
We gathered and analysed 16 discussion threads on childhood obesity taken from a Finnish Internet discussion forum, vauva.fi, between 2015 and 2021 (a total of 331 posts). For the analysis, we chose threads where the parents of a child with obesity took part. The parents’ and other commenters’ discussions were analysed and interpreted with inductive thematic analysis.
### Results
In the online discussions, childhood obesity was discussed mostly in the context of parenting, parental responsibility and lifestyle choices within the family. We identified three themes that were used to define parenting. In the theme of proving good parenting, parents and commenters listed healthy elements in their family’s lifestyle to show their responsibility and parenting skills. In the theme of blaming bad parents, other commenters pointed out mistakes in the parents’ behaviour or offered them advice. Moreover, many acknowledged that some factors causing childhood obesity were outside the parents’ influence, forming the theme of lifting the blame from parents. In addition, many parents brought up that they were genuinely ignorant of the reasons for their child’s overweight.
### Conclusions
These results are in line with previous studies suggesting that in Western cultures obesity – including childhood obesity – is typically seen as the individual’s fault and is associated with negative stigma. Consequently, counselling parents in healthcare should be expanded from supporting a healthy lifestyle to strengthening parents’ identity as being good enough parents who are already making many health enhancing efforts. Situating the family in a wider context of the obesogenic environment could ease the parents’ feelings that they have failed at parenting.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-023-15314-6.
## Background
Childhood obesity is a major concern in Finland and worldwide. In 2019, globally 38 million children under the age of 5 were overweight or obese, and the prevalence of childhood overweight among children and adolescents aged 5–19 has increased from $4\%$ [1975] to $18\%$ [2016] [1]. In 2020, in Finland, $29\%$ of boys and $18\%$ of girls aged 2–16 years were overweight or obese [2]. There is a large and fairly consistent body of evidence demonstrating that obesity in childhood has adverse consequences on premature mortality and physical morbidity in adulthood [3].
A central cause for the increase is the changes in eating and physical activity behaviours that have changed the energy balance [4]. Our environment promotes excessive food intake and does not encourage physical activity [5]. Due to several simultaneous social, technological and environmental changes [6], it has become harder for parents to ensure a healthy environment for their children. Given these developments and the multifactorial nature of childhood obesity, lifestyle change is not a straightforward issue. Previous studies among parents of children with obesity1 have shown that often the reasons for not following a healthier lifestyle are practical, such as a lack of time, resources, or uncertainty about how to maintain or achieve better lifestyle choices in the family’s everyday life and surroundings [7, 8, 9, 10]. Moreover, parents do not always recognize that their child is obese [11, 12]. Some parents feel that they already know enough about healthy lifestyles [7], and they feel that health and happiness are more important than weight [13]. Alternatively, they might acknowledge that their child is overweight, but do not perceive it as a health risk [14].
Some parents fear that treating their child’s obesity may harm the child [7, 15, 16] or that they will be blamed for the situation [16, 17]. Indeed, obesity is typically attached to many negative attributes and cultural conceptions, that have an impact on parents. More specifically, several researchers have highlighted the negative social attitudes related to overweight and obesity [18, 19, 20]. Consequently, a balance needs to be found between managing children’s obesity and considering the related psychosocial consequences.
In addition, parents of children with obesity may face stigmatization. When seeking help for their child, parents are faced with a double moral burden: they feel they are blamed and shamed for the child’s weight, but at the same time they fear the negative psychological effects of targeting the child’s weight [21]. Parents easily feel isolated and blamed for causing their children’s obesity and appreciate a supportive forum where they can share experiences [22]. Fear of stigmatization or being seen as bad parents can affect the readiness of parents to seek help or discuss the weight of their child in healthcare [21]. For the parents feeling stigmatized, anonymous discussions on online platforms, may be an alternative to discussions in healthcare encounters [23]. This study aims to explore one such platform and how childhood obesity is discussed there by parents and other commentators.
## Childhood obesity in social media
In contemporary societies, the *Internet is* a central platform where citizens search for information, inspiration, and advice on overweight, obesity, and weight management. More broadly, the Internet has increased citizen engagement with health-related issues, and various platforms have enabled citizens to search for information and support for their health-related concerns. Easy access to the Internet and its various discussion forums in Western countries, such as Finland, have made these platforms also part of many parents’ everyday lives [24].
There is little previous research on parents’ activity and argumentation on Internet forums in the context of childhood obesity. In a study based on two Australian online parenting discussion forums, four major motives for using online forums were found: seeking advice, sharing advice, social support, and making judgements [25]. On these Australian forums, parents perceived childhood obesity as a public health concern; however, many brought up having difficulties in implementing the lifestyle change messages in everyday life [9]. In 2004, in an anonymous Finnish Internet discussion, parents were mainly viewed as the primary cause of childhood obesity. Parents were negatively described as having “lousy” characters and being unable to create an “adequate” emotional bond with their children [26].
Many parents seek help from social media forums, and consequently, it is vital to understand parents’ online activity and arguments. Moreover, it is also essential to explore what kind of comments and answers these parents get and the tone of the overall discussion, because of the potential impact on others visiting the platforms. The present study fills this gap and aims to increase understanding of how childhood obesity is displayed in social media discussions. This study aims firstly to explore the discussion around childhood obesity on a Finnish discussion forum between parents having a child with obesity and people reacting to the parents, and to search for and analyse the repeated themes therein. Additionally, in the discussion we consider what additional knowledge our results provide about childhood obesity that can be useful in parents’ counselling in healthcare.
## Vauva.fi discussion forum
The dataset was acquired from Mohawk, a commercial provider that comprehensively collects and records Finnish social media content. We had access to the discussion threads that had been active for 7 years prior to February 2021. Screening the online discussion forums on the database with keywords “child obesity”, “child overweight”, and “child weight” showed that on the vauva.fi platform, childhood obesity was discussed more frequently than on other platforms.
In Finland, the vauva.fi (vauva meaning “baby” in English) discussion forum has been one of the most popular forums for years. There are about 7 million visits every month, and it reaches over 1.5 million of Finland’s 5.5 million population [27]. The sub-forums on this discussion forum are mostly related to children and family life. Moreover, there is an “open topic” sub-forum, known as a support forum for mothers and women. On vauva.fi, anyone can read the discussions without registration. However, one must register in order to participate in the discussions, with the exception of the “open topic” sub-forum. There, the platform automatically generates the cybername “visitor” for each non-registered commenter. As there is no premoderation, the messages are published as they are. However, the administrator of Sanoma Media Finland Oy deletes all the messages that break the law, and in some cases, messages that are against good manners are moderated or deleted.
## Data collection
The purpose of the data collection was to locate relevant threads discussing childhood obesity, as well as discussions between parents having a child with obesity and other commenters in the forum. The data sampling included several steps (Fig. 1).Fig. 1Steps in data collection We used the search terms “child obesity” ~ 2, “child overweight” ~ 2, and “child weight” ~ 2 (= words were not more than two words apart from each other). With these search words, in the first phase 111 posts were found in 32 discussion threads. Seven of these threads were deleted by the administrator, leaving us with 25 threads. However, the threads contained more posts than these 111 posts. We estimated that to get a comprehensive picture of the discussions, it was essential to retrieve and read (by the first author) all the 15,289 posts in these threads. While reading all these posts, the inclusion criteria for the final data became as follows: the post contained a reference to childhood obesity, and it was either written by a parent of a child with obesity or it was a response to a post by a forementioned parents.
Using these criteria, we proceeded with the data extraction. First, we eliminated all the posts not covering childhood obesity, such as posts about obesity in general, or posts commenting only on specific foods – ending up with 1475 posts. Finally, to gain an especially parental perspective, we identified posts by parents having a child with obesity and posts reacting to the parents. There were 16 threads including parents’ posts, leaving us with a total of 16 threads and 331 posts. All these threads were on the “open topic” sub-forum.
To be categorized as a parent required that the person clearly expressed having a child with overweight or obesity. Few of the posts were identified as being posted by a mother and none by a father. As the gender of the parent was not evident in most posts, we named this group “parents”. People that were not identified as having a child with obesity, but commented on the parents’ posts, we defined as “commenters”. In the following analysis, the parents are marked as numbered parents. It was not possible to mark the commenters with separate numbers, because there they were mostly written under the automatic cybername “visitor”, instead of identifiable pseudonyms.
In addition, we use the term “obesity” – meaning both obesity and overweight – as we did not have exact information on the weight of the children. However, if the commenter used either word specifically, we used that term in the extracts. Likewise in the extracts, if the gender of the child or the parent is known, we refer to the person in a gender-specific way. Otherwise, we refer to them as s/he, the child, or the parent.
The length of the posts varied from a few words to over 400 words. The number of posts to parents also varied; some parents did not receive any answers to their posts, whereas some parents received nearly 50 responses. Moreover, the number of parents and other commenters taking part in the threads varied (see Attachment 1).
## Research ethics
As regards research ethics and related legislation in Finland, the present study does not fall under the scope of the Finnish Medical Research Act and Decree ($\frac{488}{1999}$) [28] and its obligation to be reviewed and approved by an ethics board, as our research does not include medical research. At Tampere University, where the research was conducted, there are no additional binding regulations to submit to a review process by an ethics board. Due to the lack of national- or university-level regulations on social media data in Finland, ethical review was not compulsory.
Due to this situation, we carefully applied impact-driven ethics, suitable especially for research with digital media [29]. This implies taking an active role in exploring the possible negative impact of the work for those whose posts we analyse. Moreover, we have considered the Finnish Data Protection Act $\frac{1050}{2018}$ [30] and the Copyright Act ($\frac{404}{1961}$) [31], and concluded that the posts analysed here are anonymous and general enough in their style and content for it to be impossible to connect the individual posts to any single individual. Additionally, we followed the principles of Ethical Decision-Making and Internet [32] and the Ethical principles of research with human participants and ethical review in the human sciences in Finland [33].
## Data analysis
The posts were analysed with an inductive, thematic analysis. Our aim was to analyse the posts as social constructions of childhood obesity on social media platform. Thematic analysis is one potential method among qualitative methods enabling the analysis of interpretations and meanings behind the “surface” of the posts [34].
The thematic analysis was conducted following the phases presented by Braun and Clarke [35]: familiarizing ourselves with the data, generating the initial codes, searching for themes, reviewing the themes, defining and naming the themes, and finally producing the analysis. Regardless, the data process was not linear; rather the process required moving back and forth between phases and a constant return to the data.
When familiarizing ourselves with the data, we wrote down our initial thoughts and observations, and the first author identified that a major proportion of the posts concerned the reasons for and solutions to childhood obesity. Both authors discussed and verified this observation with several data examples. Next, sections of the posts where reasons and solutions were discussed were pinpointed, leading to a total of 512 sections of posts that were read and re-read several times by both authors. In this “repeated reading process” phase [35], 512 sections were further categorized into 19 initial codes related to reasons for and solutions to childhood obesity (Fig. 2). Some posts included reasons or solutions that fit several codes, and these were categorized into all suitable codes. For example, the following section was categorized with the codes “the character of the child”, “the responsibility of the parent”, and “healthy lifestyle”: “In the afternoons, I can’t watch what they eat, but based on the remains, one can eat for example three bananas or five muesli bars at once (you can guess who)” (Parent 41). The initial codes and how they were located in the parents’ and commenters’ posts are presented in Fig. 2.Fig. 2The initial codes and the frequency of their occurrence in the parents’ and commenters’ posts The posts with their codes were collated into a single table to find connections between the different codes. There, with the posts under the same code grouped together, it was easier to notice repeated themes in the data. At first, it became clear that there was an emphasis on lifestyle choices, especially healthy or unhealthy eating. However, after returning to the codes and the data several times, we observed a repeated pattern of parenthood and estimations of its quality across the codes and the posts. This finding led us to create the main themes and the sub-themes. As mentioned, this phase required us to return to the data several times, and the first author pinpointed sections to keep the themes reliable and guarantee that they reflect what was expressed in the data. Braun and Clarke call this phase the refinement of the themes and it is part of the “reviewing the themes” phase [35]. Finally, during the fifth phase of the analysis, the names of the themes were discussed several times by both authors and then determined. Additionally, appropriate examples from the data were chosen to represent the themes.
Both authors familiarized themselves with the data in all phases. The first author made suggestions in every phase and these suggestions were discussed and modified by both authors several times. If there was any disagreement about the codes or themes, they were discussed, and the disagreement solved to reach a consensus.
The data were coded using AtlasTi 9.0, a qualitative data management software programme.
## Results
The commencement of the discussion threads about childhood obesity was often based on some negative news or article from the media, such as “Alarming research: Even children can have a fatty liver and high cholesterol – parents do not recognize a child’s overweight”, or written in an accusative tone: “Are parents really so blind that they cannot see the overweight of their own child?” Only two of the 16 sampled threads concerning childhood obesity were started by parents having a child with obesity. In addition, the number of parents in the threads varied. Eight threads included only one post from a parent. In other threads, more parents took part. For example, in several threads one parent first confessed that s/he has a child with obesity, followed by other parents sharing similar situations (Attachment 1).
What followed in the discussions revolved mainly around the reasons for childhood obesity and the related solutions. The reasons and solutions were mostly intertwined in the argumentation, so the presentation of a reason typically also implied a solution and vice versa. As already shown in Fig. 2, we found that childhood obesity and its causes and solutions were discussed chiefly in terms of a healthy lifestyle and parental responsibility and choices. Furthermore, in addition to the posts that directly focused on parents’ responsibilities, the question of parenting also lay at the core of most of the other posts, where parental responsibility was considered in relation to different family situations.
As regards parenting, we found three main themes – proving good parenting, blaming bad parents, and lifting the blame from parents – and their sub-themes (Table 1), which will be analysed in the following. In addition, we pay attention to the style of argumentation. The tone of the posts from commenters varied from supportive and counselling to accusing and blaming, as the titles of the themes and the following analysis and extracts will show. Table 1Main themes and sub-themes in childhood obesity related posts in the vauva.fi discussion forumMain themeSub-themeProving good parentingMaking healthy choicesMaking an effortBlaming bad parentsMaking unhealthy choicesProblems in the family environmentLifting the blame from parentsGenes, biological factors, and illnessesCircumstances around the familyChild’s behaviour and personalityReason unknown
## Proving good parenting
Good parents were presented as active, making good lifestyle choices, and trying to change the situation that had led to their child’s obesity. We found two ways of proving good parenting: by bringing up the healthy choices one has made and showing that one is at least making an effort.
## Making healthy choices
It was very common to post about various things that had been done “right” in everyday life. These posts were mostly by parents, but also by some other commenters. In the discussions, a good parent was seen as one who is active in making healthy choices and avoids unhealthy options, especially unhealthy food choices, in the child’s everyday life. Parents attempted to convince others that even though their child was obese, they had done things that a good parent should do, such as providing their child with healthy rather than energy-dense foods and supporting a physically active lifestyle. Parents did recognize the importance of a healthy lifestyle, but often felt they had done all they could already.
We do not go in for biscuits, cakes, and buns, nor do we drink juice every day. They do not drink sodas or [eat] chips, etc. they get those maybe 5 times a year. They exercise actively every day. ( Parent 14).
Another way to show good parenting was by using other, normal weight children in the family as proof of good parenting. These statements by parents were often accompanied by lists of lifestyle factors that promote health, as was done by the following parent. If just someone would find out the reason for gaining weight? We have four children, and only one of them is overweight. The whole family eats the same food and has the same habits with snacks. This child has no extra money to buy any additional food or snacks. ( Parent 15).
A few commenters listed reasons why there were no weight issues in their families. The commenters presented themselves as successful, good parents, setting an example to other parents of how things should be done. I have an 8-year-old and two other children. No-one lies the whole day with a mobile, they go out every day, because I make them, and they do not buy candy from the grocery store without permission, because I don’t give them money for that. And this is why their bellies do not grow. ( Commenter).
Comments on lifestyle were mostly in line with the official nutrition guidelines, but there were also some posts that included a different view about a healthy lifestyle, such as the strict restriction of bread or other carbohydrate sources.
## Making an effort
Another way of presenting good parenting was showing one’s effort-making, trying one’s best to make good lifestyle choices, even when these efforts did not bring results. A good parent of a child with obesity was presented in the discussions as someone who is actively seeking a solution and is ready to ask for and receive advice. Parents brought up the efforts they had made and assured that they had spent time trying to reverse the child’s weight development. I can even make a vow that we have done more than many others for the welfare of our children. ( Parent 31).
Moreover, the commenters pushed and advised the parents to make an effort or ask for advice on how to manage the child’s weight. Parents did recognize the importance of a healthy lifestyle, but they often felt that they had already done all they could. Alternatively, some parents provided reasons why they could not implement sufficiently healthy choices in their own or their child’s everyday life. Parents wanted to present themselves as active parents who were trying their best. Simultaneously, the commenters expected the parents to try harder to change the lifestyles of the family. If a two-year-old eats an adult’s portion, how will s/he eat when s/he’s bigger. A horse’s ration? Your task is to ensure proper portion sizes, and the child may be thirsty and not hungry… *It is* the parents’ duty to see that the child eats enough, but not too much. ( Commenter).
## Blaming bad parents
The second repeated theme in the posts was the theme of blaming bad parents. Bad parenting was presented in two ways: first, by focusing on the unhealthy lifestyle of the family, and second, by highlighting the broader problems with the family and the parents’ inability to respond to the child’s emotional needs in the correct way.
## Making unhealthy choices
Commenters were active in sharing knowledge and advice on how to eat healthily, avoid childhood obesity and raise healthy children. In these statements, there was a presumption that parents having a child with obesity were doing something wrong, especially in terms of offering unhealthy foods and not being assertive enough. You should change the hard fats to soft fats, add vegetables/berries/fruits, eat according to the plate model, emphasizing vegetables, eat less sugar and white wheat, change the pasta, bread, porridge to wholewheat products, eat less meat and prefer a more vegetable-rich diet. These things will get you forward. I suppose you cook food at home, so it should not be too much effort to make these changes. No one else is doing these changes, it is easy to make excuses. ( Commenter).This answer [referring to the post of one parent] sums up what is wrong in Finnish families. It is the lack of parental authority. An eight-year-old wants to lay down and stare at a mobile. And they LET the child do this, how can this be possible? ( Commenter).
In one discussion thread, a parent started defending her situation and commenters pointed out mistakes the parent had made. When this parent continued to defend herself and challenge other commenters, the commenters became more aggressive, suggesting that the parent had failed in parenting. In the end, the commenters no longer addressed their comments to the parent but started to discuss the situation of the family by themselves. Do others see how this one mother is defending her actions, even though the son is clearly morbidly obese? And how annoying is it, when such a reasonable discussion is filled up with this mother’s defensive talk? She really does not seem to believe that her child is in bad shape and is defending herself by lifting herself and her “fancy home cooking” onto a pedestal. ( Commenter).I think this one mother knows that she is at least partly to blame for her son’s considerable overweight. Otherwise, nobody would come here all the time to defend their situation so fiercely. Your son has to lose weight significantly to have a healthy life. ( Commenter).
It was very rare for parents to confess that they had done things wrong. In these few posts, parents expressed guilt and felt responsible for the child’s obesity. I feel guilty that I let my son gain weight. ( Parent 29).I don’t get why I really have not noticed this [child’s obesity] before. The school nurse said last autumn that we are going into the upper limits of the growth charts and the family should look at what it eats. The girl is 12 years old. She likes sugary stuff; I have tried to limit it. But she eats up to three plates of even basic food. How can I deny her when she is saying that otherwise she will stay hungry? Damn, I have been a bad mother. ( Parent 53).
## Problems in the family environment
Another way of presenting bad parenting was by pointing out that the parents were creating a harmful family environment. Commenters suspected that the child’s obesity or eating was only a symptom of something else, such as depression, teasing, or simply boredom, and it was the parent’s responsibility to prevent the child from compensating for emotional needs with food. Again, the commenters blamed the parents for not meeting the emotional needs of their children and sometimes offered related advice, as in the following extracts. A healthy relationship to eating comes when eating in the child’s environment is healthy. If there are feeling-eaters around showing a model of vomiting-eating, that does not provide a healthy model for learning. ( Commenter).Have you offered your son anything else than food? Experiences, hobbies, being together, experiences in succeeding? Pleasures without food? Usually, if there is meaningfulness and passion in life, it can be anything from playing drums, dinosaurs, to camping, one does not need to binge oneself into a ball. Because then the food is in that situation where it belongs, and the meaning of life comes from somewhere else than the daily chocolate bar or ham sandwiches. ( Commenter).
In the extract below, a commenter is suggesting that some parents hide behind a rationalization that the child’s behaviour is unchangeable. Instead, s/he suggests that adults should look into the causes behind the behaviour and food preferences.[The explanation that] “the child is only greedy for delicacies” is often a simplified excuse for a situation where there are bigger problems in the background. Adults are easily blind to these, because they do not want to see the bad feelings of their own child. ( Commenter).
Most parents did not react to posts that accused them of being unable to fulfil the child’s emotional needs. Sometimes the parents did defend themselves, however, like the mother below. Honestly, I feel disgusting when you paint a picture in which my son is some kind of filthy and mentally bored outcast and us parents are people who have abandoned their child. ( Parent 31).
## Lifting the blame from parents
Finally, the reasons and solutions for childhood obesity were presented as being out of the parents’ control and hence the parents were blameless for their children’s obesity. We categorized these reasons and solutions into four groups: genes, biological factors, and illnesses; circumstances around the family; the child’s behaviour and personality; and reason unknown. Among the parents, the blameless parenting theme was intertwined with proving good parenting: often parents first presented their good parenting by listing things that they had already tried or done. Later, they stated that they would do more, but there was nothing left to do. The parents used the blameless parenting arguments more often, but commenters also gave parents absolution when they brought up causes of obesity that were not the parents’ fault and solutions that were out of the parents’ reach.
## Genes, biological factors, and illnesses
Both parents and commenters acknowledged that there were various biological or medical factors behind childhood obesity, including hereditary, genetic and hormonal factors; a hefty body size; illnesses; and a slow metabolism, as the following extracts show. Children from the same family, eating the same food, can become different in body shape because of their genes. ( Commenter).There have been [health] inspections for years and s/he is medicated constantly. Outwardly, only weight is visible from the disease, although s/he has other problems caused by the disease, which are more serious than weight. ( Parent 32).
Even though some commenters identified these factors, such as diseases, as mostly outside the parents’ influence, they nevertheless often offered advice on a healthy lifestyle. If Prader-*Will is* excluded, then [the child’s appetite] could be a condition caused by fructose, in which satiety hormones and peptides do not affect the brain. Leave out the sugar and see if the situation levels out. ( Commenter).
In addition, the mother not eating sufficiently during pregnancy, breastfeeding too much and not breastfeeding at all were presented as reasons, too. These could also be interpreted to be under the parents’ influence and hence proof of bad parenting. However, here we categorized these accounts as examples of lifting the blame from parents, because especially the parents presented these issues as something that they could not have changed in their situation. For example, the mother below suggests the quality of her milk caused the child’s obesity. My child was born big and gained weight from long breastfeeding. My milk was like cream. ( Parent 49).
Some parents were in a situation where the child became obese after being underweight. These parents felt that the previous underweight was the reason behind the obesity, and often the concern about the child not having enough food led to a situation where the child got used to big portions and favourite meals. And then suddenly everything turned upside down. The girl started to eat everything, even too much. We did not prevent this, while we remembered this skeleton-thin girl, and if she now wanted 7 meat balls and two bread rolls, she was allowed to eat them. If she wanted a third bowl of cereal, she got it. ( Parent 6).
Moreover, some parents and commenters set their hopes on future height growth to solve the obesity problem. The commenters shared experiences when their own weight was normalized during height growth.
## Circumstances around the family
There were relatively few posts where the social networks and social and health services were brought up as reasons for obesity. Both parents and commenters presented these as either a reason for childhood obesity or containing a potential solution to it. These environments were seen as ones over which parents had no influence.
Public institutions, such as child healthcare clinics and day care, were seen either as a reason or a solution to childhood obesity, both among parents and commenters. A good example of this is a lengthy thread where one parent suspected day care food to be the reason for the child’s obesity. Both my children are overweight, aged 4 and 5. They started gaining weight when they started at kindergarten and the consumption of bread and sugar bounced up significantly. In the kindergarten, they also swear by fat-free milk products. ( Parent 14).
The parent above received mostly disagreeing posts about blaming the day care, but also some posts agreeing that the day care was the reason for the obesity. In other threads, child welfare clinics and other experts were mentioned a few times as solutions for childhood obesity and mainly as places to seek help and advice. There were also posts expressing a wish to receive support from experts in healthcare. In the following extract, the commenter is optimistic about the help from the child welfare clinic, whereas the parent below is more sceptical. You should ask for help from the child welfare clinic to make food portion sizes correct. ( Commenter).In child welfare clinics, weight management issues are quite poorly taken care of. With my slightly chubby child, the advice was to add vegetables and use non-fat milk. ( Parent 8).
Grandparents were also mentioned as a reason for the child’s obesity in several posts, mostly in one thread. These were mainly responses to one parent who complained that their child was obese due to the grandparents’ treats. Some commenters questioned this explanation, whereas others supported it and shared similar experiences. In the next extract, one commenter shares similar experiences with her mother. My mom was the same with me and now she is also trying to fatten my own child. She just does not understand for some reason that you cannot take a bun or cake with coffee every day or maybe an adult can, but with a small child, it will definitely lead to a sugar spiral. ( Commenter).
A few comments were made also about the environmental influence on the child’s weight, such as chemicals and microplastics, Teflon pans, cosmetics, antibiotics, and intestinal flora. Interestingly, an obesogenic environment – such as the supply of energy-intense food and a car-dominated environment that does not encourage exercise – played no significant role in the posts. If a passive environment or energy-dense products were mentioned, it was presented in the context of the family’s everyday life as a challenge that should be solved by the parents or child.
## Child’s behaviour and personality
In several posts parents or commenters presented a certain characteristic of the child as the reason for obesity, for example the child’s habit of overeating, preference for inactivity, or personality. Parents gave examples about their child’s character and commenters about families near them – both suggesting that some children had more challenging personalities causing obesity. It is noteworthy that not only the parents but also the commenters felt that sometimes the child’s qualities were beyond the reach of the parents’ efforts. While children are different. In my friend’s family, one child is chubby, two others are normal weight, even slim and sporty. There is a healthy lifestyle in this family, but this chubby child has a huge appetite for treats. I have noticed this when the child is visiting our home. At parties, this child hogs treats above everybody else, eats double the amount of food compared to others, etc. S/he is lazy and does not like to exercise. This family, I think, has a very normal attitude towards food, treats are not a forbidden fruit, but if they are offered without limits, this one child would eat them without end. ( Commenter).
Some parents identified that their child’s energy intake was too high, but they were unsure how to solve the problem, mainly because the child’s appetite was huge, and they did not want the child to suffer from hunger. For example, the following parent suspects that the child’s obesity is perhaps related to some innate characteristics that the parent cannot change:What can one do when the child is really reluctant to drink enough water, (s)he has a huge appetite, and is very greedy for sugary treats? Age 11, so s/he is eating also in other places besides the home. ( Parent 3).
The following parent brought up several unhealthy lifestyle components that they were unable to change because the child was reluctant to make them. How do I force that 11-year-old out for a run, when he does not want to? The rest of the family cycles to the playground or takes a walk in the forest, but this first-born stays at home on the couch with his cell phone. And every now and then goes to look for something to eat from the fridge. He is keen on snacks, but also loves bread, which s/he can eat a bag a day. Probably also buys snacks secretly from the shop. ( Parent 5).
## Reason unknown
Commenters always had a reason or solution – or at least a suggestion – for the child’s obesity, but several parents expressed a genuine lack of understanding as to why their child was obese. If the cause of the obesity was considered unknown, there were no more solutions for the parents to search for. This ignorance was visible when the obesity of one child in the family was contrasted with the normal weight of the other children, as in the following extract:*Not a* single public health nurse nor doctor nor dietitian has come up with a reason why there are two slim children and one overweight in our family. We all eat the same food and there are sporty hobbies. ( Parent 36).
Another way of presenting ignorance was listing various typical reasons behind obesity and stating that none of them applied to their situation. Consequently, there were no solutions for their child’s obesity that could be adopted in the family, as the following parent presents:We eat normal home-cooked food (e.g. macaroni casserole and salad, water to drink with food), and when younger, he had a candy day. We do not purchase juice or soda! Still the boy has gone above the weight limits, big time. Tests have been taken, but no illness has been found that would explain this. ( Parent 44).
When parents explained that the reason for the child’s obesity was unknown, commenters again offered additional solutions and advice, mostly related to eating or exercise habits. Usually, parents did not reply to these posts, but if they did, and especially if they defended their choices, the commenters became more accusing in the tone of the theme of blaming bad parents.
## Discussion
This research aimed to explore the Finnish online discussion forum vauva.fi to identify, analyse and interpret discussions about childhood obesity. We have shown that childhood obesity and its causes and solutions were discussed mostly in the context of parental responsibility, the quality of parenting and a healthy lifestyle. We found three main themes that were used to define parenting: proving good parenting, blaming bad parents and lifting of the blame from parents.
The overall tendency in the posts was to associate good parenting with the family’s healthy lifestyle and consequent, lack of obesity among the children. As a result, parents having a child with obesity needed to prove their good parenting skills repeatedly on the discussion forum. A similar phenomenon was found in a study of parents of ill children: a good parent expresses both a personal sense of duty and devotion [36].
The commenters in our data provided a contrast to the parents’ posts, bringing up the theme of bad parenting – while only two of the parents stated that they were themselves bad parents. A previous study of social media discussions of childhood obesity by Kokkonen [2009] found, that parents were mainly viewed as the primary cause of the child’s overweight, and they were presented as having poor parental abilities [26]. Nnyanzi et al., have also reported that parents with normal-weight children view other parents – especially those whose children had a higher weight – as not doing things correctly [37]. Pulling together the themes of good and bad parenting, we conclude that our results reflect the cultural ethos where children with obesity are seen to signify parental failure and neglect, especially by mothers [38, 39]; the weight of one’s child has become a “litmus test of good mothering” [40].
Below, we reflect on our results with the concept of obesity stigma, meaning the tendency to attach negative attributes to obesity [41]. Several of the themes of our results reflect the cultural ethos where obese individuals are stigmatized. To begin with, the stigmatization of parents took place when parents of children with obesity were presented as failed parents who were unable to build a healthy environment for their children. Moreover, the theme of good parenting contained the potential for stigma, as good parenting was associated with the normal weight of the child – leaving other parents stigmatized as bad parents. Finally, our results suggest that children with obesity were stigmatized on the vauva.fi discussion forum too, as they were associated with negative stereotypes such as laziness and a lack of motivation and willpower.
On another level, the themes above reveal the strong tendency of modern societies to emphasize individual responsibility in health issues. Robert Crawford, for example, has pointed out that in recent decades in Western societies, there has been heightened concern about health issues from an individualistic perspective [42]. Individual-centred explanations for obesity have been reported among people taking part in lifestyle change interventions [43], among healthcare professionals [44] and among children [45]. Moreover, the centrality of lifestyle choices in our results is not a surprise, considering how obesity is presented as a result of the unhealthy lifestyles of individuals, especially a result of an unhealthy diet in the Western media, including the *Finnish media* [18, 19, 46]. There children with obesity are typically portrayed as lazy couch potatoes who should participate in the “war on obesity” along with their parents [19].
More detailed analysis of various media has shown the tones of media reporting on childhood obesity. On Twitter, childhood obesity was more often presented as a question of individual behaviour than of environment or policy [47]. Results from many countries reveal similar results: Boero [2009], has highlighted the tone of reporting on childhood obesity in the US media, which place the blame on mothers [40]. An analysis of American and Canadian parenting magazines showed that from the 1980s onwards parents have been advised to get their child eat more healthy food and less junk food, exercise more, and be less sedentary. Interestingly, since the early 2000s the advice has become more detailed, requiring a great deal more effort from the parents [48]. In the UK print media, childhood obesity has been related to individual-level drivers and solutions more frequently compared to societal-level drivers and there parents were mentioned frequently. [ 49]. Our analysis of online discussions of childhood obesity fit with this overview of how childhood obesity is presented in the Western media.
Blameless parenting was the third way of discussing childhood obesity, and it diverged from the strong tendency to define parenting by the lifestyle habits of the family. One type of blameless parenting was present in posts where parents expressed confusion about their child’s obesity despite the parents’ good intentions and healthy family habits. *Various* genetic and medical factors, the child’s behaviour and personality, and the practices of grandparents or day care centre in building eating patterns were brought up as causes of obesity. Others have also observed similar themes. Among the parents studied by Syrad et al., weight was occasionally attributed to “inherited factors”, such as genetics or puppy fat [13]. Another study found that mothers felt that one reason why they were not able to provide healthy food for the child was that the child had special needs or food preferences [50]. Lastly, among American parents, the child’s dietary or activity preferences were seen as an obstacle to the parents’ efforts to guarantee the child’s healthy diet [10].
It is noteworthy that what has been termed the obesogenic environment [5]- meaning modern technologized societies with the constant use of television and computers, a sedentary lifestyle, and easy access to energy-dense foods and large portion sizes - was not mentioned in the discussions. Although mobile phones were mentioned in the context of family life, there was no criticism of the social or cultural system as a cause for children’s greater weight. This was rather surprising given that this environment has been widely reported and discussed in the *Western media* since the early 2000s. Instead, there was a strong tendency to blame the parents for the child’s obesity.
Another obesity- related new approach we did not find in the threads, is the acceptance of bodies of all sizes, such as “body positivity” or “Health at Every Size”. These perspectives on obesity have been increasingly visible in the media in Finland [51] and elsewhere, although less so regarding children. It remains to be seen if these discourses of obesity that expand beyond individual responsibility, will change the online discussions on childhood obesity from blaming and stigmatizing towards a broader understanding of childhood obesity.
## Strengths and limitations
Anonymous social media data have their strengths and limitations. There is no way of verifying or clarifying the stories in the posts, and some of the comments might be written sarcastically. In addition, the absence of facial expressions and body gestures may make it more difficult to interpret the posts correctly, especially if there was sarcasm. However, regarding these concerns, our results are in line with previous research on perceptions of childhood obesity among parents, utilizing online discussion forum data [9, 25, 26] and with qualitative research conducted with interviews among parents [13, 37, 50] and focus groups [52].
Social media as a source of research data offers new opportunities for researchers to explore and observe citizens’ opinions, activities and interactions on different topics [53]. On anonymous Internet forums, some participants may communicate their difficulties in more intimate ways, compared to face-to-face interviews or observation encounters in healthcare. Seale et al. [ 54] have shown that interviewees typically produce retrospective accounts related to the topics of preplanned questions, whereas on the Internet, the emphasis is on participants’ more current experiences. Therefore, social media as a source of research data have the potential to provide additional knowledge on various phenomena.
The present research adds to the understanding of perceptions of childhood obesity by analysing the discussion and interaction between parents and other commentators in an online discussion forum. Many parents read online discussions and hence are affected by how obesity and parenthood are discussed there. Additionally, Internet forums are not separate from the rest of life, and consequently, they reflect the broad attitudes towards obesity in our cultures. By knowing the cultural conceptions parents confront in online forums, healthcare professionals may gain insights into how to understand parents’ concerns and reactions better.
The strength of this research is that we were able to examine parents and other commenters separately, as well as the communications between them. In particular, the online argumentation of parents of a child with obesity has been under-studied thus far, so our study produced new information on parents’ perceptions of childhood obesity. In addition, we were able to analyse more broadly the attitudes towards families that include children with obesity.
## Conclusions for healthcare
The result of our study may also be used in the development of healthcare. First, we showed that parents who take part in online discussions of childhood obesity are frequently judged as being poor parents or even as having failed in parenting. These negative experiences can weaken the willingness to seek help from support services [55] and may cause heightened sensitivity to well-intentioned questions and comments about the child’s weight from healthcare personnel. Consequently, healthcare professionals need to be careful not to reinforce the cultural model that stigmatizes children with obesity and their parents. One possibility to do this involves inclusive, affirmative, and health-oriented messages and practices in healthcare encounters that are not weight-focused [56].
Second, our results showed that many parents struggle to find ways to reverse their child’s weight development, and parents often found these struggles frustrating. However, instead of being “educated” about healthy lifestyles, parents could benefit from tools that reduce frustration when trying to change children’s food, screen, or sleep habits [52] and promote positive relationships between parents and children [57]. We also found that parents were eager to report how they prepare healthy foods for their children and push them towards physical activity. These activities should be noted in healthcare, to strengthen the parents’ sense of accomplishment. Overall, counselling could be developed in a more family-focused direction.
Finally, we showed that the obesogenic environment was not presented as an underlying factor of childhood obesity in the discussions. Discussing the pressures of the environment in healthcare could help some parents shed some of the burden and guilt they are currently feeling.
Finally, the healthcare worker’s ability to face and accept the parents’ emotions and offer support for parental skills could help parents to cope in their present situation with the child’s weight.
## Supplementary Information
Additional file 1. Attachment 1. The list of discussion threads included in the data and number of parents ($$n = 56$$), their posts ($$n = 88$$), and commenters’ posts ($$n = 243$$) in the threads.
## References
1. 1.World Health Organization. Obesity and overweight. 2021. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. Accessed 20 Jul 2021.
2. 2.Terveyden ja hyvinvoinnin laitos. Lasten ja nuorten ylipaino ja lihavuus. 2020.
https://thl.fi/fi/tilastot-ja-data/tilastot-aiheittain/lapset-nuoret-ja-perheet/lasten-ja-nuorten-ylipaino-ja-lihavuus. Accessed 11 Nov 2021
3. Reilly JJ, Kelly J. **Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: systematic review**. *Int J Obes* (2011.0) **35** 891-898. DOI: 10.1038/ijo.2010.222
4. Barlow SE. **Expert Committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report**. *Pediatrics.* (2007.0) **120** S164-S192. DOI: 10.1542/peds.2007-2329C
5. Hill JO, Peters JC. **Environmental contributions to the obesity epidemic**. *Science.* (1998.0) **280** 1371-1374. DOI: 10.1126/science.280.5368.1371
6. Ebbeling CB, Pawlak DB, Ludwig DS. **Childhood obesity: public-health crisis, common sense cure**. *Lancet* (2002.0) **360** 473-482. DOI: 10.1016/S0140-6736(02)09678-2
7. Newson L, Povey R, Casson A, Grogan S. **The experiences and understandings of obesity: families’ decisions to attend a childhood obesity intervention**. *Psychol Health* (2013.0) **28** 1287-1305. DOI: 10.1080/08870446.2013.803106
8. Lucas PJ, Curtis-Tyler K, Arai L, Stapley S, Fagg J, Roberts H. **What works in practice: user and provider perspectives on the acceptability, affordability, implementation, and impact of a family-based intervention for child overweight and obesity delivered at scale**. *BMC Public Health* (2014.0) **14** 614. DOI: 10.1186/1471-2458-14-614
9. Appleton J, Fowler C, Brown N. **Parents’ views on childhood obesity: qualitative analysis of discussion board postings**. *Contemp Nurse* (2017.0) **53** 410-420. DOI: 10.1080/10376178.2017.1358650
10. Styles JL, Meier A, Sutherland LA, Campbell MK. **Parentsʼ and Caregiversʼ concerns about obesity in young children: a qualitative study**. *Fam Community Health* (2007.0) **30** 279-295. DOI: 10.1097/01.FCH.0000290541.02834.e0
11. Falconer CL, Park MH, Croker H, Skow Á, Black J, Saxena S. **The benefits and harms of providing parents with weight feedback as part of the national child measurement programme: a prospective cohort study**. *BMC Public Health* (2014.0) **14** 549. DOI: 10.1186/1471-2458-14-549
12. Etelson D, Brand DA, Patrick PA, Shirali A. **Childhood obesity: do parents recognize this health risk?**. *Obes Res* (2003.0) **11** 1362-1368. DOI: 10.1038/oby.2003.184
13. Syrad H, Falconer C, Cooke L, Saxena S, Kessel AS, Viner R. **“Health and happiness is more important than weight”: a qualitative investigation of the views of parents receiving written feedback on their child’s weight as part of the National Child Measurement Programme**. *J Hum Nutr Diet* (2015.0) **28** 47-55. DOI: 10.1111/jhn.12217
14. Park MH, Falconer CL, Saxena S, Kessel AS, Croker H, Skow Á. **Perceptions of health risk among parents of overweight children: a cross-sectional study within a cohort**. *Prev Med* (2013.0) **57** 55-59. DOI: 10.1016/j.ypmed.2013.04.002
15. Toftemo I, Glavin K, Lagerløv P. **Parents’ views and experiences when their preschool child is identified as overweight: a qualitative study in primary care**. *Fam Pract* (2013.0) **30** 719-723. DOI: 10.1093/fampra/cmt056
16. Turner KM, Salisbury C, Shield JPH. **Parents’ views and experiences of childhood obesity management in primary care: a qualitative study**. *Fam Pract* (2012.0) **29** 476-481. DOI: 10.1093/fampra/cmr111
17. Bentley F, Swift JA, Cook R, Redsell SA. **“I would rather be told than not know” - a qualitative study exploring parental views on identifying the future risk of childhood overweight and obesity during infancy**. *BMC Public Health* (2017.0) **17** 684. DOI: 10.1186/s12889-017-4684-y
18. Harjunen H. *Neoliberal bodies and the gendered fat body* (2017.0)
19. Gard M, Wright J. *The obesity epidemic: science, morality and ideology* (2005.0)
20. Monaghan LF, Rich E, Bombak AE. *Rethinking obesity: critical perspectives in crisis times* (2022.0)
21. Davis JL, Goar C, Manago B, Reidinger B. **Distribution and disavowal: Managing the parental stigma of Children’s weight and weight loss**. *Soc Sci Med* (2018.0) **219** 61-69. DOI: 10.1016/j.socscimed.2018.10.015
22. Zenlea IS, Thompson B, Fierheller D, Green J, Ulloa C, Wills A. **Walking in the shoes of caregivers of children with obesity: supporting caregivers in paediatric weight management**. *Clin Obes* (2017.0) **7** 300-306. DOI: 10.1111/cob.12202
23. Tanis M. **Health-related on-line forums: What’s the big attraction?**. *J Health Commun* (2008.0) **13** 698-714. DOI: 10.1080/10810730802415316
24. 24.TilastokeskusInternetin käyttö medioiden seuraamiseen ja viestintään lisääntynyt2020. *Internetin käyttö medioiden seuraamiseen ja viestintään lisääntynyt* (2020.0)
25. Appleton J, Fowler C, Brown N. **Friend or foe? An exploratory study of Australian parents’ use of asynchronous discussion boards in childhood obesity**. *Collegian.* (2014.0) **21** 151-158. DOI: 10.1016/j.colegn.2014.02.005
26. Kokkonen R. **The fat child—a sign of ‘bad’ motherhood? An analysis of explanations for children’s fatness on a Finnish website**. *J Community Appl Soc Psychol* (2009.0) **19** 336-347. DOI: 10.1002/casp.1020
27. El Kamel S. *Mikä on synnytyslakki, entä pesetkö jauhelihan ennen kypsennystä? – Vauva.fi:n legendaarisia keskusteluja koottiin kirjaksi. Helsingin Sanomat* (2018.0)
28. 28.Laki lääketieteellisestä tutkimuksesta 488/1999.
https://www.finlex.fi/fi/laki/smur/1999/19990488. Accessed 4 Feb 2022.
29. Markham AN. **Afterword: ethics as impact—moving from error-avoidance and concept-driven models to a future-oriented approach**. *Soc Media Soc* (2018.0) **4** 205630511878450
30. 30.Tietosuojalaki 1050/2018.
https://www.finlex.fi/fi/laki/smur/2018/20181050. Accessed 4 Feb 2022.
31. 31.Tekijänoikeuslaki 404/1961.
https://www.finlex.fi/fi/laki/smur/1961/19610404. Accessed 4 Feb 2022.
32. Markham AN, EA Buchanan. *Internet research: ethical concerns. In: International encyclopedia of the Social & Behavioral Sciences* (2015.0) 606-13
33. 33.The Finnish National Board on Research Integrity TENKThe ethical principles of research with human participants and ethical review in the human sciences in Finland2019. *The ethical principles of research with human participants and ethical review in the human sciences in Finland* (2019.0)
34. Braun V, Clarke V. *Thematic analysis: a practical guide to understanding and doing* (2021.0)
35. Braun V, Clarke V. **Using thematic analysis in psychology**. *Qual Res Psychol* (2006.0) **3** 77-101. DOI: 10.1191/1478088706qp063oa
36. Weaver MS, October T, October T, Feudtner C, Feudtner C, Hinds PS. **“Good-parent beliefs”: research, concept, and clinical practice**. *Pediatrics.* (2020.0) **145** e20194018. DOI: 10.1542/peds.2019-4018
37. Nnyanzi LA, Summerbell CD, Ells L, Shucksmith J. **Parental response to a letter reporting child overweight measured as part of a routine national programme in England: results from interviews with parents**. *BMC Public Health* (2016.0) **16** 846. DOI: 10.1186/s12889-016-3481-3
38. Coveney J. **The government of girth**. *Health Sociol Rev* (2008.0) **17** 199-213. DOI: 10.5172/hesr.451.17.2.199
39. Friedman M. **Mother blame, Fat shame, and moral panic: “obesity” and child welfare**. *Fat Stud* (2015.0) **4** 14-27. DOI: 10.1080/21604851.2014.927209
40. Boero N. *Fat Kids, Working moms, and the “epidemic of obesity”: race, class, and mother blame* (2009.0) 113-119
41. Puhl RM, Heuer CA. **The stigma of obesity: a review and update**. *Obesity.* (2009.0) **17** 941-964. DOI: 10.1038/oby.2008.636
42. Crawford R. **Health as a meaningful social practice**. *Health.* (2006.0) **10** 401-420. DOI: 10.1177/1363459306067310
43. Jallinoja P, Pajari P, Absetz P. **Repertoires of lifestyle change and self-responsibility among participants in an intervention to prevent type 2 diabetes**. *Scand J Caring Sci* (2008.0) **22** 455-462. DOI: 10.1111/j.1471-6712.2007.00551.x
44. Zelenytė V, Valius L, Domeikienė A, Gudaitytė R, Endzinas Ž, Šumskas L. **Body size perception, knowledge about obesity and factors associated with lifestyle change among patients, health care professionals and public health experts**. *BMC Fam Pract* (2021.0) **22** 37. DOI: 10.1186/s12875-021-01383-2
45. Rees R, Oliver K, Woodman J, Thomas J. **The views of young children in the UK about obesity, body size, shape and weight: a systematic review**. *BMC Public Health* (2011.0) **11** 188. DOI: 10.1186/1471-2458-11-188
46. Kim S-H, Anne Willis L. **Talking about obesity: news framing of who is responsible for causing and fixing the problem**. *J Health Commun* (2007.0) **12** 359-376. DOI: 10.1080/10810730701326051
47. Harris JK, Moreland-Russell S, Tabak RG, Ruhr LR. **Communication about childhood obesity on twitter**. *Am J Public Health* (2014.0) **104** e62-e69. DOI: 10.2105/AJPH.2013.301860
48. Quirke L. **“Fat-proof your child”: parenting advice and “child obesity”**. *Fat Stud* (2016.0) **5** 137-155. DOI: 10.1080/21604851.2016.1145483
49. Nimegeer A, Patterson C, Hilton S. **Media framing of childhood obesity: a content analysis of UK newspapers from 1996 to 2014**. *BMJ Open* (2019.0) **9** e025646. DOI: 10.1136/bmjopen-2018-025646
50. Goldthorpe J, Ali N, Calam R. **Providing healthy diets for young children: the experience of parents in a UK inner city**. *Int J Qual Stud Health Wellbeing* (2018.0) **13** 1
51. 51.Hynnä-Granberg K. “Why can’t I take a full-shot of myself? Of course I can!” Studying selfies as socio-technological affective practices. Fem Media Stud. ahead-of-print:1–16.
52. Fuller AB, Byrne RA, Golley RK, Trost SG. **Supporting healthy lifestyle behaviours in families attending community playgroups: parents’ perceptions of facilitators and barriers**. *BMC Public Health* (2019.0) **19** 1-11. DOI: 10.1186/s12889-019-8041-1
53. Zimmer M, Kinder-Kurlanda K. *Internet research ethics for the social age : new challenges, cases, and contexts* (2017.0)
54. 54.Interviews and Internet ForumsA Comparison of Two Sources of Qualitative Data Qualitative Health Research.201020559560610.1177/104973230935409420008955. *A Comparison of Two Sources of Qualitative Data Qualitative Health Research.* (2010.0) **20** 595-606. DOI: 10.1177/1049732309354094
55. Pont SJ, Puhl R, Cook SR, Slusser W, Bolling CF, Armstrong S. **Stigma experienced by children and adolescents with obesity**. *Pediatrics.* (2017.0) **140** e20173034. DOI: 10.1542/peds.2017-3034
56. Eli K, Howell K, Fisher PA, Nowicka P. **“Those comments last forever”: parents and grandparents of preschoolers recount how they became aware of their own body weights as children**. *PLoS One* (2014.0) **9** e111974. DOI: 10.1371/journal.pone.0111974
57. 57.Sarah E., Anderson Sarah A., Keim. Parent–Child Interaction Self-Regulation and Obesity Prevention in Early Childhood Current Obesity Reports. 2016;5(2):192-200. 10.1007/s13679-016-0208-9
58. 58.Nutrition, expertise and media - MEX. https://research.tuni.fi/piiajallinoja/projektit/mex-hanke/. Accessed 20 May 2022.
|
---
title: ‘A radical operation’ – a thematic analysis of newspaper framing of bariatric
surgery in adolescents
authors:
- Sander Lefere
- Kato Verghote
- Ruth De Bruyne
- Veerle Provoost
- Priya P. Satalkar
journal: BMC Public Health
year: 2023
pmcid: PMC9993750
doi: 10.1186/s12889-023-15366-8
license: CC BY 4.0
---
# ‘A radical operation’ – a thematic analysis of newspaper framing of bariatric surgery in adolescents
## Abstract
### Background
Obesity in adolescents is a growing public health issue. Bariatric surgery is an effective, yet controversial treatment option for adolescents. The moral acceptability of this procedure by health-care professionals as well as the general public can be influenced by its portrayal in the news media. Our objective was to analyze how newspaper articles portrayed adolescent bariatric surgery, with attention to the language used and moral arguments made.
### Methods
Using an inductive thematic analysis approach, we analyzed 26 UK and 12 US newspaper articles (2014–2022) on adolescent bariatric surgery for implicit or explicit moral evaluations and use of normative language. Coding was performed after immersive reading, assisted by NVivo. Themes were identified and refined iteratively through consecutive auditing cycles to enrich the depth and rigor of our analysis.
### Results
The major themes identified related to [1] defining the burden of adolescent obesity, [2] sparking moral outrage, [3] sensation-seeking, and [4] raising ethical issues. The articles employed moral language, specifically non-neutral and negative discourse regarding surgery. Blame was attributed to adolescents or their parents. Sensationalist wording often reinforced the normative content, drawing the attention of the reader and contributing to stigmatization of adolescents with severe obesity as lacking will power and being lazy. Further moral issues that stood out were the challenges in obtaining an informed consent, and the unequal access to surgery for socially disadvantaged groups.
### Conclusions
Our findings provide insights into how adolescent bariatric surgery is represented in the print news media. Despite frequent citing of experts and studies on the efficacy, safety and unmet need for bariatric surgery, obesity and surgery in adolescents are often stigmatized and sensationalized, with (prospective) patients depicted as looking for an easy way out in the form of a solution brought by others (health systems, society, tax payers). This may increase the stigma surrounding adolescent obesity, and therefore limit the acceptability of specific treatments such as bariatric surgery.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-023-15366-8.
## Introduction
The number of adolescents with obesity has doubled over the last decades, now affecting one in every five in the United States (US). Concurrently, the prevalence of severe obesity (defined as a BMI ≥ 35 kg/m2 or ≥ $120\%$ of the age- and sex-adjusted 95th BMI percentile) has greatly increased as well [1]. Contrary to the idea of ‘growing out of obesity’, the majority of obese children and adolescents become obese adults [2]. Adolescents with obesity are at risk of metabolic and cardiovascular comorbidities, including type 2 diabetes, hypertension and non-alcoholic fatty liver disease [3, 4]. In this setting, timely treatment is of importance. Lifestyle management is the first line of treatment and can reduce comorbidities, yet especially in adolescents with severe obesity, the long-term efficacy is rather limited [5].
Over the last two decades, bariatric surgery has emerged as an effective treatment option for adolescents with severe obesity [6]. A population-based US study found that over 14.000 bariatric procedures were performed in patients ≤ 20 years of age between 2005 and 2014, although the majority were performed in young adults (mean age 18.6 years) [7]. The rate of bariatric surgeries in the US has increased gradually over time to 331 cases per 1 million adolescents [8]. Longitudinal studies have reported BMI reductions of 13–16 kg/m2 [9]. In comparison to adults, weight loss outcomes are similar in adolescents, yet resolution of comorbidities was attained in a significantly higher proportion, suggesting that early surgical intervention might be preferred [5].
Nevertheless, bariatric surgery in adolescence remains controversial and presents physiological and psychological challenges during this period in the patient’s life, when they are transitioning to adulthood and emerging independence. Even when weight loss goals are met, most patients remain overweight or obese, which might have negative psychological consequences, especially in those with unrealistic expectations regarding weight loss [10]. The lack of long-term outcome data adds to the uncertainty of whom to treat surgically.
One reason for the controversial nature of bariatric surgery in adolescents is the fact that obesity, childhood obesity in particular, has social and moral connotations attached to them. People with obesity are often portrayed as neglecting their own health, or failing to eat healthy. Parents of children with severe obesity are judged to be ignorant, irresponsible, or even bad parents [11]. Given that obesity is correlated with a low socioeconomic status or belonging to a minority, blaming people with obesity may serve to reinforce social inequalities [12]. Framing obesity as an illness might limit blame by suggesting a biological or genetic determination, yet contribute to stigmatizing people with obesity as diseased [12]. A 2001 survey revealed that $65\%$ of Americans agreed that obesity is at least partially the result of a ‘lack of willpower’ [13]. These views are important since research indicates that support for specific health policies is lower if obesity is seen as a personal issue [13, 14]. Furthermore, surgery in adolescents poses moral challenges concerning informed consent or assent, autonomy, and voluntariness [15].
News media are a major source of health information for the general public, and can potentially influence how health problems are viewed in society, increase stigmatization, and affect support for specific treatment options, prevention strategies and public health policies [16, 17]. Although more research is needed about the specific ways in which media frames influence the general public’s opinion and health policy debates, there is increasing acceptance that insight into these matters is crucial for advancing these debates [18]. Bariatric surgery in adolescents has gained significant coverage over the last decade, including articles which cite health-care professionals (HCPs), figures of authority to whom the general public might be attentive. Therefore, how adolescent bariatric surgery is framed in the news media, especially those that reach a wide audience, such as high-volume newspapers, might conceivably shape the public opinion on this issue, including moral judgments.
Our objective was to analyze how newspaper articles portrayed adolescent bariatric surgery, with attention to the language used and moral arguments made, as to our knowledge, this had not been investigated so far. We aimed to address this gap by performing qualitative thematic analysis to analyze, interpret and report moral judgement in media framing of adolescent bariatric surgery within the newspaper dataset [19].
## Study site
We selected newspapers based in the UK and US. These countries are representative of the Anglo-Saxon world, yet have distinct socio-economic and healthcare contexts that might impact how the topic is framed in the media. Specifically, in the UK, resources for healthcare interventions are allocated through the National Health Service (NHS), whereas in the US the publicly financed Medicare and Medicaid health coverage coexists with privately financed coverage, which is unaffordable for many Americans. In both countries, pediatric obesity rates are high, and adolescent bariatric surgery has been available for some time. Consequently, obesity and bariatric surgery are being discussed not only in the scientific community but also in the national and regional media.
## Sampling
Articles were obtained through the LexisUni search engine of international media. All newspapers from the US and UK were included to obtain diversity in scope, readership and publisher’s ideological orientation. We searched for articles containing keywords referring to both bariatric surgery (‘bariatric surgery’, ‘weight loss surgery’, ‘sleeve gastrectomy’, ‘gastric sleeve’, ‘gastric bypass’, ‘gastric banding’) and adolescence (‘adolescence’, ‘adolescent*’, ‘teen*’, ‘teenager*’, ‘young adult’). Given that bariatric surgery in adolescents is relatively novel, we restricted our search to articles published from January 1, 2014 to February 28, 2022. Filters for articles in English and in the category ‘Medicine & Health’ were applied.
## Data collection
The search strategy yielded 537 articles (205 UK and 332 US newspaper articles), which were downloaded in Word format. Duplicates and articles reprinted in newspapers based in countries other than the US or the UK were excluded. Selection criteria were a focus on adolescent bariatric surgery and relevance for an analysis of media framing; i.e. articles limited to describing the results of a scientific study on adolescent surgery were excluded. Given the broad search, most articles were not relevant for our purpose. After assessment of all articles by the first author, 38 articles (26 from the UK and 12 from the US) were included (Fig. 1). The list of all included articles is presented in Supplementary Table 1.Fig. 1PRISMA Flowchart of the study selection process
## Data analysis
Publication trends over time and according to media outlet were assessed to provide background information. Thematic analysis was performed [20] with a particular focus on the use of normative language and moral evaluations of adolescent bariatric surgery. The selected articles were read and re-read to get familiarized with the content. The articles were then imported into NVivo (v.1.6.1) by the first author, for inductive coding according to the methodology proposed by Braun and Clarke [19, 21]. Coding was discussed with a second author (PS). The coding was gradually abstracted to identify potential themes, which were evaluated and refined by the co-authors in a collaborative auditing cycle [22]. Consecutive auditing cycles were organized to enrich the depth and rigor of our analysis and prevent the final analysis from being a mere reflection of the first author’s subjective interpretation of the data.
## Ethics
The newspaper articles and data used for this study are publicly available and have been specifically published to reach a wider (non-scientific) audience. Personal stories of adolescents and their family, sometimes under a pseudonym, were published in these newspaper articles with consent of those involved. Moreover, personal data (age, names, location, disease course, treatment) were not directly quoted or included in our analysis. We therefore judged that our study did not invade their privacy. Similarly, the names and affiliations of HCPs quoted in the articles did not feature in our analysis. Quotation marks or bold text for emphasis within the quotes were adopted from the primary source and were not added by the authors.
As this was not feasible for practical reasons (e.g. journalists might no longer work at the newspaper, journalists might not have access to the (updated) contact information of their sources or are unable to share this for confidentiality reasons), we did not seek consent from either the journalists or the patients, their parents or HCPs portrayed in the newspaper articles. Ethical approval was obtained from the Ghent University Faculty of Arts and Philosophy Ethics committee (Nr. $\frac{2022}{11}$).
## Results
The number of articles published each year varied widely, with a peak in 2015 (Fig. 2A). The number of articles published in US newspapers was lower than in UK newspapers in every year, except for 2019, when several US news sources published articles built on findings from a scientific study. In the UK, most articles ($62\%$) were published by national tabloids, whereas the majority of US coverage was in local newspapers ($75\%$) (Fig. 2B).Fig. 2A Number of included UK and US articles published per calender year. B Breakdown of number of newspaper articles according to news publisher Four main themes were identified through thematic analysis, [1] defining the burden of adolescent obesity, [2] sparking moral outrage, [3] sensation-seeking, and [4] raising ethical issues. Several quotations are presented within the text, and additional quotations for each theme are included in Table 1.Table 1Structure of themes and subthemes with additional representative quotes not included in the main textThemeSubthemeRepresentative quotesDefining the burden of adolescent obesityStatistics“Officials at the World Health Organization (WHO) have warned that childhood obesity is one of the most serious global health challenges. Almost a third of UK pupils are already overweight by the age of 11.”Causes of obesity“He says that the rise in obesity is a "devastating" reflection of today's lifestyles: *There is* a substantial proportion of teens out there who have barely had a healthy meal in their life, are living couch-potato lifestyles and are spending hours playing computer games. […]"“ Surgery, of course, doesn't get to the root cause of why so many young Britons get fat in the first place: the proliferation of fast food outlets in deprived areas, sugary drinks and high-fat, high-sugar foods, poverty, fall-off in physical education in schools, the rise of a sedentary lifestyle and the allure of computer games and laptops. "“Furthermore, in predominantly Black and Latino North Philadelphia, almost three-fourths of the children were overweight. ”Sparking moral outrageStigmatizing obesity“She admits she felt "some disdain for moms who let their kids drink soda or eat fast food" and "sometimes looked down on the moms of fat kids". There was no junk food in her house. Her kids drank only water and ate three healthy mealsWho’s to blame?“I think the parents have to be blamed for overweight children, but they have to be helped. Although this is difficult for them—and we can understand chastising children and stopping them doing what they want is difficult—I'm afraid that's their responsibility. It comes with being a parent. "*Surgery is* a ‘quick fix’“Experts worry that teens are opting for quick-fix operations such as gastric bypass, gastric bands and gastric balloons instead of changing their diet and doing more exercise. ”“One of the criticisms of weight-loss surgery is that it doesn't necessarily promote healthy eating: It's amazing what you can liquidise, Dr. XXX says”Sensation-seekingHeadlines“FAT KIDS OP PLEA—Overweight teenagers must have weight-loss surgery to stop 'obesity apocalypse', leading doctor claims"Personal stories“A teenager who watched her obese mother die of a heart attack has undergone weight loss surgery at 16 to avoid the same fate. XXX, was just a toddler when her mother YYY, suffered a heart attack at the family's home in ZZZ. Then aged three, XXX watched in horror as her mother YYY collapsed in front of her in 2004. YYY, who weighed around 300lbs, knocked over XXX toy kitchen as she fell to the floor and had passed away by the time paramedics brought her to hospital. Terrified she would also die young, the teenager—who weighed 275lbs (19st 6lbs)—had a vertical sleeve gastrectomy at 16, and has since lost 100lbs. "‘Radical’ surgery“TEENAGERS as young as 16 have undergone radical NHS-funded weight loss surgery in Birmingham, worrying new figures show. "Raising ethical issuesAbility to give consent“Even operating on teenagers raises issues that may not apply to adults. Can a miserable adolescent, for example, really can give informed consent to such a drastic, life-changing operation? And can a teenager be expected to commit to following the very restricted diet required after the surgery, not to mention taking the needed vitamins and minerals? Are they prepared to do that for the rest of their lives?”Unmet need and disparity in access to care“Only 0.2 per cent of people who meet the official criteria—that is, having a BMI over 30 with diabetes that is not adequately controlled by medication, or having a BMI over 40 regardless of diabetes—get offered bariatric surgery. I would like that changed to it being offered to more people who at least fit the criteria as drawn up by the National Institute for Health and Care Excellence"Who should pay for surgery?“The controversial operation, […], costs the NHS about £6,000 a time. […] Urgent action is needed and unless it happens the problem of obesity and related health costs will bankrupt the NHS.”
## Defining the burden of adolescent obesity
The first theme relates to a general discussion on adolescent obesity, focusing on obesity statistics and the causes of obesity. Although not strictly normative in nature, the undertone was not neutral, indicating a moral evaluation of the current situation or judgment on the lifestyle responsible for obesity.
## Statistics
Articles often cited various statistical sources to underpin the growing problem of childhood obesity: "The new statistics come from the annual Health Survey for England, which has been running since 1991 and involves interviewing and measuring 8,000 adults and 2,000 children every year. […] a "staggering" one in four teenagers was clinically obese by the age of 15.” Descriptions similar to this one of a ‘staggering’ number of obese teenagers also featured in other articles, which regularly described the problem as an epidemic or a crisis: “[…] childhood obesity is a crisis already engulfing the UK.” In other cases, obesity was negatively framed in more explicit statements: “[…] the nation's fattest children had become even heavier in recent years, sparking fears of a costly obesity timebomb. […] Unfortunately those children who are already obese are getting even fatter”. The metaphor of obesity as an ‘exploding problem’ was recurrent in our dataset, for instance “There has been an absolute explosion in the extreme end of obesity in kids.”
## Causes of obesity
In conjunction with obesity statistics, articles discussed the causes of childhood obesity in varying levels of detail. Individual causes were often cited, for instance unhealthy eating habits: “If parents are not preparing fresh meals or are using lots of processed food then the problem just gets passed down to the younger generation." In these quotes (also see Table 1), the responsibility is lain with the parents and the adolescent, although other articles question this assumption: “XXX does not deny that parents can be responsible for their children's obesity, but he believes there are other significant factors.” Several articles also discussed societal factors linked to obesity, including poverty: “[…] in Philadelphia, where roughly a quarter of residents live below the poverty line, $41\%$ of children ages 6 to 17 are overweight or obese.”
## Sparking moral outrage
Newspaper articles often contained negative discourse on obesity in adolescents and regarding surgery, or specifically attributed blame to adolescents or their parents.
## Stigmatizing obesity
Language used to describe adolescents with obesity, irrespective of being considered for surgery, was often derogatory and stigmatizing: “A severely obese teenager is likely known by every other student in the high school not because she is a prom queen, but because she is physically the largest student in the school.” *In this* instance, there is a specific reference to the adolescents not meeting beauty norms, rather being socially isolated because of their weight. Other pejorative wording was also used, such as “These are not kids who are pleasantly plump”, invoking a contrast between adolescents who are severely obese and those who are merely ‘plump’.
A limited number of articles reacted explicitly against this dominant characterization, generally by referring to obesity as a disease that is beyond simple individual control:He speaks up now when someone speaks derisively of a person who is obese. He knows how it feels to be shunned for what medical researchers now deem a chronic disease, not a lifestyle choice. Without that experience, he said, I don’t think I would have that lens of compassion for people with their struggles.
## Who’s to blame?
As illustrated when describing the causes of obesity and stigmatization, some articles attributed blame to the adolescents and, particurarly, their parents:The two-part series called Junk Food Kids: Who's To Blame? features one 13-year-old schoolgirl who weighs 16st. XXX, who came to Britain from Romania four years ago, visits King's College Hospital in London with her parents to be assessed for the £12,000 surgery. During a consultation about the operation, XXX’s mother,[…]. Speaking in broken English, she explains: We keep on diet […].
Here, the moral indignation about obesity is reinforced by including remarks that allude to xenophobic representations of non-UK born citizens, including the idea that newcomers profit from expensive NHS-payed surgery. The failure of the parents is underlined again: “But just 15 min later, the family are shown heading straight to a nearby McDonald's for one last treat'." Notably, the family themselves seemed to accept this blame: “Her father YYY says: Dieting, changing your eating style, is about the power of willing. And we've demonstrated we're not that strong.”
## Surgery is a ‘quick fix’
A recurrent argument against bariatric surgery for adolescents was that it represents ‘choosing the easy way out’: “Concerns have been raised that people are opting for stomach surgery as an 'easy option' rather than go to the trouble of changing their lifestyle, eating more healthily and taking exercise.” While severe obesity was presented as a major problem, not all solutions were deemed acceptable, and losing weight through ‘hard work’ seemed to be valued more. This normative view was most explicitly expressed in the following:Young people should be given the chance to control their own weight, […]. Surgery takes it out of your hands. The message we are giving to young people, especially if their parents have had surgery, is that they don't really need to do anything, […], because it will be solved for them.
This view is connected to the stigmatization of obesity, stressing that individual solutions displaying a ‘work ethic’ are required to solve a problem defined by a lack of this, in line with an implicit notion of laziness ascribed to people with obesity. Notably, articles arguing that surgery is not a quick fix similarly stressed the need for lifestyle changes to achieve success after surgery, possibly from the same assumptions about the character traits of the patients: "Anyone considering weight loss surgery needs to understand that the surgeries are just a tool and they all require patients to make dietary changes and lifestyle modifications to be successful and to maintain weight loss. None of the operations are a quick fix”.
## Sensation-seeking
This theme focuses on the style rather than the content. Sensationalist wording was frequently employed, which captured the attention of the reader and often contributed to a moralizing view on these topics. Notably, sensationalism was considerably more present in UK compared to US newspaper articles.
## Headlines
Several articles featured headlines that set the tone for the article and contributed to a stigmatization of obesity in adolescents: “Teens get gastric bands as obesity time-bomb explodes” and “100,000 TEENS NEED FAT OPS; SUPER-OBESE KIDS CRISIS”. In other headlines, bariatric surgery was described as a potential solution, albeit a radical one: "A Daunting Operation Offers Relief to Obese Teenagers".
## Sensationalism in personal stories
Several articles included a personal story of an adolescent with severe obesity who has undergone, or is contemplating surgery, which were sometimes written in an unnecessarily dramatic and sensational way: "Two years earlier the fire brigade had had to demolish part of her parents' house in XXX, to take her to hospital.” This amounted to a humiliation of this girl with the aim of capturing the readers’ imagination. Highlighting certain words or sentences in upper case also added to the sensationalist presentation of adolescent obesity: “But XXX’s need to lose weight became frighteningly urgent last April when, aged just 19, she went BLIND. […] She was also at risk of developing a brain aneurysm, which meant she could have died at any moment.” Interestingly, another article discussing this girl’s condition stated that she did not go blind, but rather had transient episodes of poor vision. She was at risk of vision loss because of intracranial hypertension. This implies that the earlier newspaper had overstated her medical condition for the sake of sensation.
## ‘Radical’ surgery
Even in articles in which bariatric surgery was presented as a key therapeutic option, the radical or controversial nature of the procedure was emphasized: “The schoolboy is one of an increasing number of youngsters who have had the controversial weight-loss operations, despite recommendations that such high-risk surgery should be a last resort […].” The following description reinforced this through a vivid description: “So she has come for a Roux-en-Y gastric bypass, the most radical treatment for obesity. It is, as one surgeon puts it, "a mutilating operation" in which a person's innards are rearranged with the aim of reducing eating. And it is booming in popularity.”
## Ability to give consent
Several ethical issues related to bariatric surgery were also discussed in the articles, including the question of consent by underage adolescents: “[…] action must be taken to protect under-18 s, who are not mature enough to make a decision like this. Having a gastric band will affect the rest of your life." One comment by a surgeon refers to the need for a multidisciplinary decision on whether to go ahead with surgery:Does he think a 14-year-old can give informed consent to have surgery which comes with long-term aftercare? No, I don't. I don't believe they can make properly judged consent over a long-term issue. […] With patients he has operated on at the younger end of the age range, I judged them to have capacity and the family were in agreement, and their psychologist was.
This is crucial, given that adolescents frequently have unrealistic expectations, which need to be tempered: “Although it has "definitely been worth it", she wasn't prepared for how difficult living with a gastric band would be. […] *It is* a lifelong commitment, which as a teenager she says she didn't really grasp.”
## Unmet need and disparity in access to care
Despite frequent negative views on obesity, and the criticism of surgery as a quick fix in some articles, most articles advocated for surgery as a tool to treat adolescents with severe obesity. One issue repeatedly identified in this context is the clinical unmet need and disparities in access, as only a fraction of eligible adolescents are being treated surgically: “*Recent data* showed just 2,000 weight loss operations are carried out on children and teenagers each year, despite surgery rates having tripled in the last 20 years." Because of the potential side effects, surgery is often not even presented as an option to patients:An estimated three to four million adolescents are heavy enough to meet the criteria for bariatric surgery, Dr. XXX said. But only about 1,000 teenagers a year have the operation. Many medical centers will not perform it on teenagers and many pediatricians never mention it to their heavy patients.
Especially in the US articles, unequal access based on income and ethnicity was discussed: “Childhood obesity disproportionately affects children of color and those in low-income populations, Dr. XXX said. Those getting access to surgery are almost exclusively middle- and upper-class white adolescents.” A major additional barrier cited multiple times in the US context, was that both private and public insurers would not cover the surgery for underaged patients, contributing to the disparities in access.
## Who should pay for surgery?
Although cost-effective, bariatric surgery is a relatively expensive treatment, and several articles connected the previous themes to the cost posed to the society: “"A GIRL who weighs 25 stone at 16 is making a desperate effort to slim down with a £6,000 gastric bypass op on the NHS. […] Instead, she will have the surgery to stop her eating so much, with the bill picked up by the taxpayer." *In this* extract, there is a direct link between stigmatization of obesity and indignation that the taxpayer, through the NHS, will have to pay for the treatment. A similar statement was quoted above in the section on blame. In one article, the question was directly posed to the readers: “What do you think—SHOULD GASTRIC BANDS BE PAID FOR BY NHS?” However, other articles stressing the unmet need for surgery argue that bariatric surgery is cost-effective: “We know we should be doing more adult bariatric surgery. This is probably down to cost, he says, though surgery is cost effective to the health service in the long term."
## Discussion
This qualitative study aimed to investigate the moral framing of adolescent bariatric surgery in UK and US news media using thematic analysis. The predominant themes identified related to [1] defining the burden of adolescent obesity, [2] sparking moral outrage, [3] sensation-seeking, and [4] raising ethical issues.
The first theme captured statistics on the prevalence of adolescent obesity, as well as the broader discussion on the causes of childhood obesity, which could be classified as person-level or system-level. *The* general public does not view these as mutually exclusive, as a UK study reported an agreement of approximately $60\%$ with both ‘People are overweight because they lack willpower’ and ‘People are overweight because there are so many unhealthy foods around’ [23]. While both ways of causal reasoning were found in our dataset, a recent study found that discussions of social and economic aspects related to obesity in UK newspaper articles have decreased over time, with increasing emphasis placed on individual factors [24]. Moreover, concentrating on individual causes was connected to the subthemes of whom to attribute blame to, and to what extent the taxpayers should contribute to surgical treatment. The latter point is made more salient by the repeated description of bariatric surgery in adolescents as ‘radical’, a specific framing of the intervention that can contribute to moral outrage.
Personal stories, and even article headlines, contained non-neutral, vivid and often sensationalist terms, signaling a moral evaluation or judgment of obesity. Indeed, there was some evidence of disparaging descriptions of adolescents with severe obesity, by both journalists and HCPs. A focus on individual rather than systemic causes of obesity in news articles could contribute to the further stigmatization of people with obesity [25], including by HCPs [26]. Stigmatization of obesity is often societally tolerated on the belief that it will motivate those affected to ‘correct themselves’ and lose weight, yet research clearly indicates that stigmatization creates additional barriers to attain healthy lifestyles and thereby worsens obesity [27]. Bariatric surgery is often viewed negatively as a ‘quick fix’ option because not all weight-loss options are evaluated equally, in part because some do not conform to the societal norms of hard work and personal responsibility, or of being able to tackle your own problems with your own resources. And although scientific studies and HCPs were extensively quoted, often endorsing bariatric surgery to counter adolescent obesity, and stressing the gap between the number of eligible patients for surgery and the number of procedures performed, the frequent referrals to bariatric surgery as ‘radical’ or even ‘mutilating’ seem to reflect an ambivalence by journalists towards these recommendations [28].
Informed consent or assent was identified as a specific ethical challenge, with several articles raising the question whether and to what extent obese adolescents could give consent for surgery. Even in adults, and more so in adolescents, difficulties can arise when informing patients about potential complications, expected long-term benefits and mandatory changes to lifestyle [15]. Health illiteracy can be an additional obstacle in obese adolescents and their family. Furthermore, negative or stigmatizing reporting in the media might also impair patients’ autonomy [15] by affecting which treatments are viewed by patients and their families in their particular socio-economic context as feasible or even desirable.
Our finding that sensationalism (language used in news title and text) was more present in the UK articles (in our sample) is intriguing. One potential explanation for this is that most relevant US articles were published in local newspapers, in contrast to UK articles, published mainly in tabloid newspapers supportive of conservative ideology. In contrast, disparity in access to treatment, including surgery, is an important ethical theme [15] which featured most prominently in our analysis of US newspaper articles. Emphasizing this in media articles could help build support for policy initiatives to tackle this issue.
Summarizing the implications from our study to formulate recommendations for improving future discussions of adolescent obesity and bariatric surgery in the media, we would stress the need for both HCPs and journalists to be mindful of how the use of language from certain registers might frame the topic. Since HCPs are often interviewed, they can leverage this opportunity to stimulate a more constructive and well-balanced discussion by providing accurate information to journalists – although there is a paucity of literature on this topic – and thereby become actively involved in destigmatizing health issues [29]. In a similar vein, journalists who write about health topics should be aware of how they present their story, because this determines what the public, and indirectly the policymakers, will consider the facts to be. Framing the public discussion in a certain direction could preclude other views on the issue [18]. Interestingly, a study on a range of public health service messaging also found that these often focused on individual responsibility or perpetuated stereotypes, and concluded that by understanding the problems with these stereotypes, health communicators should be educated on developing alternative messaging that are less harmful [30]. A comparable approach for journalists, for instance through medical education workshops and the introduction of editorial policies with the aim of avoiding certain language, might improve the coverage in the future. Furthermore, policymakers should resist the sensationalist characterization of obesity, instead recognizing that childhood obesity is a complex issue without clear-cut or one-dimensional solutions. Lastly, as mentioned, the framing of obesity in the media might affect which treatments are viewed as acceptable by patients and their families, as well as the general public. Heightened awareness of this can assist HCPs to identify underlying preconceptions and misconceptions of patients and their family, which can be addressed when discussing and evaluating treatment options [31].
Our study has several limitations. First, as articles were searched for using one database, LexisUni, we cannot exclude that relevant articles were omitted because they were not included. It is unclear if the relatively limited number of articles retrieved from, mostly local, US newspapers, is partly the result of our search methodology or reflects a true underreporting compared to UK news media. These findings are mirrored in a 2022 news media survey, with a comparable usage of print and online news media for both countries, but with a bigger role for regional or local newspapers in the US [32]. In addition, we did not include televised news broadcasts or social media news articles in our analysis, which might report differently on adolescent bariatric surgery than classic newspaper articles.
## Conclusions
In conclusion, this study investigated the moral framing of adolescent bariatric surgery in newspaper articles, and identified implicit and explicit moral framing. Despite frequent citing of experts and studies on the efficacy, safety and unmet need of surgery, obesity and surgery in adolescents were often stigmatized and sensationalized with (prospective) patients depicted as looking for an easy way out in the form of a solution brought by others (HCPs and the public in the form of taxpayers), in contrast to one they would have to work for. Representing patients this way may contribute to the stigma surrounding bariatric surgery, particularly in adolescents.
## Supplementary Information
Additional file 1: Supplementary Table 1. Overview of included newspaper article metadata.
## References
1. Bolling CF, Armstrong SC, Reichard KW, Michalsky MP. **Section On Obesity SOS. Metabolic and Bariatric Surgery for Pediatric Patients With Severe Obesity**. *Pediatrics* (2019.0) **144** e20193224. DOI: 10.1542/peds.2019-3224
2. Ryder JR, Jacobs DR, Sinaiko AR, Kornblum AP, Steinberger J. **Longitudinal Changes in Weight Status from Childhood and Adolescence to Adulthood**. *J Pediatr* (2019.0) **214** e2
3. Lefere S, Dupont E, De Guchtenaere A. **Intensive lifestyle management improves steatosis and fibrosis in pediatric non-alcoholic fatty liver disease**. *Clin Gastroenterol Hepatol* (2022.0) **20** 2317-2326. DOI: 10.1016/j.cgh.2021.11.039
4. Twig G, Zucker I, Afek A. **Adolescent Obesity and Early-Onset Type 2 Diabetes**. *Diabetes Care* (2020.0) **43** 1487-1495. DOI: 10.2337/dc19-1988
5. Malhotra S, Czepiel KS, Akam EY. **Bariatric surgery in the treatment of adolescent obesity: current perspectives in the United States**. *Expert Rev Endocrinol Metab* (2021.0) **16** 123-134. DOI: 10.1080/17446651.2021.1914585
6. Paulus GF, de Vaan LE, Verdam FJ, Bouvy ND, Ambergen TA, van Heurn LW. **Bariatric surgery in morbidly obese adolescents: a systematic review and meta-analysis**. *Obes Surg* (2015.0) **25** 860-878. DOI: 10.1007/s11695-015-1581-2
7. Griggs CL, Perez NP, Goldstone RN. **National Trends in the Use of Metabolic and Bariatric Surgery Among Pediatric Patients With Severe Obesity**. *JAMA Pediatr* (2018.0) **172** 1191-1192. DOI: 10.1001/jamapediatrics.2018.3030
8. Bouchard ME, Tian Y, Linton S. **Utilization Trends and Disparities in Adolescent Bariatric Surgery in the United States 2009–2017**. *Childhood obesity (Print)* (2022.0) **18** 188-196. DOI: 10.1089/chi.2021.0201
9. Armstrong SC, Bolling CF, Michalsky MP, Reichard KW. **Section On Obesity SOS. Pediatric Metabolic and Bariatric Surgery: Evidence, Barriers, and Best Practices**. *Pediatrics* (2019.0) **144** e20193223. DOI: 10.1542/peds.2019-3223
10. Li MK, Sathiyamoorthy T, Regina A, Strom M, Toulany A, Hamilton J. **"Your own pace, your own path": perspectives of adolescents navigating life after bariatric surgery**. *Int J Obes* (2021.0) **45** 2546-2553. DOI: 10.1038/s41366-021-00928-w
11. Appleton J, Fowler C, Brown N. **Parents' views on childhood obesity: qualitative analysis of discussion board postings**. *Contemp Nurse* (2017.0) **53** 410-420. DOI: 10.1080/10376178.2017.1358650
12. Saguy AC, Riley KW. **Weighing both sides: Morality, mortality, and framing contests over obesity**. *J Health Polit Policy Law* (2005.0) **30** 869-921. DOI: 10.1215/03616878-30-5-869
13. Oliver JE, Lee T. **Public opinion and the politics of obesity in America**. *J Health Polit Policy Law* (2005.0) **30** 923-954. DOI: 10.1215/03616878-30-5-923
14. Barry CL, Brescoll VL, Brownell KD, Schlesinger M. **Obesity metaphors: how beliefs about the causes of obesity affect support for public policy**. *Milbank Q* (2009.0) **87** 7-47. DOI: 10.1111/j.1468-0009.2009.00546.x
15. Hofmann B. **Bariatric surgery for obese children and adolescents: a review of the moral challenges**. *BMC Med Ethics* (2013.0) **14** 18. DOI: 10.1186/1472-6939-14-18
16. Barry CL, Jarlenski M, Grob R, Schlesinger M, Gollust SE. **News media framing of childhood obesity in the United States from 2000 to 2009**. *Pediatrics* (2011.0) **128** 132-145. DOI: 10.1542/peds.2010-3924
17. Saguy AC, Gruys K. **Morality and Health: News Media Constructions of Overweight and Eating Disorders**. *Soc Probl* (2010.0) **57** 231-250. DOI: 10.1525/sp.2010.57.2.231
18. Koon AD, Hawkins B, Mayhew SH. **Framing and the health policy process: a scoping review**. *Health Policy Plan* (2016.0) **31** 801-816. DOI: 10.1093/heapol/czv128
19. Braun V, Clarke V. **Using thematic analysis in psychology**. *Qual Res Psychol* (2006.0) **3** 77-101. DOI: 10.1191/1478088706qp063oa
20. Islam S, Fitzgerald L. **Indigenous obesity in the news: a media analysis of news representation of obesity in Australia's Indigenous population**. *BMC obesity* (2016.0) **3** 30. DOI: 10.1186/s40608-016-0109-1
21. Braun V, Clarke V. **Reflecting on reflexive thematic analysis**. *Qual Res in Sport Exercise and Health* (2019.0) **11** 589-597. DOI: 10.1080/2159676X.2019.1628806
22. Provoost V. **Interdisciplinary Collaborative Auditing as a Method to Facilitate Teamwork/Teams in Empirical Ethics Projects**. *AJOB Empir Bioeth* (2020.0) **11** 14-16. DOI: 10.1080/23294515.2019.1705431
23. Beeken RJ, Wardle J. **Public beliefs about the causes of obesity and attitudes towards policy initiatives in Great Britain**. *Public Health Nutr* (2013.0) **16** 2132-2137. DOI: 10.1017/S1368980013001821
24. Baker P, Brookes G, Atanasova D, Flint SW. **Changing frames of obesity in the UK press 2008–2017**. *Soc Sci Med* (2020.0) **264** 113403. DOI: 10.1016/j.socscimed.2020.113403
25. Foley K, McNaughton D, Ward P. **Monitoring the 'diabetes epidemic': A framing analysis of United Kingdom print news 1993–2013**. *PLoS ONE* (2020.0) **15** e0225794. DOI: 10.1371/journal.pone.0225794
26. Kaminsky J, Gadaleta D. **A study of discrimination within the medical community as viewed by obese patients**. *Obes Surg* (2002.0) **12** 14-18. DOI: 10.1381/096089202321144513
27. Pont SJ, Puhl R, Cook SR, Slusser W, Section On O, Obesity S. **Stigma Experienced by Children and Adolescents With Obesity**. *Pediatrics* (2017.0) **140** e20173034. DOI: 10.1542/peds.2017-3034
28. Shaw RL, Giles DC. **Motherhood on ice? A media framing analysis of older mothers in the UK news**. *Psychol Health* (2009.0) **24** 221-236. DOI: 10.1080/08870440701601625
29. Zhang Y, Jin Y, Stewart S, Porter J. **Framing responsibility for depression: how US news media attribute causal and problem-solving responsibilities when covering a major public health problem**. *JACR* (2016.0) **44** 118-35
30. Coleman R, Major LH. **Ethical Health Communication: A Content Analysis of Predominant Frames and Primes in Public Service Announcements**. *J Mass Media Ethics* (2014.0) **29** 91-107. DOI: 10.1080/08900523.2014.893773
31. Foley K, Ward P, McNaughton D. **Innovating Qualitative Framing Analysis for Purposes of Media Analysis Within Public Health Inquiry**. *Qual Health Res* (2019.0) **29** 1810-1822. DOI: 10.1177/1049732319826559
32. 32.Newman N, Fletcher R, Robertson C, Eddy K, Kleis Nielsen R. Digital News Report 2022. RISJ. 2022.
|
---
title: Pectin modulates intestinal immunity in a pig model via regulating the gut
microbiota-derived tryptophan metabolite-AhR-IL22 pathway
authors:
- Guoqi Dang
- Xiaobin Wen
- Ruqing Zhong
- Weida Wu
- Shanlong Tang
- Chong Li
- Bao Yi
- Liang Chen
- Hongfu Zhang
- Martine Schroyen
journal: Journal of Animal Science and Biotechnology
year: 2023
pmcid: PMC9993796
doi: 10.1186/s40104-023-00838-z
license: CC BY 4.0
---
# Pectin modulates intestinal immunity in a pig model via regulating the gut microbiota-derived tryptophan metabolite-AhR-IL22 pathway
## Abstract
### Background
Pectin is a heteropolysaccharide that acts as an intestinal immunomodulator, promoting intestinal development and regulating intestinal flora in the gut. However, the relevant mechanisms remain obscure. In this study, pigs were fed a corn-soybean meal-based diet supplemented with either $5\%$ microcrystalline cellulose (MCC) or $5\%$ pectin for 3 weeks, to investigate the metabolites and anti-inflammatory properties of the jejunum.
### Result
The results showed that dietary pectin supplementation improved intestinal integrity (Claudin-1, Occludin) and inflammatory response [interleukin (IL)-10], and the expression of proinflammatory cytokines (IL-1β, IL-6, IL-8, TNF-α) was down-regulated in the jejunum. Moreover, pectin supplementation altered the jejunal microbiome and tryptophan-related metabolites in piglets. Pectin specifically increased the abundance of Lactococcus, Enterococcus, and the microbiota-derived metabolites (skatole (ST), 3-indoleacetic acid (IAA), 3-indolepropionic acid (IPA), 5-hydroxyindole-3-acetic acid (HIAA), and tryptamine (Tpm)), which activated the aryl hydrocarbon receptor (AhR) pathway. AhR activation modulates IL-22 and its downstream pathways. Correlation analysis revealed the potential relationship between metabolites and intestinal morphology, intestinal gene expression, and cytokine levels.
### Conclusion
In conclusion, these results indicated that pectin inhibits the inflammatory response by enhancing the AhR-IL22-signal transducer and activator of transcription 3 signaling pathway, which is activated through tryptophan metabolites.
## Introduction
Weaning is one of the most critical periods in both animal production and infant growth and development. The gastrointestinal tract of animals is not fully developed at this stage [1]. It is susceptible to changes in feeding patterns and nutrition, leading to stress and diarrhea. Given the omnivorous and physiological similarities between weaned piglets and human infants, the piglet is regarded as the most suitable animal model for studying gut health [2].
Pectin, predominantly composed of α-1,4-linked D-galacturonic acid (GalA) monomers, is abundant in citrus, apple, lemon peels and pulp. As a typical fermentable dietary fiber, pectin can regulate the human and animal intestinal microbiota [3–5]. It can also strengthen the mucus layer to restrict the entry of hazardous substances [6, 7]. Furthermore, it can enhance the integrity of the epithelial cell layer [8] and maintain intestinal integrity in piglets exposed to lipopolysaccharide or high-fat diet [9].
The gastrointestinal tract is home to a diverse community of trillions of microorganisms collectively known as the gut microbiota [10], and this intricate community is central to gut health and disease [11]. Moreover, the gut microbiota is associated with its ability to defend against enteropathogens, absorb nutrients, and maintain a healthy immune system [12–14]. However, pectin can also have direct effects in the small-intestinal sites [7]. It has been shown that the non-esterified GalA residues rich in pectin can bind to toll like receptor 2 (TLR2) via ionic bonds [15]. The pectin suppresses the TLR$\frac{2}{1}$ signal (TLR2 can form heterodimers with TLR1), and then IL-6 secretion is reduced, thereby reducing the inflammatory response and ameliorating the damage [16].
Recently, many studies have focused on the function of microbial tryptophan catabolites in the gut and their contributions to host physiology [17]. For instance, aryl hydrocarbon receptor (AhR) ligands; 3-indole ethanol (IE), 3-indole pyruvate (IPA), and 3-indole aldehyde (IA) reduce gut permeability [18]. Serotonin (5-HT), another catabolite, plays an important role in gastrointestinal absorption, transit, and secretion. Besides, it also regulates mood, behavior, pain modulation, and cognitive function via the central nervous system [19]. According to a representative study, pectin supplementation may not only alter the intestinal flora of mice, but also increase the tryptophan metabolites of the flora by activating the AHR pathway [20]. Previous research from our laboratory has demonstrated the anti-inflammatory effects of pectin on gut immunity [21]. However, the precise mechanism by which pectin promotes gut health remains unknown.
Thus, the intriguing question was whether microbial tryptophan catabolite is the link between pectin and the gut health regulator. To bridge this knowledge, we examined the changes in serum, gut microbiota, and tryptophan metabolite following pectin supplementation in al pig model. This study gave a novel perspective for promoting a new understanding of how pectin promotes gut health.
## Ethics statement
All animal experiments were approved by the Animal Ethics Committees of Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (Ethics Code Permit IAS2019-37).
## Standards and chemicals
Pectin extracted from citrus peel (Henan Yuzhong Biotechnology Co., Ltd., Zhengzhou, China) mainly consisted of galacturonic acid (white powder, with purity of > $81.4\%$, DM: $13.5\%$). Microcrystalline cellulose (MCC) is a β-1,4-multi-bonded linear carbohydrate consisting of glucose residues with $99.5\%$ purity. ( Beijing NCC Technology R&D Center, China). Reference standards for tryptophan (Trp), tryptamine (Tpm), 3-indoleacetic acid (IAA) and kynurenine (Kyn) were purchased from Sigma-Aldrich (St. Louis, MO, USA); 5-hydroxyindole-3-acetic acid (HIAA) and skatole (ST) were obtained from Cato Research Chemicals Inc. (Eugene, OR, USA); 3-indolylpropionic acid (IPA) and serotonin (5-HT) were from Laboratory of the Government Chemist (Teddington, UK) and Beijing Wokai Biotechnology Co., Ltd. (China), respectively. Assay kits, including interleukin IL-17, IL-22 were purchased from Nanjing Jiancheng Bioengineering Institute (Jiangsu, China).
## Experimental design and animal care
A total of 16 crossbred barrows aged 21 d (6.77 ± 0.92 kg; Duroc × Landrace × Yorkshire) were randomly assigned to one of two diets with eight piglets per treatment. No antibiotics were administered to the piglets throughout the 4-week experiment. Piglets were fed ad libitum and had free access to water. A corn-soybean basal diet was formulated to meet nutritional requirements of National Research Council (NRC, 2012) [22]. After a 3-d of adaption, piglets were fed a diet containing either $5\%$ microcrystalline cellulose (w/w) as the control (CON) group or $5\%$ pectin (w/w) as the treatment (PEC) group for 3 weeks. All piglets were housed in separated pens with daily-cleaned plastic slatted floors.
## Sample collection
Blood samples were acquired from the jugular vein via a sterilized syringe before the pigs were sacrificed at the end of the experiment. The serum was then separated by centrifugation for 10 min at 3000 × g at 4 °C and stored in aliquots at −80 °C for cytokines analysis. The middle section (2 cm) of the jejunum was obtained and fixed in $4\%$ paraformaldehyde for histological examination. The intestinal segment was washed with ice-cold phosphate buffered saline (PBS), and the mucosa was scraped off using a glass microscope slide. Mucosa samples were immediately snap-frozen in liquid nitrogen and stored at −80 °C to further investigate the bacterial community, genes, and protein expression.
## Intestinal morphology
The hematoxylin-eosin (HE) staining of the jejunum was performed according to the methods as previously described [23]. Briefly, specimens of jejunum were embedded in paraffin, sectioned (5 μm thickness), and stained with HE for histological evaluation [villus height (VH)]. Microphotographs were taken with a Leica DM2000 light microscope (Leica, Wetzlar, Germany) at a magnification of 40. VH was performed using Image Pro software [24].
## Serum inflammatory cytokines
The ELISA kit was employed to detect serum cytokines as previous describe [25]. Quantitative analysis of pro-inflammatory cytokine (IL-17), and anti-inflammatory cytokine (IL-22) in serum were measured by ELISA kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China) according to the detection kit instructions.
## Quantitative real-time (qRT) PCR analysis
Total RNA was extracted from the jejunum mucosa, using the RNeasy Mini Kit (GeneBetter, Beijing, China). The concentration of each RNA sample was quantified using the NanoDrop 2000 (Nanodrop Technologies, Wilmington, DE, USA). The cDNA was transcribed at 37 °C for 15 min and 85 °C for 5 s using the PrimeScriptTM RT reagent kit with gDNA Eraser (Takara Biomedical Technology in Beijing, China). qRT-PCR with 40 amplification cycles was conducted with a commercial kit (PerfectStart Green qPCR SuperMix, TransGen Biotech, Beijing, China). In detail, a total of 10 μL reaction mixture contain 1 μL of cDNA, 0.4 μL forward primer, 0.4 μL reverse primer, 0.2 μL of ROX, and 3 μL of PCR-grade water. *The* gene of β-actin was used as an internal control. Primers used were listed in Table 1. The relative gene expression level between the control group and the treatment group was calculated by the 2-ΔΔCt method, and the value was normalized to the internal control. Table 1Primer sequences used for real-time PCRGenePrimerNucleotide sequences (5′ to 3′)β-actinFGCGTAGCATTTGCTGCATGARGCGTGTGTGTAACTAGGGGTZO-1FCTCCAGGCCCTTACCTTTCGRGGGGTAGGGGTCCTTCCTATOccludinFCAGGTGCACCCTCCAGATTGRTATGTCGTTGCTGGGTGCATClaudin-1FTCGACTCCTTGCTGAATCTGRTTACCATACCTTGCTGTGGCIL-1βFGCCAGTCTTCATTGTTCAGGTTTRCCAAGGTCCAGGTTTTGGGTIL-6FTCCAATCTGGGTTCAATCARTCTTTCCCTTTTGCCTCAIL-8FTACGCATTCCACACCTTTCRGGCAGACCTCTTTTCCATTIL-10FTCGGCCCAGTGAAGAGTTTCRGGAGTTCACGTGCTCCTTGAIL-17FCTCTCGTGAAGGCGGGAATCRGTAATCTGAGGGCCGTCTGGTNF-αFCGTCGCCCACGTTGTAGCCAATRGCCCATCTGTCGGCACCACCAhRFCATGCTTTGGTCTTTTATGCRTTCCCTTTCTTTTTCTGTCCCYP1A1FCCTTCACCATCCCTCACAGTRATCACCTTTTCACCCAGTGCCYP1B1FAATAACGGGGGAAATTCCTGRCACCGAAACACAATGCAATCRegIIIγFAACCTGGATGGGTGCAGACGTGRTTGGTTCCAAGCCCTCGGTGIL-22FCTACATCACCAACCGCACCTRTCAGAGTTGGGGAACAGCACZO-1 Zonula occludens-1, IL-1 Interleukin 1, IL-6 Interleukin 6, IL-8 Interleukin 8, IL-10 Interleukin 10, IL-17 Interleukin17, TNF-α *Tumor necrosis* factor-alpha, CYP1A1 Cytochrome P450, family 1, subfamily A, polypeptide 1, CYP1B1 Cytochrome P450, family 1, subfamily B, polypeptide 1, IL-22 Interleukin 22
## Western blotting assay
Total protein was extracted from jejunum tissue using RIPA lysis buffer (Thermo Fisher Scientific Inc., Waltham, MA, USA). It was quantified with the BCA protein assay kit (Cat# 23225, Thermo, Waltham, MA, USA). Total proteins in the amount of 30 μg were loaded for separation onto $10\%$ SDS-PAGE. The proteins were then transferred onto a polyvinylidene difluoride (PVDF) membrane at 90 V for 1.5 h using the wet transfer method. The membranes were then incubated in $5\%$ skimmed milk for 2 h at room temperature for blocking. After incubation with a primary antibody Occludin (Thermo Fisher Scientific Inc., #40-4700, 1:5000), Claudin-1 (Thermo Fisher Scientific Inc., #51-9000, 1:5000), IL-22 (Abcam, #ab193813, 1:2000), STAT3 (Biowordtechnology; #AP0365, 1:1000), P-STAT3 (Biowordtechnology; #AP0248, 1:1000), and β-actin (CST, #4970 T, 1:4000) overnight at 4 °C, the membranes were incubated with HRP-labeled goat anti-mouse or goat anti-rabbit secondary antibodies (1:5000). Protein blots were visualized using a gel imaging system (Tanon 2500R; Tanon Science & Technology Co., Ltd., Shanghai, China). The band density was quantified using Image J 10.0 software and normalized to β-actin.
## 16S ribosomal RNA (rRNA) amplicon sequencing
Genomic DNA was extracted from the jejunum mucosa using the EZNATM Soil DNA Kit (D5625-02, Omega Bio-Tek Inc., Norcross, GA, USA), as directed by the manufacturer. The hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified by a two-step PCR using specific primers (338F, 5′-ACTCCTACGGGAGGCAGCAG-3′ and 806R, 5′-GGACTACH VGGGTWTCTAAT-3′) with unique 8-bp barcodes to facilitate multiplexing. The amplicons were sequenced using the Illumina HiSeq sequencing platform, as previously described. The Majorbio Cloud Platform (www.majorbio.com) was used to analyze the raw data. The raw reads were deposited to the Sequence Read Archive (SRA) database (Accession Number: PRJNA876628) of NCBI. A more detailed methodology was described previously [21].
## Trp and its metabolites analysis by liquid chromatograph-mass spectrometer (LC-MS)
Methanol was used to extract Trp and its metabolites (ST, IAA, IPA, HIAA, Tpm, 5-HT, Kyn) from the jejunum mucosa. The methanol extraction solutions were pre-cooled for 30 min at −20 °C. After being vortexed for 1 min, the samples were grinded 3 times (30 s for each time and 10 s intervals) with high throughput Tissuelyser instrument (Scientz-48, Jingxin, Shanghai, China). The supernatant was collected after centrifugation at 10,000 × g for 5 min and filtered through 0.22 μm filter membranes (Jin Teng, Tianjin, China).
LC-MS analysis was performed on Agilent 1290 UHPLC electrospray ionization-time-of-flight mass spectrometer (ESI-TOF-MS) coupled with Agilent 1260 SFC-Ultivo equipped with an Agilent ZORBAX Eclipse XDB-C18 column (3.0 mm × 150 mm, 1.8 μm). A linear gradient was obtained by mixing eluent A (water + $0.1\%$ formic acid) and eluent B ($100\%$ methanol). The elution gradient for 5-HT and ST was as follows: 0–0.5 min ($20\%$ B), 0.5–1 min ($20\%$–$40\%$ B), 1–3 min ($65\%$–$75\%$ B), 3–4 min ($75\%$–$90\%$ B), 4–7 min ($90\%$–$100\%$ B) at the flow rate of 0.5 mL/min. For the remaining metabolites (Trp, IAA, IPA, HIAA, Tpm, Kyn), the elution gradient was set as follows: 0–0.5 min ($20\%$ B), 0.5–1 min ($20\%$–$40\%$ B), 1–2 min ($40\%$–$50\%$ B), 2–3 min ($50\%$–$80\%$ B), 3–4 min ($80\%$ B), 4–7 min ($80\%$–$85\%$ B), 7–9 min ($85\%$–$100\%$ B), 9–11 min ($100\%$ B), and the flow rate was 0.3 mL/min, and the column temperature was 40 °C. The amount of each metabolite was calculated according to standard curves with known metabolite levels.
## Statistical analysis
Data conforming to normal distribution were compared using Student t-test, while those with non-normally distributed were tested using Kruskal-Wallis test (CYP1A1, serum IL-17, TNF-α). These analyses were performed using the JMP software (JMP R version 10.0.0, SAS Institute, Cary, NC, USA) for Windows.
Raw data obtained from gut microbiota were processed using the free online platform of Majorbio I-Sanger Cloud Platform (www.i-sanger.com). For β-diversity, principal-coordinate analysis (PCoA) plots were produced using Bray-Curtis distances, and community significance was confirmed with a Wilcoxon Rank-Sum test. All data were presented as mean ± standard error of the mean (SEM). Acceptable significance levels were at ∗$P \leq 0.05$ and ∗∗P ≤ 0.01.
Spearman’s or Mantel’s correlation was used to analyze the correlation between the mucosal tryptophan metabolites, gene expression (inflammatory cytokines, STAT3/IL-22 pathway), and tryptophan and its derivatives in the jejunum.
## Dietary pectin supplementation improved the integrity of jejunum
To determine the effects of pectin supplementation on intestinal integrity, HE staining, qPCR, and western blotting methods were used to examine the jejunum morphology and tight junctions. Histopathology staining results showed that the villus height was increased significantly ($P \leq 0.05$) in the PEC group than in control (Fig. 1A–B). Additionally, the mRNA expression levels of tight junction proteins Claudin-1 ($$P \leq 0.005$$), Occludin ($$P \leq 0.016$$), and zonula occludens-1 (ZO-1, $$P \leq 0.108$$) were increased (Fig. 1C–E). Western blotting results showed that the protein level of Claudin-1 increased greatly ($P \leq 0.05$), however, the level of Occludin did not change significantly (Fig. 1F). It was indicated that pectin supplementation improved intestinal barrier and gut integrity. Fig. 1Effects of pectin on jejunal morphology in piglets. A Representative images of hematoxylin-eosin staining in the jejunum; B *Jejunal villus* height; C Jejunal mRNA expression levels of Claudin-1; D Jejunal mRNA expression levels of Occludin; E Jejunal mRNA expression levels of ZO-1 ($$n = 6$$). F Jejunal protein expression levels of tight junction proteins (Occludin, Claudin-1) ($$n = 4$$). Data were expressed as mean ± SEM. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$
## Pectin supplementation altered the expression levels of inflammatory cytokines in the jejunal mucosa and serum
The inflammatory cytokines were also detected in the jejunal mucosa and serum. In the jejunal mucosa, pectin supplementation downregulated the expression of pro-inflammatory cytokines, IL-1β (Fig. 2A; $P \leq 0.05$), IL-6 (Fig. 2B; $P \leq 0.05$), IL-8 (Fig. 2C; $P \leq 0.05$), IL-17 (Fig. 2D; $$P \leq 0.066$$), and TNF-α (Fig. 2E; $P \leq 0.05$). On the other hand, the expression of the anti-inflammatory cytokine IL-10 (Fig. 2F; $$P \leq 0.088$$) was increased in the PEC group compared to the control group. Additionally, after pectin supplementation, a diminished expression level of IL-17 (Fig. 2G; $P \leq 0.05$) and an enhanced expression level of IL-22 (Fig. 2H; $P \leq 0.008$) was observed in the serum. Thus, pectin supplementation in the diet regulated the jejunum inflammatory responses in piglets. Fig. 2Pectin supplement altered the expression levels of inflammatory cytokines in the jejunum and serum. A IL-1β; B IL-6; C IL-8; D IL-17; E TNF-α; F IL-10; A–F were detected levels in Jejunum; G IL-17; and H IL-22 were detected in serum, $$n = 6$.$ * $P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$; data are presented as the mean ± SEM ($$n = 6$$)
## Pectin supplementation altered the bacterial community in jejunum mucosa
Following size filtering, quality control, and chimera checking, 16S rRNA amplicon sequencing results revealed a total of 859,243 reads ranging from 35,227 to 74,138 reads per sample, to examine the effect of pectin on microbial population in the jejunum. Sequencing counts were normalized to acquire normalized reads for each sample into operational taxonomic units (OTUs) based on $97\%$ identity.
As indicated in Fig. 3, a Venn diagram was utilized to reveal the common and unique OTUs in the control and/or pectin supplementation groups. Pigs in the CON and pectin groups had 367 and 1025 distinct OTUs, respectively, and 769 common OTUs (Fig. 3A). Additionally, alpha diversity (Sobs indexes) revealed that the gut microbial flora diversity of pectin-treated piglets was significantly different from that of the control piglets, at the OTU level in the jejunal mucosa (Fig. 3B). This was further supported by the beta diversity presented in PCoA (Fig. 3C). The composition of the gut microbial community was then analyzed at the genus level. Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria constituted the majority of the microbiota at the phylum level (Fig. 3D). Pectin boosted the abundance of Proteobacteria, whereas decreased the abundance of Actinobacteria (Fig. 3E). Noticeable alterations in their microbial composition were detected at the genus level (Fig. 3F). Pectin significantly reduced the relative abundance of Streptococcus, Prevotella_9, Megamonas, Eubacterium, Megasphaera, Prevotella_2, and Actidaminococcus, whereas it increased the relative abundance of Enterococcus, Lactococcus, and Morganella (Fig. 3G, $P \leq 0.05$).Fig. 3Effects of pectin on the jejunum microbial diversity. A Venn diagram; B *The alpha* diversity indices (Sobs); C *The beta* diversity presented by the PCoA plot based on the OTU level; D The abundance of the intestinal microbiota composition at the phylum level; E Differences in microbial community composition between two groups at phylum level; F The abundance of the intestinal microbiota composition at the genus level; G Differences in microbial community composition between two groups at phylum level. Data were expressed as mean ± SEM, $$n = 8$.$ * $P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$
## Pectin altered the levels of microbiota-derived tryptophan metabolites in jejunum
Trp is an important metabolite related to gut microbiota. Various diets and bacterial populations influenced the concentration of Trp and its derivatives. The Trp-derived metabolites in the jejunal mucosa were determined to evaluate whether a change in the intestinal microbiota affects the production of Trp and its related metabolites after pectin supplementation. The concentration of Trp was significantly lower in the pectin group compared to the CON group (Fig. 4A). As for indole derivatives, the concentrations of ST (Fig. 4B), IAA (Fig. 4C), 3- IPA (Fig. 4D), HIAA (Fig. 4E), and Tpm (Fig. 4F) were significantly higher in the pectin-fed piglets than in the CON group ($P \leq 0.05$). Particularly, the content of IPA reached extremely significant levels (Fig. 4D; $P \leq 0.001$). Additionally, two other pathway metabolites, 5-HT and Kyn were not significantly different between these two groups (Fig. 4G–H). Accordingly, adding pectin facilitated tryptophan metabolism towards the indoles as AhR ligands in the piglet intestine. Fig. 4Effect of pectin on the jejunum microbial tryptophan metabolism concentration. A Trp (tryptophan); B ST (Skatole); C IAA (3-Indole acetic acid); D IPA (3-indolepropionic). E 5-Hydroxyindole-3-acetic acid; F Tpm (Tryptamine); G 5-HT (5-hydroxytryptamine); H Kyn (Kynurenine). Data are presented as the mean ± SE, ($$n = 8$$). * $P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$
## The changed Trp metabolism by pectin supplementation activated the AhR/IL-22/STAT3 signaling pathway in jejunal mucosa of piglets
Microbial derived tryptophan catabolites (indole compounds) are ligands for the AhR and act on the AhR in lymphoid cells. Therefore, we investigated the effect of pectin on the AhR signaling pathway. We analyzed the expression of AhR activation in the jejunum. All changes in expression [AhR (Fig. 5A), IL-22 (Fig. 5B), cytochrome P450 1A1 (CYP1A1, Fig. 5C), cytochrome P450 1B1 (CYP1B1, Fig. 5D), recombinant regenerating islet derived protein 3 gamma (RegIIIγ, Fig. 5E)] were significantly increased ($P \leq 0.05$). Similar results were obtained using WB analysis of the IL-22-STAT3 pathway (Fig. 5F). The protein levels of IL-22 and P-STAT/STAT3 were increased, however, not to a significant level (Fig. 5G–H, $P \leq 0.05$). These results mentioned above suggested that pectin can activate the AhR-IL-22-STAT3 signaling pathways. Fig. 5Dietary pectin supplementation influenced AhR activation and relative downstream genes expression in the jejunum of weaned piglets. A AhR; B IL-22; C CYP1A1. D CYP1B1. E RegIIIγ. F Protein abundances of IL-22, STAT3 and p-STAT3; G Protein abundances of IL-22; H The protein rate of p-STAT3/STAT3. Data are presented as the mean ± SE, ($$n = 6$$). * $P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$
## AhR activation in mucosa is potentially correlated with mucosal tryptophan metabolites
Spearman rank correlations coefficient and significance tests revealed a correlation between the various bacteria and the tryptophan metabolites (Fig. 6A). The concentration of Trp was significantly and negatively correlated with the abundance of Lactococcus, whereas it was significantly and positively linked to the abundance of Prevotella_9. The concentration of IAA was positively correlated with the abundance of Enterococcus, Lactococcus, and Rothia, but inversely correlated with Prevotella_9 and Megamonas. IPA was positively correlated with the abundance of Lactococcus and Rothia, while it was negatively correlated with Prevotella_9 and Megasphaera. Tpm had a significantly positive relationship with Enterococcus and Rothia, whereas it had a significantly negative relationship with Megasphaera. Fig. 6Heat maps of the Spearman rank correlation coefficient and significant tests between the differential bacteria and tryptophan metabolites (A). Pairwise comparisons if metabolites are demonstrated with a color gradient denoting Spearman’s correlation coefficient. Trp, ST, IAA, IPA, HIAA, and Tpm are related to inflammatory cytokines, jejunum morphology indices by partial spearman tests. Edge width corresponds to the Partial Spearman’s r statistic for the corresponding distance correlations and edge color denotes the statistical significance (B). Data are presented as the mean ± SE, ($$n = 6$$). * $P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$ Moreover, the Mantel test demonstrated a significant correlation between Trp and the gene expression levels of Claudin-1, IL-17, CYP1A1, and RegIIIγ (Mantel’s r > 0.25, $P \leq 0.05$, Fig. 6B). Beyond that, five genes (Occludin, IL-6, IL-10, IL-22, AhR) showed a significant correlation with ST (Mantel’s r > 0.25, $P \leq 0.05$). IAA significantly correlated with Occludin, ZO-1, IL-22, CYP1A1, and CYP1B1. Additionally, we found that Claudin-1, CYP1B1, and RegIIIγ showed a significant association with IPA. HIAA was also associated with eight genes (Occludin, ZO-1, IL-10, IL-22, AhR, CYP1A1, CYP1B1, and RegIIIγ). There was a significant relationship between Tpm and ZO-1, IL-6, TNF-α, IL-22, AhR, and RegIIIγ.
## Discussion
During the weaning transition period, piglets are susceptible to infection by various pathogens and non-pathogens, associated with a disrupted state of microbiota and an immature immune system [26]. Emerging evidence demonstrated that pectin could enhance anti-inflammatory properties, regulate the host microbiome [27], boost markers of mucus barrier function, modulate immunological activity [21, 28], and promote gut integrity [29]. In this study, we demonstrated that supplementing piglets with pectin can boost their anti-inflammatory activity, which may be associated with changes in microbial tryptophan metabolites induced by pectin supplementation.
In this study, pectin was found to increase the villus height, suggesting that it enhances intestinal health status and promotes nutrient absorption. Previous research has also shown that dietary fiber treatment can improve the morphological structure of the jejunum of piglets, as evidenced by an increase in VH and a decrease in crypts depth (CD), resulting in decreased intestinal mucosal permeability [24, 30] and increased the intestinal barrier protection [31, 32].
As an essential protein in the intestine, tight junction proteins play a crucial role in gut barrier function, particularly in maintaining the integrity of the intestinal barrier and preventing the spread of harmful substances [33]. Recent research showed that feeding piglets inulin or pectin increases the gene expression of Claudin-1, Occludin, and ZO-1 [21, 34]. As expected, the results of our study were consistent with the previous work. Thus, pectin supplementation during the weaning transition period improved the intestinal barrier function of piglets.
Subsequently, we also investigated whether pectin may have an additional beneficial effect on the intestinal tract. As is well-known, TNF-α, IL-1β, and IL-6 are essential proinflammatory response indicators. Additionally, macrophages produce IL-8, a small inflammatory cytokine. This indicates that variations in these proinflammatory cytokine levels may reflect the inflammatory response status. A previous study revealed that low-methoxyl pectin might downregulate the mRNA levels of TNF-α, IL-1β, and IL-6 in ileum colonic tissues [35]. Furthermore, pectin extract from apples might decrease the gene expression of IL-6 in mouse ileum tissue [9]. Similarly, pectin derived from oranges and lemons can improve intestinal inflammation by inhibiting the initiation of IL-6 secretion [15]. In this study, we observed that administration of pectin reduced the mRNA expression of IL-1β, IL-6, IL-8, and TNF-α in jejunal mucosa of piglets. Moreover, there are several trials suggested that a low degree of methyl-esterification-(DM) pectin could suppress TLR2-TLR1 by directly blocking of TLR2 receptor [36], then slow down capsule implantation induced increasing in pro-inflammatory cytokine (TNF-α, IL-6), and promoted the release of IL-10 [37]. IL-17 is also a proinflammatory cytokine mainly produced by Th17 cells and is associated with the pathogenesis of many autoimmune inflammatory diseases [38]. IL-10, which is primarily secreted by Tregs, reduces Th17 development and function, as well as inhibits the secretion of proinflammatory cytokines and chemokines [39]. Research showed that the decreased expression of IL-10 and the increased expression of IL-17 might aggravate intestinal inflammation [40]. Interestingly, the pectin treatment decreased the mRNA expression level of IL-17 while increasing the mRNA expression level of IL-22, which was consistent with the data mentioned above from the previous studies. Moreover, the levels of serum cytokines IL-17 and IL-22 were consistent with those of the jejunal mucosa. Thus, we proposed that pectin may modulate mucosal immunity by increasing anti-inflammatory cytokines and decreasing pro-inflammatory cytokines.
Due to its fermentation properties on microorganisms, most previous studies on dietary fiber focused on the hindgut [41, 42]. In contrast, pectin was found to modulate the microbial composition of the foregut in this study, although the foregut is not the primary site of microbial fermentation. Wu et al. [ 21] reported that the supplementation of pectin in the piglet diet decreased the diversity and abundance of microorganisms in the small intestine. Similar results were obtained in other studies [43]. In contrast, other studies observed an increased abundance and diversity of ileal microbial in piglets [44, 45]. We also hypothesized that pectin supplementation could affect gut health by altering the microbial composition or in other ways. As predicted by the preceding analysis, our data revealed that pectin significantly changed the composition and structure of the gut microbiota and increased the OTU number and alpha diversity. A healthy gut microbiota typically consists of four main phyla: Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria [46]. In the present study, we observed that pectin reduced the abundance of Actinobacteria in the jejunal mucosa. It has been reported that the abundance of Actinobacteria was highly expressed in the gut of goats with diarrhea [47]. Proteobacteria, an intestinal commensal bacteria, was also significantly increased by pectin in the present study. However, pectin significantly decreased the abundance of Streptococcus, a known facultative-anaerobe bacterium and an opportunistic pathogen. Specifically, *Streptococcus infection* may cause mucosal damage [48] and is associated with an increased risk of colorectal cancer [49]. Our study also found that the indole-derivatives-producing bacteria (Lactobacillus and Enterococcus) in the gut showed a notable increase in pectin group. Additionally, pectin significantly reduced the abundance of Prevotella_9, Megamonas, Eubacterium, Megasphaera, Prevotella_2, and Acidaminococcus. Prevotella_9 and Megasphaera are generally regarded as opportunistic pathogens [50, 51]. Moreover, Megamonas, Eubacterium, and Acidaminococcus are commonly believed to be associated with fatty acid metabolism [52–55], which significantly decreased in pectin group. Morganella, the Gram-negative bacillus, belongs to the Enterobacteriaceae family. This study observed that pectin supplementation significantly increased the abundance of Morganella. Therefore, our study showed that adding pectin significantly reduced the abundance of harmful bacteria (Streptococcus) and increased the abundance of beneficial bacteria (Enterococcus and Lactobacillus) in the intestinal mucosa, to promote intestinal health.
Lactobacillus and Enterococcus are related to tryptophan metabolism. Specifically, tryptophan is an important amino acid that mammals must obtain from their diet, and it can be transformed into indole and indole derivatives by the gut microbiota [56, 57]. Then, it can alter the immune-related signaling pathways that regulate inflammation in the gut [58]. Only a few commensal species, including Enterococcus [59, 60] and Lactobacillus [61], are known to produce indole derivatives, and many more are likely to be discovered. In this study, pectin supplementation decreased the concentration of Trp while increasing the amount of indole derivatives (ST, IAA, IPA, HIAA, Tpm). Previous studies suggested that dietary fiber supplementation increased the levels of indole derivates, including IPA [62]. Other studies also showed that pectin can alleviate alcoholic hepatitis by promoting the elevation of microbial metabolites, IAA, and Indole-3-lactic acid [20]. This accorded with our research that pectin promotes the metabolism of tryptophan, shifting the metabolic direction toward the metabolism of indoles. As a whole, pectin increased the content of indole derivatives in the gut, which had a beneficial effect on intestinal immunity.
Group 3 innate lymphoid cells (ILC3) are greatly enriched in the lamina propria of the jejunum, the highly expressed AhR in ILC3 can be activated by tryptophan metabolites (indole derivates) as ligands, thereby promoting the production of IL-22 by ILC3 cells [63–66], and activates downstream pathways by inducing phosphorylation of Stat3, further promoting the production of antimicrobial peptides and mucins [67, 68]. Representative research showed that pectin supplementation altered the intestinal flora of mice, increased the tryptophan metabolites of the flora, and reduced alcohol-induced liver damage by activating the AhR pathway [20, 29]. In this study, we found that pectin treatment enhanced the levels of AhR, IL-22 and p-Stat3 downstream of AhR, which plays as an essential role in promoting the production of antimicrobial molecules (CYP1A1, CYP1B1, and RegIIIγ). Consequently, these antimicrobial molecules exerted the protective function of the intestinal barrier. This indicated that the altered in the microbiota structure and metabolite concentration in jejunal mucosa observed following pectin treatment was the basis of immunomodulation, with the activated AhR/IL-22/Stat3 signaling pathway providing a plausible mechanism.
## Conclusions
In conclusion, our study found that adding pectin to the model could improve the intestinal integrity and gut immunity by promoting tryptophan metabolism. This indicated that dietary pectin supplementation altered jejunal microbial composition, thus promoting microbial tryptophan metabolism. Increased metabolites can act as ligands or signaling molecules to modulate the intestinal immune response through the AhR/IL-22/State3 pathway, ultimately reducing proinflammatory factors and enhancing intestinal barrier function. These results provided a reliable theoretical foundation and guidance for using pectin in mammals as a prospective intestinal health defender.
## References
1. 1.Wu H, Wang Y, Li H, Meng L, Zheng N, Wang J. Effect of food endotoxin on infant health. Toxins (Basel). 2021;13(5):298. 10.3390/toxins13050298.
2. Tang S, Chen Y, Deng F, Yan X, Zhong R, Meng Q. **Xylooligosaccharide-mediated gut microbiota enhances gut barrier and modulates gut immunity associated with alterations of biological processes in a pig model**. *Carbohydr Polym* (2022.0) **294** 119776. DOI: 10.1016/j.carbpol.2022.119776
3. Bang SJ, Kim G, Lim MY, Song EJ, Jung DH, Kum JS. **The influence of in vitro pectin fermentation on the human fecal microbiome**. *AMB Express* (2018.0) **8** 98. DOI: 10.1186/s13568-018-0629-9
4. Elshahed MS, Miron A, Aprotosoaie AC, Farag MA. **Pectin in diet: interactions with the human microbiome, role in gut homeostasis, and nutrient-drug interactions**. *Carbohydr Polym* (2021.0) **255** 117388. DOI: 10.1016/j.carbpol.2020.117388
5. Chung WS, Walker AW, Louis P, Parkhill J, Vermeiren J, Bosscher D. **Modulation of the human gut microbiota by dietary fibres occurs at the species level**. *BMC Biol* (2016.0) **14** 3. DOI: 10.1186/s12915-015-0224-3
6. Maria-Ferreira D, Nascimento AM, Cipriani TR, Santana-Filho AP, Watanabe PDS, Sant Ana DMG. **Rhamnogalacturonan, a chemically-defined polysaccharide, improves intestinal barrier function in DSS-induced colitis in mice and human Caco-2 cells**. *Sci Rep* (2018.0) **8** 12261. DOI: 10.1038/s41598-018-30526-2
7. Beukema M, Faas MM, de Vos P. **The effects of different dietary fiber pectin structures on the gastrointestinal immune barrier: impact via gut microbiota and direct effects on immune cells**. *Exp Mol Med* (2020.0) **52** 1364-1376. DOI: 10.1038/s12276-020-0449-2
8. 8.Wilms E, Jonkers D, Savelkoul HFJ, Elizalde M, Tischmann L, de Vos P, et al. The impact of pectin supplementation on intestinal barrier function in healthy young adults and healthy elderly. Nutrients. 2019;11(7):1554. 10.3390/nu11071554.
9. Jiang T, Gao X, Wu C, Tian F, Lei Q, Bi J. **Apple-derived pectin modulates gut microbiota, improves gut barrier function, and attenuates metabolic endotoxemia in rats with diet-induced obesity**. *Nutrients* (2016.0) **8** 126. DOI: 10.3390/nu8030126
10. 10.Sasaki H, Lyu Y, Nakayama Y, Nakamura F, Watanabe A, Miyakawa H, et al. Combinatorial effects of soluble, insoluble, and organic extracts from Jerusalem artichokes on gut microbiota in mice. Microorganisms. 2020;8(6):954. 10.3390/microorganisms8060954.
11. Zhang Q, Hu J, Feng JW, Hu XT, Wang T, Gong WX. **Influenza infection elicits an expansion of gut population of endogenous Bifidobacterium animalis which protects mice against infection**. *Genome Biol* (2020.0) **21** 99. DOI: 10.1186/s13059-020-02007-1
12. Ichikawa-Seki M, Motooka D, Kinami A, Murakoshi F, Takahashi Y, Aita J. **Specific increase of Fusobacterium in the faecal microbiota of neonatal calves infected with Cryptosporidium parvum**. *Sci Rep* (2019.0) **9** 12517. DOI: 10.1038/s41598-019-48969-6
13. Pilla R, Gaschen FP, Barr JW, Olson E, Honneffer J, Guard BC. **Effects of metronidazole on the fecal microbiome and metabolome in healthy dogs**. *J Vet Intern Med* (2020.0) **34** 1853-1866. DOI: 10.1111/jvim.15871
14. 14.Mills S, Stanton C, Lane JA, Smith GJ, Ross RP. Precision nutrition and the microbiome, part I: current state of the science. Nutrients. 2019;11(4):923. 10.3390/nu11040923.
15. Beukema M, Jermendi E, van den Berg MA, Faas MM, Schols HA, de Vos P. **The impact of the level and distribution of methyl-esters of pectins on TLR2-1 dependent anti-inflammatory responses**. *Carbohydr Polym* (2021.0) **251** 117093. DOI: 10.1016/j.carbpol.2020.117093
16. Krishnan R, Ko D, Foster CE, Liu W, Smink AM, de Haan B. **Immunological challenges facing translation of alginate encapsulated porcine islet xenotransplantation to human clinical trials**. *Methods Mol Biol* (2017.0) **1479** 305-333. DOI: 10.1007/978-1-4939-6364-5_24
17. Zhang LS, Davies SS. **Microbial metabolism of dietary components to bioactive metabolites: opportunities for new therapeutic interventions**. *Genome Med* (2016.0) **8** 46. DOI: 10.1186/s13073-016-0296-x
18. 18.Scott SA, Fu J, Chang PV. Microbial tryptophan metabolites regulate gut barrier function via the aryl hydrocarbon receptor. P Natl Acad Sci USA. 2020. 10.1073/pnas.2000047117.
19. Mawe GM, Hoffman JM. **Serotonin signalling in the gut--functions, dysfunctions and therapeutic targets**. *Nat Rev Gastroenterol Hepatol* (2013.0) **10** 473-486. DOI: 10.1038/nrgastro.2013.105
20. Wrzosek L, Ciocan D, Hugot C, Spatz M, Dupeux M, Houron C. **Microbiota tryptophan metabolism induces aryl hydrocarbon receptor activation and improves alcohol-induced liver injury**. *Gut* (2021.0) **70** 1299-1308. DOI: 10.1136/gutjnl-2020-321565
21. 21.Wu W, Zhang L, Xia B, Tang S, Xie J, Zhang H. Modulation of pectin on mucosal innate immune function in pigs mediated by gut microbiota. Microorganisms. 2020;8(4):535. 10.3390/microorganisms8040535.
22. 22.National Animal Health Monitoring System (NAHMS)Swine 2012 part ii: reference of swine health and health management in the United States2012Washington, DCUSDA. *Swine 2012 part ii: reference of swine health and health management in the United States* (2012.0)
23. Xia B, Wu W, Fang W, Wen X, Xie J, Zhang H. **Heat stress-induced mucosal barrier dysfunction is potentially associated with gut microbiota dysbiosis in pigs**. *Anim Nutr* (2022.0) **8** 289-299. DOI: 10.1016/j.aninu.2021.05.012
24. 24.Wen X, Zhong R, Dang G, Xia B, Wu W, Tang S, et al. Pectin supplementation ameliorates intestinal epithelial barrier function damage by modulating intestinal microbiota in lipopolysaccharide-challenged piglets. J Nutr Biochem. 2022;10:9107. 10.1016/j.jnutbio.2022.109107.
25. Shrestha S, Noh JM, Kim SY, Ham HY, Kim YJ, Yun YJ. **Angiotensin converting enzyme inhibitors and angiotensin ii receptor antagonist attenuate tumor growth via polarization of neutrophils toward an antitumor phenotype**. *Oncoimmunology* (2016.0) **5** e1067744. DOI: 10.1080/2162402X.2015.1067744
26. Wang T, Yao W, Li J, Shao Y, He Q, Xia J. **Dietary garcinol supplementation improves diarrhea and intestinal barrier function associated with its modulation of gut microbiota in weaned piglets**. *J Anim Sci Biotechnol* (2020.0) **11** 12. DOI: 10.1186/s40104-020-0426-6
27. Yu C, Wu D, Zhu K, Hou L, Hang X, Ding T. **Challenges of pectic polysaccharides as a prebiotic from the perspective of fermentation characteristics and anti-colitis activity**. *Carbohydr Polym* (2021.0) **270** 118377. DOI: 10.1016/j.carbpol.2021.118377
28. Xie J, Yu R, Qi J, Zhang G, Peng X, Luo J. **Pectin and inulin stimulated the mucus formation at a similar level: an omics-based comparative analysis**. *J Food Sci* (2020.0) **85** 1939-1947. DOI: 10.1111/1750-3841.15163
29. 29.Schanz O, Chijiiwa R, Cengiz SC, Majlesain Y, Weighardt H, Takeyama H, et al. Dietary AhR ligands regulate AhRR expression in intestinal immune cells and intestinal microbiota composition. Int J Mol Sci. 2020;21(9):3189. 10.3390/ijms21093189.
30. Abdollahi A, Karimi A, Sadeghi AA, Bedford MR, Ashengroph M. **The effects of the fiber source and xylanase supplementation on production, egg quality, digestibility, and intestinal morphology in the aged laying hen**. *Poultry Sci* (2021.0) **100** 100936. DOI: 10.1016/j.psj.2020.12.033
31. Tejeda OJ, Kim WK. **Effects of fiber type, particle size, and inclusion level on the growth performance, digestive organ growth, intestinal morphology, intestinal viscosity, and gene expression of broilers**. *Poultry Sci* (2021.0) **100** 101397. DOI: 10.1016/j.psj.2021.101397
32. Wang Y, Lu WQ, Li DF, Liu XT, Wang HL, Niu S. **Energy and ileal digestible amino acid concentrations for growing pigs and performance of weanling pigs fed fermented or conventional soybean meal**. *Asian-Australas J Anim Sci* (2014.0) **27** 706-716. DOI: 10.5713/ajas.2013.13612
33. Stan TL, Soylu-Kucharz R, Burleigh S, Prykhodko O, Cao L, Franke N. **Increased intestinal permeability and gut dysbiosis in the r6/2 mouse model of Huntington’s disease**. *Sci Rep* (2020.0) **10** 18270. DOI: 10.1038/s41598-020-75229-9
34. He J, Xie H, Chen D, Yu B, Huang Z, Mao X. **Synergetic responses of intestinal microbiota and epithelium to dietary inulin supplementation in pigs**. *Eur J Nutr* (2021.0) **60** 715-727. DOI: 10.1007/s00394-020-02284-3
35. 35.Sun Y, He Y, Wang F, Zhang H, de Vos P, Sun J. Low-methoxyl lemon pectin attenuates inflammatory responses and improves intestinal barrier integrity in caerulein-induced experimental acute pancreatitis. Mol Nutr Food Res. 2017;61(4):1600885. 10.1002/mnfr.201600885.
36. Sahasrabudhe NM, Beukema M, Tian L, Troost B, Scholte J, Bruininx E. **Dietary fiber pectin directly blocks toll-like receptor 2-1 and prevents doxorubicin-induced ileitis**. *Front Immunol* (2018.0) **9** 383. DOI: 10.3389/fimmu.2018.00383
37. Jermendi E, Beukema M, van den Berg MA, de Vos P, Schols HA. **Revealing methyl-esterification patterns of pectins by enzymatic fingerprinting: beyond the degree of blockiness**. *Carbohydr Polym* (2022.0) **277** 118813. DOI: 10.1016/j.carbpol.2021.118813
38. 38.Patel P, Malipatlolla DK, Devarakonda S, Bull C, Rascon A, Nyman M, et al. Dietary oat bran reduces systemic inflammation in mice subjected to pelvic irradiation. Nutrients. 2020;12(8):2172. 10.3390/nu12082172.
39. Stankovic N, Surbatovic M, Stanojevic I, Simic R, Djuricic S, Milickovic M. **Possible cytokine biomarkers in pediatric acute appendicitis**. *Ital J Pediatr* (2019.0) **45** 125. DOI: 10.1186/s13052-019-0726-7
40. Shi Y, Liu Z, Cui X, Zhao Q, Liu T. **Intestinal vitamin D receptor knockout protects from oxazolone-induced colitis**. *Cell Death Dis* (2020.0) **11** 461. DOI: 10.1038/s41419-020-2653-3
41. Fischer F, Romero R, Hellhund A, Linne U, Bertrams W, Pinkenburg O. **Dietary cellulose induces anti-inflammatory immunity and transcriptional programs via maturation of the intestinal microbiota**. *Gut Microbes* (2020.0) **12** 1-17. DOI: 10.1080/19490976.2020.1829962
42. Chen Y, Xie Y, Zhong R, Liu L, Lin C, Xiao L. **Effects of xylo-oligosaccharides on growth and gut microbiota as potential replacements for antibiotic in weaning piglets**. *Front Microbiol* (2021.0) **12** 641172. DOI: 10.3389/fmicb.2021.641172
43. 43.Wu W, Zhang L, Xia B, Tang S, Liu L, Xie J, et al. Bioregional alterations in gut microbiome contribute to the plasma metabolomic changes in pigs fed with inulin. Microorganisms. 2020;8(1):111. 10.3390/microorganisms8010111.
44. Tang X, Zhang L, Fan C, Wang L, Fu H, Ren S. **Dietary fiber influences bacterial community assembly processes in the gut microbiota of Durco× Bamei crossbred pig**. *Front Microbiol* (2021.0) **12** 688554. DOI: 10.3389/fmicb.2021.688554
45. Pu G, Li P, Du T, Niu Q, Fan L, Wang H. **Adding appropriate fiber in diet increases diversity and metabolic capacity of distal gut microbiota without altering fiber digestibility and growth rate of finishing pig**. *Front Microbiol* (2020.0) **11** 533. DOI: 10.3389/fmicb.2020.00533
46. 46.Jin J, Zhang L, Jia J, Chen Q, Yuan Z, Zhang X, et al. Effects of maternal low-protein diet on microbiota structure and function in the Jejunum of Huzhu Bamei suckling piglets. Animals (Basel). 2019;9(10):713. 10.3390/ani9100713.
47. Li A, Yang Y, Qin S, Lv S, Jin T, Li K. **Microbiome analysis reveals gut microbiota alteration of early-weaned Yimeng black goats with the effect of milk replacer and age**. *Microb Cell Factories* (2021.0) **20** 78. DOI: 10.1186/s12934-021-01568-5
48. Flemer B, Warren RD, Barrett MP, Cisek K, Das A, Jeffery IB. **The oral microbiota in colorectal cancer is distinctive and predictive**. *Gut* (2018.0) **67** 1454-1463. DOI: 10.1136/gutjnl-2017-314814
49. Kasai C, Sugimoto K, Moritani I, Tanaka J, Oya Y, Inoue H. **Comparison of human gut microbiota in control subjects and patients with colorectal carcinoma in adenoma: terminal restriction fragment length polymorphism and next-generation sequencing analyses**. *Oncol Rep* (2016.0) **35** 325-333. DOI: 10.3892/or.2015.4398
50. Li P, Niu Q, Wei Q, Zhang Y, Ma X, Kim SW. **Microbial shifts in the porcine distal gut in response to diets supplemented with Enterococcus faecalis as alternatives to antibiotics**. *Sci Rep* (2017.0) **7** 41395. DOI: 10.1038/srep41395
51. Chen B, Gao LL, Pan Q. **Woody forages effect the intestinal bacteria diversity of golden pompano Trachinotus ovatus**. *AMB Express* (2018.0) **8** 29. DOI: 10.1186/s13568-018-0550-2
52. Polansky O, Sekelova Z, Faldynova M, Sebkova A, Sisak F, Rychlik I. **Important metabolic pathways and biological processes expressed by chicken cecal microbiota**. *Appl Environ Microb* (2015.0) **82** 1569-1576. DOI: 10.1128/AEM.03473-15
53. Stinson LF, Gay MCL, Koleva PT, Eggesbo M, Johnson CC, Wegienka G. **Human milk from atopic mothers has lower levels of short chain fatty acids**. *Front Immunol* (2020.0) **11** 1427. DOI: 10.3389/fimmu.2020.01427
54. Dai ZL, Wu GY, Zhu WY. **Amino acid metabolism in intestinal bacteria: links between gut ecology and host health**. *Front Biosci* (2011.0) **16** 1768-1786. DOI: 10.2741/3820
55. Dai ZL, Li XL, Xi PB, Zhang J, Wu G, Zhu WY. **Metabolism of select amino acids in bacteria from the pig small intestine**. *Amino Acids* (2012.0) **42** 1597-1608. DOI: 10.1007/s00726-011-0846-x
56. Zelante T, Iannitti RG, Fallarino F, Gargaro M, De Luca A, Moretti S. **Tryptophan feeding of the IDO1-AhR axis in host-microbial symbiosis**. *Front Immunol* (2014.0) **5** 640. DOI: 10.3389/fimmu.2014.00640
57. 57.Zhang B, Xu Y, Liu S, Lv H, Hu Y, Wang Y, et al. Dietary supplementation of foxtail millet ameliorates colitis-associated colorectal cancer in mice via activation of gut receptors and suppression of the STAT3 pathway. Nutrients. 2020;12(8):2367. 10.3390/nu12082367.
58. Keszthelyi D, Troost FJ, Masclee AA. **Understanding the role of tryptophan and serotonin metabolism in gastrointestinal function**. *Neurogastroenterol Motil* (2009.0) **21** 1239-1249. DOI: 10.1111/j.1365-2982.2009.01370.x
59. Wlodarska M, Luo C, Kolde R, d'Hennezel E, Annand JW, Heim CE. **Indoleacrylic acid produced by commensal peptostreptococcus species suppresses inflammation**. *Cell Host Microbe* (2017.0) **22** 25-37.e26. DOI: 10.1016/j.chom.2017.06.007
60. 60.Zhu X, Fu B, Dong M, Guo Y, Cao Z, Wu J. Effects of long-term antibiotic treatment on mice urinary aromatic amino acid profiles. Biosci Rep. 2021;41(1):BSR20203498. 10.1042/BSR20203498.
61. Lamas B, Natividad JM, Sokol H. **Aryl hydrocarbon receptor and intestinal immunity**. *Mucosal Immunol* (2018.0) **11** 1024-1038. DOI: 10.1038/s41385-018-0019-2
62. Kundi ZM, Lee JC, Pihlajamaki J, Chan CB, Leung KS, So SSY. **Dietary fiber from oat and rye brans ameliorate Western diet-induced body weight gain and hepatic inflammation by the modulation of short-chain fatty acids, bile acids, and tryptophan metabolism**. *Mol Nutr Food Res* (2021.0) **65** e1900580. DOI: 10.1002/mnfr.201900580
63. Qian M, Liu J, Zhao D, Cai P, Pan C, Jia W. **Aryl hydrocarbon receptor deficiency in intestinal epithelial cells aggravates alcohol-related liver disease**. *Cell Mol Gastroenterol Hepatol* (2022.0) **13** 233-256. DOI: 10.1016/j.jcmgh.2021.08.014
64. Alexeev EE, Lanis JM, Kao DJ, Campbell EL, Kelly CJ, Battista KD. **Microbiota-derived indole metabolites promote human and murine intestinal homeostasis through regulation of interleukin-10 receptor**. *Am J Pathol* (2018.0) **188** 1183-1194. DOI: 10.1016/j.ajpath.2018.01.011
65. Dong F, Hao F, Murray IA, Smith PB, Koo I, Tindall AM. **Intestinal microbiota-derived tryptophan metabolites are predictive of ah receptor activity**. *Gut Microbes* (2020.0) **12** 1-24. DOI: 10.1080/19490976.2020.1788899
66. 66.Zelante T, Iannitti RG, Cunha C, De Luca A, Giovannini G, Pieraccini G, et al. Tryptophan catabolites from microbiota engage aryl hydrocarbon receptor and balance mucosal reactivity via interleukin-22. Immunity. 2013;39(2):372–85. 10.1016/j.immuni.2013.08.003.
67. Wang J, Wang P, Tian H, Tian F, Zhang Y, Zhang L. **Aryl hydrocarbon receptor/il-22/stat3 signaling pathway is involved in the modulation of intestinal mucosa antimicrobial molecules by commensal microbiota in mice**. *Innate Immun* (2018.0) **24** 297-306. DOI: 10.1177/1753425918785016
68. 68.Elshaer D, Moniruzzaman M, Yi TO, Zhi Q, Schreiber V, Begun J, et al. Facile synthesis of dendrimer like mesoporous silica nanoparticles to enhance targeted delivery of interleukin-22. Biomater Sci-UK. 2021;9(22):7402–11. 10.1039/D1BM01352A.
|
---
title: 'Water intake, hydration status and 2-year changes in cognitive performance:
a prospective cohort study'
authors:
- Stephanie K. Nishi
- Nancy Babio
- Indira Paz-Graniel
- Lluís Serra-Majem
- Jesús Vioque
- Montserrat Fitó
- Dolores Corella
- Xavier Pintó
- Aurora Bueno-Cavanillas
- Josep A. Tur
- Laura Diez-Ricote
- J. Alfredo Martinez
- Carlos Gómez-Martínez
- Andrés González-Botella
- Olga Castañer
- Andrea Alvarez-Sala
- Cristina Montesdeoca-Mendoza
- Marta Fanlo-Maresma
- Naomi Cano-Ibáñez
- Cristina Bouzas
- Lidia Daimiel
- María Ángeles Zulet
- John L. Sievenpiper
- Kelly L. Rodriguez
- Zenaida Vázquez-Ruiz
- Jordi Salas-Salvadó
journal: BMC Medicine
year: 2023
pmcid: PMC9993798
doi: 10.1186/s12916-023-02771-4
license: CC BY 4.0
---
# Water intake, hydration status and 2-year changes in cognitive performance: a prospective cohort study
## Abstract
### Background
Water intake and hydration status have been suggested to impact cognition; however, longitudinal evidence is limited and often inconsistent. This study aimed to longitudinally assess the association between hydration status and water intake based on current recommendations, with changes in cognition in an older Spanish population at high cardiovascular disease risk.
### Methods
A prospective analysis was conducted of a cohort of 1957 adults (aged 55–75) with overweight/obesity (BMI between ≥ 27 and < 40 kg/m2) and metabolic syndrome from the PREDIMED-Plus study. Participants had completed bloodwork and validated, semiquantitative beverage and food frequency questionnaires at baseline, as well as an extensive neuropsychological battery of 8 validated tests at baseline and 2 years of follow-up. Hydration status was determined by serum osmolarity calculation and categorized as < 295 mmol/L (hydrated), 295–299.9 mmol/L (impending dehydration), and ≥ 300 mmol/L (dehydrated). Water intake was assessed as total drinking water intake and total water intake from food and beverages and according to EFSA recommendations. Global cognitive function was determined as a composite z-score summarizing individual participant results from all neuropsychological tests. Multivariable linear regression models were fitted to assess the associations between baseline hydration status and fluid intake, continuously and categorically, with 2-year changes in cognitive performance.
### Results
The mean baseline daily total water intake was 2871 ± 676 mL/day (2889 ± 677 mL/day in men; 2854 ± 674 mL/day in women), and $80.2\%$ of participants met the ESFA reference values for an adequate intake. Serum osmolarity (mean 298 ± 24 mmol/L, range 263 to 347 mmol/L) indicated that $56\%$ of participants were physiologically dehydrated. Lower physiological hydration status (i.e., greater serum osmolarity) was associated with a greater decline in global cognitive function z-score over a 2-year period (β: − 0.010; $95\%$ CI − 0.017 to − 0.004, p-value = 0.002). No significant associations were observed between water intake from beverages and/or foods with 2-year changes in global cognitive function.
### Conclusions
Reduced physiological hydration status was associated with greater reductions in global cognitive function over a 2-year period in older adults with metabolic syndrome and overweight or obesity. Future research assessing the impact of hydration on cognitive performance over a longer duration is needed.
### Trial registration
International Standard Randomized Controlled Trial Registry, ISRCTN89898870. Retrospectively registered on 24 July 2014
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12916-023-02771-4.
## Background
Cognitive decline is an important public health concern given 55 million people have been diagnosed with dementia and almost 80 million people are projected to be affected by 2030 [1]. Cognitive function is particularly important, especially as the population ages, because it determines the maintenance of our independence, the performance of everyday activities, and the quality of life [1]. Cognitive decline and dementia have a diverse etiology, and effective treatment is still not available [2]. For this reason, prevention strategies targeting modifiable factors, such as nutritional habits and dietary intake to slow the development of cognitive impairment, remain a promising public health approach [2]. Evidence to date assessing individual dietary factors and dietary patterns in relation to cognitive health has been summarized in the Lancet Neurology [3], while there is information related to various macro- and micronutrients and patterns, evidence related to water appears limited.
Water intake is a nutritional habit that is often overlooked, yet it is considered essential for the optimal physiological function of the human body [4]. As the most abundant component of the human body, water intake and optimal hydration are considered key aspects for the proper functioning of organ systems, aiding, among others, in efficient digestion, elimination of toxins, energy production, thermoregulation, and joint lubrication, as well as a multitude of biochemical reactions [5, 6]. Furthermore, proper hydration is thought to be important for optimal cognitive functioning as it plays a vital role in neural conductivity [7].
Dehydration occurs when the body loses more water than is taken in, whereas hypohydration refers to the state of water deficit; these conditions result in less-than-optimal hydration and may elicit adverse physiological consequences [8]. For the purposes of this paper, dehydration will be the term used to encompass the state of improper hydration due to unbalanced water loss or water deficit. In Europe, the percentage of the population reported to have inadequate water intake is estimated to vary from 5 to $35\%$ [9–11]. Understanding water intake and hydration status is of particular importance in older adults as this population tends to be less likely to meet recommendations on water intake and is at greater risk for dehydration due to blunted sensitivity to thirst signals, lower body reserves due to reduced muscle mass, reduced ability to deal with heat stress, and use of medications or laxatives with diuretic effects [12–15]. Multiple health organizations and dietary guidelines have acknowledged that adequate hydration status is associated with the preservation of physical and mental functions and that water intake is the best way to achieve hydration [16–19].
To maintain adequate hydration status, several countries and public organizations have proposed water intake recommendations for the public [20, 21]. These recommendations have been determined based on data from population studies and accounted for water from beverage and food sources [22]. For instance, the European Food Safety Authority (EFSA), based on data from population studies from 13 European countries, proposed the dietary reference values (DRV) for the adequate intake (AI) of water which increases with age up to 2.5 L and 2.0 L of water daily for men and women (aged 14 to 70 years), respectively [20]. Similarly, the Institute of Medicine (IOM) proposed increasing water intake levels with age up to 3.7 L/day and 2.7 L/day (aged 19 to 70 years) in men and women, respectively. Yet, the World Health Organization and other guidelines related to cognitive health do not currently include recommendations related to water intake or hydration status [23–25]. Further understanding of health behaviors, such as cognitive functioning, related to something as fundamental as water intake can have a substantial impact on public health.
Inadequate water intake and dehydration have been associated with existing signs of cognitive impairment among older adults living in long-term care facilities [26–29]. Moreover, acute studies have shown that dehydration and water supplementation affect mood and cognitive performance [30]. However, fluid and water intake has received limited attention in epidemiological studies, and the literature scarcely examines water intake as a predictor of cognitive performance among older adults. The few studies that have assessed hydration status as a potential predictor of cognitive function among community-dwelling older adults have been inconclusive [31–34]. Furthermore, to date, few studies have prospectively captured and examined the impact of water and hydration status on cognitive function over a multi-year period. Therefore, the objective of the present analyses was to prospectively investigate the relation between hydration status, water intake, and 2-year changes in cognitive performance in community-dwelling older adults with metabolic syndrome and overweight or obesity.
## Study design
This prospective cohort study is based on data collected during the first 2 years of the PREDIMED-Plus (PREvención con DIeta MEDiterránea Plus) study. Briefly, the PREDIMED-Plus study is an ongoing randomized, parallel-group, 6-year multicenter, controlled trial designed to assess the effect of lifestyle interventions on the primary prevention of cardiovascular disease. The primary aim of the trial is to assess the effects of an intensive weight loss intervention based on an energy-reduced Mediterranean diet (MedDiet), physical activity promotion, and behavioral support (intervention group) compared to usual care and dietary counseling only with an energy-unrestricted MedDiet (control group) on the prevention of cardiovascular events. Details of the design and methods of PREDIMED-Plus have been previously described [35, 36] and are available at https://www.predimedplus.com/.
## Ethics, consent, and permissions
The PREDIMED-Plus study protocol and procedures were approved by the Research Ethics Committees from each of the participating centers, and the study was registered with the International Standard Randomized Controlled Trial Registry (ISRCTN; ISRCTN89898870). All participants provided written informed consent.
## Study participants
PREDIMED-Plus participants were recruited from 23 centers across Spain between September 2013 and December 2016. A total of 6874 adults met the eligibility criteria and were randomly allocated in a 1:1 ratio to either the intervention or the control group. Couples sharing the same household were randomized together, and the couple was used as a unit of randomization. Eligible participants were community-dwelling adults (aged 55 to 75 for men; 60 to 75 for women) with overweight or obesity (BMI: 27 to 40 kg/m2) who met at least three criteria for metabolic syndrome [35], without previous cardiovascular events or diagnosed neurodegenerative diseases at baseline. All participants provided written informed consent.
The present longitudinal analysis involves a sub-study conducted in 10 of the 23 PREDIMED-Plus recruiting centers. Of the participants in the PREDIMED-Plus sub-study who had a completed validated 32-item Spanish fluid intake questionnaire, participants were excluded if they did not have a completed baseline Food Frequency Questionnaire (FFQ) or who reported implausible total energy intakes based on those proposed by Willet (≤ 500 and ≤ 3500 kcal/day in women and ≤ 800 and ≤ 4000 kcal/day in men) [37]. For the water and fluid intake analyses, using the interquartile range method (using a 1.5 multiplier for the first and third quartiles), participants with extreme intakes of fluid (daily fluid intakes for men < 188 mL or > 3862 mL and women < 263 mL or > 3539 mL) were excluded for the assessment of water and fluid intakes. Similarly, participants without blood sample values for urea, sodium, potassium, glucose, and serum osmolarity values < 100 mmol/L were also excluded from the analyses of hydration status and cognition. Furthermore, associations were tested for those participants who had completed the various cognitive tests.
## Assessment of water and fluid intake
A validated, semi-quantitative 32-item Beverage Intake Assessment Questionnaire (BIAQ) [10] and a 143-item validated semi-quantitative FFQ [38] specifying usual portion sizes, were administered by trained dietitians to assess habitual fluid and dietary intakes, respectively. These two questionnaires have been validated within populations of older, Spanish individuals, which are analogous to the current study population, and both have been found to be reproducible with relative validity [10, 38]. The BIAQ recorded the frequency of consumption of various beverage types during the month prior to the visit date. The average daily fluid intake from beverages was estimated from the servings of each type of beverage. The questionnaire items on beverages included: tap water, bottled water, natural fruit juices, bottled fruit juices, natural vegetable juices, bottled vegetable juices, whole milk, semi-skimmed milk, skimmed milk, drinking yogurt, milkshakes, vegetable drinks, soups, jellies and sorbets, soda, light/zero soda, espresso, coffee, tea, beer, non-alcoholic beer, wine, spirits, mixed alcoholic drinks, energy drinks, sports drinks, meal replacement shakes, and other beverages. The water and nutrient contents of the beverages were estimated mainly using the CESNID Food Composition Tables [39], complemented with data from the BEDCA Spanish Database of Food Composition [40].
The FFQ collected data on food intake based on the year prior to the visit according to nine possible frequency categories, which ranged from “never or almost never” to “> 6 portions/day” and based on the dietary guidelines for the Spanish population [41]. The information collected was converted into grams per day, multiplying portion sizes by consumption frequency and dividing the result by the period assessed. Ten food groups composed of vegetables, fruits, legumes, cereals, dairy beverages, meat and poultry, fats, nuts, fish/seafood, and other foods were determined to assess the contribution of foods to total water intake. Food groups and energy intake were estimated using Spanish food composition tables [42, 43]. Drinking water intake, water intake from all fluids, total water intake, EFSA total fluid water intake (TFWI), and EFSA total water intake (TWI) were computed (descriptions summarized in Additional file 1: Table S1). Drinking water intake was estimated based on tap and bottled water intakes based on BIAQ responses. Water intake from all fluids was computed from tap and bottled water, plus water from other beverages based on responses to the BIAQ. Total water intake encompassed water intake from all fluids in addition to water present in food sources based on responses to the FFQ. Water intake was further categorized based on established reference values. The EFSA recommendations for total water intake (EFSA TWI) for older adults (2.5 L/day and 2.0 L/day for men and women, respectively) in conditions of moderate environmental temperature and moderate physical activity [20] were used as reference values. Further categorizations were determined based on total water intake from fluids alone, based on EFSA recommendations (EFSA TFWI), where recommended levels for older adults are set to at least 2.0 L/day and 1.6 L/day for men and women, respectively [20].
## Assessment of hydration status
Hydration status was estimated based on calculated serum osmolarity (SOSM), which is considered a more reliable biomarker of hydration status than urinary markers in older adults [44]. Fasting serum glucose, urea, sodium, and potassium were measured by standard methods. Blood urea nitrogen was determined from urea values using the conversion factor of 0.357 and reported in mmol/L. With all relevant serum analyte measures presented in mmol/L, SOSM was estimated using the following equation [45]:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{SOSM}=1.86\ast \left(\textrm{sodium}+\textrm{potassium}\right)+1.15\ast \textrm{glucose}+\textrm{blood}\ \textrm{urea}\ \textrm{nitrogen}+14$$\end{document}SOSM=1.86*sodium+potassium+1.15*glucose+bloodureanitrogen+14 Based on this equation, dehydration, impending dehydration, and hydrated statuses were defined as SOSM > 300, 295–300, and < 295 mmol/L, respectively [28, 46].
## Assessment of cognitive performance
A battery of 8 neuropsychological tests assessing different cognitive domains was administered at baseline and 2-year follow-up by trained staff to assess cognitive performance. The following tests were assessed: the Mini-Mental State Examination (MMSE), two Verbal Fluency Tests (VFTs), two Digit Span Tests (DSTs) of the Wechsler Adult Intelligence Scale-III (WAIS-III), the Clock Drawing Test (CDT), and two Trail Making Tests (TMTs).
Briefly, a Spanish-validated version of the MMSE questionnaire, a commonly used cognitive screening test, was used in the present analysis [47]. A higher MMSE score indicates better cognitive performance [48]. Verbal ability and executive function were evaluated using the VFTs, which consist of two parts: the semantic verbal fluency task-animal category version (VFT-a) and the phonemic verbal fluency task-letter “p” version (VFT-p) [49]. The DST of the WAIS-III Spanish version assessed attention and memory. The DST Forward Recall (DST-f) is representative of attention and short-term memory capacity, and the DST Backward Recall (DST-b) is considered as a test of working memory capacity [50, 51]. The CDT-validated Spanish version was mainly used to evaluate visuospatial and visuo-constructive capacity [52–54]. Lastly, the TMT, another tool often used to assess executive function, consists of two parts. Part A (TMT-A) assessed attention and processing speed capacities, and part B (TMT-B) further examined cognitive flexibility [55]. All instruments included in the cognitive battery have been standardized for the Spanish population in the age range of the study population.
To assess overall cognitive function, a global cognitive function (GCF) score was determined as the main outcome measure, in addition to evaluating the individual neuropsychological tests (supplementary analyses). Raw scores at baseline and scores of changes at 2 years of follow-up for each individual cognitive assessment, as well as GCF, were standardized using the mean and standard deviation from the baseline measurements as normative data, creating z-scores [56]. GCF was calculated as a composite z-score of all 8 assessments, adding or subtracting each individual test value based on whether a higher score indicates higher or lower cognitive performance, respectively, using the formula:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{GCF}=\left({\textrm{Z}}_{\textrm{MMSE}}+{\textrm{Z}}_{\textrm{CDT}}+{\textrm{Z}}_{\textrm{VFT}-\textrm{a}}+{\textrm{Z}}_{\textrm{VFT}-\textrm{p}}+\left(-{\textrm{Z}}_{\textrm{TMT}-\textrm{A}}\right)+\left(-{\textrm{Z}}_{\textrm{TMT}-\textrm{B}}\right)+{\textrm{Z}}_{\textrm{DST}-\textrm{f}}+{\textrm{Z}}_{\textrm{DST}-\textrm{b}}\right)/8$$\end{document}GCF=ZMMSE+ZCDT+ZVFT-a+ZVFT-p+-ZTMT-A+-ZTMT-B+ZDST-f+ZDST-b/8
## Covariate assessment
The trained staff collected baseline socio-demographic (i.e., sex, age, education level, and civil status) and lifestyle (i.e., physical activity, total energy intake, alcohol intake, caffeine consumption [57], sleeping habits, and smoking status) related variables, as well as information about medication use, in face-to-face interviews using self-reported general questionnaires and a 143-item validated semi-quantitative FFQ for the dietary related variables [38], which were further estimated using Spanish food composition tables [42, 43]. Leisure time physical activity was estimated using the validated Minnesota-REGICOR Short Physical Activity questionnaire [58]. These socio-demographic and lifestyle variables were considered as possible covariates because of reports that younger adults, women, individuals with higher educational attainment, married, more active, greater consumers, and non-smokers tend to consume higher amounts of fluids from beverages and foods and hence more likely to meet recommendations on water intake [59, 60]. Alcohol was accounted for as a potential covariate as it may act as a diuretic at certain levels [61] as well as being associated with an elevated risk of dementia when consumed regularly [62]. Similarly, caffeinated beverages may have a mild diuretic effect [63], as well as may affect attention and alertness [64] and could be associated with reduced cognitive decline and dementia risk [65]. Sleeping habits have also been associated with cognitive health [66]. Anthropometric measures, including weight and height were measured by trained staff using calibrated scales and wall-mounted stadiometers, respectively. Body mass index (BMI), which may modify the relationship between water intake and hydration status [67], was calculated as weight in kilograms divided by height in meters squared. History of chronic disease (i.e., type 2 diabetes, hypertension, and hypercholesterolemia) was self-reported or collected from patient medical records and included as these conditions may cause fluid imbalance, cause dehydration, and have been associated with cognitive performance possibly leading to mild cognitive impairment [68–70]. Depressive symptomatology was evaluated using the Beck Depression Inventory-II (BDI-II), given the association observed with cognitive health [71] where depressive symptomatology risk was established as a score ≥ 14 [72].
## Statistical analyses
For the present analyses, a prospective cohort study was conducted within the framework of the PREDIMED-Plus study using the database updated to December 22, 2020. Participants were categorized into quantiles based on baseline water intake (drinking water, water intake from all fluids, total water intake), recommended categories of water intake (EFSA TWI, EFSA, TFWI), and hydration status according to serum osmolarity. Baseline characteristics of participants for each category and quantile of water intake and hydration status were presented as numbers and percentages using Pearson’s chi-square test for categorical variables and means ± standard deviations or median (interquartile range [P25–P75]) using one-way ANOVA or Kruskal Wallis test for continuous variables, as appropriate.
Multivariable linear regression models were fitted to assess longitudinal associations comparing the 2-year change in cognitive function across baseline variables of hydration status and water intake and for meeting the EFSA recommendations for TWI and TFWI [20]. When analyses were performed with categorical variables, p for trend was calculated. The p for linear trend was calculated by assigning the median value of each category and modeling it as a continuous variable.
Multivariable linear regression models were adjusted for several potential confounders. Model 1 adjusted for age (years), sex (man or woman), intervention group, participating center size (< 100, 100 to < 150, 150 to < 200, ≥ 200 participants for hydration status and < 100, 100 to < 200, 200 to < 300, ≥ 300 participants for fluid-related analyses), respective baseline cognitive function score, and corrected for clusters (to account for couples living in the same household being randomized as a single unit). Model 2 was additionally adjusted for BMI (kg/m2), educational level (primary, secondary, or college), civil status (single, divorced or separated, married, widower), smoking status (current, former, or never), and physical activity (METs/min/day). Model 3 was additionally adjusted for sleeping habits (hours of nighttime sleep), depressive symptomatology (yes/no), diabetes prevalence (yes/no), hypertension (yes/no), hypercholesterolemia (yes/no), total energy intake (kJ per day), alcohol consumption in g/day (and adding the quadratic term), and caffeine intake (g/day). To assess the linear trend, the median value of each category of exposure variables (hydration status and various assessments of water and fluid intake) was assigned to each participant and was modeled as continuous variables in linear regression models. The Bonferroni correction was used to correct for multiple comparisons and reduce the risk of a type 1 error.
Several stratified and sensitivity analyses were additionally performed to test the robustness of the findings. First, sex-stratified regression approaches were employed to examine the relationships between hydration status and these water and fluid intake categories and 2-year changes in global cognitive function. Sensitivity analyses were additionally performed, testing the addition of estimated glomerular filtration rate (eGFR), an indicator of renal function derived based on serum creatinine level, age, and sex [73], or dietary intake covariates (amount [g/day] of vegetables, fruits, legumes, grains, non-fluid dairy, meat, oils, fish, nuts, and pastries determined via the validated 143-item semi-quantitative FFQ [19]) to the multivariable models, but also after removal of participants with baseline MMSE < 24 (mild dementia and poorer) [74], or the removal of participants with extreme GCF z-scores at baseline (< $5\%$ and > $95\%$).
The data were analyzed using the Stata 14 software program (StataCorp LP, TX, USA), and the results were originally considered statistically significant at a p-value (2-tailed) < 0.05, and after the Bonferroni correction, statistical significance was considered at a p-value (2-tailed) < 0.005.
## Results
A total of 1957 participants (mean age 65.0 ± 4.9 years and $50.5\%$ women) were available for the assessment of water and fluid intake and 1192 participants for the assessment of hydration status after excluding missing values or implausible data (Additional file 1: Fig. S1). Table 1 presents the baseline characteristics of the study population according to sex, water (tap and bottle) intake amount, and hydration status. The median (range) consumption of drinking water intake in men and women was 900 (0 to 3100) and 900 (0 to 2700) mL/day, respectively. Compared with participants in the group with the lowest drinking water intake (< 500 mL/day), those with the highest drinking water intake (1.8 to 3.1 L/day) were more likely to be younger ($p \leq 0.001$), have a higher BMI ($$p \leq 0.001$$), and have a lower alcohol intake ($p \leq 0.001$). Compared to participants considered to be hydrated according to serum osmolarity status, participants considered to be dehydrated tended to be older ($$p \leq 0.008$$), women ($$p \leq 0.008$$), have type 2 diabetes ($p \leq 0.001$), have depressive symptoms ($$p \leq 0.025$$), and less likely to drink alcohol ($$p \leq 0.002$$). Participants involved in the present analyses did not differ from the rest of the participants enrolled in the PREDIMED-Plus trial in terms of age, sex, BMI, and prevalence of obesity and type 2 diabetes ($p \leq 0.05$ for all comparisons). Furthermore, $80\%$ of participants ($69\%$ of men and $90\%$ of women) met the EFSA fluid intake recommendations based on questionnaire responses, yet serum osmolarity values indicated over $50\%$ of participants were physiologically dehydrated with only $10\%$ of participants being considered physiologically hydrated based on serum osmolarity levels. Table 1Baseline characteristics of the participants according to sex, categories of water intake, and hydration statusTraitTotalWomenMenp-valueCategories of water intakeCategories of estimated SOSMaLowestLow-moderateHigh-moderateHighestp-valueHydratedbImpending dehydratedDehydratedp-valueMedian900900900400 ml/day900 ml/day1300 ml/day1800 ml/day292 mmol/L298 mmol/L304 mmol/LRange(0–3100)(0–2700)(0–3100)(0–500)(800–1000)(1071–1400)(1800–3100)(263–294.9)(295–299.9)(300–346.5)Frequency, n1957989968390744546277123400669Socio-demographic data Age (years)65. 0± 4.966.2 ± 4.063.70 ± 5.36< 0.00165.0 ± 4.965.4 ± 4.864.9 ± 5.064.0 ± 4.7< 0.00164.2 ± 5.064.3 ± 5.065.2 ± 4.80.008 Sex (women)989 (50.5)989 (50.5)968 (49.5)188 (48.2)383 (51.5)280 (51.3)138 (49.8)0.73147 (38.2)190 (47.5)353 (52.8)0.008 Education level Primary school1023 (52.3)629 (63.6)394 (40.7)188 (48.2)399 (53.6)297 (54.4)139 (50.2)68 (55.3)211 (52.8)372 (55.6) High school545 (27.9)235 (23.8)310 (32.0)111 (28.5)201 (27.0)148 (27.1)85 (30.7)29 (25.6)112 (28.0)184 (27.5) College389 (19.9)125 (12.6)264 (27.3)< 0.00191 (23.3)144 (19.4)101 (18.5)53 (19.1)0.38126 (21.1)77 (19.3)113 (16.9)0.636 *Civil status* Single, divorced or separated242 (12.4)136 (13.8)106 (11.0)47 (12.1)96 (12.9)62 (11.4)37 (13.4)17 (13.8)46 (11.5)77 (11.5) Married1513 (77.3)684 (69.2)829 (85.6)300 (76.9)560 (75.3)423 (79.1)221 (79.8)97 (78.9)319 (79.8)520 (77.7) Widower202 (10.3)169 (17.1)33 (3.4)< 0.00143 (11.0)88 (11.8)52 (9.5)19 (6.9)0.3119 (7.3)35 (8.8)72 (10.8)0.645Lifestyle factors Body mass index, kg/m232.6 ± 3.533.0 ± 3.632.3 ± 3.4< 0.00132.3 ± 3.432.7 ± 3.532.5 ± 3.533.3 ± 3.50.00132.3 ± 3.132.5 ± 3.432.8 ± 3.50.245 Physical activity (MET min/day)348.7 ± 334.4284.0 ± 244.6414.8 ± 395.5< 0.001330.5 ± 322.4345.2 ± 335.4364.1 ± 348.9352.6 ± 325.60.502423.9 ± 338.2364.5 ± 337.9344.2 ± 346.80.057 *Smoking status* Never smoked904 (46.2)704 (71.2)200 (20.7)182 (46.7)334 (44.9)263 (48.17)125 (45.1)47 (38.2)175 (43.8)333 (49.8) Former smoker817 (41.8)212 (21.4)605 (62.5)163 (41.8)305 (41.0)222 (40.7)127 (45.9)56 (45.5)177 (44.3)269 (40.2) Current smoker236 (12.1)73 (7.4)163 (16.8)< 0.00145 (11.5)105 (14.1)61 (11.2)25 (9.0)0.72920 (16.3)48 (12.0)67 (10.0)0.057 Sleeping status, weekdays ≤ 6 h697 (35.6)381 (38.5)316 (32.6)141 (36.2)264 (35.5)191 (35.0)101 (36.5)45 (36.6)140 (35.0)237 (35.4) 6 to ≤ 8 h1096 (56.0)517 (52.3)579 (59.8)215 (55.1)412 (55.4)315 (57.7)154 (55.6)65 (52.9)227 (56.8)366 (54.7) > 8 h164 (8.4)91 (9.2)73 (7.5)0.00334 (8.7)68 (9.1)40 (7.3)22 (7.9)0.92913 (10.6)33 (8.3)66 (9.9)0.863 Sleeping status, weekends ≤ 6 h600 (30.7)344 (34.9)256 (26.5)128 (32.9)217 (29.3)161 (29.5)94 (34.2)35 (28.5)122 (30.7)208 (31.1) 6 to ≤ 8 h1111 (56.9)517 (52.4)594 (61.5)207 (53.2)431 (58.1)320 (58.6)153 (55.6)69 (56.1)233 (58.5)362 (54.2) > 8 h241 (12.4)125 (12.7)116 (12.0)< 0.00154 (13.9)94 (12.7)65 (11.9)28 (10.2)0.43719 (15.5)43 (10.8)98 (14.7)0.371Disease prevalence at recruitment Diabetes618 (31.6)283 (28.6)335 (34.6)0.004118 (30.3)236 (31.7)179 (32.8)85 (30.7)0.85130 (24.4)89 (22.3)241 (36.0)< 0.001 Hypertension1640 (83.8)834 (84.3)806 (83.3)0.523326 (83.6)626 (84.1)453 (83.0)235 (84.8)0.90296 (78.1)329 (82.3)568 (84.9)0.136 Hypercholesterolemia1411 (72.1)761 (77.0)650 (67.2)< 0.001274 (72.1)544 (73.5)380 (71.0)177 (70.8)0.74288 (71.5)292 (73.0)487 (72.8)0.950 Depressive symptoms428 (21.9)314 (31.8)114 (11.8)< 0.00194 (24.1)154 (20.7)116 (21.3)64 (23.1)0.55120 (16.3)81 (20.3)171 (25.6)0.025Biochemical parameters *Serum potassium* (mmol/L)4.3 ± 0.44.3 ± 0.44.3 ± 0.40.3554.4 ± 0.44.3 ± 0.44.28 ± 0.374.3 ± 0.40.0034.2 ± 0.44.3 ± 0.44.3 ± 0.40.005 *Serum sodium* (mmol/L)139.4 ± 14.9139.0 ± 17.1139.9 ± 12.40.219140.1 ± 12.6139.1 ± 15.7139.6 ± 14.5138.8 ± 16.60.492137.6 ± 2.1140.2 ± 1.4142.4 ± 2.1< 0.001 Glucose (mmol/L)6.3 ± 1.76.3± 1.66.4 ± 1.80.0566.29 ± 1.566.34 ± 1.756.27 ± 1.646.43 ± 1.950.5825.7 ± 0.96.0 ± 1.26.7 ± 2.0< 0.001 *Blood urea* nitrogen (BUN, mmol/L)6.6 ± 1.86.6 ± 1.76.7 ± 1.90.7916.6 ± 1.76.7 ± 1.86.7 ± 1.96.7 ± 1.80.9805.9 ± 1.56.1 ± 1.47.0 ± 1.9< 0.001 SOSM (mmol/L)a298.4 ± 24.4297.6 ± 28.0299.1 ± 20.20.298299.2 ± 21.6297.6 ± 26.6299.3 ± 19.1296.9 ± 31.10.580292.3 ± 3.6297.9 ± 1.4304.1 ± 3.8< 0.001Dietary intake Alcohol intake (g/day)9.8 ± 13.34.0 ± 6.715.7 ± 15.6< 0.00112.5 ± 16.29.8 ± 12.98.7 ± 12.28.2 ± 11.1< 0.00113.3 ± 16.511.3 ± 15.39.1 ± 13.20.002 Energy intake (kcal/day)2391.7 ± 541.42238.3 ± 493.12548.6 ± 543.8< 0.0012422.1 ± 559.52391.2 ± 530.42374.7 ± 549.02384.1 ± 30.80.6112400.0 ± 598.12342.4 ± 549.32365.7 ± 554.40.579 Caffeine intake (mg/day)33.6 ± 31.829.6 ± 27.937.6 ± 34.9< 0.00138.3 ± 35.231.6 ± 29.732.6 ± 31.334.0 ± 32.60.00735.7 ± 35.136.9 ± 35.335.8 ± 31.90.869 Mediterranean diet score (17-point)8.5 ± 2.68.8 ± 2.68.1 ± 2.6< 0.0018.4 ± 2.68.3 ± 2.68.6 ± 2.68.7 ± 2.60.06198.7 ± 2.78.6 ± 2.68.6 ± 2.50.889 Drinking water intake (ml/day)1031.2 ±465.21033.1 ± 458.81029.30.8551352.9 ± 123.4892.1 ± 30.11300.5 ± 17.31829.2 ± 155.7< 0.0011036.0 ± 499.21034.7 ± 466.61044.3 ± 478.80.946 Total fluid intake (ml/day)1842.4 ± 592.01797.2 ± 579.71888.7 ± 601.10.0011232.1 ± 454.21691.2 ± 388.52103.5 ± 406.72593.5 ± 413.5< 0.0011931.1 ± 643.31886.5 ± 662.41874.4 ± 646.00.672 Total water intake (ml/day)2871.1 ± 676.22853.5 ± 674.82889.2 ± 677.40.24342241.2 ± 500.92709.8 ± 493.73146.0 ± 516.03646.0 ± 562.8< 0.0012947.2 ± 710.12869.6 ± 743.42852.4 ± 735.20.421 Meets EFSA TWI recommendations1569 (80.2)898 (90.8)671 (69.3)< 0.001186 (47.7)595 (80.0)512 (93.8)276 (99.6)<0.00199 (80.5)309 (77.3)521 (77.9)0.750Cognitive function tests GCFc0.04 ± 0.62− 0.15 ± 0.610.22 ± 0.58< 0.0010.10 ± 0.580.04 ± 0.64− 0.03 ± 0.660.11 ± 0.550.0180.02 ± 0.670.13 ± 0.560.01 ± 0.640.028 MMSE28.14 ± 1.9427.82 ± 2.1828.46 ± 1.60< 0.00128.33 ± 1.8428.04 ± 1.9828.02 ± 2.0228.36 ± 1.740.00828.23 ± 1.9128.20 ± 2.0928.14 ± 2.040.824 CDT5.81 ± 1.315.68 ± 1.385.94 ± 1.23< 0.0015.83 ± 1.375.77 ± 1.315.80 ± 1.275.86 ± 1.310.7675.75 ± 1.505.90 ± 1.275.89 ± 1.200.481 VFT-a15.78 ± 4.7414.75 ± 4.5416.82 ± 4.71< 0.00116.32 ± 4.5415.61 ± 4.9815.49 ± 4.7316.03 ± 4.310.03215.77 ± 5.0415.72 ± 4.5815.62 ± 4.700.913 VFT-p11.95 ± 4.4011.31 ± 4.3412.61 ± 4.36< 0.00112.23 ± 4.4711.93 ± 4.4911.65 ± 4.4112.23 ± 3.980.14511.99 ± 4.1112.19 ± 4.3511.46 ± 4.320.023 TMT-A55.17 ± 31.4061.62 ± 32.5648.94 ± 28.88< 0.00153.73 ± 28.7557.04 ± 33.2155.77 ± 32.5551.02 ± 27.040.03756.72 ± 28.4554.97 ± 30.3357.56 ± 36.130.474 TMT-B136.9 ± 78.4156.6 ± 82.5116.8 ± 68.3< 0.001129.9 ± 75.4138.0 ± 79.2142.9 ± 81.4132.2 ± 72.60.06153.2 ± 92.3139.6 ± 81.0146.4 ± 82.40.217 DST-f8.66 ± 2.468.13 ± 2.329.19 ± 2.48< 0.0018.61 ± 2.338.74 ± 2.488.50 ± 2.578.86 ± 2.340.238.26 ± 2.628.74 ± 2.448.73 ± 2.530.176 DST-b5.06 ± 2.204.53 ± 1.975.58 ± 2.28< 0.0015.04 ± 2.255.15 ± 2.154.85 ± 2.205.27 ± 2.190.0494.89 ± 2.305.11 ± 2.115.02 ± 2.160.638Data are n (%) or mean ± SD for categorical and quantitative variables, respectively. The exception is the diet score is represented as mean (range)Chi-squared is used for categorical variables and one-way ANOVA for quantitative variablesSOSM > 300 mmol/L represented a dehydrated state, 295 to 300 mmol/L represented a state of impending dehydration, and < 294 mmol/L represented a hydrated stateAbbreviations: CDT, Clock Drawing Test; DASH, Dietary Approaches to Stop Hypertension; DST-f, Digit Span Test Forward; DST-b, Digit Span Test Backward; GCF, global cognitive function; MMSE, Mini-Mental State Examination; TMT-A, Trail Making Test Part A; TMT-B, Trail Making Test Part B; VFT-a, Verbal Fluency tasks semantical; VFT-p, Verbal Fluency Tasks PhonologicalaSOSM represents calculated serum osmolarity and was calculated using the formula SOSM = 1.86 × (Na+ + K+) + 1.15 × glucose + BUN + 14, where all analytes are in mmol/LbHydrated includes participants considered to be hydrated (SOSM 285 to < 295 mmol/L) and overhydrated (SOSM < 285 mmol/L). These groups were combined due to the small number of participants ($$n = 3$$) considered to be overhydrated. Assessment with the removal of these participants did not modify the findingscGCF was calculated using the formula GCF= (ZMMSE + ZCDT + ZVFT-a + ZVFT-p + (−ZTMT-A) + (−ZTMT-B) + ZDST-f + ZDST-b)/8 Figures 1 and 2 summarize the multivariable-adjusted β-coefficients ($95\%$ CIs) of water intake and hydration status categorically and continuously, respectively, with 2-year changes in GCF z-scores (full details from all models are presented in Additional file 1: Tables S2-S4). Categorical analyses showed a non-significant trend towards participants considered to have a dehydrated status (β: − 0.11; $95\%$ CI: − 0.24 to 0.02; p for trend = 0.058) to have a greater decline in global cognitive function compared to those who were considered hydrated (Fig. 1). No significant associations were observed between the various classifications of water intake (i.e., drinking water, all fluids, water from beverage and food sources, and based on ESFA water and fluid recommendations) and 2-year changes in GCF in the multivariable-adjusted models. The results of the continuous linear regression analyses suggest the presence of a significant association between hydration status and global cognitive decline over a 2-year period (β: − 0.10; $95\%$ CI: − 0.02 to − 0.004; $$p \leq 0.002$$) (Fig. 2).Fig. 1Hydration status, water and fluid intakes categorically with 2-year changes in global cognitive function (z-scores). Data are presented as beta-coefficients and $95\%$ CI. Multivariable linear regression models were adjusted for baseline covariates, including baseline GCF score, age (years), sex, intervention PREDIMED-Plus randomized group, and participating center (for hydration status: ≤ 100, 100 to < 150, 150 to < 200, > 200 participants; for fluid-related exposures: ≤ 100, 100 to < 200, 200 to < 300, > 300 participants), body mass index (kg/m2), educational level (primary, secondary, or college), civil status (single, divorced or separated, married, widower), smoking habit (current, former, or never), physical activity (METs/min/day), sleep status (hours per day), depressive symptomatology (yes/no), diabetes prevalence (yes/no), hypertension (yes/no), hypercholesterolemia (yes/no), energy intake (kcal/day), alcohol consumption in g/day (and adding the quadratic term), and caffeine intake (mg/day). aHydration status refers to serum osmolarity, where dehydration, impending dehydration, and hydrated statuses were defined as SOSM > 300, 295–300, and < 295 mmol/L, respectively. bDrinking water refers to tap and bottled water intakes. cWater, all fluids refers to tap and bottled water, plus water from other beverages and fluid food sources, such as soups and smoothies. dTotal water refers to water from all fluids in addition to water present in food sources. eEFSA TFWI refers to the recommended levels of total fluid water intake for older adults at 2.0 L/day and 1.6 L/day for men and women, respectively. fEFSA TWI refers to the recommended levels of total water intake, from fluids and foods, for older adults at 2.5 L/day and 2.0 L/day for men and women, respectively. Abbreviations: EFSA, European Food Safety Authority; TFWI, total fluid water intake; TWI, total water intake; SOSM, serum osmolarityFig. 2Hydration status, water and fluid intakes continuously with 2-year changes in global cognitive function (z-scores). Data are presented as beta-coefficients and $95\%$ CI. Multivariable linear regression models were adjusted for baseline covariates, including baseline GCF score, age (years), sex, intervention PREDIMED-Plus randomized group, and participating center (≤ 100, 100 to < 150, 150 to < 200, > 200 participants), body mass index (kg/m2), educational level (primary, secondary, or college), civil status (single, divorced or separated, married, widower), smoking habit (current, former, or never), physical activity (METs/min/day), sleep status (hours per day), depressive symptomatology (yes/no), diabetes prevalence (yes/no), hypertension (yes/no), hypercholesterolemia (yes/no), energy intake (kcal/day), alcohol consumption in grams/day (and adding the quadratic term), and caffeine intake (mg/day). Beta represents the changes in global cognitive function, expressed as z-scores, with each hydration or fluid intake component continuously. Bonferroni correction analyses have been run to correct for multiple comparisons and indicate a $p \leq 0.005$ is statistically significant Additional file 1: Tables S2-S4 show the unadjusted and multivariable-adjusted β-coefficients ($95\%$ CIs) of water and fluid intake (from both beverage and food sources, assessed individually and combined), as well as hydration status with changes in the global and individual assessments of cognitive function over a 2-year period. When each neuropsychological test was investigated separately, participants with the highest category of intake of drinking water (1.0 to 1.5 L/day) non-statistically significantly presented with a 0.17-point increase (β: 0.17; $95\%$ CI: 0.02 to 0.32; p for trend = 0.021) in DST-f z-score compared to those with the lowest water intake (< 0.5 L/day) over a 2-year period (Additional file 1: Table S2). Total fluid intake showed similar findings where participants in the highest category of intake of total fluid water (2.5 L, range 2.2 to 3.4 L/day) presented with a 0.12-point increase (β: 0.12; $95\%$ CI: 0 to 0.24; p for trend = 0.041) in DST-f z-score compared to those with the lowest water intake (1.1 L, range 0.4 to 1.4 L/day) over a 2-year period (Additional file 1: Table S2). No other associations in the multivariable-adjusted categorical or continuous analyses were observed. Furthermore, post hoc analyses of available data showed no significant differences in changes over time in the various fluid intake assessment variables (over a 2-year duration) or hydration status (over a 1-year duration) ($p \leq 0.005$) among the participants.
## Stratified analyses
When the analyses were stratified by sex, no changes in significance were observed with the associations between water and fluid intakes, in either categorical or continuous investigations, and global cognitive function. However, when hydration-related analyses were restricted to men or women participants, findings were attenuated when women only were assessed both categorically and continuously ($p \leq 0.005$), whereas in men those with a SOSM ≥ 300 mmol/L, indicating a dehydrated status, showed a higher cognitive decline over the 2-year period compared to those hydrated (β: − 0.20, $95\%$ CI: − 0.36 to − 0.04, $$p \leq 0.012$$ and p-trend = 0.002), and the significance remained in the continuous analyses (β: − 0.013, $95\%$ CI: − 0.022 to − 0.004, $$p \leq 0.004$$) (Additional file 1: Figs. S2-S3).
## Sensitivity analyses
Regarding the sensitivity analyses, associations additionally adjusted for eGFR did not significantly modify the findings (Additional file 1: Tables S5-S7). Moreover, associations additionally adjusted for intake of dietary variables, where applicable, including the amount (g/day) of vegetables, fruits, legumes, grains, non-fluid dairy, meat, oils, fish, nuts, and pastries did not significantly modify the findings (Additional file 1: Tables S5-S7). Furthermore, the main results did not substantially change after the removal of extreme GCF baseline z-scores (< $5\%$ and > $95\%$). Whereas following the removal of participants with a baseline MMSE score < 24 ($$n = 51$$), the categorical association of hydration status with GCF became greater showing a more dehydrated status related to greater 2-year global cognitive decline compared to hydrated participants (p-trend = 0.046), and the association of the continuous variables remained significant ($$p \leq 0.002$$). The findings related to water and fluid intake did not substantially change with the removal of participants with a baseline MMSE score < 24 (Additional file 1: Tables S5-S7).
## Discussion
To the best of our knowledge, this is the first multi-year prospective cohort study to assess the association between water intake (from fluid and food sources) and hydration status, with subsequent changes in cognitive performance in older Spanish adults with metabolic syndrome and overweight or obesity. In this large sample of older Spanish adults, poorer hydration status was associated with greater global cognitive decline over a 2-year period, particularly in men, whereas water intake, from fluid and/or food sources, and meeting related EFSA recommendations, was not associated with global cognitive function. Nonetheless, when results of cognitive function tests were considered independently, water intake (> 1.5 L/day compared to < 0.5 L/day from tap or bottled water and 2.2 to 4.4 L/day compared to < 1.4 L/day of water from all fluid sources) was related to better attention and short-term memory, as assessed by DST-f, over a 2-year period.
Despite the general acknowledgment that an appropriate level of fluid intake and hydration status is important for health, there have been limited investigations to date assessing the relationship between fluid intake or hydration status and cognitive function. Existing evidence suggests that good hydration status may be associated with better cognitive test results and that mild, induced dehydration can impair cognitive abilities [75], but findings are not consistent and there are only a few studies exploring the relationship of hydration status and hardly any assessing amount of water intake, with cognitive performance in older community-dwelling adults.
Of the few relevant and recent studies that have been conducted, one cross-sectional analysis of 2506 community-dwelling older American adults (aged ≥ 60 years) from the Nutrition and Health Examination Survey (NHANES) 2011–2014 cycles assessed both hydration status and water intake in relation to cognitive performance [34]. In comparison with the present findings, this study found women (but not men) considered to be hydrated, based on a SOSM level between 285 and 289 mmol/L, had better attention and processing speeds, based on a Digit Symbol Substitution test (DSST), than women not at optimal hydration [34]. These cross-sectional findings differ from the present observations where global cognitive function, but not individual tests related to attention and processing, was associated with hydration status. Whereas, correspondingly, water intake (but not hydration status) was positively associated with DST-f, which is similar to the DSST in that it is an indicator of attention as well as short-term memory capacity, and this was seen across all older adults (both men and women). Additionally, in the NHANES study, cognitive test scores were significantly lower among adults who failed to meet EFSA recommendations on adequate intake (AI) of water in bivariate analyses, yet this significance was attenuated in the multivariable analyses among both women and men. Yet, using the alternative AI of daily water intake of 1500 mL or more, which is comparable to the highest drinking water intake group in the present study, women scored higher on the Animal Fluency Test, a measure of verbal fluency and hence executive function, and DSST than women with intake levels below this amount, and findings among men trended in the same direction [34].
Similarly, hydration status has been associated with cognitive function in two cross-sectional studies of older community-dwelling adults by Suhr and colleagues [32, 33]. First, Suhr et al. showed that in 28 healthy community-dwelling older adults (aged 50 to 82 years), a lower hydration status, determined in this study via total body water measured using the bioelectrical impedance method, was related to a decreased psychomotor processing speed, poorer attention, and memory [33]. A second cross-sectional study by Suhr et al. [ 32] conducted in 21 postmenopausal women (aged 50 to 78 years) reported a positive association between total body water, also measured by the bioelectrical impedance method, and working memory or memory skills.
Moreover, the possible relation between hydration and cognitive function shown by the present findings and demonstrated by the abovementioned cross-sectional studies in older adults align with acute heat- and exercise-induced dehydration studies in younger populations, as illustrated by a meta-analysis of 33 trials (pre-post or crossover design) of 413 adults free of disease (aged < 45 years). It should be noted that these studies assessed dehydration over a period of less than 72 h with 27 ($81\%$) of the studies including only men participants, and 25 ($76\%$) studies involving recreationally or highly athletic individuals. The authors concluded that despite variability among the included studies, dehydration impaired cognitive performance, particularly for tasks involving attention, executive function, and motor coordination when water deficits exceeded $2\%$ body mass loss [76].
Conversely, a cross-sectional study conducted in Poland among 60 community-dwelling older adults (aged 60 to 93 years) found no significant relationship between cognitive performance, as assessed using the MMSE, TMT, and the Babcock Story Recall Test, and hydration status as assessed by urine specific gravity [31]. The discrepancy between the findings from this cross-sectional study and the present PREDIMED-Plus analyses might be because all participants in the cross-sectional study from Poland were considered to be adequately hydrated and hence the authors of that study could not assess the impact of a dehydrated state on cognition.
A noteworthy consideration when interpreting the literature and the main findings of the current study for practical use and in the determination of potential mechanisms of action is the distinction between water intake and water balance (related to hydration status) within the body. When homeostasis of fluids within the body is disrupted, modifying water intake may impact cognitive function, yet due to the dynamic complexity of body water regulation impacting hydration status may be dependent on individualized physiological water intake needs [8]. Thus, while the biological mechanism by which water intake and a hydrated status may reduce the risk of cognitive decline is unclear, evidence suggests that aspects related to hydration and fluid homeostasis or a lack thereof, such as hormone regulation and changes in brain structure, could be a key underlying factor.
Several mechanisms regulate water intake and output to maintain serum osmolarity, and hence hydration status, within a narrow range. Elevated blood osmolarity resulting in the secretion of antidiuretic hormone (ADH), also known as vasopressin or arginine vasopressin, a peptide hormone which acts primarily in the kidneys to increase water reabsorption, is one such mechanism that works to return osmolarity to baseline and preserve fluid balance [77]. In addition to its role in mediating the physiological functions related to water reabsorption and homeostasis, evidence has suggested that ADH participates in cognitive functioning [78] and that the associated cognitive modulations may further interplay with sex hormones [79]. Antidiuretic hormone may be influenced by the androgen sex hormone, which is generally more abundant in the brains of males than in females [80]. As a result, the impact of ADH on cognition could be greater in males [80].
Exercise- and heat-induced acute dehydration studies implicate possible modifications to the brain structure as another potential mechanism of action for an association between water intake, hydration status, and cognitive function. Evidence has proposed that acute dehydration can lead to a reduction in brain volume and subtle regional changes in brain morphology such as ventricular expansion, effects that may be reversed following acute rehydration [81, 82]. Acute dehydration studies have further implicated hydration status in affecting cerebral hemodynamics and metabolism resulting in declines in cerebral blood flow and oxygen supply [83, 84]. A lower vascular and neuronal oxygenation could potentially compromise the cerebral metabolic rate for oxygen, thereby contributing to reductions in cognitive performance [81, 85–88]. Nonetheless, other potential unknown mechanisms cannot be disregarded.
There are several limitations and strengths of the present analyses that need to be acknowledged. The first notable limitation is that the results may not be generalizable to other populations since the participants are older Spanish individuals with metabolic syndrome and overweight or obesity. Second, measurement error and recall bias are possibilities given the use of questionnaires to estimate water and fluid intake and that these rely on responders’ memory which is a component of cognitive function. However, these questionnaires have been validated and determined as reliable methods of assessing long-term intake in the present study population [37, 38]. Third, despite its longitudinal design, water and fluid intake and hydration status were only considered at baseline; however, as the questionnaires measure habitual beverage and food intake, and older adults are considered to have reasonably stable dietary habits [37, 38], this is not expected to significantly impact the findings. Along these lines, the possible effect of seasonality on water intake and osmolarity was not considered a concern in the present analyses as the validation of the fluid questionnaire measurements included assessments at various points throughout the year (baseline vs. 6 months vs 1 year) with no significant differences observed in fluid consumption across the different time points assessed [10]. Hence, the finding of no difference between 6-month intervals, suggests no significant differences between opposing seasons (e.g., winter vs. summer; spring vs. fall). Furthermore, SOSM determination may not necessarily detect acute dehydration or rehydration immediately prior to the cognitive testing, and it is unknown whether observed elevated SOSMs were due to inadequate water intake, ADH abnormality, or other factors. While it is possible that the hydration status of some individuals was misclassified because serum osmolarity was estimated as opposed to being directly measured, the equation has been shown to predict directly measured serum osmolarity well in older adult men and women with and without diabetes or renal issues with a good diagnostic accuracy of dehydration and has been considered a gold standard for the identification of impending and current water-loss dehydration in older adults [44, 45, 89–91]. Lastly, a discrepancy was observed between the percentage of individuals that were considered to have met EFSA fluid intake recommendations and those considered to be dehydrated based on calculated osmolarity. This may have been due to the fact that the EFSA fluid intake recommendations are meant for individuals in good health [20]; whereas the present study population had overweight or obesity, and it has been shown that individuals with higher BMIs have higher water needs related to metabolic rate, body surface area, body weight, and water turnover rates related to higher energy requirements, greater food consumption, and higher metabolic production [92]. Strengths of the present analyses include the longitudinal, prospective design, the large sample size, the use of an extensive cognitive test battery, the use of validated questionnaires, and the robustness of the current findings due to the adjustment of relevant covariates.
## Conclusions
Findings suggest that hydration status, specifically poorer hydration status, may be associated with a greater decline in global cognitive function in older adults with metabolic syndrome and overweight or obesity, particularly in men. Further prospective cohort studies and randomized clinical trials are required to confirm these results and to better understand the link between water and fluid intake, hydration status, and changes in cognitive performance to provide guidance for guidelines and public health.
## Supplementary Information
Additional file 1: Table S1. Hydration and water intake definitions. Table S2. Associations between cognitive assessments and water and fluid intake exposures. Table S3. Associations between cognitive assessments and EFSA fluid intake related guidelines. Table S4. Associations between cognitive assessments and hydration status. Table S5. Sensitivity analysis in global cognitive function according to water and fluid intake related exposures Table S6. Sensitivity analysis in global cognitive function according to EFSA fluid intake related guidelines. Table S7. Sensitivity analysis in global cognitive function according to hydration status. Fig. S1. Flow diagram of participants. Fig. S2. Continuous sensitivity analysis by sex. Fig. S3. Categorical sensitivity analysis by sex.
## References
1. 1.Gauthier S, Rosa-Neto P, Morais JA, Webster C. World Alzheimer Report 2021: Journey through the diagnosis of dementia. London: Alzheimer´s Disease International; 2021.
2. Petersen RC, Lopez O, Armstrong MJ, Getchius TSD, Ganguli M, Gloss D. **Practice guideline update summary: mild cognitive impairment report of the guideline development, dissemination, and implementation**. *Neurology.* (2018.0) **90** 126-135. DOI: 10.1212/WNL.0000000000004826
3. Scarmeas N, Anastasiou CA, Yannakoulia M. **Nutrition and prevention of cognitive impairment**. *Lancet Neurol* (2018.0) **17** 1006-1015. DOI: 10.1016/S1474-4422(18)30338-7
4. Johnson EC, Adams WM. **Water intake, body water regulation and health**. *Nutrients.* (2020.0) **12** 702. DOI: 10.3390/nu12030702
5. Lorenzo I, Serra-Prat M, Carlos YJ. **The role of water homeostasis in muscle function and frailty: a review**. *Nutrients.* (2019.0) **11** 1857-1872. DOI: 10.3390/nu11081857
6. Jéquier E, Constant F. **Water as an essential nutrient: the physiological basis of hydration**. *Eur J Clin Nutr* (2010.0) **64** 115-123. DOI: 10.1038/ejcn.2009.111
7. Kleiner SM. **Water**. *J Am Diet Assoc* (1999.0) **99** 200-206. DOI: 10.1016/S0002-8223(99)00048-6
8. Armstrong L, Johnson E. **Water intake, water balance, and the elusive daily water requirement**. *Nutrients.* (2018.0) **10** 1928. DOI: 10.3390/nu10121928
9. Nissensohn M, Sánchez-Villegas A, Ortega RM, Aranceta-Bartrina J, Gil Á, González-Gross M. **Beverage consumption habits and association with total water and energy intakes in the Spanish population: findings of the ANIBES study**. *Nutrients.* (2016.0) **8** 232-250. DOI: 10.3390/nu8040232
10. Ferreira-Pêgo C, Nissensohn M, Kavouras SA, Babio N, Serra-Majem L, Águila AM. **Relative validity and repeatability in a Spanish population with metabolic syndrome from the PREDIMED-PLUS study**. *Nutrients.* (2016.0) **8** 457-470. DOI: 10.3390/nu8080475
11. Nissensohn M, Sánchez-Villegas A, Galan P, Turrini A, Arnault N, Mistura L. **Beverage consumption habits among the European population: association with total water and energy intakes**. *Nutrients.* (2017.0) **9** 383-396. DOI: 10.3390/nu9040383
12. Rolls BJ, Phillips PA. **Aging and disturbances of thirst and fluid balance**. *Nutr Rev* (2009.0) **48** 137-144. DOI: 10.1111/j.1753-4887.1990.tb02915.x
13. Kenney WL, Tankersley CG, Newswanger DL, Hyde DE, Puhl SM, Turner NL. **Age and hypohydration independently influence the peripheral vascular response to heat stress**. *J Appl Physiol* (1990.0) **68** 1902-1908. DOI: 10.1152/jappl.1990.68.5.1902
14. Begg DP. **Disturbances of thirst and fluid balance associated with aging**. *Physiol Behav* (2017.0) **178** 28-34. DOI: 10.1016/j.physbeh.2017.03.003
15. Hooper L, Bunn D, Jimoh FO, Fairweather-Tait SJ. **Water-loss dehydration and aging**. *Mech Ageing Dev* (2014.0) **136–137** 50-58. DOI: 10.1016/j.mad.2013.11.009
16. Lieberman HR. **Hydration and cognition: a critical review and recommendations for future research**. *J Am Coll Nutr* (2007.0) **26** 555S-561S. DOI: 10.1080/07315724.2007.10719658
17. Murray B. **Hydration and physical performance**. *J Am Coll Nutr* (2007.0) **26** 542S-548S. DOI: 10.1080/07315724.2007.10719656
18. Alfredo Martínez Hernández J, Cámara Hurtado M, Maria Giner Pons R, González Fandos E, López García E. **Informe del Comité Científico de la Agencia Española de Seguridad Alimentaria y Nutrición (AESAN) de revisión y actualización de las Recomen-daciones Dietéticas Para la población española**. *Revista del Comité Científico de la AESAN* (2020.0) **32** 11-58
19. 19.U.S. Department of Agriculture, U.S. Department of Health and Human ServicesDietary guidelines for Americans, 2020-202520209. *Dietary guidelines for Americans, 2020-2025* (2020.0)
20. **Scientific opinion on dietary reference values for water**. *EFSA J* (2010.0) **8** 1459-1507
21. 21.Institute of MedicineDietary reference intakes for water, potassium, sodium, chloride, and sulfate2005Washington, D.C.National Academies Press. *Dietary reference intakes for water, potassium, sodium, chloride, and sulfate* (2005.0)
22. Gandy J. **Water intake: validity of population assessment and recommendations**. *Eur J Nutr* (2015.0) **54 Suppl 2** S11-S16. DOI: 10.1007/s00394-015-0944-8
23. 23.Risk reduction of cognitive decline and dementia: WHO guidelines. Geneva: World Health Organization; 2019. Licence: CC BY-NC-SA 3.0 IGO.
24. Volkert D, Chourdakis M, Faxen-Irving G, Frühwald T, Landi F, Suominen MH. **ESPEN guidelines on nutrition in dementia**. *Clin Nutr* (2015.0) **34** 1052-1073. DOI: 10.1016/j.clnu.2015.09.004
25. 25.National Institute for Health and Care Excellence (NICE)Dementia, disability and frailty in later life-mid-life approaches to delay or prevent onset. NICE guideline2015156. *Dementia, disability and frailty in later life-mid-life approaches to delay or prevent onset. NICE guideline* (2015.0) 1-56
26. Armstrong-Esther CA, Browne KD, Armstrong-Esther DC, Sander L. **The institutionalized elderly: dry to the bone!**. *Int J Nurs Stud* (1996.0) **33** 619-628. DOI: 10.1016/S0020-7489(96)00023-5
27. Marra MV, Simmons SF, Shotwell MS, Hudson A, Hollingsworth EK, Long E. **Elevated serum osmolality and total water deficit indicate impaired hydration status in residents of long-term care facilities regardless of low or high body mass index**. *J Acad Nutr Diet* (2016.0) **116** 828-836. DOI: 10.1016/j.jand.2015.12.011
28. Hooper L, Bunn DK, Downing A, Jimoh FO, Groves J, Free C. **Which frail older people are dehydrated? The UK DRIE study**. *J Gerontol Ser A Biol Sci Med Sci* (2016.0) **71** 1341-1347. DOI: 10.1093/gerona/glv205
29. Lauriola M, Mangiacotti A, D’Onofrio G, Cascavilla L, Paris F, Paroni G. **Neurocognitive disorders and dehydration in older patients: clinical experience supports the hydromolecular hypothesis of dementia**. *Nutrients.* (2018.0) **10** 562-576. DOI: 10.3390/nu10050562
30. Majdi M, Hosseini F, Naghshi S, Djafarian K, Shab-Bidar S. **Total and drinking water intake and risk of all-cause and cardiovascular mortality: a systematic review and dose-response meta-analysis of prospective cohort studies**. *Int J Clin Pract* (2021.0) **75** e14878. DOI: 10.1111/ijcp.14878
31. Bialecka-Dębek A, Pietruszka B. **The association between hydration status and cognitive function among free-living elderly volunteers**. *Aging Clin Exp Res* (2019.0) **31** 695-703. DOI: 10.1007/s40520-018-1019-5
32. Suhr JA, Patterson SM, Austin AW, Heffner KL. **The relation of hydration status to declarative memory and working memory in older adults**. *J Nutr Health Aging* (2010.0) **14** 840-843. DOI: 10.1007/s12603-010-0108-8
33. Suhr JA, Hall J, Patterson SM, Niinistö RT. **The relation of hydration status to cognitive performance in healthy older adults**. *Int J Psychophysiol* (2004.0) **53** 121-125. DOI: 10.1016/j.ijpsycho.2004.03.003
34. Bethancourt HJ, Kenney WL, Almeida DM, Rosinger AY. **Cognitive performance in relation to hydration status and water intake among older adults, NHANES 2011–2014**. *Eur J Nutr* (2020.0) **59** 3133-3148. DOI: 10.1007/s00394-019-02152-9
35. Martínez-González MA, Buil-Cosiales P, Corella D, Bulló M, Fitó M, Vioque J. **Cohort profile: design and methods of the PREDIMED-plus randomized trial**. *Int J Epidemiol* (2019.0) **48** 387-388o. DOI: 10.1093/ije/dyy225
36. Salas-Salvadó J, Díaz-López A, Ruiz-Canela M, Basora J, Fitó M, Corella D. **Effect of a lifestyle intervention program with energy-restricted Mediterranean diet and exercise on weight loss and cardiovascular risk factors: one-year results of the PREDIMED-plus trial**. *Diabetes Care* (2019.0) **42** 777-788. DOI: 10.2337/dc18-0836
37. Willett W. *Nutritional epidemiology, 3rd edn. Chapter 11* (2013.0) 306
38. Fernández-Ballart JD, Piñol JL, Zazpe I, Corella D, Carrasco P, Toledo E. **Relative validity of a semi-quantitative food-frequency questionnaire in an elderly Mediterranean population of Spain**. *Br J Nutr* (2010.0) **103** 1808-1816. DOI: 10.1017/S0007114509993837
39. Palma I, Farra A, Cantós D. *Tablas de composición de alimentos por medidas caseras de consumo habitual en España* (2008.0)
40. 40.RedBEDCA, AESANBase de Datos Española de Composición de Alimentos2006. *Base de Datos Española de Composición de Alimentos* (2006.0)
41. Aranceta Bartrina J, Arija Val V, Maíz Aldalur E, Martínez de la Victoria Muñoz E, Ortega Anta RM. **Dietary guidelines for the Spanish population (SENC, December 2016); the new graphic icon of healthy nutrition**. *Nutr Hosp* (2016.0) **33** 1-48
42. 42.Moreiras O, Carbajal A, Cabrera L, Cuadrado C. Tablas de composición de alimentos Guía de prácticas [Food Composition Tables. Practice Guide]. 19th ed. Madrid: Piramide; 2018. p. 496.
43. 43.Mataix Verdú J, García Diz L, Mañas Almendros M, Martinez de Vitoria E, Llopis González J. Tablas de composición de alimentos [Food composition tables]. 5th ed. Granada; Universidad de Granada; 2013.
44. 44.Hooper L, Abdelhamid A, Attreed NJ, Campbell WW, Channell AM, Chassagne P, et al. Clinical symptoms, signs and tests for identification of impending and current water-loss dehydration in older people. Cochrane Database Syst Rev. 2015. 10.1002/14651858.CD009647.pub2.
45. Khajuria A, Krahn J. **Osmolality revisited - deriving and validating the best formula for calculated osmolality**. *Clin Biochem* (2005.0) **38** 514-519. DOI: 10.1016/j.clinbiochem.2005.03.001
46. Hooper L, Bunn DK, Abdelhamid A, Gillings R, Jennings A, Maas K. **Water-loss (intracellular) dehydration assessed using urinary tests: how well do they work? Diagnostic accuracy in older people**. *Am J Clin Nutr* (2016.0) **104** 121-131. DOI: 10.3945/ajcn.115.119925
47. Blesa R, Pujol M, Aguilar M, Santacruz P, Bertran-Serra I, Hernández G. **Clinical validity of the “mini-mental state” for Spanish speaking communities**. *Neuropsychologia.* (2001.0) **39** 1150-1157. DOI: 10.1016/S0028-3932(01)00055-0
48. Folstein MF, Folstein SE, McHugh PR. **“Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician**. *J Psychiatr Res* (1975.0) **12** 189-198. DOI: 10.1016/0022-3956(75)90026-6
49. Benton AL, Hamsher K, Sivan AB. *Multilingual aphasia examination* (1994.0)
50. Wechsler D. *Wechsler adult intelligence scale-III* (1997.0)
51. Rossi L, Neer C-R, Lopetegui S. *Escala de inteligencia Para adultos de WECHSLER. WAIS-III Índice de comprensión verbal. Normas Para los subtests: Vocabulario, analogías e información, Para la ciudad de La Plata Edades: 16 a 24 Años* (2008.0) 223-236
52. Aprahamian I, Martinelli JE, Neri AL, Yassuda MS. **O Teste do Desenho do Relógio: Revisão da acurácia no rastreamento de demência**. *Dementia e Neuropsychologia* (2009.0) **3** 74-80. DOI: 10.1590/S1980-57642009DN30200002
53. Paganini-Hill A, Clark LJ. **Longitudinal assessment of cognitive function by clock drawing in older adults**. *Dement Geriatr Cogn Dis Extra* (2011.0) **1** 75-83. DOI: 10.1159/000326781
54. Del Ser QT, García De Yébenes MJ, Sánchez Sánchez F, Frades Payo B, Rodríguez Laso Á, Bartolomé Martínez MP. **Evaluación cognitiva del anciano. Datos normativos de una muestra poblacional española de más de 70 años**. *Med Clin (Barc)* (2004.0) **122** 727-740. DOI: 10.1016/S0025-7753(04)74369-9
55. Llinàs-Reglà J, Vilalta-Franch J, López-Pousa S, Calvó-Perxas L, Torrents Rodas D, Garre-Olmo J. **The trail making test: association with other neuropsychological measures and normative values for adults aged 55 years and older from a Spanish-speaking population-based sample**. *Assessment.* (2017.0) **24** 183-196. DOI: 10.1177/1073191115602552
56. Brouwer-Brolsma EM, Dhonukshe-Rutten RAM, van Wijngaarden JP, van de Zwaluw NL, In’t Veld PH, Wins S. **Cognitive performance: a cross-sectional study on serum vitamin D and its interplay with glucose homeostasis in Dutch older adults**. *J Am Med Dir Assoc* (2015.0) **16** 621-627. DOI: 10.1016/j.jamda.2015.02.013
57. Paz-Graniel I, Babio N, Becerra-Tomás N, Toledo E, Camacho-Barcia L, Corella D. **Association between coffee consumption and total dietary caffeine intake with cognitive functioning: cross-sectional assessment in an elderly Mediterranean population**. *Eur J Nutr* (2021.0) **60** 2381-2396. DOI: 10.1007/s00394-020-02415-w
58. Molina L, Sarmiento M, Peñafiel J, Donaire D, Garcia-Aymerich J, Gomez M. **Validation of the REGICOR short physical activity questionnaire for the adult population**. *PLoS One* (2017.0) **12** e0168148. DOI: 10.1371/journal.pone.0168148
59. Paz-Graniel I, Babio N, Serra-Majem L, Vioque J, Zomeño MD, Corella D. **Fluid and total water intake in a senior Mediterranean population at high cardiovascular risk: demographic and lifestyle determinants in the PREDIMED-plus study**. *Eur J Nutr* (2020.0) **59** 1595-1606. DOI: 10.1007/s00394-019-02015-3
60. Drewnowski A, Rehm CD, Constant F. **Water and beverage consumption among adults in the United States: cross-sectional study using data from NHANES 2005–2010**. *BMC Public Health* (2013.0) **13** 1068. DOI: 10.1186/1471-2458-13-1068
61. Polhuis KCMM, Wijnen AHC, Sierksma A, Calame W, Tieland M. **The diuretic action of weak and strong alcoholic beverages in elderly men: a randomized diet-controlled crossover trial**. *Nutrients.* (2017.0) **9** 660-673. DOI: 10.3390/nu9070660
62. Xu W, Wang H, Wan Y, Tan C, Li J, Tan L. **Alcohol consumption and dementia risk: a dose–response meta-analysis of prospective studies**. *Eur J Epidemiol* (2017.0) **32** 31-42. DOI: 10.1007/s10654-017-0225-3
63. Zhang Y, Coca A, Casa DJ, Antonio J, Green JM, Bishop PA. **Caffeine and diuresis during rest and exercise: a meta-analysis**. *J Sci Med Sport* (2015.0) **18** 569-574. DOI: 10.1016/j.jsams.2014.07.017
64. van den Berg B, de Jong M, Woldorff MG, Lorist MM. **Caffeine boosts preparatory attention for reward-related stimulus information**. *J Cogn Neurosci* (2020.0) **33** 104-118. DOI: 10.1162/jocn_a_01630
65. Chen JQA, Scheltens P, Groot C, Ossenkoppele R. **Associations between caffeine consumption, cognitive decline, and dementia: a systematic review**. *J Alzheimers Dis* (2020.0) **78** 1519-1546. DOI: 10.3233/JAD-201069
66. Xu W, Tan CC, Zou JJ, Cao XP, Tan L. **Sleep problems and risk of all-cause cognitive decline or dementia: an updated systematic review and meta-analysis**. *J Neurol Neurosurg Psychiatry* (2020.0) **91** 236-244. DOI: 10.1136/jnnp-2019-321896
67. Rosinger AY, Lawman HG, Akinbami LJ, Ogden CL. **The role of obesity in the relation between total water intake and urine osmolality in US adults, 2009-2012**. *Am J Clin Nutr* (2016.0) **104** 1554-1561. DOI: 10.3945/ajcn.116.137414
68. You Y, Liu Z, Chen Y, Xu Y, Qin J, Guo S. **The prevalence of mild cognitive impairment in type 2 diabetes mellitus patients: a systematic review and meta-analysis**. *Acta Diabetol* (2021.0) **58** 671-685. DOI: 10.1007/s00592-020-01648-9
69. Iadecola C, Gottesman RF. **Neurovascular and cognitive dysfunction in hypertension: epidemiology, pathobiology, and treatment**. *Circ Res* (2019.0) **124** 1025-1044. DOI: 10.1161/CIRCRESAHA.118.313260
70. An Y, Zhang X, Wang Y, Wang Y, Liu W, Wang T. **Longitudinal and nonlinear relations of dietary and serum cholesterol in midlife with cognitive decline: results from EMCOA study**. *Mol Neurodegener* (2019.0) **14** 51-70. DOI: 10.1186/s13024-019-0353-1
71. John A, Patel U, Rusted J, Richards M, Gaysina D. **Affective problems and decline in cognitive state in older adults: a systematic review and meta-analysis**. *Psychol Med* (2019.0) **49** 353-365. DOI: 10.1017/S0033291718001137
72. Sanz J, Luis A, Carmelo P, Resumen V. **Adaptación española del Inventario Para la Depresión de Beck-II (BDI-II): 2. Propiedades psicométricas en población general the Spanish adaptation of Beck’s depression inventory-II (BDI-II): 2. Psychometric properties in the general population**. *Clínica y Salud* (2003.0) **14** 249-280
73. 73.National Institute of Diabetes and Digestive and Kidney Diseases. Estimating glomerular filtration rate. https://www.niddk.nih.gov/health-information/professionals/clinical-tools-patient-management/kidney-disease/laboratory-evaluation/glomerular-filtration-rate/estimating. Accessed 1 Jun 2022.
74. 74.Dementia Care Central, National Institute on Aging. Mini-Mental State Exam (MMSE) Alzheimer’s/Dementia Test: administration, accuracy and scoring. 2020. https://www.dementiacarecentral.com/mini-mental-state-exam/. Accessed 5 Jul 2021.
75. Masento NA, Golightly M, Field DT, Butler LT, van Reekum CM. **Effects of hydration status on cognitive performance and mood**. *Br J Nutr* (2014.0) **111** 1841-1852. DOI: 10.1017/S0007114513004455
76. Wittbrodt MT, Millard-Stafford M. **Dehydration impairs cognitive performance: a meta-analysis**. *Med Sci Sports Exerc* (2018.0) **50** 2360-2368. DOI: 10.1249/MSS.0000000000001682
77. Kanbay M, Yilmaz S, Dincer N, Ortiz A, Sag AA, Covic A. **Antidiuretic hormone and serum osmolarity physiology and related outcomes: what is old, what is new, and what is unknown?**. *J Clin Endocrinol Metab* (2019.0) **104** 5406-5420. DOI: 10.1210/jc.2019-01049
78. Insel TR. **Translating oxytocin neuroscience to the clinic: a National Institute of Mental Health perspective**. *Biol Psychiatry* (2016.0) **79** 153-154. DOI: 10.1016/j.biopsych.2015.02.002
79. Lu Q, Lai J, Du Y, Huang T, Prukpitikul P, Xu Y. **Sexual dimorphism of oxytocin and vasopressin in social cognition and behavior**. *Psychol Res Behav Manag* (2019.0) **12** 337-349. DOI: 10.2147/PRBM.S192951
80. Bluthe R, Gheusi G, Dantzer R. **Gonadal steroids influence the involvement of arginine vasopressin in social recognition in mice**. *Psychoneuroendocrinology.* (1993.0) **18** 323-335. DOI: 10.1016/0306-4530(93)90028-J
81. Kempton MJ, Ettinger U, Schmechtlg A, Winter EM, Smith L, McMorris T. **Effects of acute dehydration on brain morphology in healthy humans**. *Hum Brain Mapp* (2009.0) **30** 291-298. DOI: 10.1002/hbm.20500
82. Duning T, Kloska S, Steinstrater O, Kugel H, Heindel W, Knecht S. **Dehydration confounds the assessment of brain atrophy**. *Neurology.* (2005.0) **64** 548-550. DOI: 10.1212/01.WNL.0000150542.16969.CC
83. Trangmar S, Chiesa S, Kalsi K, Secher N, González-Alonso J. **Hydration and the human brain circulation and metabolism**. *Nutr Hosp* (2015.0) **1** 10261
84. Rasmussen P, Nybo L, Volianitis S, Møller K, Secher NH, Gjedde A. **Cerebral oxygenation is reduced during hyperthermic exercise in humans**. *Acta Physiol* (2010.0) **199** 63-70. DOI: 10.1111/j.1748-1716.2010.02084.x
85. Piil JF, Lundbye-Jensen J, Trangmar SJ, Nybo L. **Performance in complex motor tasks deteriorates in hyperthermic humans**. *Temperature.* (2017.0) **4** 420-428. DOI: 10.1080/23328940.2017.1368877
86. Ogoh S. **Relationship between cognitive function and regulation of cerebral blood flow**. *J Physiol Sci* (2017.0) **67** 345-351. DOI: 10.1007/s12576-017-0525-0
87. 87.Ogoh S, Tsukamoto H, Hirasawa A, Hasegawa H, Hirose N, Hashimoto T. The effect of changes in cerebral blood flow on cognitive function during exercise. Physiol Rep. 2014;2(9):e12163.
88. Claassen JAHR, Thijssen DHJ, Panerai RB, Faraci FM. **Regulation of cerebral blood flow in humans: physiology and clinical implications of autoregulation**. *Physiol Rev* (2021.0) **101** 1487-1559. DOI: 10.1152/physrev.00022.2020
89. Hooper L, Abdelhamid A, Ali A, Bunn DK, Jennings A, John WG. **Diagnostic accuracy of calculated serum osmolarity to predict dehydration in older people: adding value to pathology laboratory reports**. *BMJ Open* (2015.0) **5** e008846. DOI: 10.1136/bmjopen-2015-008846
90. Lacey J, Corbett J, Forni L, Hooper L, Hughes F, Minto G. **A multidisciplinary consensus on dehydration: definitions, diagnostic methods and clinical implications**. *Ann Med* (2019.0) **51** 232-251. DOI: 10.1080/07853890.2019.1628352
91. Siervo M, Bunn D, Prado CM, Hooper L. **Accuracy of prediction equations for serum osmolarity in frail older people with and without diabetes**. *Am J Clin Nutr* (2014.0) **100** 867-876. DOI: 10.3945/ajcn.114.086769
92. Chang T, Ravi N, Plegue MA, Sonneville KR, Davis MM. **Inadequate hydration, BMI, and obesity among US adults: NHANES 2009-2012**. *Ann Fam Med* (2016.0) **14** 320-324. DOI: 10.1370/afm.1951
|
---
title: Bone marrow stromal cells generate an osteoinductive microenvironment when
cultured on titanium–aluminum–vanadium substrates with biomimetic multiscale surface
roughness
authors:
- Michael B Berger
- D Joshua Cohen
- Kyla B Bosh
- Marina Kapitanov
- Paul J Slosar
- Michael M Levit
- Michelle Gallagher
- Jeremy J Rawlinson
- Zvi Schwartz
- Barbara D Boyan
journal: Biomedical Materials (Bristol, England)
year: 2023
pmcid: PMC9993812
doi: 10.1088/1748-605X/acbf15
license: CC BY 4.0
---
# Bone marrow stromal cells generate an osteoinductive microenvironment when cultured on titanium–aluminum–vanadium substrates with biomimetic multiscale surface roughness
## Abstract
Osseointegration of titanium-based implants possessing complex macroscale/microscale/mesoscale/nanoscale (multiscale) topographies support a direct and functional connection with native bone tissue by promoting recruitment, attachment and osteoblastic differentiation of bone marrow stromal cells (MSCs). Recent studies show that the MSCs on these surfaces produce factors, including bone morphogenetic protein 2 (BMP2) that can cause MSCs not on the surface to undergo osteoblast differentiation, suggesting they may produce an osteogenic environment in vivo. This study examined if soluble factors produced by MSCs in contact with titanium–aluminum–vanadium (Ti6Al4V) implants possessing a complex multiscale biomimetic topography are able to induce osteogenesis ectopically. Ti6Al4V disks were grit-blasted and acid-etched to create surfaces possessing macroscale and microscale roughness (MM), micro/meso/nanoscale topography (MN), and macro/micro/meso/nanoscale topography (MMNTM). Polyether-ether-ketone (PEEK) disks were also fabricated by machining to medical-grade specifications. Surface properties were assessed by scanning electron microscopy, contact angle, optical profilometry, and x-ray photoelectron spectroscopy. MSCs were cultured in growth media (GM). Proteins and local factors in their conditioned media (CM) were measured on days 4, 8, 10 and 14: osteocalcin, osteopontin, osteoprotegerin, BMP2, BMP4, and cytokines interleukins 6, 4 and 10 (IL6, IL4, and IL10). CM was collected from D14 MSCs on MMNTM and tissue culture polystyrene (TCPS) and lyophilized. Gel capsules containing active demineralized bone matrix (DBM), heat-inactivated DBM (iDBM), and iDBM + MMN-GM were implanted bilaterally in the gastrocnemius of athymic nude mice ($$n = 8$$ capsules/group). Controls included iDBM + GM; iDBM + TCPS-CM from D5 to D10 MSCs; iDBM + MMN-CM from D5 to D10; and iDBM + rhBMP2 (R&D Systems) at a concentration similar to D5–D10 production of MSCs on MMNTM surfaces. Legs were harvested at 35D. Bone formation was assessed by micro computed tomography and histomorphometry (hematoxylin and eosin staining) with the histology scored according to ASTM 2529–13. DNA was greatest on PEEK at all time points; DNA was lowest on MN at early time points, but increased with time. Cells on PEEK exhibited small changes in differentiation with reduced production of BMP2. Osteoblast differentiation was greatest on the MN and MMNTM, reflecting increased production of BMP2 and BMP4. Pro-regenerative cytokines IL4 and IL10 were increased on Ti-based surfaces; IL6 was reduced compared to PEEK. None of the media from TCPS cultures was osteoinductive. However, MMN-CM exhibited increased bone formation compared to iDBM and iDBM + rhBMP2. Furthermore, exogenous rhBMP2 alone, at the concentration found in MMN-CM collected from D5 to D10 cultures, failed to induce new bone, indicating that other factors in the CM play a critical role in that osteoinductive microenvironment. MSCs cultured on MMNTM Ti6Al4V surfaces differentiate and produce an increase in local factors, including BMP2, and the CM from these cultures can induce ectopic bone formation compared to control groups, indicating that the increased bone formation arises from the local response by MSCs to a biomimetic, multiscale surface topography.
## Introduction
Orthopedic and dental implant success is related to the extent of osseointegration around an implanted material. Osseointegration involves a complex biological cascade that occurs immediately after implant placement and encompasses a regenerative immune response to the implanted biomaterial resulting in cellular recruitment, differentiation, and activation of progenitor cells that secrete and mineralize organic matrix, anchoring the implant with native bone tissue [1, 2]. Currently, compromised bone architectures and dysregulation of osseointegration are leading causes of insufficient integration, implant loosening, and ultimate failure [3–7].
Titanium (Ti) and its alloys are often used for implants that interface with bone due to superior material properties, including a naturally occurring passivated oxide layer that prevents material corrosion and provides excellent wear resistance, robust mechanical properties, and tunable surface properties [8, 9]. Polyether-ether-ketone (PEEK) polymers have been investigated for use in bone because they have a higher mechanical modulus than other biocompatible polymers, ranging from 3.6 GPa to upwards of 18 GPa [8], yet lower than metals. Moreover, PEEK is radiolucent, which allows clinicians to view bone growth radiographically. However, PEEK possess dramatically different surface chemistries compared to titanium and its alloys. PEEK-based interbody fusion devices often require the use of recombinant human bone morphogenetic protein-2 (BMP2), particularly when iliac crest bone graft is not available to achieve sufficient fusion [8, 10, 11]. In addition to concerns related to the use of BMP2 and high material costs [12, 13], studies investigating the use of PEEK during osseointegration have observed biological responses that lead to fibrous encapsulation, implant migration, or non-unions [14–17].
Implants fabricated using Ti and its alloys (e.g. titanium–aluminum–vanadium [Ti6Al4V]) have improved biocompatibility compared to PEEK and their ability to support osseointegration has been further improved by modifying their surface topography through a variety of methods to control cellular response [18–20]. Biomimicry can be achieved by creating a surface topography similar to native bone after osteoclastic surface resorption. Surfaces possessing a biomimetic topography have been shown to increase osteoblast progenitor cell differentiation, maturation, and activity in vitro and increase implant retention rates in vivo. Analysis of these biomimetic surfaces has focused primarily on parameters such as: macroscale roughness, microscale roughness, mesoscale and nanoscale feature formation, surface wettability, and surface and bulk chemical properties [21–24].
Numerous studies have assessed the effects of a variety of nano-topographies on the responses of bone marrow stromal cells (MSCs) to implant surfaces [25–29], and have shown that osteoblastic differentiation is enhanced compared to unmodified surfaces. However, studies assessing the effects of surface parameters on the osteoblastic differentiation of human MSCs indicate that the best results are achieved when there is a multiscale arrangement that includes macro, micro, meso and nanoscale topography. In addition to promoting osteoblast differentiation, these surfaces also result in the production of factors than can control the responses of other cells through paracrine signaling [30–32]. Yet, not all surfaces that possess a complex multiscale topography elicit comparable responses. Studies have demonstrated that a combination of proprietary grit-blasting and acid-etching alter subtle implant surface properties, including kurtosis (pointedness or peakedness) and skewness (symmetry or asymmetry of distribution relative to a bell curve). While surfaces may demonstrate similar levels of average microroughness, in vitro models show that cells are able to differentiate between these topographic properties [33].
There is a considerable body of literature demonstrating that MSCs and normal human osteoblasts (NHOsts) will form multi-layered nodules when cultured on tissue culture polystyrene (TCPS) surfaces for 21 d, particularly when they are grown in a culture media containing supplements including dexamethasone and beta-calcium phosphate [34, 35]. In contrast, when these cells are cultured on a Ti6Al4V surface that has a multiscale surface topography that is similar to that of an osteoclast resorption pit, they exhibit osteoblast properties within 7 d, even when using growth media without these supplements [36, 37]. Not only do they produce proteins that are associated with well-differentiated osteoblasts such as osteocalcin (OCN), they also synthesize and secrete proteins that can act on other cells not on the surface, such as BMPs, vascular endothelial growth factor, and transforming growth factor beta-1 (TGFβ1) needed for osteogenesis [33, 38, 39].
These in vitro observations suggest that cells on the biomimetic surface can modulate the local factors in the surrounding environment that influence overall osteogenesis around an implant. This modulation can be further assessed with negative controls. For example, addition of anti-BMP2 antibody to the MSC cultures, as described above, blocks the surface effect on osteoblast differentiation, indicating that BMP2 acts in an autocrine manner. It also blocks production of factors associated with vasculogenesis and modulation of the immune mediators, indicating that the factors produced by MSCs cultured on Ti6Al4V disks with a multiscale biomimetic topography can induce osteoblast differentiation of MSCs on inserts above the surface via paracrine regulation and that BMP2 is one of the regulatory factors involved [40, 41].
These culture experiments strongly support the hypothesis that surface topography can promote osseointegration by inducing MSCs to generate an osteogenic or osteoinductive microenvironment. Testing that hypothesis, however, requires a model to assess bone formation, and in vitro models are limited in demonstrating mineralized tissue outcomes. To address this, we adapted an international standard that is recognized by the United States Food and Drug Administration for demonstrating osteoinduction by demineralized bone matrix (DBM) in vivo. We reasoned that biomimetic implants themselves are not osteoinductive by the standard definition that bone must form in a site that would otherwise not form bone, such as muscle [42, 43]. Instead, we focused on the microenvironment, represented by the factors produced by cells in response to surface chemistry and topography, and we tested those factors present in the culture media for osteoinductivity using the standard in vivo model. We assessed the conditioned media (CM) generated by MSCs cultured on three different multiscale Ti6Al4V surface topographies and machined PEEK for production of factors associated with osteogenesis, and then based on production of BMP2, we analyzed the in vivo osteoinduction ability of CM generated by MSCs cultured on a biomimetic surface with macroscale/microscale/mesoscale/nanoscale topography. CM from MSCs cultured on TCPS, media incubated on TCPS without cells, and rhBMP2 at the concentration present in the combined CM from MSCs on the multiscale biomimetic surface as well as active DBM and heat inactivated DBM (iDBM) were used as controls.
## Substrate preparation
PEEK discs were prepared from 15 mm diameter rods of PEEK bulk material and machined into 1.6 mm thick discs. Titanium–aluminum–vanadium (Ti6Al4V) discs were prepared by Medtronic (Minneapolis, MN) from 15 mm diameter rods of grade 23-alloyed Ti6Al4V machined into 1.6 mm thick disks. A portion of the Ti6Al4V surfaces underwent a grit blasting, chemical masking and dual acid etching process to generate macroscale peaks and valleys creating a patented macroscale/microscale rough Ti6Al4V surface (MM) [44]. A subset of the MM discs received an additional patented proprietary grit-blasting and acid-etching procedure [44–46], to create a complex surface texture at the macro/micro/nanoscale (MMNTM). The remaining portion of machined Ti6Al4V also underwent the proprietary grit-blasting and acid-etching procedure to generate a complex surface at the micro/meso/nanoscale (MN) [44, 45, 47]. All discs were cleaned, packaged, and gamma irradiated according to the manufacturer’s protocols.
## Scanning electron microscopy (SEM)
Surface topography was qualitatively assessed using SEM (Hitachi SU-70, Tokyo, Japan). Surfaces were secured on SEM imaging mounts by carbon tape and imaged with 32 μA ion current, 5 kV accelerating voltage and 4 mm working distance. PEEK samples were platinum sputter coated at 1 × 10−4 vacuum for 90 s before imaging. Surface homogeneity was determined by viewing multiple locations across the implant surface and three representative locations were imaged at magnifications of 35, 100, 1000, 2000, 10 000, 50 000, and 100 000X to ensure homogenous assessment, with at least two disks per group imaged.
## Laser confocal microscopy (LCM)
Surface microroughness was qualitatively assessed by LCM (Zeiss LSM 710). Single plane and Z-stacks were obtained with a Plan Apochromat 20×/0.8 M27 objective with a 5× optical zoom, using a 405 nm laser in reflection mode at $25\%$ power. Scan parameters were 0.79 μs pixel dwell, 0.40 μm pixel size, 600.9 μm pinhole, 202.20 × 202.20 μm image size, and step size of 1 μm. No digital gain was used. Digital bandpass filtering was used: Gaussian 1st order filter with thresholds at 2 μm and 75 μm. Average surface roughness (Sa) was defined as the average absolute distance in the z-plane; peak-to-valley distant (Sz) was defined as arithmetic average peak-to-valley height of 25 uniform areas within each field of view calculated within the topography module using ZEN software (Zeiss [48]) and shown as the mean and standard deviation (SD) for $$n = 10$$ samples from two discs per group.
## Sessile drop contact angle
Surface energy was determined by sessile drop test using a goniometer (CAM 250, Ramé-Hart). Samples ($$n = 2$$) were measured in five different locations and dried with nitrogen between measurements. The 3 µl of ultrapure water was used per drop measurement, and angle measurements were taken every 5 s for a total of 15 s. Those four measurements were then averaged to produce 1 of the 5 total measurements per disc. Measurements are shown as mean ± SD of ten samples per group.
## X-ray photoelectron spectroscopy (XPS)
XPS was used to analyze surface chemistry (PHI VersaProbe III Scanning XPS, Physical Electronics Inc., Chanhassen, MN). Copper clips and instrument mount were sonicated in ethanol for 10 min prior to securing samples. Analysis was conducted using a 25 watt, 50 kV x-ray gun with a spot size of 200 µm, 20 ms dwelling time and 1 eV step size. Survey scan was taken at 280 000 eV scan resolution, region scans were taken at 55 000 eV for six elements: oxygen, carbon, titanium, aluminum, vanadium, and nitrogen. Five locations were analyzed per discs with two discs per group. Measurements are shown as mean ± SD of ten samples per group.
## X-ray diffraction spectroscopy (XRD)
XRD was used to analyze bulk chemistry (PANalytical MPD X-‘Pert Pro, Panalytical, Almelo, Netherlands). Samples were placed in the center of the three-dimensional analysis mount. Analysis was conducted using a 40 A, 45 kV x-ray gun from 30° to 90°, 18.87 ms dwelling time per step and 0.0001 degrees per step. One central location was analyzed per disc with two discs per group.
## Cell culture
Human female MSCs obtained after informed consent and isolated from adult bone marrow (Donor #8011L, Texas A&M Institute for Regenerative Medicine, College Station, TX) were cultured in MSC growth medium (GM) comprised of αMEM with 4 µM L-glutamine and $16.5\%$ fetal bovine serum at 37 °C in $5\%$ CO2 and $100\%$ humidity and cultured to confluence in T75 flasks (Corning Inc., Oneonta, NY) before plating on the surfaces. For biological analysis, all four groups of surfaces were placed in a 24-well plate (Corning Inc.), and cells were plated at a density of 20 000 cells ml−1 at 0.5 ml per well. MSCs cultured on TCPS served as optical cell culture controls. Twenty-four hour after plating, GM were changed with subsequent media changes every 48 h after that for three, seven, nine and thirteen days. At each designated time point, cells were incubated for 24 h with fresh GM before harvest. Thus, cells designated as day 3 and day 7 were harvested on day 4 and 8. Cells designated as day 9 and 13 were harvested on day 10 and 14. Thus capturing the cell behavior for the full 24 h of the day of interest. These media were then defined as CM to indicate changes in the local environmental factors produced by the cells in the culture. CM were then collected from surfaces and stored at −80 °C, and MSCs were rinsed twice with 1× poshphate buffered saline (PBS), and placed in 0.5 ml of Triton-X100 and stored at −80 °C for biological assays.
## Factor production
Cell layers were lysed by ultrasonication at 40 V for 15 s well−1 (VCX 130; Vibra-Cell, Newtown, CT). The QuantiFluor* dsDNA system (Promega, Madison, WI) was used to determine total DNA content by fluorescence. Enzyme-linked immunosorbent assays were used to determine the levels of osteogenic and immunogenic factors in the CM. OCN (Thermo Fisher), osteoprotegerin (OPG; R&D Systems, Inc.), osteopontin (OPN; R&D Systems, Inc.), BMP2 and BMP4 (R&D Systems, Inc.), and interleukins 4, 6 and 10 (IL4, IL6, IL10; R&D Systems, Inc.) were quantified according to the manufacturer’s protocol.
## Osteoinduction by local factors produced by MSCs in response to surface properties
All in vivo studies were carried out after approval from Virginia Commonwealth University’s Institutional Animal Care and Use Committee according to ARRIVE guidelines under protocol number AD10000675. Surgeries were conducted in two phases. Twenty-nine Crl:NU(NCr)-Foxn1nu mice, 8–9 weeks old, were purchased from Charles River (Wilmington, MA), 16 for the first phase of the study and 13 for the second phase. In vivo studies were conducted with an $$n = 8$$ implants per group based on a power analysis using an alpha of 0.05 and a power of $80\%$ (delta = 5, sigma = 3, $m = 1$) to reveal a minimum of $$n = 7$$ per group for the study to yield statistical significance, 1 extra animal was included to ensure power if an animal reached a humane endpoint. Bilateral implants were placed, so the number of mice per experimental group was four to reduce the number of animals necessary for this study.
Phase 1 was performed to establish the system. We first assessed whether the in vivo assay was sensitive to DBM, which is a known osteoinduction agent (gift of LifeNet Health, Virginia Beach, VA). We also examined the osteoinduction ability of heat iDBM; lyophilized growth media, which included $10\%$ fetal bovine serum (FBS) (GM); and lyophilized CM from MSCs cultured for 14 d on TCPS in GM. These media and cell controls were included to determine if agents present in the growth media, either as a function of the $10\%$ FBS or as a result of the growth of MSCs on a non-Ti6Al4V surface, resulted in detectable bone formation in the mouse muscle pouch model. MSCs were cultured on TCPS at a seeding density of 10 000 cells cm−2 and GM were changed every 48 h. At day 14 [49–51], GM were changed and after 24 h, the now-CM were collected and stored at −80 °C. Growth media were incubated on TCPS without MSCs for 24 h to create the growth media control group.
In Phase 2, we tested the hypothesis that CM generated by MSCs on MMNTM surfaces would support osteoinduction. Implant groups consisted of: [1] 20 mg iDBM, [2] 20 mg iDBM + 10ng of aqueous rhBMP2 (R&D Systems) added at the time of surgery in a concentration similar to the averaged in vitro production over 5 d of culture of MSCs on MMNTM surfaces (MMN-CM concentration) [40, 52], and [3] 20 mg iDBM + 10 mg of lyophilized media from MSCs cultured on MMNTM surfaces from day 5 through day 10 (D5–D10). For the rhBMP2 implant group, 10 µl PBSl containing 10 ng of rhBMP2 was added in the gel capsule and re-capped by the surgeon before placement into the muscle pouch.
## Preparation of implants
Based on the in vitro study comparing the response of MSCs to the three Ti6Al4V surfaces, we selected CM from cultures grown on MMNTM substrates to assess osteoinductivity of CM generated by MSCs undergoing surface mediated differentiation in response to complex surface topographies. BMP2 production was elevated on all Ti6Al4V substrates on days 7, 10 and 14 compared to PEEK. We selected the MMNTM surface for the in vivo assessment in part because BMP2 was highest in the day 14 MSCs cultured on MMN, but also because the pro-regenerative cytokines IL4 and IL10 were elevated whereas the pro-inflammatory IL6 was reduced.
Accordingly, MSCs were cultured on MMNTM and TCPS at a seeding density of 10 000 cells cm−2. After plating, GM were changed at 24 h, and then every 48 h. Beginning at day 5, GM were changed daily through day 10 and each day’s CM were collected and stored at −80 °C. Media were pooled into a 50 ml conical tube and frozen at −80 °C prior to lyophilization. To lyophilize the media, the frozen media were uncapped, tightly covered with parafilm, and an 18-gauge needle was used to create holes in the layer in order to allow the evaporating liquid to escape the tube. The lyophilized proteins were then homogenized using weighing spatulas within a biological safety cabinet. Samples were measured and 10 mg of each group’s lyophilized powder was and placed into a UV sterilized size 5 gel capsule (Size 5, Torpac, Fairfield, NJ). Briefly, larger diameter halves of sterilized UV gel capsules were placed vertically in sterilized PCR tube holders and 20 mg of aDBM or iDBM was loaded into each half gel capsule massed by microbalance. All capsules were then capped with the smaller diameter half. All gel capsules and holders were sterilized again by UV light for 24 h in the biological safety cabinet ($$n = 8$$ implants/group). All groups were implanted on the day of surgery as described below in the mouse surgery section.
## Bilateral implantation of gel capsules into the muscle pouch of the gastrocnemius
Mice were acclimated for 5 d prior to surgery. Implants were randomized and implanted bilaterally into the hind limbs for $$n = 2$$ two per mouse. Each group consisted of $$n = 8$$ implants/group.
Anesthesia was induced with $4\%$ isoflurane in 400 ml min−1 of oxygen until no response to toe pinch was observed. The surgical site was then prepared bilaterally using alternating washes of chlorhexidine and $70\%$ isopropanol, repeated three times. Animals were then injected with 1 mg kg−1 of Sustained Release Buprenorphine LAB (Zoopharm, Laramie, Colorado) to provide 72 h of post-operative analgesia. Animals were transported to the procedure table and anesthesia was maintained with $2\%$ isoflurane in 400 ml min−1 of oxygen, adjusting as necessary. A 1 cm longitudinal incision was made in the skin overlying the gastrocnemius muscle, exposing the muscle. A muscle pocket was formed between the gastrocnemius and the biceps femoris using blunt dissection. Care was used to gently dissect in order to maintain a bloodless field. A sterile, size 5 gelatin capsule loaded based on the group designation was inserted into this muscle pocket. The muscles and skin were then reapproximated and closed with 7 mm wound clips to avoid suture-related calcification. The same procedure was then conducted on the contralateral side. From induction to post-operative recovery, animals were kept euthermic by placing them on a water-circulated heating pad. Animals were closely observed the first 72 h following surgery and subsequently observed and weighed weekly. Wound clips were removed 14 d postoperatively. Thirty-five days post-operatively animals were killed via CO2 inhalation and the hindlimbs were harvested and stored in $10\%$ buffered formalin [43, 53].
## Micro-CT analysis
Bone formation was assessed quantitatively by micro-CT using Dataviewer and Skyscan for scanning, imaging, and reconstructing to visualize and evaluate the new bone formation in the muscle pockets of the hind limbs. The legs were scanned in the 15 ml Eppendorf tubes packed with gauze above and below the leg to secure the sample in place. The tube was positioned so the long axis of the leg was perpendicular to the axis of the x-ray beam. All samples were calibrated under identical parameters at 50 kV and 150 µA, with 320 ms exposure time, at 17.9 µm magnified pixel size.
The micro-CT images were used to quantify new bone formation. The region of interest for bone quantification did not include the femur, tibia, or fibula. The mineralized bone tissue was differentiated from non-mineralized tissues using a fixed threshold of [70, 255]. To determine the total bone volume of the new bone formation, the programs CTAn and CTvox (Bruker) were used to trace an area of interest by drawing a circular contour area per slice and quantifying the areas that remained white at the given threshold. With these areas summed over all the relevant slices, the bone volume was measured in mm3.
## Histological processing
Whole muscle was processed by Histion (Everett, WA) as described previously [53]. Briefly, samples were localized as needed using radiographic imaging on site. Samples were then decalcified in formic acid and subsequently embedded using a formalin-fixed paraffin-embedded tissue processing method. Samples were stained by hematoxylin and eosin (H&E) to assess the implant and muscle histology. One section was taken from the approximate center of the implant mass or as closely as possible to a trans-axial plane through the femur. Images of each sample were taken at 10X magnification using a light microscope (Carl Zeiss Meditec AG, Jena, Germany). Images were stitched and the lighting normalized across all stitched images boxes using Zen Pro.
## Histomorphometric analysis
Stitched images were used for histomorphometric tracing of demineralized bone chips and live bone analysis by a trained researcher using a bamboo drawing pen and image-J (NIH) to quantify each area, as described previously [53]. All areas of demineralized bone tissue were summated, and areas of live bone were summated, and percent live bone were calculated.
## Histological ranking
The histology images were ranked according to a scoring system adapted from ASTM2529-13 on a scale of 0 through 4 (standard guide for in vivo evaluation of osteoinductive potential for materials containing demineralized bone). Scores were determined by the percent new bone formation compared to total mineral tissue in the explants scored by histomorphometry. Samples were scored with 0 if no bone formation was calculated, 1 if there were less than $10\%$ new bone-forming elements, 2 if there were between $10\%$ and $20\%$ bone-forming elements, 3 if there were between $21\%$ and $30\%$ new bone-forming elements, and 4 if greater than $30\%$ bone-forming elements were present based on the histomorphometry analysis [43].
## Statistical analysis
Data are means ± standard deviation for material characterization including optical profilometry, contact angle assessment, and XPS described in the methods. Data are means ± standard error mean of six independent cultures/variable for in vitro; in vitro cell experiments were repeated to ensure the validity of the results. Statistical analysis among groups at each time point was performed by one-way analysis of variance (ANOVA) and multiple comparisons between the groups were conducted with a two-tailed Tukey correction. Unpaired t-tests were used to compare analyses with only two groups and ANOVA for analyses with groups of 3 or more. Grubb’s outlier test was used to identify outliers. A p-value of less than 0.05 was considered statistically significant. All statistical analysis was performed with GraphPad Prism version 5.04.
## Evaluation of substrates
SEM of PEEK samples showed circular ridge formation during disc machining and increased magnification showed these micro ridges possessed microscale and submicron structure due to the machining processes. The end result was uneven gash and tear structures in concentric patterns (figures 1(A)–(C)). Grit-blasting and acid-etching the Ti6Al4V disks generated complex multiscale surface topographies at macro, micro, and nanoscale. MM and MMNTM substrates possessed similar macroscale topography (figures 1(D) and (J)) with bubble-like morphologies seen at the macroscale. MN substrates possessed a similar texture at the macroscale imaging as the MMNTM surface but lacked the circular cavities created by dual acid etching (figure 1(G)). These surface topographies at the microscale were comprised of smaller ridges and structures. However, MM substrates possessed only slight microstructures with heterogeneous topographies different from MN and MMNTM surfaces across the surface (figure 1(E)). MMNTM and MN possessed similar micron scale morphologies (figures 1(H) and (K)). All groups had dissimilar nanoscale topography, but MMNTM and MN had similar micro-/nano-scale ridge formations (figures 1(F), (I) and (L)) dispersed across the substrate surfaces.
**Figure 1.:** *Scanning electron microscopy of PEEK and Ti6Al4V substrates possessing complex topography at the macro, micro, and meso/nanoscale. (LEFT) Macroscale SEM images captured at 35X. (MIDDLE) Microscale SEM images captured at 10 000X. (RIGHT) Nanoscale images were taken at 100 000X. Group names are: poly-ether-ether-ketone (PEEK), macro-/micro-rough (MM), micro-/nano-rough (MN), and macro-/micro-/nano-rough (MMNTM) Ti6Al4V surfaces. White arrows, microscale roughness generated by 2nd step acid-etching. Black arrows, multiscale micro-/nano-ridge structures generated by 2nd acid-etching step.*
Reflective optical profilometry showed MM substrates had increased average microroughness compared to PEEK and processing to generate nanostructure formation further increased average microroughness on MN and MMNTM substrates in a macroscale topography dependent manner (figure 2(A)). MMNTM was significantly higher than MM and PEEK peak-to-valley height; MMNTM and MN substrates were similar, and MN and MM substrates were also similar. MN was significantly higher than PEEK where MM and PEEK did not have differences in peak-to-valley height (figure 2(B)).
**Figure 2.:** *(A) Average areal microroughness and (B) average peak to valley distance for the 4 substrates using optical profilometry. (C) Quantification of surface wettability by sessile drop contact angle measurement. Groups not sharing letters are significantly different at p < 0.05. Groups not sharing letters are significantly different at p < 0.05 (D) semi-quantitative x-ray photoelectron spectroscopy analysis of surface elemental composition, and (E) x-ray diffraction profiles of the surface and bulk chemistry of the Ti6Al4V substrates. Tr is trace amounts of each element were detected by XPS of bulk structure and circles are titanium (Ti) whereas squares are predicted to be titania (TiO2) based on elemental reference standards for EDX [88]. Group names are poly-ether-ether-ketone (PEEK), macro-/micro-rough (MM), micro-/nano-rough (MN), and macro-/micro-/nano-rough (MMNTM) Ti6Al4V surfaces.*
Surface wettability analysis showed PEEK substrates were less wettable compared to MM Ti6Al4V substrates. Nanostructure formation of the Ti6Al4V substrates also reduced the wettability of MN and MMNTM substrates to the same level found on PEEK (figure 2(C)). All substrates demonstrated intermediate wettability independent of surface processing. Surface chemistry was characterized by XPS. XPS analysis showed carbon and oxygen content matching the chemical formula for PEEK polymer. All Ti6Al4V groups had similar concentrations of oxygen, carbon, titanium, aluminum, and vanadium (figure 2(D)). XRD profiles were then taken for the Ti6Al4V substrates. Profiles for each group (MM, MN, and MMNTM) showed similar peak location in the bulk of the titanium alloy, MMNTM substrates contained an additional peak 37°, which is most likely TiO2 (figure 2(E)).
## Cellular response to surface texture
Total DNA content at day 3 showed less MSC DNA on MN surfaces. At day 7, all Ti6Al4V groups had decreased total DNA content compared to PEEK, and MN was further significantly decreased. However, by day 10, Ti6Al4V substrates possessing macroscale roughness (MM and MMNTM) had the same total DNA content as MN and all Ti6Al4V groups were less than PEEK. At day 14, PEEK had the highest DNA content, followed by MN, and the lowest total DNA content was on microroughened surfaces, MM and MMNTM (figure 3(A)).
**Figure 3.:** *Cellular response to PEEK and Ti6Al4V surfaces. (A) Total DNA content was determined in the cell layer lysate. (B) Osteocalcin, (C) osteopontin, (D) bone morphogenetic protein 2 and (E) BMP4, (F) osteoprotegerin were determined in the conditioned media at days 4 (designated D3), 8 (designated D7), 10 and 14. Groups not sharing letters are significantly different at p < 0.05 within each time point between surfaces. Group names are poly-ether-ether-ketone (PEEK), macro/micro-rough (MM), micro/meso/nano-rough (MN), and macro/micro/meso/nano-rough (MMNTM) Ti6Al4V surfaces.*
OCN and OPN were determined in the CM to assess osteoblastic differentiation of MSCs. OCN increased temporally on Ti6Al4V substrates. At days 3 and 7 MSCs produced low amounts of OCN but it was increased on MN (figure 3(B)). OCN was increased on the Ti6Al4V surfaces at day 10; the nano-modified groups produced the highest concentration of OCN and MM groups produced the same as MMNTM. MMNTM produced the most OCN at day 14 and MM and MN substrates produced similar levels (figure 3(B)). OPN was increased on Ti6Al4V substrates at day 3 and 7, compared to PEEK and surface morphology did not change OPN production. MN was increased compared to PEEK at day 10, while MM and MMNTM were not. At day 14, MSCs produced more OPN on MMNTM surfaces compared to MM and MN, and MM substrate MSCs produced more OPN than PEEK (figure 3(C)).
Paracrine signaling factors BMP2, BMP4, and OPG were measured in the CM to evaluate how MSCs were altering the microenvironment at the surface. BMP2 was increased at day 3 on MN, compared to MM and MMNTM, and MM was increased compared to PEEK. However, all Ti6Al4V groups were increased compared to PEEK at day 7 and day 10. MMNTM produced the greatest amount of BMP2 on day 14 and MM and MMNTM were similar, while MN and PEEK were similar (figure 3(D)). BMP4 responded similarly; however, at day 3 and 7 MM and MMNTM were increased compared to PEEK and MM and MN were not different from each other at day 7. All Ti6Al4V groups were increased compared to machined PEEK at day 10, and MM and MMNTM both produced similar amounts of BMP4 at day 14 and MN and PEEK were not different (figure 3(E)). OPG was increased on nanomodified Ti6Al4V surfaces at day 3 and was further increased on MN compared to the other Ti6Al4V surfaces. At day 7, MN produced the most, followed by MM, and MMNTM was not different from PEEK. However, 3 d later at day 10 OPG was increased on the Ti6Al4V surfaces compared to PEEK. Macroroughened Ti6Al4V groups were increased on day 14, and MN was not different from PEEK (figure 3(F)).
## Immuno-regulation by soluble cytokines
Production of the proinflammatory IL6 and anti-inflammatory IL4 and IL10 was assessed over the 14 d. IL6 was higher on MM surfaces compared to MN at day 3. However, at days 7, 10, and 14 the amount of IL6 produced by MSCs was higher on PEEK substrates compared to all Ti6Al4V surfaces, and at days 7 and 10 the presence of nanostructures further decreased IL6 production (figure 4(A)). IL4 and IL10 were not altered significantly at day 3, only MM and MN cultures had increased IL4 and IL10 compared to PEEK at day 3. IL4 was increased on Ti6Al4V surfaces at day 7, while MN was the only group producing more IL10 than PEEK at day 7. Both IL4 and IL10 were increased on microroughened surfaces at day 10 and PEEK and MN were not different from each other for IL4. At day 14, MMNTM produced the highest concentration of IL4 and IL10, but was not different than MM group for IL4. MM was not different from MN or PEEK for IL4, but was different from PEEK for IL10 (figures 4(B) and (C)).
**Figure 4.:** *Cellular response to PEEK and Ti6Al4V surfaces. (A) Interleukin 6, (B) interleukin 4, and (C) interleukin 10 were determined in the conditioned media at days 4 (designated 3D), 8 (designated 7D), 10 and 14. Groups not sharing letters are significantly different at p < 0.05 within each time point between surfaces. Group names are poly-ether-ether-ketone (PEEK), macro/micro-rough (MM), micro/meso/nano-rough (MN), and macro/micro/meso/nano-rough (MMNTM) Ti6Al4V surfaces.*
## Mineralization in an in vivo model of ectopic osteogenesis
In order to evaluate the effect of paracrine signaling factors secreted by cells in contact with MMNTM Ti alloy surfaces, we first validated the ASTM in vivo assay for osteoinductivity. At the micro-CT level, active DBM demonstrated robust mineralization throughout the implanted tissue (BV ∼ 3 mm3) as a positive control. Furthermore, iDBM and iDBM supplemented with lyophilized media and CM from MSCs cultured on TCPS showed little mineralization as shown by the micro-CT reconstructions (figure 5(A)) and quantification (figure 5(B)). These results demonstrate that MSCs cultured on TCPS, and media alone are negative controls and incapable of osteoinduction in the standard assay and show that the system can identify ectopic osteogenesis if it is present.
**Figure 5.:** *Ectopic models of osteogenesis in athymic nude mice gastrocnemius. Phase 1: (A) micro-CT quantification of mineralized tissue within the muscle pouch of the gastrocnemius for active DBM (aDBM), heat inactivated DBM (iDBM), iDBM supplemented with lyophilized growth media (TCPS-no cells), and iDBM supplemented with lyophilized conditioned media from MSCs cultured on TCPS for 14 d (TCPS + cells). (B) Representative micrographs of the four groups. Phase 2: (C) micro-CT quantification of mineralized tissue within the muscle pouch of the gastrocnemius for iDBM, iDBM supplemented with 10 ng of rhBMP2, and iDBM supplemented with lyophilized conditioned media collected from MSCs cultured on MMNTM surfaces beginning on day 5 through day 10 of culture and pooled. (D) Representative micrographs of the three groups. Arrows are markers of mineralized tissue formation within the region of interest of the micro-CT scans. Groups not sharing letters are significantly different at p < 0.05. White boxes are scale bars at 1 mm.*
We then evaluated the effect of iDBM supplemented with rhBMP2 at a similar level produced by the cells in culture (rhBMP2; 10 ng, R&D Systems) or iDBM supplemented with 10 mg lyophilized media from MSCs cultured on MMNTM Ti alloy substrates [40, 53]. As expected with the replicated negative control, iDBM alone was not osteoinductive, nor was iDBM supplemented with 10 ng BMP2. In contrast, iDBM plus CM from MMNTM implants demonstrated increased bone volume compared to both BMP2 and iDBM groups (figures 5(C) and (D)) as quantified by micro-CT. To further evaluate this effect, MMNTM and iDBM groups were processed histologically with H&E staining for quantitative histomorphometry to determine new bone formation. These data showed increased live bone area (figure 6(C)) and live bone perimeter in MMNTM samples compared to iDBM (figure 6(D)). Histological slices of each group showed greater areas of cell-populated canaliculi in bone pieces from the MMNTM group (figures 6(A) and (B)). Furthermore, histological samples were ranked according to ASTM 2529–13 to determine osteogenic activity of DBM lots. iDBM had a new bone formation percentage of $9.9\%$ and an average rank of 1.375 (table 1), failing the osteogenic ranking assessment. MMNTM samples demonstrated an increased averaged new bone percentage of $31.8\%$ and an average histological ranking of 3.125 (table 1), passing the assessment of osteogenic activity according to the standard.
**Figure 6.:** **Histological analysis* of new bone formation ectopically. (A) and (B) Representative histological images of the H&E staining for both the (A) iDBM and the (B) MMNTM groups. (C) Quantification of the live bone area using quantitative histomorphometry. (D) Quantification of the live bone perimeter using quantitative histomorphometry. (E) Ranked scoring of each sample according to an adapted ASTM Standard 2529–13 (standard guide for in vivo evaluation of osteoinductive potential for materials containing demineralized bone). Black arrows mark areas of live bone with cells populating lacunae. Black lines are scale bars at 1 mm. Groups not sharing letters are significantly different at $p \leq 0.05.$* TABLE_PLACEHOLDER:Table 1.
## Discussion
The present data demonstrate that Ti6Al4V surfaces possessing a combination of complex topographies like macroroughness, microtopography, and nanoscale feature formation temporally regulate the production of osteogenic factors over time. Surfaces possessing a subset of these complex structures are also capable of inducing osteogenic differentiation of MSCs into osteoblasts and the rate of this differentiation and regulation of local factors is influenced by the surface properties. Moreover, paracrine signaling factors produced by cells cultured on substrates comprising complex multiscale biomimetic topographies at the macro/micro/meso/nanoscale can induce bone formation. This study proves that the microenvironment—as created by cells on these surfaces in an in vitro culture—is osteoinductive in a standard ectopic model of osteogenesis. This finding supports further hypotheses that an osteoinductive microenvironment during implantation may improve osseointegration with new bone formation.
Surface properties have long been known to be important for the integration of Ti-based implants. Substrates created by machining and processed by grit-blasting and acid-etching result in Ti6Al4V surfaces possessing microscale roughness that have demonstrated success clinically in the field of periodontology and implant dentistry [24, 54–56]. In the present study, further processing of roughened surfaces using proprietary grit-blasting and acid-etching create additional microstructure and nanotexture formation; and reduce surface wettability in a manner similar to previous studies [33, 57]. However, in this study we have demonstrated a surface processing method that creates a similar evenly dispersed, heterogenous nano-topography regardless of the starting macroroughness of the implant surface, as seen in the SEM images of both MN and MMNTM surfaces.
PEEK implant electron micrographs show complex tear morphology due to the machining process and confirms the roughness quantified by optical profilometry. However, these surface properties were not sufficient to generate an osteogenic response, likely governed by differences in bulk chemistry and limited multiscale topography.
Macroscale electron micrographs show similar morphology between MM and MMNTM surfaces that confirms the same processing treatment prior to modification at the micron and nanoscale. The macroscale surface modification generated by dual acid etching resulted in similar reductions in DNA content by day 7 and increased OCN and OPG. This was correlated with early increases in BMP2 and BMP4. Addition of micro and nanotopography to either the macro-textured surface or a machined surface, caused a further increase in BMP production, which was sustained through day 14 of culture on the MMNTM surface. These results confirm the importance of the biomimetic multiscale topography to attain the full osteogenic panoply of outcomes.
These results also demonstrate the importance of assessing surface properties using multiple parameters. Implant modifications can induce surface morphologies that vary significantly from each other but have matching quantifiable surface roughness [54, 58–60]. The use of scanning electron micrographs and qualification of surfaces at the macro-, micro-, and nanoscale provides important predictive information on potential effectiveness of surface treatment on altering cellular response.
Biologically, nanostructures have been shown to alter cell proliferation and attachment and macroscale roughness affects production of cytokines over a longer period [13, 25, 27, 29, 61–63]. Ti6Al4V surfaces possess superior material properties, such as wear resistance, and alloy crystalline phases that allow tuning for physical morphologies that closely resemble the biological surface of native bone. The result is cellular phenotypes with reduced production of proinflammatory IL6 signaling during surface-mediated osteogenesis and increased production of pro-regeneration cytokines like IL4 and IL10 that have been shown to increase implant retention [61, 63–65]. Newer polymeric materials for orthopedic and dental applications like PEEK, have mechanical moduli that are more similar to cortical bone but have also been shown to increase inflammatory profiles in preclinical and basic research studies [65–67].
Wettability is another key regulator of cellular response to a biomaterial. In this study, while all substrates possess intermediate wettability, the microroughened Ti6Al4V substrates had increased surface wettability compared to PEEK polymers. Moreover, nano feature formation decreased the wettability of Ti6Al4V surfaces, suggesting the nanofeatures alter the interaction of liquids on the implant surface. This is unsurprising: surface roughness and the formation of nanostructures have been shown to directly alter the contact angle and surface wettability of surfaces including Ti alloys [64, 68].
In vitro surface wettability has been studied with respect to osteoblast adhesion and behavior on dental implant surfaces. Increasing surface wettability and creating hydrophilic implant surfaces have been shown to alter protein adsorption [69, 70], cell attachment [20, 71], and production of important paracrine signaling molecules that modulate immune response to an implanted material [66, 72]. Although surface processing reduced overall wettability of the surface, the cellular response was still enhanced, suggesting that topography plays a larger role in longer term cellular response. Additional further processing of MMNTM and MN surfaces to increase surface wettability may lead to increased cellular response during surface mediated osteogenesis. MSCs are sensitive to surface topography both at the microscale and to submicron surface textures [73]. MSC response to these four surfaces showed a phenotypic decrease in cellular proliferation on Ti6Al4V surfaces compared to PEEK over 14 d. Moreover, these cells were also sensitive to small morphological changes by delaying proliferation on only MN modified surfaces. The presence of macroscale roughness on the implant surface did decrease proliferation compared to PEEK, but slight proliferation still occurred until day 14. These effects could be attributed to differences in surface area as well as to alterations in adsorbed proteins on the substrates. These binding epitopes are then sensed by MSC receptors on the implant surface and previous literature has shown that activation of these receptors can lead to reduced proliferation as maturation occurs [31, 37, 74].
Osteoblast differentiation is inversely related to proliferation; thus, peak production of factors associated with osteogenesis will occur at different times depending on the substrate surface properties, like topography or wettability. This physiologic balance and temporal regulation of proliferation vs differentiation of progenitor cells is important to ensure sufficient cell population within the implant micro-environment while also promote regeneration [1, 75]. Osteoblast maturation is jointly regulated by macroscale topographies, microscale roughness and nanotexture formation [25, 39]. Furthermore, MSCs still differentiate into osteoblasts in a surface-mediated manner in growth media despite lacking any exogenous media supplements found in osteogenic media. This observation is supported by the increasing production of both OCN and OPN as well as paracrine signaling factors BMP2, BMP4, and OPG. MSCs on machined PEEK do not undergo surface-mediated differentiation into osteoblasts demonstrated by low levels of both OCN and OPN. These data suggest that cells sense the various types of surface chemistry, surface modifications, and the combination of modifications have additive effects on cellular response. These considerations are important preclinically as recent reports have shown variability in personalized responses to implant surfaces possessing microroughness alone as well as sex differences in regenerative response during culture or after implant placement [76, 77]. Therefore, implant surfaces that possess a hierarchy of topography may facilitate osteogenesis throughout the integration process.
Interleukins were quantified to determine how MSCs were regulating immune lineage cells. Proinflammatory IL6 was upregulated on PEEK surfaces compared with all titanium surfaces at 7 d and remained upregulated thereafter. Small levels of IL6 have been shown to be important to regulate cellular migration around an implant [78]. However, prolonged elevated levels of IL6 can result in a cytotoxic microenvironment and in vivo can result in fibrous tissue formation and implant failure. These results are similar to published clinical data showing PEEK implants may result in nonunion or subsidence [14, 61].
Anti-inflammatory IL4 and IL10 were increased on Ti6Al4V substrates compared with PEEK with the greatest trends in upregulate at 10 and 14 d. Both of these anti-inflammatory cytokines have been investigated previously in the immune system and are increased in a microroughness and hydrophilic manner [79, 80]. MSCs have been shown to exert immunomodulatory signals and it is likely, in this system, that MSCs on microroughened and nanotextured surfaces increase production of IL4 and IL10, which polarizes macrophages into a regenerative M2 response [63, 79–82]. Previous studies using similar surfaces have demonstrated distinct differences between bulk chemical structure and overall cellular response. PEEK has been shown to increase pro-inflammatory cytokines and reduction in pro-regenerative cytokines [65]. These distinct differences have also been correlated in the clinic with increased rates of subsidence after fusion [10, 14, 17, 83–85].
Previous studies have used CM or trans-well co-cultures in vitro to assess paracrine signaling of soluble factors necessary for osteogenesis. These studies show that surfaces possessing microroughness are able to initiate the production of soluble signaling factors into the peri-implant microenvironment [40, 41]. The hypothesis that factors produced by cells in contact with the surface are important clinically is supported by the observation that in periodontology, micro/meso/nano-textured surfaces applied to threaded endosseous implants have demonstrated strong clinical success with increased implant retention rates and reduced implant failures [86].
In vitro studies are often limited with one cell type on the surface acting as the signal to osteoprogenitor cells, rather than the more complex collection of cells present in vivo and the temporal regulation of local factors from these varied cell populations. The mouse muscle model for assessing osteoinductivity enabled the capsule-based implantation of a combination of soluble factors secreted by osteoprogenitor cells. This usage further enabled the observation that the microenvironment with these factors signaled other cell types to produce new bone ectopically in vivo. Our previous observation, that inhibiting the action of BMP2 blocked the surface effect in vitro [40], suggested, unsurprisingly, that BMP2 is one important factor responsible for the osteoinduction observed in vivo. The observation, however, that iDBM + BMP2 was not osteoinductive in the present study, indicated that BMP2 is not the only constituent of the microenvironment, the CM generated by MSCs on MNN™ surfaces, that is playing a role.
There are several reasons that iDBM + BMP2 was not effective. The concentration of BMP2 in the CM was much lower than is used clinically [87], and may not have been sufficient to induce osteoinduction under the conditions of the study. We used BMP2 purchased from a supplier other than BMP2 commonly used clinically. Finally, the delivery system with iDBM and the carrier with a gel capsule is not the delivery system that is used clinically.
In this study, Ti6Al4V surfaces that possess the multiscale biomimetic structures at the macro, micro, meso and nanoscale stimulate MSCs to generate factors that support bone formation ectopically in a mouse model of osteoinduction. Ectopic bone formation is the gold standard to determine the effectiveness of an implanted biomaterial with regards to support de novo osteogenesis in vivo and ASTM 2529–13 is the active standard for evaluating materials containing DBM for osteoinductive potential [43]. In many cases, the use of this standard is limited because the actual material cannot be implanted in rodent muscle. Instead, we focused on the chemical environment generated by MSCs cultured on the MMNTM surface. Additionally, inflammatory regulation is an important factor in development of new bone in vivo, but it was necessary to use athymic nude mice as the CM were generated by human MSCs, which is also a potential limitation as these mice lack a competent fully developed immune system seen in other preclinical models of implantation into bone.
Even with these limitations, we present compelling data demonstrating that MSCs cultured on biomimetic Ti6Al4V substrates generate a microenvironment that is osteoinductive in this ASTM standard assay. Cell culture media alone and CM from MSCs cultured on tissue culture plastic for 14 d are unable to stimulate bone formation ectopically. These samples lack the soluble signaling factors necessary for mineralization to occur. In contrast, the addition of lyophilized CM from MSCs differentiating in response to MMNTM surface properties from day 5 to day 10 of culture possess the combination of factors to stimulate bone formation ectopically. Moreover, when BMP2 is supplemented into iDBM, at in vitro concentrations similar to MSCs cultured in contact with the MMNTM surface, osteoinductivity does not occur suggesting it is a combination of multiple soluble factors that are required for osteogenesis.
Measuring the basic biological response to implant surfaces is clinically important to support common methods to restore function and quality of life for patients [1, 2]. For treatment of chronic back pain, loss of teeth, or trauma, to name a few, dental and orthopedic implants must maintain stability to restore function. Physicians in these fields currently rely on the structural stability of titanium or PEEK materials and in challenging scenarios, they may consider options to further facilitate bone formation by using exogenous sources of bone inductive proteins like rhBMP2. That is, the clinician considers, not only the material itself, but the local peri-implant microenvironment and cellular signaling needed to support bone formation as well. This study suggests that a titanium material with a biomimetic surface can contribute to the cellular response and an osteoinductive microenvironment that facilitates new bone formation. This finding elucidates new approaches and important factors to improve implant fixation and patient quality of life especially those suffering compromised bone qualities [59]. The development of biomimetic implant surface topographies has been shown to increase osteogenesis and cellular differentiation of progenitor cells, and we continue to assess how these effects can support treatment planning for physicians.
## Conclusions
Collectively, these data show that MSCs are sensitive to a combination of titanium implant surface properties including macro and microscale roughness and nanoscale feature formation. In the in vitro portion of this study, MSCs cultured on the MM, MN, and MMNTM substrates produced a positive combination of factors compared to controls. In the in vivo portion of this study, new bone formation was sensitive to the combination of factors observed in various culture media. With an ectopic model of osteoinduction, this study found that the combination of osteogenic soluble signaling factors, observed in the CM from MMN™ substrates and implanted with a gel carrier into muscle, created an osteoinductive microenvironment.
## Data availability statement
The data that support the findings of this study are available upon reasonable request from the authors.
## Author contributions
M B B and D J C contributed equally as first authors and can place their names first in CVs. M B B, D J C, J J R, B D B, and Z S contributed to experimental design and analysis of results. M G prepared the test surfaces and contributed to the experimental design and data analysis. M B B, and M L conducted assays, assisted on the surgery, analyzed data, and contributed to data interpretation. J P provided expertise on the mechanical testing and aided in the testing. M B B and M L wrote the first manuscript draft. D J C performed the surgery, collected data, and analyzed data. All authors contributed to the interpretation of the results and final manuscript.
## Conflict of interest
B D B is a paid consultant for Medtronic (Minneapolis, MN) and Spineology Inc. (St. Paul, MN), chief scientific officer for Pascal Medical Corporation (Richmond, VA), and an unpaid consultant for Institut Straumann AG (Basel, Switzerland), Curiteva (Tanner, AL), and Nexus Spine (Salt Lake City, UT). Z S is an unpaid consultant for AB Dental (Ashdod, Israel) and an unpaid consultant for Institut Straumann AG (Basel, Switzerland). P J S is a paid consultant of Medtronic; M G and J J R are employees of Medtronic
## References
1. Terheyden H, Lang N P, Bierbaum S, Stadlinger B. **Osseointegration—communication of cells**. *Clin. Oral Implants Res.* (2012.0) **23** 1127-35. DOI: 10.1111/j.1600-0501.2011.02327.x
2. Albrektsson T, Johansson C. **Osteoinduction, osteoconduction and osseointegration**. *Eur. Spine J.* (2001.0) **10** S96-101. DOI: 10.1007/s005860100282
3. Naujokat H, Kunzendorf B, Wiltfang J. **Dental implants and diabetes mellitus—a systematic review**. *Int. J. Implant Dent.* (2016.0) **2** 5. DOI: 10.1186/s40729-016-0038-2
4. Coelho P G, Benjamin P, Tovar N, Koopmans S-J, Plana N, Graves D T, Engebretson S, van Beusekom H M M, Oliveira P G F P, Dard M. **Effect of obesity/metabolic syndrome and diabetes on osseointegration of dental implants in a miniature swine model. A pilot study**. *J. Oral Maxillofac. Surg.* (2018.0) **76** 1677-87. DOI: 10.1016/j.joms.2018.02.021
5. Moy P K, Medina D, Shetty V, Aghaloo T. **Dental implant failure rates and associated risk factors**. *Int. J. Oral Maxillofac. Implants* (2005.0) **20** 569-78. PMID: 16161741
6. Turri A, Rossetti P, Canullo L, Grusovin M, Dahlin C. **Prevalence of peri-implantitis in medically compromised patients and smokers: a systematic review**. *Int. J. Oral Maxillofac. Implants* (2016.0) **31** 111-8. DOI: 10.11607/jomi.4149
7. Jacobs R. **Preoperative radiologic planning of implant surgery in compromised patients**. *Periodontol. 2000* (2003.0) **33** 12-25. DOI: 10.1046/j.0906-6713.2002.03302.x
8. Schwitalla A, Muller W. **PEEK dental implants: a review of the literature**. *J. Oral Implantol.* (2013.0) **39** 743-9. DOI: 10.1563/AAID-JOI-D-11-00002
9. Steinemann G. **Titanium—the material of choice?**. *Periodontol. 2000* (1998.0) **17** 7-21. DOI: 10.1111/j.1600-0757.1998.tb00119.x
10. Girasole G, Muro G, Mintz A, Chertoff J. **Transforaminal lumbar interbody fusion rates in patients using a novel titanium implant and demineralized cancellous allograft bone sponge**. *Int. J. Spine Surg.* (2013.0) **7** e95-100. DOI: 10.1016/j.ijsp.2013.08.001
11. Flouzat-Lachaniette C-H, Ghazanfari A, Bouthors C, Poignard A, Hernigou P, Allain J. **Bone union rate with recombinant human bone morphogenic protein-2 versus autologous iliac bone in PEEK cages for anterior lumbar interbody fusion**. *Int. Orthop.* (2014.0) **38** 2001-7. DOI: 10.1007/s00264-014-2301-6
12. Hyzy S L, Olivares-Navarrete R, Schwartz Z, Boyan B D. **BMP2 induces osteoblast apoptosis in a maturation state and noggin-dependent manner**. *J. Cell. Biochem.* (2012.0) **113** 3236-45. DOI: 10.1002/jcb.24201
13. Hyzy S L, Olivares-Navarrete R, Ortman S, Boyan B D, Schwartz Z. **Bone morphogenetic protein 2 alters osteogenesis and anti-inflammatory profiles of mesenchymal stem cells induced by microtextured titanium**. *Tissue Eng.* (2017.0) **23** 1132-41. DOI: 10.1089/ten.TEA.2017.0003
14. Yang J J, Yu C H, Chang B, Yoem J S, Lee J H, Lee C-K. **Subsidence and nonunion after anterior cervical interbody fusion using a stand-alone polyetheretherketone (peek) cage**. *Clin. Orthop. Surg.* (2011.0) **3** 16-23. DOI: 10.4055/cios.2011.3.1.16
15. El Awadly T, Wu G, Ayad M, Radi I, Wismeijer D, El Fetouh H A, Osman R B. **A histomorphometric study on treated and untreated ceramic filled PEEK implants versus titanium implants: preclinical**. *Clin. Oral Implants Res.* (2019.0) **31** 246-54. DOI: 10.1111/clr.13562
16. William J, Richard D, Bailey A. **An analysis of fusion cage migration in unilateral and bilateral fixation with transforaminal lumbar interbody fusion**. *Eur. Spine J.* (2013.0) **22** 439-45. DOI: 10.1007/s00586-012-2458-x
17. Jiya T U, van Royen T S B J, Resorbable P Á. **Posterior lumbar interbody fusion using non resorbable poly-ether-ether-ketone versus resorbable poly-L-lactide-co-D, L-lactide fusion devices. Clinical outcome at a minimum of 2-year follow-up**. *Eur. Spine J.* (2011.0) **20** 618-22. DOI: 10.1007/s00586-010-1568-6
18. Franchi M, Orsini E, Martini D, Ottani V, Fini M, Giavaresi G, Giardino R, Ruggeri A. **Destination of titanium particles detached from titanium plasma sprayed implants**. *Micron* (2007.0) **38** 618-25. DOI: 10.1016/j.micron.2006.09.005
19. Cooper L F, Masuda T, Whitson S, Yliheikkila P, Felton D. **Formation of mineralizing osteoblast cultures on machined, titanium oxide grit-blasted, and plasma-sprayed titanium surfaces**. *Int. J. Maxillofac. Implants* (1999.0) **14** 37-47
20. Gittens R A, Scheideler L, Rupp F, Hyzy S L, Geis-Gerstorfer J, Schwartz Z, Boyan B D. **A review on the wettability of dental implant surfaces II: biological and clinical aspects**. *Acta Biomater.* (2014.0) **10** 2907-18. DOI: 10.1016/j.actbio.2014.03.032
21. Wegst U G K, Bai H, Saiz E, Tomsia A P, Ritchie R O. **Bioinspired structural materials**. *Nat. Mater.* (2015.0) **14** 23-36. DOI: 10.1038/NMAT4089
22. Wennerberg A, Albrektsson T. **Effects of titanium surface topography on bone integration: a systematic review**. *Clin. Oral Implants Res.* (2009.0) **20** 172-84. DOI: 10.1111/j.1600-0501.2009.01775.x
23. Anselme K. **Osteoblast adhesion on biomaterials**. *Biomaterials* (2000.0) **21** 667-81. DOI: 10.1016/S0142-9612(99)00242-2
24. Gittens R A, Olivares-Navarrete R, Schwartz Z, Boyan B D. **Implant osseointegration and the role of microroughness and nanostructures: lessons for spine implants**. *Acta Biomater.* (2014.0) **10** 3363-71. DOI: 10.1016/j.actbio.2014.03.037
25. Tetè S, Mastrangelo F, Quaresima R, Vinci R, Sammartino G, Stuppia L, Gherlone E. **Influence of novel nano-titanium implant surface on human osteoblast behavior and growth**. *Implant Dent.* (2010.0) **19** 520-31. DOI: 10.1097/ID.0b013e3182002eac
26. Kikuchi T, Yoshida M, Matsuura S, Natsui S, Tsuji E, Habazaki H, Suzuki R O. **Rapid reduction of titanium dioxide nano-particles by reduction with a calcium reductant**. *J. Phys. Chem. Solids* (2014.0) **75** 1041-8. DOI: 10.1016/j.jpcs.2014.04.016
27. Guadarrama Bello D, Fouillen A, Badia A, Nanci A. **A nanoporous titanium surface promotes the maturation of focal adhesions and formation of filopodia with distinctive nanoscale protrusions by osteogenic cells**. *Acta Biomater.* (2017.0) **60** 339-49. DOI: 10.1016/j.actbio.2017.07.022
28. Webster T J, Ejiofor J U. **Increased osteoblast adhesion on nanophase metals: Ti, Ti6Al4V, and CoCrMo**. *Biomaterials* (2004.0) **25** 4731-9. DOI: 10.1016/j.biomaterials.2003.12.002
29. Khang D, Lu J, Yao C, Haberstroh K M, Webster T J. **The role of nanometer and sub-micron surface features on vascular and bone cell adhesion on titanium**. *Biomaterials* (2008.0) **29** 970-83. DOI: 10.1016/j.biomaterials.2007.11.009
30. Lotz E M, Olivares-Navarrete R, Hyzy S L, Berner S, Schwartz Z, Boyan B D. **Comparable responses of osteoblast lineage cells to microstructured hydrophilic titanium–zirconium and microstructured hydrophilic titanium**. *Clin. Oral Implants Res.* (2017.0) **28** e51-9. DOI: 10.1111/clr.12855
31. Olivares-Navarrete R, Raz P, Zhao G, Chen J, Wieland M, Cochran D L, Chaudhri R A, Ornoy A, Boyan B D, Schwartz Z. **Integrin α2β1 plays a critical role in osteoblast response to micron-scale surface structure and surface energy of titanium substrates**. *Proc. Natl Acad. Sci. USA* (2008.0) **105** 15767-72. DOI: 10.1073/pnas.0805420105
32. Lossdörfer S, Schwartz Z, Wang L, Lohmann C H, Turner J D, Wieland M, Cochran D L, Boyan B D. **Microrough implant surface topographies increase osteogenesis by reducing osteoclast formation and activity**. *J. Biomed. Mater. Res.* (2004.0) **70A** 361-9. DOI: 10.1002/jbm.a.30025
33. Olivares-Navarrete R, Hyzy S L, Berg M E, Schneider J M, Hotchkiss K, Schwartz Z, Boyan B D. **Osteoblast lineage cells can discriminate microscale topographic features on titanium-aluminum-vanadium surfaces**. *Ann. Biomed. Eng.* (2014.0) **42** 2551-61. DOI: 10.1007/s10439-014-1108-3
34. Langenbach F, Handschel J. **Effects of dexamethasone, ascorbic acid and β-glycerophosphate on the osteogenic differentiation of stem cells**. *Stem Cell Res. Ther.* (2013.0) **4** 117/1-117/7. DOI: 10.1186/scrt328
35. Lian J B, Stein G S. **Concepts of osteoblast growth and differentiation: basis for modulation of bone cell development and tissue formation**. *Crit. Rev. Oral Biol. Med.* (1992.0) **3** 269-305. DOI: 10.1177/10454411920030030501
36. Lotz E M, Berger M B, Boyan B D, Schwartz Z. **Regulation of mesenchymal stem cell differentiation on microstructured titanium surfaces by semaphorin 3A**. *Bone* (2020.0) **134**. DOI: 10.1016/j.bone.2020.115260
37. Keselowsky B G, Wang L, Schwartz Z, Garcia A J, Boyan B D. **Integrin a5 controls osteoblastic proliferation and differentiation responses to titanium substrates presenting different roughness characteristics in a roughness independent manner**. *J. Biomed. Mater. Res.* (2007.0) **80** 700-10. DOI: 10.1002/jbm.a
38. Rani V D, Manzoor K, Menon D, Selvamurugan N, Nair S V. **The design of novel nanostructures on titanium by solution chemistry for an improved osteoblast response**. *Nanotechnology* (2009.0) **20**. DOI: 10.1088/0957-4484/20/19/195101
39. Gittens R A, Olivares-Navarrete R, McLachlan T, Cai Y, Hyzy S L, Schneider J M, Schwartz Z, Sandhage K H, Boyan B D. **Differential responses of osteoblast lineage cells to nanotopographically-modified, microroughened titanium-aluminum-vanadium alloy surfaces**. *Biomaterials* (2012.0) **33** 8986-94. DOI: 10.1016/j.biomaterials.2012.08.059
40. Berger M B, Bosh K B, Jacobs T W, Joshua Cohen D, Schwartz Z, Boyan B D. **Growth factors produced by bone marrow stromal cells on nanoroughened titanium–aluminum–vanadium surfaces program distal MSCs into osteoblasts via BMP2 signaling**. *J. Orthop. Res.* (2020.0) **39** 1908-20. DOI: 10.1002/jor.24869
41. Olivares-Navarrete R, Hyzy S L, Hutton D L, Erdman C P, Wieland M, Boyan B D, Schwartz Z. **Direct and indirect effects of microstructured titanium substrates on the induction of mesenchymal stem cell differentiation towards the osteoblast lineage**. *Biomaterials* (2010.0) **31** 2728-35. DOI: 10.1016/j.biomaterials.2009.12.029
42. Barradas A M C, Yuan H, van Blitterswijk C A, Habibovic P. **Osteoinductive biomaterials: current knowledge of properties, experimental models and biological mechanisms**. *Eur. Cell Mater.* (2011.0) **21** 407-29. DOI: 10.22203/eCM.v021a31
43. **F2529-13: standard guide for**. (2021.0) 1-15. DOI: 10.1520/F2529-13R21
44. Patterson C, Schneider J, Ullrich P, Berg M. **US9314337B2: microstructured implant surfaces**. (2016.0)
45. Ullrich P, Berg M, Patterson C. **US009655745B2: methods for manufacturing implants having integration surfaces**
46. Ullrich P F, Patterson C J, Schneider J, Berg M E. **US9848995B2: process for fabricating bioactive vertebral endplate bone-contacting surfaces on a spinal implant**. (2017.0)
47. Ullrich P, Patterson C. **US009655745B2: processes for producing regular repeating patterns on surfaces of interbody devices**. (2015.0)
48. **LSM710, LSM780, LSM 710 NLO, LSM 780 NLO, and ConfoCor3 operational manual**. (2010.0)
49. von Knoch F, Jaquiery C, Kowalsky M, Schaeren S, Alabre C, Martin I, Rubash H E, Shanbhag A S. **Effects of bisphosphonates on proliferation and osteoblast differentiation of human bone marrow stromal cells**. *Biomaterials* (2005.0) **26** 6941-9. DOI: 10.1016/j.biomaterials.2005.04.059
50. Qi H, Aguiar D J, Williams S M, la Pean A, Pan W, Verfaillie C M. **Identification of genes responsible for osteoblast differentiation from human mesodermal progenitor cells**. *Proc. Natl Acad. Sci.* (2003.0) **100** 3305-10. DOI: 10.1073/pnas.0532693100
51. Kuhn L T, Liu Y, Advincula M, Wang Y-H, Maye P, Goldberg A J. **A nondestructive method for evaluating**. *Tissue Eng.* (2010.0) **16** 1357-66. DOI: 10.1089/ten.tec.2009.0701
52. Erickson D M, Harris S E, Dean D D, Harris M A, Wozney J M, Boyan B D, Schwartz Z. **Recombinant bone morphogenetic protein (BMP)-2 regulates costochondral growth plate chondrocytes and induces expression of BMP-2 and BMP-4 in a cell maturation-dependent manner**. *J. Orthop. Res.* (1997.0) **15** 371-80. DOI: 10.1002/jor.1100150309
53. Schwartz Z, Hyzy S L, Moore M A, Hunter S A, Ronholdt C J, Sunwoo M, Boyan B D. **Osteoinductivity of demineralized bone matrix is independent of donor bisphosphonate use**. *J. Bone Joint Surg.* (2011.0) **93** 2278-86. DOI: 10.2106/JBJS.J.01469
54. Buser D, Schenk R K, Steinemann S, Fiorellini J P, Fox C H, Stich H. **Influence of surface characteristics on bone integration of titanium implants. A histomorphometric study in miniature pigs**. *J. Biomed. Mater. Res.* (2018.0) **25** 889-902. DOI: 10.1002/jbm.820250708
55. Müller F, Al-nawas B, Storelli S, Quirynen M, Hicklin S, Castro-laza J. **Small-diameter titanium grade IV and titanium-zirconium implants in edentulous mandibles: five-year results from a double- blind, randomized controlled trial**. *BMC Oral Health* (2015.0) **15** 1-10. DOI: 10.1186/s12903-015-0107-6
56. Lang N P, Salvi G E, Huynh-Ba G, Ivanovski S, Donos N, Bosshardt D D. **Early osseointegration to hydrophilic and hydrophobic implant surfaces in humans**. *Clin. Oral Implants Res.* (2011.0) **22** 349-56. DOI: 10.1111/j.1600-0501.2011.02172.x
57. Olivares-Navarrete R, Hyzy S L, Gittens R A, Schneider J M, Haithcock D, Ullrich P, Slosar P J, Schwartz Z, Boyan B D. **Rough titanium alloys regulate osteoblast production of angiogenic factors**. *Spine J.* (2013.0) **13** 1563-70. DOI: 10.1016/j.spinee.2013.03.047
58. le Guéhennec L A, Soueidan P, Layrolle Y. **Surface treatments of titanium dental implants for rapid osseointegration**. *Dent. Mater.* (2007.0) **23** 844-54. DOI: 10.1016/j.dental.2006.06.025
59. Boyan B D, Lotz E M, Schwartz Z. **Roughness and hydrophilicity as osteogenic biomimetic surface properties**. *Tissue Eng.* (2017.0) **23** 1479-89. DOI: 10.1089/ten.TEA.2017.0048
60. Matteson J L, Greenspan D C, Tighe T B, Gilfoy N, Stapleton J J. **Assessing the hierarchical structure of titanium implant surfaces**. *J. Biomed. Mater. Res.* (2016.0) **104** 1083-90. DOI: 10.1002/jbm.b.33462
61. Hyzy S L, Olivares-Navarrete R, Hutton D L, Tan C, Boyan B D, Schwartz Z. **Microstructured titanium regulates interleukin production by osteoblasts, an effect modulated by exogenous BMP-2**. *Acta Biomater.* (2013.0) **9** 5821-9. DOI: 10.1016/j.actbio.2012.10.030
62. Salou L, Hoornaert A, Louarn G, Layrolle P. **Enhanced osseointegration of titanium implants by nanostructured surface: an experimental study in rabbits**. *Acta Biomater.* (2012.0) **11** 494-502. DOI: 10.1016/j.actbio.2014.10.017
63. Wang Y, Qi H, Miron R J, Zhang Y. **Modulating macrophage polarization on titanium implant surface by poly(dopamine)-assisted immobilization of IL4**. *Clin. Implant Dent. Relat. Res.* (2019.0) **21** 977-86. DOI: 10.1111/cid.12819
64. Tugulu S, Löwe K, Scharnweber D, Schlottig F. **Preparation of superhydrophilic microrough titanium implant surfaces by alkali treatment**. *J. Mater. Sci. Mater. Med.* (2010.0) **21** 2751-63. DOI: 10.1007/s10856-010-4138-x
65. Olivares-Navarrete R, Hyzy S L, Slosar P J, Schneider J M, Schwartz Z, Boyan B D. **Implant materials generate different peri-implant inflammatory factors: poly-ether-ether-ketone promotes fibrosis and microtextured titanium promotes osteogenic factors**. *Spine* (2015.0) **40** 399-404. DOI: 10.1097/BRS.0000000000000778
66. Hotchkiss K M, Reddy G B, Hyzy S L, Schwartz Z, Boyan B D, Olivares-Navarrete R. **Titanium surface characteristics, including topography and wettability, alter macrophage activation**. *Acta Biomater.* (2016.0) **31** 425-34. DOI: 10.1016/j.actbio.2015.12.003
67. Trindade R, Albrektsson T, Galli S, Prgomet Z, Tengvall P, Wennerberg A. **Bone immune response to materials, part i: titanium, peek and copper in comparison to sham at 10 days in rabbit tibia**. *J. Clin. Med.* (2018.0) **7** 526. DOI: 10.3390/jcm7120526
68. Yoshimitsu Z, Nakajima A, Watanabe T. **2002 Effects of surface structure on the hydrophobicity and sliding behavior of water droplets**. *Langmuir* **18** 5818-22. DOI: 10.1021/la020088p
69. Rupp F, Scheideler L, Rehbein D, Axmann D, Geis-Gerstorfer J. **Roughness induced dynamic changes of wettability of acid etched titanium implant modifications**. *Biomaterials* (2004.0) **25** 1429-38. DOI: 10.1016/j.biomaterials.2003.08.015
70. Kopf B S, Ruch S, Berner S, Spencer N D, Maniura-Weber K. **The role of nanostructures and hydrophilicity in osseointegration:**. *J. Biomed. Mater. Res.* (2015.0) **103** 2661-72. DOI: 10.1002/jbm.a.35401
71. Yang J, Zhou Y, Wei F, Xiao Y. **Blood clot formed on rough titanium surface induces early cell recruitment**. *Clin. Oral Implants Res.* (2015.0) **27** 1031-8. DOI: 10.1111/clr.12672
72. Zhao G, Schwartz Z, Wieland M, Rupp F, Geis-Gerstorfer J, Cochran D L, Boyan B D. **High surface energy enhances cell response to titanium substrate microstructure**. *J. Biomed. Mater. Res.* (2005.0) **74** 49-58. DOI: 10.1002/jbm.a.30320
73. Garcia A J. **Get a grip: integrins in cell-biomaterial interactions**. *Biomaterials* (2005.0) **26** 7525-9. DOI: 10.1016/j.biomaterials.2005.05.029
74. Hefti T, Frischherz M, Spencer N D, Hall H, Schlottig F. **A comparison of osteoclast resorption pits on bone with titanium and zirconia surfaces**. *Biomaterials* (2010.0) **31** 7321-31. DOI: 10.1016/j.biomaterials.2010.06.009
75. Pešáková V, Kubies D, Hulejová H, Himmlová L. **The influence of implant surface properties on cell adhesion and proliferation**. *J. Mater. Sci. Mater. Med.* (2007.0) **18** 465-73. DOI: 10.1007/s10856-007-2006-0
76. Zhukareva V, Obrocka M, Houle J D, Fischer I, Neuhuber B. **Secretion profile of human bone marrow stromal cells: donor variability and response to inflammatory stimuli**. *Cytokine* (2010.0) **50** 317-21. DOI: 10.1016/j.cyto.2010.01.004
77. Berger M B, Cohen D J, Levit M M, Puetzer J L, Boyan B D, Schwartz Z. **Hydrophilic implants generated using a low-cost dielectric barrier discharge plasma device at the time of placement exhibit increased osseointegration in an animal pre-clinical study: an effect that is sex-dependent**. *Dent. Mater.* (2022.0) **38** 632-45. DOI: 10.1016/j.dental.2022.02.002
78. Yoshitake F, Itoh S, Narita H, Ishihara K, Ebisu S. **Interleukin-6 directly inhibits osteoclast differentiation by suppressing receptor activator of NF-kappaB signaling pathways**. *J. Biol. Chem.* (2008.0) **283** 11535-40. DOI: 10.1074/jbc.M607999200
79. Hotchkiss K M, Clark N M, Olivares-Navarrete R. **Macrophage response to hydrophilic biomaterials regulates MSC recruitment and T-helper cell populations**. *Biomaterials* (2018.0) **182** 202-15. DOI: 10.1016/j.biomaterials.2018.08.029
80. Abaricia O, Shah A H, Chaubal M, Hotchkiss K M, Olivares-navarrete R. **Wnt signaling modulates macrophage polarization and is regulated by biomaterial surface properties**. *Biomaterials* (2020.0) **243**. DOI: 10.1016/j.biomaterials.2020.119920
81. Hachim D, Lopresti S T, Yates C C, Brown B N. **Shifts in macrophage phenotype at the biomaterial interface via IL-4 eluting coatings are associated with improved implant integration**. *Biomaterials* (2017.0) **112** 95-107. DOI: 10.1016/j.biomaterials.2016.10.019
82. Gao L, Li M, Yin L, Zhao C, Chen J, Zhou J, Duan K, Feng B. **Dual-inflammatory cytokines on TiO**. *J. Biomed. Mater. Res.* (2018.0) **106A** 1878-86. DOI: 10.1002/jbm.a.36391
83. Campbell P G, Cavanaugh D A, Nunley P, Utter P A, Kerr E, Wadhwa R. **PEEK versus titanium cages in lateral lumbar interbody fusion: a comparative analysis of subsidence**. *J. Neurosurg. Spine* (2020.0) **49** E10. DOI: 10.3171/2020.6.FOCUS20367
84. Suh P B, Puttlitz C, Lewis C, Bal B S, McGilvray K. **The effect of cervical interbody cage morphology, material composition, and substrate density on cage subsidence**. *JAAOS—J. Am. Acad. Ortho. Surg.* (2017.0) **25** 160-8. DOI: 10.5435/JAAOS-D-16-00390
85. Nakanishi Y, Naito K, Yamagata T, Yoshimura M, Shimokawa N, Nishikawa M, Ohata K, Takami T. **Safety of anterior cervical discectomy and fusion using titanium-coated polyetheretherketone stand-alone cages: multicenter prospective study of incidence of cage subsidence**. *J. Clin. Neurosci.* (2020.0) **74** 47-54. DOI: 10.1016/j.jocn.2020.01.056
86. Buser D, Janner S F M, Wittneben J G, Brägger U, Ramseier C A, Salvi G E. **10-year survival and success rates of 511 titanium implants with a sandblasted and acid-etched surface: a retrospective study in 303 partially edentulous patients**. *Clin. Implant Dent. Relat. Res.* (2012.0) **14** 839-51. DOI: 10.1111/j.1708-8208.2012.00456.x
87. Tannoury C A, An H S. **Complications with the use of bone morphogenetic protein 2 (BMP-2) in spine surgery**. *Spine J.* (2014.0) **14** 552-9. DOI: 10.1016/j.spinee.2013.08.060
88. Murray J. **Ti (titanium) binary alloy phase diagrams. alloy phase diagrams**. *ASM Int.* (2016.0) 615-8. DOI: 10.31399/asm.hb.v03.a0006213
|
---
title: 'Blood pressure lowering effects of β‐blockers as add‐on or combination therapy:
A meta‐analysis of randomized controlled trials'
authors:
- Qian‐Hui Guo
- Zhi‐Ming Zhu
- Ying‐Qing Feng
- Jin‐Xiu Lin
- Ji‐Guang Wang
journal: The Journal of Clinical Hypertension
year: 2023
pmcid: PMC9994166
doi: 10.1111/jch.14616
license: CC BY 4.0
---
# Blood pressure lowering effects of β‐blockers as add‐on or combination therapy: A meta‐analysis of randomized controlled trials
## Abstract
The authors performed a meta‐analysis to assess the efficacy of non‐atenolol β‐blockers as add‐on to monotherapy or as a component of combination antihypertensive therapy in patients with hypertension. The authors searched and identified relevant randomized controlled trials from PubMed until November 2021. Studies comparing blood pressure lowering effects of β‐blockers with diuretics, calcium channel blockers (CCBs), angiotensin‐converting enzyme inhibitors (ACEIs), or angiotensin receptor blockers (ARBs) were included. The analysis included 20 studies with 5544 participants. β‐blockers add‐on to monotherapy significantly reduced systolic and diastolic blood pressure as compared with non‐β‐blocker monotherapy (weighted mean difference in mm Hg [$95\%$ confidence interval]: −4.1 [−6.0, −2.2] and −3.7 [−4.6, −2.8], respectively). These results were consistent across the comparisons with diuretics (systolic pressure, −10.2 [−14.2, −6.2]; diastolic pressure, −5.4 [−8.2, −2.6]), CCBs (systolic pressure, −4.1 [−7.1, −1.0]; diastolic pressure, −2.8 [−4.1, −1.5]), and ACEIs/ARBs (systolic pressure, −2.9 [−4.3, −1.5]; diastolic pressure, −4.2 [−5.0, −3.4]). There was no significant difference in blood pressure lowering effects between combinations with and without a β‐blocker (systolic pressure, −1.3 mm Hg [−5.8, 3.2]; diastolic pressure, −.3 mm Hg [−2.7, 2.1]). Metoprolol add‐on or combination therapy had a significantly greater blood pressure reduction than non‐β‐blocker therapy (systolic pressure, −3.6 mm Hg [−5.9, −1.3]; diastolic pressure, −2.1 mm Hg [−3.5, −.7]). In conclusion, non‐atenolol β‐blockers are effective in lowering blood pressure as add‐on to monotherapy or as a component of combination antihypertensive therapy. In line with the current hypertension guideline recommendations, β‐blockers can and should be used in combination with other antihypertensive drugs.
## INTRODUCTION
Current guidelines for the management of hypertension differ for the use of β‐blockers, despite the same clinical evidence. The Chinese Hypertension League (CHL) guidelines recommend β‐blockers as one of the first‐line antihypertensive drugs, 1 whereas the European Society of Cardiology (ESC)/European Society of Hypertension (ESH), 2 and the International Society of Hypertension (ISH) 3 guidelines recommend the use of β‐blockers at any treatment step for patients with cardiovascular diseases such as myocardial infarction, heart failure, angina, or atrial fibrillation. The American Heart Association (AHA)/American College of Cardiology (ACC) 4 and the Japanese Society of Hypertension (JSH) 5 guidelines recommend β‐blockers for resistant hypertension and for patients with cardiovascular diseases. In consideration of the high prevalence of resistant hypertension and cardiovascular diseases, β‐blockers are often indispensable as a component of combination therapy for blood pressure control and for cardiovascular prevention and protection. However, the reluctance to use β‐blockers as part of the first‐line therapy for uncomplicated hypertension is influenced by studies with atenolol, which showed inferior effects in comparison with other classes of antihypertensive drugs. 6, 7, 8 Indeed, in the LIFE (Losartan Intervention For Endpoint Reduction in Hypertension) 6 and ASCOT‐BPLA (the blood pressure‐lowering arm of the Anglo‐Scandinavian Cardiac Outcomes Trial) 7 trials with an antihypertensive regimen based on an angiotensin‐receptor blocker (ARB) and calcium‐channel blocker (CCB) as the comparator, respectively, atenolol‐based antihypertensive regimen was less efficacious in reducing blood pressure as well as the risk of cardiovascular events.
There is emerging clinical trial evidence that antihypertensive therapy with a β‐blocker other than atenolol may be particularly efficacious in blood pressure control. 9, 10, 11 Therefore, we performed a systematic review and meta‐analysis of randomized controlled trials to assess the effects of β‐blockers, other than atenolol, as a component of antihypertensive therapy on systolic and diastolic blood pressure in patients with hypertension.
## Search strategy and selection criteria
We performed systematic review of randomized controlled trials with a parallel‐group design that compared blood pressure lowering effects of non‐atenolol β‐blockers as add‐on to monotherapy or as a component of combination antihypertensive therapy in patients with hypertension. We searched MEDLINE (PubMed) databases from inception to 28 November 2021. The complete search strategy is provided in Supplementary Appendix A.
Other components of combination therapy included diuretics, CCBs, angiotensin‐converting enzyme inhibitors (ACEIs), and ARBs. Among β‐blockers, atenolol was excluded, and metoprolol, bisoprolol, acebutolol, esmolol, carvedilol, labetalol, arotinolol, bevantolol, celiprolol, nebivolol, and bucindolol were included in the search. There was no restriction in terms of study duration or type of blood pressure measurement device; however, studies with a sample size smaller than 50 participants were excluded.
## Data extraction and synthesis
Studies retrieved from the MEDLINE database search were first assessed for relevance through screening of titles and abstracts. The full texts of relevant studies were then assessed for eligibility according to the inclusion and exclusion criteria set for this meta‐analysis. Prespecified data were extracted from each of the included studies by one researcher using a standardized *Excel data* extraction sheet, and independently reviewed by two researchers. The prespecified data extracted for each eligible study included study design, intervention characteristics, baseline characteristics of interest, and study outcomes. Any disagreements during data extraction were resolved by consensus (Figure 1).
**FIGURE 1:** *Flow diagram of selection procedure for included studies.*
## Outcome measures
The outcomes of interest for the present meta‐analysis included change from baseline in clinic systolic and diastolic blood pressure and heart rate and the proportion of patients with blood pressure response.
## Assessment of selection bias
Selection bias in eligible trials was assessed by the Cochrane collaboration's tool (version 2.0); further details are provided in Supplementary Appendices B and C. Selection bias was concurrently examined by the two data reviewers for the randomization process, deviation from intended intervention, missing outcome data, measurement of the outcome, and selection of the reported results. The overall bias for each trial was categorized as “low risk”, “some concerns”, and “high risk”.
## Statistical analyses
All statistical analyses (pooled analyses, funnel plot, sensitivity analysis, and Harbord/Egger's test) were performed using the Stata software (version 15.0). Data were extracted using a standardized data form. The relative risks or weighted mean difference (WMD) and $95\%$ confidence intervals ($95\%$ CIs) were calculated for each outcome using a fixed‐effects (Mantel–Haenszel method, inverse variance method) and random‐effects model, respectively, in the absence and presence of heterogeneity. All tests were two‐sided and P ≤.05 was considered statistically significant. For all the outcomes of interest, subgroup analyses were performed according to therapeutic regimen.
Statistical heterogeneity was assessed using the I2 statistic and chi‐squared test. I2 statistic with values < 25, 25–50, and > $50\%$ indicated low, moderate, and high heterogeneity, respectively. The chi‐squared test was used as inferential statistics of heterogeneity with a significance level at $P \leq .1.$ Potential publication bias was evaluated using the funnel plot and the Harbord or Egger's test. To evaluate the influence of each study on the overall estimate, sensitivity analysis was performed in which the meta‐analysis was re‐estimated by omitting each study in turn. An individual study was suspected of excessive influence if the point estimate of its omitted analysis laid outside the CI of the combined analysis, or its omitted analysis estimate differed in significance relative to the combined analysis.
## Characteristics of the included studies
Of the 1117 titles and abstracts identified in the initial search of the database, a total of 66 studies were shortlisted for full‐text review; of these, 26 papers met the inclusion criteria and reported at least one outcome of interest. 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37 Six papers were excluded because of insufficient data 12, 13, 14, 15, 16, 17; hence, 20 papers (reporting 21 treatment comparisons) comprising 5544 patients were included in the final analysis. 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37 The trials were segregated and analyzed separately based on the β‐blocker interventions as add‐on to monotherapy or as a component of combination therapy. A total of 15 papers reported effects of β‐blockers as an add‐on to monotherapy compared with a non‐β‐blocker monotherapy, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32 while five papers compared blood pressure lowering effects of combination therapy with and without β‐blockers. 33, 34, 35, 36, 37 The study selection process is described in Figure 1 and details of the included studies are summarized in Table 1. Study duration ranged from 4 to 36 weeks. Of the 20 trials included in the analysis, 15 showed low risk of selection bias for the six domains of the Cochrane Risk of Bias instrument, and five trials showed some concerns of selection bias (Supplementary Appendices B and C).
**TABLE 1**
| Study (first author, year) | Intervention | Number of patients | Patients (inclusion criteria) | Follow‐up (weeks) |
| --- | --- | --- | --- | --- |
| β‐blocker add‐on therapy versus non‐β‐blocker monotherapy | β‐blocker add‐on therapy versus non‐β‐blocker monotherapy | β‐blocker add‐on therapy versus non‐β‐blocker monotherapy | β‐blocker add‐on therapy versus non‐β‐blocker monotherapy | β‐blocker add‐on therapy versus non‐β‐blocker monotherapy |
| β‐blocker + diuretic | | | | |
| Bichisao and coworkers, 1989 31 | Metoprolol/chlorthalidone SR (200/25 mg/day) versus chlorthalidone (25 mg/day) | 545 | Hypertensive outpatients, at stage I or II, with supine DBP > 95 mm Hg and < 115 mm Hg | 8 weeks |
| Labetalol/HCTZ Multicenter Study Group, 1985 32 | Labetalol (200–800 mg/day) + HCTZ (50 mg/day) versus HCTZ (50 mg/day) | 174 | Adults aged 19—70 years with essential hypertension (standing DBP > 95 mm Hg) | 10 weeks |
| Frishman and coworkers, 1994 28 | Bisoprolol (10 mg/day) + HCTZ (25 mg/day) versus HCTZ (25 mg/day) | 63 | Adults aged ≥21 years with stage I and stage II hypertension (DBP 95–115 mm Hg) | 4 weeks |
| Frishman and coworkers, 1995 27 | Bisoprolol (5 mg/day) + HCTZ (6.25 mg/day) versus HCTZ (25 mg/day) | 283 | Adults aged ≥21 years with stage I and stage II hypertension (DBP 95–115 mm Hg) | 4 weeks |
| Prisant and coworkers, 1998 25 | Bisoprolol/HCTZ (up to 10/6.25 mg OD) versus enalapril (up to 40 mg OD) Bisoprolol/HCTZ (up to 10/6.25 mg OD) versus amlodipine (up to 10 mg OD) | 462 | Adults with stage I and II hypertension (DBP 95–114 mm Hg) | 12 weeks |
| β‐blocker + CCB | | | | |
| Dahlöf and coworkers, 1990 30 | Metoprolol/felodipine ER (100/10 mg/day) + versus felodipine ER (10 mg/day) | 107 | Adults aged 20–70 years with newly diagnosed or previously treated hypertension (DBP > 95 mm Hg) | 12 weeks |
| Wetzchewald and coworkers, 1992 29 | Metoprolol (100–200 mg OD) + felodipine (10–20 mg OD) versus felodipine (10–40 mg OD) | 76 | Adults with DBP ≥95 mm Hg | 36 weeks |
| Andersson and coworkers, 1999 26 | Metoprolol/felodipine (50/5 or 100/10 mg OD) versus enalapril (10–20 mg OD) | 120 | Adults aged 20–70 years, with supine DBP 95–115 mm Hg | 8 weeks |
| Waeber and coworkers, 1999 23 | Metoprolol/felodipine (50/5 to 100/10 mg/day) versus enalapril (10–20 mg/day) | 642 | Adults with uncomplicated essential hypertension (seated DBP 95–110 mm Hg) | 12 weeks |
| Zannad and coworkers, 1999 24 | Metoprolol/felodipine (50/5 mg OD) versus amlodipine (5 mg OD) | 245 | Patients aged 30–75 years with mild‐to‐moderate uncomplicated primary hypertension and DBP of 95–115 mm Hg | 6 weeks |
| Frishman and coworkers, 2006 22 | Metoprolol/felodipine ER (400/20 mg OD) versus felodipine ER (20 mg OD) | 173 | Adults aged 18–80 years with uncomplicated essential hypertension (seated DBP 95–114 mm Hg) | 9 weeks |
| Devi and coworkers, 2011 21 | Metoprolol ER/amlodipine (50/5 mg OD) versus amlodipine (5 mg OD) | 163 | Adults aged 18–80 years with a SBP of 140–179 mm Hg and DBP of 90–114 mm Hg | 8 weeks |
| β‐blocker + ACEI/ARB | | | | |
| Deedwania and coworkers, 2013 19 | Nebivolol (5–40 mg/day) + lisinopril (10 mg/day) or losartan (50 mg/day) versus lisinopril (10 mg/day) or losartan (50 mg/day) | 325 | Adults aged 18–80 years with a diagnosis of primary hypertension; DBP 90–110 mm Hg if untreated, 85–105 mm Hg if taking one antihypertensive medication, or 80–95 mm Hg if taking two antihypertensive medications | 12 weeks |
| Weiss and coworkers, 2013 20 | Nebivolol (5–40 mg/day) + lisinopril (10–20 mg/day) or losartan (100–200 mg/day) versus lisinopril (10–20 mg/day) or losartan (100–200 mg/day) | 491 | Adults aged 18–85 years with a diagnosis of primary hypertension; SBP 170–200 mm Hg if untreated, 155–180 mm Hg if taking one antihypertensive medication, or 140–170 mm Hg if taking two antihypertensive medications | 12 weeks |
| Giles and coworkers, 2014 18 | Nebivolol/valsartan (10/160 mg/day for Weeks 1–4 and 20/320 mg/day for Weeks 5–8) versus valsartan (160 mg/day for Weeks 1–4 and 320 mg/day for Weeks 5–8) | 1108 | Men and women aged ≥18 years with stage I or II hypertension (JNC7 criteria) with a recent DBP of ≥90 mm Hg and < 110 mm Hg if receiving hypertensive treatment, or ≥95 mm Hg and < 110 mm Hg at screening if untreated | 8 weeks |
| β‐blocker combination therapy | β‐blocker combination therapy | β‐blocker combination therapy | β‐blocker combination therapy | β‐blocker combination therapy |
| Breithaupt‐Grögler K and coworkers, 1998 37 | Metoprolol/HCTZ (100/12.5 mg) versus verapamil SR/trandolapril (180/1 mg) | 51 | Adults with hypertension (DBP ≥90 and < 115 mm Hg) | 24 weeks |
| Klein and coworkers, 1998 36 | Metoprolol/felodipine (50/5 mg OD for Weeks 1–4 and 100/10 mg OD for Weeks 5–8) versus captopril/HCTZ (25/25 mg OD for Weeks 1–4 and 50/25 mg OD for Weeks 5–8) | 109 | Adults aged 20—70 years with mild‐to‐moderate primary hypertension (DBP 95–115 mm Hg) | 8 weeks |
| Pareek and coworkers, 2010 35 | Metoprolol ER/amlodipine (25/2.5 mg – 50/5 mg OD) versus losartan (25–50 mg OD) + amlodipine (2.5–5 mg OD) | 148 | Patients aged 18—75 years with mild‐to‐moderate hypertension (DBP 90–109 mm Hg) | 12 weeks |
| Grassi and coworkers, 2017 34 | Nebivolol/HCTZ (5/12.5 mg OD) versus irbesartan/HCTZ (150/12.5 mg OD) | 122 | Elderly aged > 60 years with isolated systolic hypertension (SBP ≥140 mm Hg and DBP < 90 mm Hg) | 12 weeks |
| Farag and coworkers, 2018 33 | Nebivolol (5 mg OD) + valsartan (160 mg OD) versus amlodipine/valsartan (10/160 mg OD) | 137 | Patients with essential hypertension of stage II or more severe (defined as either SBP ≥160 mm Hg or DBP ≥100 mm Hg) | 12 weeks |
## Efficacy of β‐blockers as an add‐on to antihypertensive therapy
Of the 15 add‐on therapy trials, nine and 11 reported changes in sitting/supine systolic and diastolic blood pressure, respectively. Overall, the β‐blocker add‐on therapy significantly reduced systolic and diastolic blood pressure more than the non‐β‐blocker monotherapy (WMD [$95\%$ CI]: −4.1 mm Hg [−6.0, −2.2] and −3.7 mm Hg [−4.6, −2.8], respectively). In the analysis according to the antihypertensive medication, systolic blood pressure reduction was significantly and consistently greater for the β‐blocker add‐on to a diuretic (WMD: −10.2 mm Hg, $95\%$ CI: −14.2, −6.2), a CCB (WMD: −4.1 mm Hg, $95\%$ CI: −7.1, −1.0), or an ACEI/ARB (WMD: −2.9 mm Hg, $95\%$ CI: −4.3, −1.5; Figure 2A), compared to the respective monotherapies. Similar results were observed for diastolic blood pressure, with the corresponding WMD of −5.4 mm Hg ($95\%$ CI: −8.2, −2.6), −2.8 mm Hg ($95\%$ CI: −4.1, −1.5), and −4.2 mm Hg ($95\%$ CI: −5.0, −3.4), respectively (Figure 2B).
**FIGURE 2:** *Treatment effects of β‐blocker add‐on therapy on (A) systolic and (B) diastolic blood pressure in the sitting or supine position, and (C) diastolic blood pressure response. Black symbols represent point estimate of each individual trial. Horizontal lines denote 95% CIs of each individual trial. Diamonds represent overall or subtotal pooled estimate and 95% CI of trials. ACEI, angiotensin‐converting enzyme inhibitor; ARB, angiotensin receptor blocker; BB, β‐blocker; CCB, calcium channel blocker; CI, confidence interval; WMD, weighted mean difference*
Ten studies reported effects on the proportion of patients who achieved a diastolic blood pressure control (< 90 mm Hg or < 95 mm Hg, or a ≥10 mm Hg reduction from baseline). Overall, the probability of patients achieving a diastolic blood pressure response was $34\%$ higher with the β‐blocker add‐on therapy than non‐β‐blocker monotherapy (odds ratio [OR]: 1.34, $95\%$ CI: 1.12, 1.61). In the analysis according to antihypertensive medication, the likelihood of achieving a diastolic blood pressure response was higher for the β‐blocker add‐on to a diuretic (OR: 1.50, $95\%$ CI: 1.20, 1.88) and to an ACEI/ARB (OR: 1.25, $95\%$ CI: 1.01, 1.55), but not to a CCB (OR: 1.13; $95\%$ CI:.82, 1.55), compared to the respective non‐β‐blocker monotherapies (Figure 2A).
Three trials (all with metoprolol) reported treatment effects on heart rate. β‐blocker add‐on led to a greater reduction in heart rate by 6.9 bpm ($95\%$ CI: −8.6, −5.2) than non‐β‐blocker monotherapy in a fixed‐effect model (Supplementary Appendix D).
None of the included studies had excessive influence on the overall results of the meta‐analysis (Supplementary Appendix E). Funnel plots did not show any evidence of publication bias (Supplementary Appendix F).
## Efficacy of β‐blockers as a component of combination antihypertensive therapy
Five trials reported changes in sitting/supine systolic and diastolic blood pressure between combination antihypertensive therapy with and without β‐blocker as a component. There was no significant difference between β‐blocker combination and non‐β‐blocker combination therapies for the reduction in systolic and diastolic blood pressure (WMD [$95\%$ CI]: −1.3 mm Hg [−5.8, 3.2] and −.3 mm Hg [−2.7, 2.1], respectively; Figure 3A and B).
**FIGURE 3:** *Treatment effects of β‐blocker combination therapy on (A) systolic and (B) diastolic blood pressure in the sitting or supine position. Black symbols represent point estimate of each individual trial. Horizontal lines denote 95% CIs of each individual trial. Diamonds represent overall or subtotal pooled estimate and 95% CI of trials. BB, β‐blocker; CI, confidence interval; WMD, weighted mean difference.*
## Subgroup analysis of metoprolol add‐on or combination antihypertensive therapy
Seven and nine trials reported changes in sitting/supine systolic and diastolic blood pressure with metoprolol add‐on and combination therapy, respectively. Overall, the metoprolol add‐on or combination therapy was associated with a significantly greater reduction in systolic and diastolic blood pressure than non‐β‐blocker therapy (WMD [$95\%$ CI]: −3.6 mm Hg [−5.9, −1.3] and −2.1 mm Hg [−3.5, −.7], respectively; Figure 4A and B).
**FIGURE 4:** *Treatment effects on (A) systolic and (B) diastolic blood pressure in the sitting or supine position in metoprolol trials. Black symbols represent point estimate of each individual trial. Horizontal lines denote 95% CIs of each individual trial. BB, β‐blocker; CI, confidence interval; WMD, weighted mean difference.*
## DISCUSSION
In the present meta‐analysis, β‐blocker add‐on therapy was associated with a significantly greater reduction in systolic and diastolic blood pressure compared with non‐β‐blocker monotherapy. The between‐group WMD was −4.1 and −3.7 mm Hg, respectively, consistently in favor of the β‐blocker add‐on therapy compared to various antihypertensive therapies such as diuretics, CCBs and ACEIs/ARBs. Combination therapy containing a β‐blocker was as effective as non‐β‐blocker combination therapy for blood pressure lowering effects. Similar results were observed for the analysis restricted to metoprolol trials.
This meta‐analysis investigates the blood pressure lowering effect of β‐blockers as add‐on and a component of combination antihypertensive therapy. The results of the analysis support the position that beta‐blockers can be used in combination with the other classes of antihypertensive drugs at any stage of treatment for blood pressure control, including as a first‐choice therapy. The findings of the analysis are also in line with results of previous meta‐analyses of β‐blocker therapy for the management of hypertension and other cardiovascular diseases and with the view expressed by Mancia and coworkers 38 and Esler and coworkers 39 in their recent reviews. After thorough review of the evidence, these authors highlighted favorable effects of β‐blockers in about 50 different clinical conditions that may coexist with hypertension. The authors also argued that downgrading β‐blockers from first‐choice therapy to use on specific conditions alone may not be justified based on the evidence that β‐blockers are as effective as other antihypertensive drugs in lowering blood pressure. 2, 38, 39 However, it is noteworthy that β‐blockers are a heterogenous class of drugs with differences in physiochemical properties and receptor selectivity, leading to variable pharmacological effects and efficacy in patients with hypertension. Previous randomized controlled trials often used propranolol or atenolol in the evaluation of the efficacy of β‐blockers for the primary prevention of cardiovascular events. Propranolol is a non‐selective β‐blocker. Atenolol is a hydrophilic β1 selective agent with a relatively short half‐life. The once‐daily dosing of atenolol was probably sub‐optimal, as indicated by significantly lower blood pressure reduction in the atenolol group than in the comparator groups in the LIFE and ASCOT‐BPLM trials. 6, 7 The results of these atenolol trials probably do not apply to other β1 selective blockers such as metoprolol. Indeed, in the Metoprolol Atherosclerosis Prevention in Hypertensives (MAPHY) trial, metoprolol treatment led to a significant reduction in cardiovascular mortality and morbidity, including stroke, compared with diuretics. 40, 41 Furthermore, a previous meta‐analysis has shown that β‐blockers are effective as other classes of antihypertensive drugs in preventing cardiovascular events, especially when trials with atenolol were excluded. 42 The findings of our current study further add to the evidence in supporting the efficacy of β‐blockers in hypertension.
If used properly, such as in combination with other classes of antihypertensive drugs, β‐blockers could be particularly effective in blood pressure lowering and in cardiovascular disease prevention. The observed 3–4 mm Hg superior blood pressure lowering effect may explain the results from several recent observational studies demonstrating outcome benefits of β‐blockers in cardiovascular disease prevention. 43, 44 *In a* real‐world evidence study of long‐term effectiveness of β‐blockers, using data from patients registered in the UK Clinical Practice Research Datalink between 2000 and 2014 ($$n = 100$$ 066), 44 patients receiving β‐blocker therapy ($$n = 4240$$) had a lower risk of mortality than those receiving other antihypertensive therapy. The risk reduction in mortality was sustained for 15 years (hazard ratio,.52; $95\%$ CI:.27,.10), with a 2–3 year delayed effect after the combination of β‐blocker therapy. 44 Similar findings were reported from the long‐term follow‐up (20 years) of the UKPDS study, where the risk of mortality was lower with a β‐blocker than with an ACEI in patients with diabetes mellitus and hypertension. 43 The observed greater blood pressure lowering effect of β‐blockers add‐on to combination ACEIs/ARBs is noteworthy. Such a combination is not recommended by any current hypertension guidelines, because β‐blockers suppress renin secretion and reduce the plasma levels of angiotensin II and, therefore, perceived to be less additive in antihypertensive efficacy to ACEIs/ARBs. 45 The additive effect of β‐blockers add‐on to ACEIs/ARBs on blood pressure reduction was indeed less than the add on to diuretics or CCBs, but still appreciable in size, at 2–4 mm Hg greater than antihypertensive therapy with non‐β‐blockers. Of note, combination therapy with ACEIs/ARBs and a β‐blocker is recommended as first‐line therapy in patients with heart failure or myocardial infarction. 46, 47, 48, 49 While the majority of these patients also have hypertension, the recommendations are not solely based on the blood pressure lowering effects of β‐blockers, but rather on their cardioprotective effects. The efficacy of β‐blockers with ACEI/ARBs combination therapy in hypertension remains unexplored and should be further investigated in randomized controlled trials.
The present meta‐analysis should be interpreted within the context of its limitations. First, the analysis was based on summary statistics instead of individual patient data, hence reaching less standardization. Second, the number of trials and study patients were relatively small for some of the subgroup analyses, for example, the combination of β‐blockers and ACEIs/ARBs and the metoprolol versus non‐metoprolol subgroup analyses. The pooled estimates on the subgroup analyses require confirmation in further randomized controlled trials. In addition, the dose of the study drugs varied across studies. However, sensitivity analyses did not show any excessive influence of any individual study on the pooled estimates. Finally, the duration of the included studies was relatively short (ranged from 4 to 36 weeks). The study results cannot be extrapolated to longer‐term duration. Nonetheless, metoprolol demonstrated sustained blood pressure lowering effects in the long‐term MAPHY trial with a median follow‐up of 4.2 years. 40, 41 In conclusion, this meta‐analysis showed that β‐blockers (excluding atenolol) as add‐on to several other classes of antihypertensive drugs, such as diuretics, CCBs and even ACEIs/ARBs, were efficacious in further lowering blood pressure and that combination therapy with β‐blockers is as effective as non‐β‐blocker combination therapy. Thus, in line with the current guideline recommendations, β‐blockers can and should be used in combination with other classes of antihypertensive drugs at any stage of treatment.
## AUTHOR CONTRIBUTIONS
All authors contributed to the meta‐analysis design and data analysis, and to the drafting, review and final approval of the manuscript.
## CONFLICT OF INTEREST
Qian‐Hui Guo and Ji‐Guang Wang were financially supported by grants from the National Natural Science Foundation of China (91639203, 82070435, and 82000394) and the Ministry of Science and Technology (2018YFC1704902), Beijing; the Shanghai Commissions of Science and Technology (19DZ2340200) and Health (“Three‐year Action Program of Shanghai Municipality for Strengthening the Construction of Public Health System”, GWV‐10.1‐XK05, and a special grant for “leading academics”); and the Clinical Research Program, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (grant number: 2018CR010), Shanghai, China.
## Unknown
Zhi‐Ming Zhu, Ying‐Qing Feng, Jin‐Xiu Lin declare no conflict of interest.
## References
1. Liu J. **Highlights of the 2018 Chinese hypertension guidelines**. *Clin Hypertens* (2020.0) **26**
2. Williams B, Mancia G, Spiering W. **ESC/ESH guidelines for the management of arterial hypertension**. *Eur Heart J* (2018.0) **39** 3021-3104. PMID: 30165516
3. Unger T, Borghi C, Charchar F. **2020 International Society of Hypertension global hypertension practice guidelines**. *Hypertension* (2020.0) **75** 1334-1357. PMID: 32370572
4. Whelton PK, Carey RM, Aronow WS. **Guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: a report of the American College of Cardiology/American Heart Association Task Force on clinical practice guidelines**. *Circulation* **138** e484-e594. PMID: 30354654
5. Umemura S, Arima H, Arima S. **The Japanese Society of Hypertension guidelines for the management of hypertension (JSH 2019)**. *Hypertens Res* **42** 1235-1481. PMID: 31375757
6. Dahlof B, Devereux RB, Kjeldsen SE. **Cardiovascular morbidity and mortality in the Losartan Intervention For Endpoint reduction in hypertension study (LIFE): a randomised trial against atenolol**. *Lancet* (2002.0) **359** 995-1003. PMID: 11937178
7. Dahlof B, Sever PS, Poulter NR. **Prevention of cardiovascular events with an antihypertensive regimen of amlodipine adding perindopril as required versus atenolol adding bendroflumethiazide as required, in the Anglo‐Scandinavian Cardiac Outcomes Trial‐Blood Pressure Lowering Arm (ASCOT‐BPLA): a multicentre randomised controlled trial**. *Lancet* (2005.0) **366** 895-906. PMID: 16154016
8. Williams B, Lacy PS, Thom SM. **Differential impact of blood pressure‐lowering drugs on central aortic pressure and clinical outcomes: principal results of the Conduit Artery Function Evaluation (CAFE) study**. *Circulation* (2006.0) **113** 1213-1225. PMID: 16476843
9. Mahmud A, Feely J. **Beta‐blockers reduce aortic stiffness in hypertension but nebivolol, not atenolol, reduces wave reflection**. *Am J Hypertens* (2008.0) **21** 663-667. PMID: 18437130
10. Dhakam Z, Yasmin McEnieryCM, Burton T, Brown MJ, Wilkinson IB. **A comparison of atenolol and nebivolol in isolated systolic hypertension**. *J Hypertens* (2008.0) **26** 351-356. PMID: 18192850
11. Bonet S, Agusti A, Arnau JM. **Beta‐adrenergic blocking agents in heart failure: benefits of vasodilating and non‐vasodilating agents according to patients' characteristics: a meta‐analysis of clinical trials**. *Arch Intern Med* (2000.0) **160** 621-627. PMID: 10724047
12. Radchenko GD, Sirenko YM, Kushnir SM, Torbas OO, Dobrokhod AS. **Comparative effectiveness of a fixed‐dose combination of losartan + HCTZ versus bisoprolol + HCTZ in patients with moderate‐to‐severe hypertension: results of the 6‐month ELIZA trial**. *Vasc Health Risk Manage* (2013.0) **9** 535-549
13. Vitale C, Marazzi G, Iellamo F. **Effects of nebivolol or irbesartan in combination with hydrochlorothiazide on vascular functions in newly‐diagnosed hypertensive patients: the NINFE (Nebivololo, Irbesartan Nella Funzione Endoteliale) study**. *Int J Cardiol* (2012.0) **155** 279-284. PMID: 22078979
14. Takase B, Takeishi Y, Hirai T. **Comparative effects of amlodipine monotherapy and combination therapy with betaxolol on cardiac autonomic nervous activity and health‐related quality of life in patients with poorly controlled hypertension**. *Circ J* (2008.0) **72** 764-769. PMID: 18441457
15. Lacourciere Y, Lefebvre J, Poirier L, Archambault F, Arnott W. **Treatment of ambulatory hypertensives with nebivolol or hydrochlorothiazide alone and in combination. A randomized, double‐blind, placebo‐controlled, factorial‐design trial**. *Am J Hypertens* (1994.0) **7** 137-145. PMID: 8179848
16. Lejeune P, Gunselmann W, Hennies L. **Effects of BAY l 5240, a fixed combination of low dose nifedipine and acebutolol on hypertension: comparison with standard dose nifedipine**. *Eur J Clin Pharmacol* (1985.0) **28** 17-21. PMID: 3886399
17. Stimpel M, Koch B. **Antihypertensive treatment with moexipril plus HCTZ vs metoprolol plus HCTZ in patients with mild‐to‐moderate hypertension**. *J Hum Hypertens* (1997.0) **11** 133-137. PMID: 9140801
18. Giles TD, Weber MA, Basile J. **Efficacy and safety of nebivolol and valsartan as fixed‐dose combination in hypertension: a randomised, multicentre study**. *Lancet* (2014.0) **383** 1889-1898. PMID: 24881993
19. Deedwania P, Shea J, Chen W, Brener L. **Effects of add‐on nebivolol on blood pressure and glucose parameters in hypertensive patients with prediabetes**. *J Clin Hypertens (Greenwich)* (2013.0) **15** 270-278. PMID: 23551727
20. Weiss RJ, Stapff M, Lin Y. **Placebo effect and efficacy of nebivolol in patients with hypertension not controlled with lisinopril or losartan: a phase IV, randomized, placebo‐controlled trial**. *Am J Cardiovasc Drugs* (2013.0) **13** 129-140. PMID: 23519546
21. Devi P, Xavier D, Sigamani A. **Effect of fixed dose combinations of metoprolol and amlodipine in essential hypertension: MARS–a randomized controlled trial**. *Blood Press Suppl* (2011.0) **2** 5-12. PMID: 22352120
22. Frishman WH, Hainer JW, Sugg J, Group M‐FACTStudy. **A factorial study of combination hypertension treatment with metoprolol succinate extended release and felodipine extended release results of the Metoprolol Succinate‐Felodipine Antihypertension Combination Trial (M‐FACT)**. *Am J Hypertens* (2006.0) **19** 388-395. PMID: 16580575
23. Waeber B, Detry JM, Dahlof B. **Felodipine‐metoprolol combination tablet: a valuable option to initiate antihypertensive therapy?**. *Am J Hypertens* (1999.0) **12** 915-920. PMID: 10509550
24. Zannad F, Boivin JM. **Ambulatory 24‐h blood pressure assessment of the felodipine‐metoprolol combination versus amlodipine in mild to moderate hypertension. Lorraine General Physician Investigators Group**. *J Hypertens* (1999.0) **17** 1023-1032. PMID: 10419077
25. Prisant LM, Neutel JM, Papademetriou V, DeQuattro V, Hall WD, Weir MR. **Low‐dose combination treatment for hypertension versus single‐drug treatment‐bisoprolol/hydrochlorothiazide versus amlodipine, enalapril, and placebo: combined analysis of comparative studies**. *Am J Ther* (1998.0) **5** 313-321. PMID: 10099075
26. Andersson OK. **Improved efficacy with maintained tolerability in the treatment of primary hypertension. Comparison between the felodipine‐metoprolol combination tablet and monotherapy with enalapril. Swedish Multicentre Group**. *J Hum Hypertens* (1999.0) **13** 55-60. PMID: 9928753
27. Frishman WH, Burris JF, Mroczek WJ. **First‐line therapy option with low‐dose bisoprolol fumarate and low‐dose hydrochlorothiazide in patients with stage I and stage II systemic hypertension**. *J Clin Pharmacol* (1995.0) **35** 182-188. PMID: 7751430
28. Frishman WH, Bryzinski BS, Coulson LR. **A multifactorial trial design to assess combination therapy in hypertension. Treatment with bisoprolol and hydrochlorothiazide**. *Arch Intern Med* (1994.0) **154** 1461-1468. PMID: 8018001
29. Wetzchewald D, Klaus D, Garanin G, Lohr E, Hoffmann J. **Regression of left ventricular hypertrophy during long‐term antihypertensive treatment–a comparison between felodipine and the combination of felodipine and metoprolol**. *J Intern Med* (1992.0) **231** 303-308. PMID: 1532616
30. Dahlof B, Hosie J. **Antihypertensive efficacy and tolerability of a fixed combination of metoprolol and felodipine in comparison with the individual substances in monotherapy. The Swedish/United Kingdom Study Group**. *J Cardiovasc Pharmacol* (1990.0) **16** 910-916. PMID: 1704983
31. Bichisao E, Merlini L, Gambini O, Alberti D, Pollavini G. **Chlorthalidone alone or in fixed combination with slow‐release metoprolol in the management of arterial hypertension: a long‐term study of 545 patients**. *J Int Med Res* (1989.0) **17** 339-349. PMID: 2676651
32. **Labetalol and hydrochlorothiazide in hypertension**. *Clin Pharmacol Ther* (1985.0) **38** 24-27. PMID: 2860990
33. Farag SM, Rabea HM, Mahmoud HB. **Effect of amlodipine/valsartan versus nebivolol/valsartan fixed dose combinations on peripheral and central blood pressure**. *High Blood Press Cardiovasc Prev* (2018.0) **25** 407-413. PMID: 30390203
34. Grassi G, Seravalle G, Brambilla G. **Multicenter randomized double‐blind comparison of nebivolol plus HCTZ and irbesartan plus HCTZ in the treatment of isolated systolic hypertension in elderly patients: results of the NEHIS study**. *Adv Ther* (2017.0) **33** 2173-2187. PMID: 27771847
35. Pareek A, Chandurkar NB, Sharma R, Tiwari D, Gupta BS. **Efficacy and tolerability of a fixed‐dose combination of metoprolol extended release/amlodipine in patients with mild‐to‐moderate hypertension: a randomized, parallel‐group, multicentre comparison with losartan plus amlodipine**. *Clin Drug Investig* (2010.0) **30** 123-131
36. Klein G. **Combination therapy with felodipine and metoprolol compared with captopril and hydrochlorothiazide**. *Blood Press* (1998.0) **7** 308-312. PMID: 10321444
37. Breithaupt‐Grogler K, Gerhardt G, Lehmann G, Notter T, Belz GG. **Blood pressure and aortic elastic p ation therapy**. *Int J Clin Pharmacol Ther* (1998.0) **36** 425-431. PMID: 9726695
38. Mancia G, Kjeldsen SE, Kreutz R, Pathak A, Grassi G, Esler M. **Individualized beta‐blocker treatment for high blood pressure dictated by medical comorbidities: indications beyond the 2018 European Society of Cardiology/European Society of Hypertension guidelines**. *Hypertension* (2022.0) **79** 1153-1166. PMID: 35378981
39. Esler M, Kjeldsen SE, Pathak A, Grassi G, Kreutz R, Mancia G. *Diverse pharmacological properties, trial results, comorbidity prescribing and neural pathophysiology suggest European hypertension guideline downgrading of beta‐blockers is not justified* (2022.0) 210-224
40. Wikstrand J, Warnold I, Olsson G, Tuomilehto J, Elmfeldt D, Berglund G. **Primary prevention with metoprolol in patients with hypertension. Mortality results from the MAPHY study**. *JAMA* (1988.0) **259** 1976-1982. PMID: 3346979
41. Wikstrand J, Warnold I, Tuomilehto J. **Metoprolol versus thiazide diuretics in hypertension. Morbidity results from the MAPHY Study**. *Hypertension* (1991.0) **17** 579-588. PMID: 2013485
42. Kuyper LM, Khan NA. **Atenolol vs nonatenolol beta‐blockers for the treatment of hypertension: a meta‐analysis**. *Can J Cardiol* (2014.0) **30** S47-53. PMID: 24750981
43. Holman RR, Paul SK, Bethel MA, Neil HA, Matthews DR. **Long‐term follow‐up after tight control of blood pressure in type 2 diabetes**. *N Engl J Med* (2008.0) **359** 1565-1576. PMID: 18784091
44. Sabido M, Hohenberger T, Grassi G. **Pharmacological intervention in hypertension using beta‐blockers: real‐world evidence for long‐term effectiveness**. *Pharmacol Res* (2018.0) **130** 191-197. PMID: 29366925
45. Izzo JL, Khan SU, Saleem O, Osmond PJ. **Ambulatory 24‐hour cardiac oxygen consumption and blood pressure‐heart rate variability: effects of nebivolol and valsartan alone and in combination**. *J Am Soc Hypertens* (2015.0) **9** 526-535. PMID: 26116459
46. Heidenreich PA, Bozkurt B, Aguilar D. **AHA/ACC/HFSA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Joint Committee on clinical practice guidelines**. *Circulation* (2022.0) **145** e895-e1032. DOI: 10.1161/CIR.0000000000001063
47. McDonagh TA, Metra M, Adamo M. **2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure**. *Eur Heart J* (2021.0) **42** 3599-3726. PMID: 34447992
48. Collet JP, Thiele H, Barbato E. **2020 ESC guidelines for the management of acute coronary syndromes in patients presenting without persistent ST‐segment elevation**. *Eur Heart J* (2021.0) **42** 1289-1367. PMID: 32860058
49. Ibanez B, James S, Agewall S. **ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST‐segment elevation: the task force for the management of acute myocardial infarction in patients presenting with ST‐segment elevation of the European Society of Cardiology (ESC)**. *Eur Heart J* **39** 119-177. PMID: 28886621
|
---
title: 'Rapid response nursing triage outcomes for COVID-19: factors associated with
patient’s participation in triage recommendations'
authors:
- Jyu-Lin Chen
- Chen-Xi Lin
- Mijung Park
- Jerry John Nutor
- Rosalind de Lisser
- Thomas J. Hoffmann
- Hannah J. Kim
journal: BMC Medical Informatics and Decision Making
year: 2023
pmcid: PMC9994385
doi: 10.1186/s12911-023-02139-x
license: CC BY 4.0
---
# Rapid response nursing triage outcomes for COVID-19: factors associated with patient’s participation in triage recommendations
## Abstract
### Background
COVID-19 is an ongoing global health crisis with prevention and treatment recommendations rapidly changing. Rapid response telephone triage and advice services are critical in providing timely care during pandemics. Understanding patient participation with triage recommendations and factors associated with patient participation can assist in developing sensitive and timely interventions for receiving the treatment to prevent adverse health effects of COVID-19.
### Methods
This cohort study aimed to assess patient participation (percentage of patients who followed nursing triage suggestions from the COVID hotline) and identify factors associated with patient participation in four quarterly electronic health records from March 2020 to March 2021 (Phase 1: 14 March 2020–6 June 2020; Phase 2: 17 June 2020–16 September 2020; Phase 3: 17 September 2020–16 December 2020; Phase 4: 17 December 2020–16 March 2021). All callers who provided their symptoms (including asymptomatic with exposure to COVID) and received nursing triage were included in the study. Factors associated with patient participation were identified using multivariable logistic regression analyses, including demographic variables, comorbidity variables, health behaviors, and COVID-19-related symptoms.
### Results
The aggregated data included 9849 encounters/calls from 9021 unique participants. Results indicated: [1] $72.5\%$ of patient participation rate; [2] participants advised to seek emergency department care had the lowest patient participation rate ($43.4\%$); [3] patient participation was associated with older age, a lower comorbidity index, a lack of unexplained muscle aches, and respiratory symptoms. The absence of respiratory symptoms was the only factor significantly associated with patient participation in all four phases (OR = 0.75, 0.60, 0.64, 0.52, respectively). Older age was associated with higher patient participation in three out of four phases (OR = 1.01–1.02), and a lower Charlson comorbidity index was associated with higher patient participation in phase 3 and phase 4 (OR = 0.83, 0.88).
### Conclusion
Public participation in nursing triage during the COVID pandemic requires attention. This study supports using a nurse-led telehealth intervention and reveals crucial factors associated with patient participation. It highlighted the importance of timely follow-up in high-risk groups and the benefit of a telehealth intervention led by nurses serving as healthcare navigators during the COVID-19 pandemic.
## Background
The novel coronavirus (SARS-CoV-2; 2019-nCoV; COVID-19) is an urgent and ongoing global health crisis. As of 18 January 2022, California had about 6.8 million confirmed cases, with 77,306 deaths due to COVID-19 complications and a $21.1\%$ test-positive rate [1]. To prepare for a sudden surge in patients needing critical medical care and to reduce the possibility of overwhelming health systems, healthcare providers must work together to manage the influx of potential patients at an early stage. This includes managing the influx at an early stage, and identifying patients in the community early through triage hotlines and clinics to provide appropriate and timely care for patients during the COVID-19 pandemic [2]. Nurses are uniquely positioned to lead community-based triage efforts to identify and recommend care for patients during the ongoing COVID-19 pandemic.
The outbreak of COVID-19 has impacted the usual face-to-face interactions between nurses and their patients. To provide high-quality clinical care and increase access to healthcare systems, many hospitals have increased the use of virtual and other telehealth systems to triage and care for patients. One of these technologies, Telephone Triage and Advice Services (TTAS), allows patients to speak to a nurse over the telephone to receive assessment and health advice [3]. Nurses can quickly obtain detailed travel and exposure histories and act as first responders for patients at elevated risk [4]. Automated screening algorithms may be built into the intake process, and local epidemiologic information can standardize screening and practice patterns across hospitals. Respiratory symptoms, which may be early signs of COVID-19, are most commonly evaluated using the TTAS approach.
Current research has revealed that TTAS are safe, promote care access, and increase service utilization [5–7]. During the ongoing pandemic, TTAS capabilities may have the potential to address increasing demands placed on overwhelmed health systems [7]. The COVID-19 pandemic has created and reinforced the opportunity and advantage for nurses and healthcare providers to provide assessment and recommendations via phone, especially when face-to-face interaction is limited and a large number of patients seek care recommendations. TTAS also enables access to care for people living in remote or underserved communities and those with underlying comorbid conditions with limited resources. COVID-19 is an ongoing public health challenge and mitigating public transmission with adherence to COVID-19 measures is needed. TTAS is run and led by nurses, serving as critical healthcare navigators who provide an essential function in the fight against COVID-19, offering patient education and triaging patients to the appropriate level of care. Patient participation is a patient’s subsequent action adherence to the health providers’ recommendation. Nevertheless, there is little evidence that documents the impact of a nurse-led COVID-19 rapid response telephone triage center on patient participation with the recommendations, and few studies have examined factors associated with patient participation with the COVID-19 triage recommendations [8, 9].
Understanding patient participation with triage recommendations and factors associated with patient participation (such as age, sex, health conditions, and symptoms) can assist in developing sensitive and timely interventions for receiving the treatment needed to prevent adverse health effects of COVID-19. Lessons revealed in this study can inform healthcare providers and policymakers in making recommendations for managing an early response to public health concerns.
## Methods
This study aimed to (a) assess patient participation rate with COVID-19 nursing triage recommendations in a large healthcare center in the United States and (b) identify factors (demographic data such as age, gender, comorbidity, obesity status, health behaviors such as smoking, drinking, and illicit drug used and symptoms) associated with patient participation with nurse recommendations at a University-Affiliated Medical Center. In this study, patient participation is defined as a patient’s subsequent action adherence to the telephone triage recommendation within 14 days of the initial triage call in the three-month phase.
A cohort study design was used to follow the patients over the two weeks after initiation of the triage hotline and advised them to follow the recommendations based on risk factors and reported symptoms. This study included patients from a large University-affiliated Medical Center in Northern California participating in the COVID-19 nursing triage hotline between 14 March 2020 and 21 March 2021. This study included a convenience sample of individuals who called the hotline, reported symptoms (including asymptomatic responses from patients who had exposure to COVID-19), and received nursing triage. Callers who called the hotline for COVID prevention or other COVID-related information without providing their symptom information for triage are omitted. This study utilized extensive data from the Medical Center’s electronic health records system. Study participants' consent was not required to use data from the electronic health records. This study has been approved by the Institutional Ethics Committee on Human Research of the University of California, San Francisco [20-31034].
A nurse triage hotline was established in a large University-affiliated medical center, Rapid Response Nursing Triage Hotline for COVID-19 called RN TO COVID, led by nurses who serve as health care navigators. Patients at this University-affiliated health center were advised to contact the COVID-19 hotline with questions or if they were experiencing COVID-19-related symptoms. All calls were taken by a triage nurse, and patients were asked about their symptoms, exposures, and comorbidities. The triage nurse then made care recommendations based on pre-determined triage protocols created by current clinical practice guidelines and COVID-19 symptomatology. Patients without symptoms but who had exposure to COVID-19 were asked to monitor their conditions and call back if they developed symptoms. Patients received four possible recommendations when they reported symptoms: self-care (SC), video acute care clinic visit (VACC within 24 h), respiratory symptom clinic (RSC in-person care within 12 h), and emergency department care (ED immediate care). A recent publication provides information on this triage protocol [10].
This study utilized extensive data from the Medical Center’s electronic health record system (Epic), available to hospital clinicians and researchers. Data were analyzed in four phases, based on quarterly periods of data collection from March 2020 to March 2021 (Phase 1: 14 March 2020–6 June 2020; Phase 2: 17 June 2020–16 September 2020; Phase 3: 17 September 2020–16 December 2020; Phase 4: 17 December 2020–16 March 2021). Individuals were included if they called the COVID-19 hotline, provided their symptoms, and received telephone triage decisions during the study period. This study used only initial call data per patient every three months. A response to the triage recommendation was determined by a patient’s action documented in the electronic medical record within 14 days of the triage call. Patients’ participation in the recommendation (VACC, RSC, and ED) were obtained in the electronic medical record system at the University-affiliated hospital system. All data extracted were de-identified. R v4.1.0 [11] and STATA SE v16.0 [12] were used for data cleaning and analysis.
## Measures
Dependent variable: patient participation in COVID-19 triage recommendation is defined as the patient’s subsequent action adherence to the telephone triage recommendation within 14 days of the initial triage call in the three-month phase. Independent variables include demographic characteristics (age, gender), comorbidity (Charlson comorbidity index and obesity status), health behaviors (smoking, drinking, and illicit drug use), and symptoms (fever, unexplained muscle aches, eye-nose-throat (ENT) symptoms, eye symptom-(i.e., eye redness and/or discharge, respiratory symptom, upper respiratory infection (URI) symptom, gastrointestinal (GI symptom), and altered mental status).
Charlson comorbidity index (CCI) is a weighted index as a continuous variable to predict the risk of death within one year of hospitalization for patients with specific comorbid conditions. Higher scores indicate a more severe condition and a poorer prognosis and have been used consistently in the literature [13]. Body Mass Index (BMI) was defined as the body mass (kg) divided by the square of the height (m2) and was calculated using self-reported weight and height. We defined obesity as having a BMI equal to or above 30 [14]. This was a dichotomous variable. Health behaviors included smoking, drinking, and illicit drug use as binary variables (0 = never, 1 = quit or currently use). COVID-related symptoms included fever, unexplained muscle aches, ENT, eye symptoms, respiratory symptoms, URI symptoms, GI symptoms, and altered mental status, which were included in the hotline nursing triage (0 = No, 1 = Yes).
## Analysis
Descriptive analyses were used to describe demographic characteristics and patient participation. Chi-square tests were performed to examine differences in the prevalence of obesity, health behaviors, and COVID-19-related symptoms between patients who followed the triage recommendation and those who did not follow the recommendation. Multivariable logistic regression analyses were performed to determine the odds ratios (ORs) and $95\%$ confidence intervals (CIs) between demographic variables (gender and age), comorbidity variables (CCI and BMI), health behaviors (smoking, drinking, and illicit drug used), and COVID-related symptoms and patient participation based on four different pandemic phases and all four phases. All statistical analyses were performed with STATA SE v16.0 with a significance value set at $p \leq 0.05$ and a Bonferroni corrected (for 15 tests) significance level of 0.0033 for logistic regression models.
## Results
The analysis included 9849 encounters/calls from 9021 unique participants (mean age = 45.4 years, SD = 21.1). Overall, about $39.5\%$ of patients self-identified as male, ranging from $38.8\%$ in phase 2 to $40.3\%$ in phase 4. On average, $21.8\%$ of patients were obese, ranging from $19.5\%$ in phase 3 and $24\%$ in phase 1. In terms of health behaviors, in the overall sample, $30.1\%$, $66.1\%$, and $23.8\%$ of patients reported smoking, drinking, and using illicit drugs, respectively (Table 1). The most commonly reported symptoms included URI symptoms ($37.6\%$), respiratory symptoms ($30\%$), unexplained muscle aches ($17.8\%$), and GI symptoms ($13\%$, Table 1).Table 1Characteristics of participantsPhase 1Phase 2Phase 3Phase 4Total($$n = 1769$$)($$n = 2887$$)($$n = 3250$$)($$n = 1943$$)($$n = 9849$$)DemographicAge, mean (SD)49.67 (19.85)46.33 (20.90)43.12 (20.78)44.04 (22.35)45.42 (21.10)SexMale709 ($40.1\%$)1123 ($38.8\%$)1274 ($39.1\%$)785 ($40.3\%$)3891 ($39.5\%$)Female1059 ($59.9\%$)1770 ($61.2\%$)1978 ($60.9\%$)1163 ($59.7\%$)5970 ($60.5\%$)ComorbidityCharlson comorbidity index, mean (SD)0.99 (1.37)0.82 (1.35)0.61 (1.14)0.72 (1.24)0.76 (1.27)Obesity (Yes)398 ($24.0\%$)580 ($22.1\%$)579 ($19.5\%$)409 ($23.2\%$)1966 ($21.8\%$)Health behaviors (Yes/No)Smoking (Yes)563 ($33.2\%$)819 ($30.1\%$)849 ($28.2\%$)539 ($30.3\%$)2770 ($30.1\%$)Drinking (Yes)965 ($61.2\%$)1623 ($66.0\%$)1933 ($70.7\%$)992 ($63.2\%$)5513 ($66.1\%$)Illicit drug use (Yes)331 ($21.4\%$)574 ($24.1\%$)689 ($25.8\%$)344 ($22.5\%$)1938 ($23.8\%$)Symptoms (Yes/No)Fever (Yes)267 ($15.1\%$)0 ($0\%$)0 ($0\%$)0 ($0\%$)267 ($2.7\%$)Unexplained muscle aches (Yes)387 ($21.9\%$)632 ($21.9\%$)481 ($14.8\%$)257 ($13.2\%$)1757 ($17.8\%$)ENT (Yes)99 ($5.6\%$)152 ($5.3\%$)120 ($3.7\%$)76 ($3.9\%$)447 ($4.5\%$)Eye symptom (Yes)40 ($2.3\%$)72 ($2.5\%$)63 ($1.9\%$)35 ($1.8\%$)210 ($2.1\%$)Respirator symptom (Yes)831 ($47.0\%$)840 ($29.1\%$)809 ($24.9\%$)474 ($24.4\%$)2954 ($30.0\%$)URI symptom (Yes)804 ($45.5\%$)1192 ($41.3\%$)1128 ($34.7\%$)583 ($30.0\%$)3707 ($37.6\%$)GI symptom (Yes)329 ($18.6\%$)403 ($14.0\%$)350 ($10.8\%$)196 ($10.1\%$)1278 ($13.0\%$)Altered mental (Yes)5 ($0.3\%$)8 ($0.3\%$)5 ($0.2\%$)3 ($0.2\%$)21 ($0.2\%$)Phase 1: 14 March 2020–6 June 2020. Phase 2: 17 June 2020–16 September 2020. Phase 3: 17 September 2020–16 December 2020. Phase 4: 17 December 2020–16 March 2021 On average, of the total cohort, about $3.4\%$ of patients ($$n = 331$$) were directed to the ED, $14.2\%$ to RSC ($$n = 1401$$), $18.3\%$ to Self-care ($$n = 1798$$), $21.1\%$ to VACC ($$n = 2082$$), and $43\%$ to self-monitor ($$n = 4238$$, Table 2).Table 2Call results/recommendationPhaseED (%)RSC (%)SELF CARE (%)VACC (%)Monitor (%)Total180 (5.5)256 (14.5)446 (25.2)566 (32.0)421 (23.8)1769295 (3.3)544 (18.8)617 (21.4)514 (17.8)1117 (38.7)28873100 (3.0)377 (11.6)485 (19.0)618 (19.0)1670 (51.4)3250456 (2.9)224 (11.5)256 (13.2)377 (19.4)1030 (53.1)1943Total331 (3.4)1401 (14.2)1798 (12.3)2082 (21.1)4238 (43.0)9849ED Emergency department immediate care; RSC Respiratory symptom clinic in-person care within 12 h; VACC Video acuter care clinic visits within 24 h The overall patient participation rate for the following triage recommendations was $72.5\%$; in the four phases, patient participation rates were $69\%$, $68.7\%$, $77.8\%$, and $72.5\%$, respectively. Significant differences were found among the four recommendations across the four study phases. Participants advised to seek ED care had the lowest patient participation ($43.4\%$), in comparison with participants advised to seek care at a video acute care clinic ($57.6\%$), a respiratory screening center ($71.6\%$), or to administer self-care ($67.7\%$) (Table 3).Table 3Patient participation in nurse recommendationsRecommendationPatient participation (%)Phase 1–4Phase 1Phase 2Phase 3Phase 4ED137 (41.4)37 (46.2)28 (29.5)49 (49.0)23 (41.1)RSC804 (57.4)158 (61.7)334 (61.4)216 (57.3)96 (42.9)VACC1403 (67.6)360 (63.6)357 (69.5)436 (70.6)250 (66.3)SELF CARE1290 (71.6)335 (75.1)325 (52.7)412 (85.1)218 (85.2)SELF MONITER3507 (82.7)331 (78.6)940 (84.2)1414 (84.6)822 (79.8)All7141 (72.5)1221 (69.0)1984 (68.7)2527 (77.8)1409 (72.5)Pearson chi2156.4220***33.9584***281.3123***218.3143***181.9150***ED Emergency department immediate care; RSC Respiratory symptom clinic in-person care within 12 h; VACC Video acuter care clinic visits within 24 h***$p \leq 0.001$ In all 4 phases of aggregated data, older age, a lower Charlson comorbidity index (CCI), and lack of unexplained muscle aches and respiratory symptoms were associated with higher patient participation (Pseudo R2 = − 0.02; $p \leq 0.001$; Table 4). The absence of respiratory symptoms was the only factor significantly associated with higher patient participation in all periods [OR = 0.75, 0.60, 0.64, 0.52; $95\%$ CI = (0.57, 0.91), (0.56, 0.85), (0.45, 0.75), (0.39, 0.70), respectively]. Older age was associated with higher patient participation in three out of four phases [OR = 1.01–1.02; $95\%$ CI = (1.01, 1.02)], and a lower Charlson comorbidity index (CCI) was associated with higher patient participation in phase 3 and phase 4 [OR = 0.83, 0.88; $95\%$ CI = (0.76, 0.90), (0.80, 0.97)] (Table 4).Table 4Logistic Regression model for patient’s participation in phases 1–; All phasesPhase 1Phase 2Phase 3Phase 4Sex1.051.061.001.230.98[0.94, 1.17][0.84, 1.34][0.82, 1.22][0.99, 1.52][0.76, 1.27]Age1.01***1.02***1.01***1.011.01**[1.006, 1.01][1.01, 1.02][1.00, 1.02][1.00, 1.01][1.00, 1.02]Charlson comorbidity index0.92***0.931.020.83***0.88*[0.88, 0.96][0.85, 1.01][0.95, 1.10][0.76, 0.90][0.80, 0.97]Obesity1.011.051.241.001.05[0.95, 1.08][0.81, 1.35][0.99, 1.55][0.96, 1.04][0.79, 1.39]Smoking1.101.071.230.961.08[0.97, 1.23][0.83, 1.39][1.00, 1.52][0.77, 1.21][0.82, 1.43]Drinking0.940.890.980.910.93[0.84, 1.05][0.70, 1.13][0.80, 1.21][0.73, 1.14][0.71, 1.21]Drug used1.091.261.140.911.09[0.96, 1.25][0.93, 1.70][0.90, 1.44][0.71, 1.15][0.80, 1.50]Fever0.790.851.001.001.00[0.60, 1.04][0.63, 1.14][1.00, 1.00][1.00, 1.00][1.00, 1.00]Unexplained muscle aches0.82**0.930.840.65**1.01[0.75, 0.52][0.69, 1.26][0.67, 1.05][0.49, 0.86][0.69, 1.49]ENT symptom1.160.861.341.211.08[0.91,1.47][0.53, 1.39][0.89, 2.02][0.73, 2.02][0.59, 1.95]Eye symptom0.830.710.920.721.02[0.60, 1.15][0.35, 1.45][0.54, 1.57][0.38, 1.34][0.42, 2.52]Respiratory symptom0.60***0.72**0.69**0.58***0.52***[0.54, 0.67][0.57, 0.91][0.56, 0.85][0.45, 0.75][0.39, 0.70]URI symptom0.931.240.65***1.271.03[0.82, 1.05][0.95, 1.62][0.53, 0.80][0.98, 1.65][0.76, 1.40]GI symptom0.890.980.970.860.77[0.77, 1.04][0.73, 1.32][0.74, 1.26][0.63, 1.18][0.51, 1.15]Altered mental0.741.001.240.371.00[0.27, 2.05][1.00, 1.00][0.24, 6.41][0.05, 2.88][1.00, 1.00]Observations76691484225024971433Exponentiated coefficients; $95\%$ confidence intervals in brackets*$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$
## Discussion
This study aimed to assess the utilization of the COVID-19 hotline and patient participation with COVID-19 nursing triage recommendations and identify factors associated with patient participation in the early COVID-19 pandemic. More than 9,800 patients called the nurse-led triage COVID hotline during the first 12 months of the pandemic. The top three reported symptoms reported by patients include URI symptoms ($37.6\%$), respiratory symptoms ($30\%$), and unexplained muscle aches ($17.8\%$). Of patients who reported symptoms, $21.1\%$ were advised to VACC, $18.3\%$ to Self-care, $14.2\%$ to RSC, and $3.4\%$ of patients to ED. In this study, the average patient participation rate of nursing triage recommendations was $72.5\%$, ranging from 68.7 to $77.8\%$. We found that participants advised to seek ED care had the lowest patient participation ($43.4\%$). The absence of respiratory symptoms, older age, and lower Charlson comorbidity index are associated with higher patient participation in the recommendations.
The triage nurses’ recommendations were based on the triage protocol, including symptoms and other high-risk factors (such as age and other chronic health conditions). The COVID-19 hotline was heavily utilized by people seeking nursing triage and recommendations in the community. Our study found that about one-third of patients reported URI or respiratory symptoms. These respiratory-related symptoms reported by patients are consistent with studies suggesting that COVID-19 primarily targets the respiratory system. The virus can enter the respiratory tract through the mucous membranes of the mouth, nose, and eyes [15–17]. A recent systematic review and meta-analysis study on the prevalence of symptoms in adults from nine countries found that the most common clinical presentation of severe COVID-19 was fever ($78\%$), followed by cough ($57\%$) and fatigue ($31\%$) [15]. Current evidence suggests that individuals infected with COVID-19 experience various symptoms, from mild to severe conditions, and symptoms typically occur 2–14 days after virus exposure [15]. The time gap between exposure to COVID and the onset of symptoms may be responsible for the low prevalence of some symptoms in our study since some callers reached the hotline after being exposed to COVID but without symptoms. As the virus mutates and evolves, the continued assessment of the symptoms reported by people with COVID-19 is critical in understanding the symptom presentation and influencing how patients perceive the need to follow the nurses’ recommendations and receive treatment when necessary. The average patient participation rate in nursing triage recommendations was $72.5\%$, ranging from 68.7 to $77.8\%$. Patients advised ED care had the lowest patient participation rate ($43.4\%$), which is very concerning as the progression of severe symptoms can occur quickly and increase the risk for further health deterioration and delayed necessary hospitalization. Our findings are consistent with national data indicating that ED visits were lower between April and June 2020 [18]. Delayed ED care increases the risk of not receiving appropriate care and not getting an accurate diagnosis, and prolonged hospitalization increases mortality [19, 20]. A current study in Sweden found the overall 60-day mortality rate as $17.4\%$ during the first year of the pandemic [20]. Studies have found high mortality rates ranging from 35 to $62\%$ for critically ill patients with coronavirus [21–23]. Some possible rationales for non-adherence to the recommendation to ED care could include that individuals were concerned about the long waiting time in the ED or the likelihood of being infected with COVID while staying in the ED. Additionally, during the stay-at-home order, individuals may have had limited assistance at home, thus impacting their ability to seek acute ED care, and others may have been concerned about the cost of ED care. Timely treatment is essential in reducing the mortality associated with COVID-19. Future studies will need to explore the individual perception of the severity of illness and individuals’ rationale for not going to ED when advised.
Our study found that common factors associated with patient participation in the triage recommendation include the absence of respiratory symptoms and older and lower comorbidity. As the most common symptoms reported by people with COVID-19 include fever and dry cough [17], individuals without respiratory and lower comorbidity may be more likely to be asked to perform self-care or VACC, which is easier to follow through. Consistent with previous studies, individuals with a medical history and old age have an increased risk for infection and poorer COVID-19 outcomes due to weaker immune systems [24–26]. In the early pandemic, although there was limited knowledge of COVID-19 and its impact on health, there were several public health campaigns and regular announcements related to this newly discovered virus on media. These public announcements discussed the incidence of COVID-19 and shared information on the high-risk group for COVID-19 infection and mortality (such as older age and individuals with existing health conditions). The media coverage of COVID-19 influence individuals’ perception of their risk and decision on behavior changes (i.e., wearing a face mask, keeping social distance, frequent handwashing, getting medical attention) and vaccination intention [27, 28]. As older age has been reported to increase the risk for COVID-19-related hospitalization and mortality, individuals with older age may be more aware of their risk for COVID-19 and the health effects of COVID-19; thus, they are more likely to follow the nurses’ recommendations. To increase patient participation in nurses’ recommendations, public health organizations may need to engage various communities in health literacy champions that are culturally and linguistically appropriate to increase the understanding of the current recommendations for public health issues. Future studies may also need to explore the role of media on COVID-19 behavior change and medical care-seeking behaviors.
The results of this study provide recommendations for healthcare providers and policymakers regarding the management of the ongoing COVID-19 response and future public health emergencies. First, the utilization and participation rate of the nurse-based telephone triage system was moderate to high. A nurse-based telephone triage system based on the most up-to-date evidence can be quickly established to respond to emergency health events. The timeliness of the response is feasible and critical in providing timely and appropriate care. Second, timely follow-up with the patient regarding the recommendations, understanding the patient’s hesitancy in following the recommendation, and adapting policy in real-time are important factors in addressing the ongoing COVID-19 pandemic, as well as improving patient health outcomes. Third, patients advised to seek emergency department care had the lowest patient participation ($43.4\%$), revealing the importance of timely follow-up for high-risk groups. Future clinical pathways or practice guidelines may need to consider factors (respiratory symptoms, younger age, and higher Charlson comorbidity index) in clinical decision-making.
This study is one of the first to explore the utilization of the nurse-led triage COVID-19 hotline. The results of the study should be interpreted with caution. Some limitations of the study include convenient sampling, the use of only electronic medical record data within a university-affiliated hospital, and self-reported data. The fewer report of some symptoms, such as altered mental conditions and fevers, may cause the models to be unstable, and the results must be interpreted carefully. Moreover, the percentage of variance explained by the model and ORs for age is small and other variables such as living alone or with family, history of illness, and previous relationships with healthcare workers could be included in future analyses. Another limitation is that we were unable to truly validate whether patients have performed self-care. We assumed that patients who did not use other services (such as VCC, RSC, and ED) within 14 days performed self-care. Future studies may reach out to patients concerning their self-care behaviors.
Future studies may need to consider other potential factors (such as symptom severity, medical history, knowledge about COVID-19, exposure to COVID-19, social determinants of health variables, access to treatment, and social support) related to patient participation in recommendations made by nurses and matching groups. Future work may have broader generalizability if other settings were included and a mixed-method design was used to examine barriers and facilitators following triage recommendations.
## Conclusion
The average patient participation rate was $72.5\%$, with the ED being the lowest rate. The absence of respiratory symptoms, older age, and lower Charlson comorbidity index are associated with higher patient participation. Public participation in nursing triage during the COVID pandemic requires attention. This study supports using a nurse-led telehealth intervention, where nurses serve as critical healthcare navigators, providing assessment and care recommendations during the COVID-19 pandemic. While many adhered to nurse triage recommendations, those determined to be most critical did not; this is a concerning finding that warrants future study.
## References
1. 1.California State Government. Tracking COVID-19 in California. 2022. [Cited 2022 1/18]. Available from: https://covid19.ca.gov/state-dashboard/.
2. Eggleton EJ. **Simple, fast and affordable triaging pathway for COVID-19**. *Postgrad Med J* (2021.0) **97** 192-195. DOI: 10.1136/postgradmedj-2020-138029
3. Purc-Stephenson RJ, Thrasher C. **Nurses’ experiences with telephone triage and advice: a meta-ethnography**. *J Adv Nurs* (2010.0) **66** 482-494. DOI: 10.1111/j.1365-2648.2010.05275.x
4. Bolina AF, Bomfim E, Lopes-Junior LC. **Frontline nursing care: the COVID-19 pandemic and the brazilian health system**. *SAGE Open Nurs* (2020.0) **6** 2377960820963771. PMID: 33415303
5. Chambers D. **Digital and online symptom checkers and health assessment/triage services for urgent health problems: systematic review**. *BMJ Open* (2019.0) **9** e027743. DOI: 10.1136/bmjopen-2018-027743
6. Lake R. **The quality, safety and governance of telephone triage and advice services: an overview of evidence from systematic reviews**. *BMC Health Serv Res* (2017.0) **17** 614. DOI: 10.1186/s12913-017-2564-x
7. McLean S. **The impact of telehealthcare on the quality and safety of care: a systematic overview**. *PLoS ONE* (2013.0) **8** e71238. DOI: 10.1371/journal.pone.0071238
8. Caponnetto V. **The COVID-19 pandemic as an opportunity to improve health care through a nurse-coordinated multidisciplinary model in a headache specialist center: the implementation of a telemedicine protocol**. *Telemed J E Health* (2022.0) **28** 1016-1022. DOI: 10.1089/tmj.2021.0414
9. Raesi R. **The impact of education through nurse-led telephone follow-up (telenursing) on the quality of life of COVID-19 patients**. *J Egypt Public Health Assoc* (2021.0) **96** 30. DOI: 10.1186/s42506-021-00093-y
10. Judson TJ. **Rapid design and implementation of an integrated patient self-triage and self-scheduling tool for COVID-19**. *J Am Med Inform Assoc* (2020.0) **27** 860-866. DOI: 10.1093/jamia/ocaa051
11. 11.Team RC. R: a language and environment for statistical computing. 2013.
12. 12.StataCorpStata statistical software: release 172021College Station, TXStataCorp LP. *Stata statistical software: release 17* (2021.0)
13. Nanda S. **Body mass index, multi-morbidity, and COVID-19 risk factors as predictors of severe COVID-19 outcomes**. *J Prim Care Community Health* (2021.0) **12** 21501327211018559. DOI: 10.1177/21501327211018559
14. Wu S. **Metabolic health has greater impact on diabetes than simple overweight/obesity in Mexican Americans**. *J Diabetes Res* (2016.0) **2016** 4094876. DOI: 10.1155/2016/4094876
15. Grant MC. **The prevalence of symptoms in 24,410 adults infected by the novel coronavirus (SARS-CoV-2; COVID-19): a systematic review and meta-analysis of 148 studies from 9 countries**. *PLoS ONE* (2020.0) **15** e0234765. DOI: 10.1371/journal.pone.0234765
16. Brosnahan SB. **COVID-19 and respiratory system disorders: current knowledge, future clinical and translational research questions**. *Arterioscler Thromb Vasc Biol* (2020.0) **40** 2586-2597. DOI: 10.1161/ATVBAHA.120.314515
17. Harrison AG, Lin T, Wang P. **Mechanisms of SARS-CoV-2 transmission and pathogenesis**. *Trends Immunol* (2020.0) **41** 1100-1115. DOI: 10.1016/j.it.2020.10.004
18. Smulowitz PB. **National trends in ED visits, hospital admissions, and mortality for medicare patients during the COVID-19 pandemic**. *Health Aff (Millwood)* (2021.0) **40** 1457-1464. DOI: 10.1377/hlthaff.2021.00561
19. Bae SJ. **Comparison of the clinical process and outcomes in patients after coronavirus infection 2019 outbreak**. *Medicina (Kaunas)* (2021.0) **57** 1086. DOI: 10.3390/medicina57101086
20. Stralin K. **Mortality in hospitalized COVID-19 patients was associated with the COVID-19 admission rate during the first year of the pandemic in Sweden**. *Infect Dis (Lond)* (2022.0) **54** 145-151. DOI: 10.1080/23744235.2021.1983643
21. Bhatraju PK. **Covid-19 in critically Ill patients in the Seattle region—case series**. *N Engl J Med* (2020.0) **382** 2012-2022. DOI: 10.1056/NEJMoa2004500
22. Gupta S. **Factors associated with death in critically ill patients with coronavirus disease 2019 in the US**. *JAMA Intern Med* (2020.0) **180** 1436-1447. DOI: 10.1001/jamainternmed.2020.3596
23. Yang X. **Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study**. *Lancet Respir Med* (2020.0) **8** 475-481. DOI: 10.1016/S2213-2600(20)30079-5
24. Barek MA, Aziz MA, Islam MS. **Impact of age, sex, comorbidities and clinical symptoms on the severity of COVID-19 cases: a meta-analysis with 55 studies and 10014 cases**. *Heliyon* (2020.0) **6** e05684. DOI: 10.1016/j.heliyon.2020.e05684
25. Jiang Y. **Risk factors associated with 28-day all-cause mortality in older severe COVID-19 patients in Wuhan, China: a retrospective observational study**. *Sci Rep* (2020.0) **10** 22369. DOI: 10.1038/s41598-020-79508-3
26. Leijte WT. **Mortality and re-admission after hospitalization with COVID-19**. *Ned Tijdschr Geneeskd* (2020.0) **164** D5423. PMID: 33332036
27. Gozzi N. **Collective response to media coverage of the COVID-19 pandemic on Reddit and Wikipedia: mixed-methods analysis**. *J Med Internet Res* (2020.0) **22** e21597. DOI: 10.2196/21597
28. Zhou WK. **Effects of media reporting on mitigating spread of COVID-19 in the early phase of the outbreak**. *Math Biosci Eng* (2020.0) **17** 2693-2707. DOI: 10.3934/mbe.2020147
|
---
title: 'A prediction model for predicting the risk of acute respiratory distress syndrome
in sepsis patients: a retrospective cohort study'
authors:
- Chi Xu
- Lei Zheng
- Yicheng Jiang
- Li Jin
journal: BMC Pulmonary Medicine
year: 2023
pmcid: PMC9994387
doi: 10.1186/s12890-023-02365-z
license: CC BY 4.0
---
# A prediction model for predicting the risk of acute respiratory distress syndrome in sepsis patients: a retrospective cohort study
## Abstract
### Background
The risk of death in sepsis patients with acute respiratory distress syndrome (ARDS) was as high as 20–$50\%$. Few studies focused on the risk identification of ARDS among sepsis patients. This study aimed to develop and validate a nomogram to predict the ARDS risk in sepsis patients based on the Medical Information Mart for Intensive Care IV database.
### Methods
A total of 16,523 sepsis patients were included and randomly divided into the training and testing sets with a ratio of 7:3 in this retrospective cohort study. The outcomes were defined as the occurrence of ARDS for ICU patients with sepsis. Univariate and multivariate logistic regression analyses were used in the training set to identify the factors that were associated with ARDS risk, which were adopted to establish the nomogram. The receiver operating characteristic and calibration curves were used to assess the predictive performance of nomogram.
### Results
Totally 2422 ($20.66\%$) sepsis patients occurred ARDS, with the median follow-up time of 8.47 (5.20, 16.20) days. The results found that body mass index, respiratory rate, urine output, partial pressure of carbon dioxide, blood urea nitrogen, vasopressin, continuous renal replacement therapy, ventilation status, chronic pulmonary disease, malignant cancer, liver disease, septic shock and pancreatitis might be predictors. The area under the curve of developed model were 0.811 ($95\%$ CI 0.802–0.820) in the training set and 0.812 ($95\%$ CI 0.798–0.826) in the testing set. The calibration curve showed a good concordance between the predicted and observed ARDS among sepsis patients.
### Conclusion
We developed a model incorporating thirteen clinical features to predict the ARDS risk in patients with sepsis. The model showed a good predictive ability by internal validation.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12890-023-02365-z.
## Background
Sepsis is defined as a life-threatening organ dysfunction, which caused by a dysregulation of the host’s response to infection [1]. It is estimated that more than 19 million people suffer from sepsis each year, and it has become one of the major threats to human mortality [2]. Acute respiratory distress syndrome (ARDS) is regarded as the earliest and most common complication of sepsis, leading to the excessive and uncontrolled inflammatory reactions and increased mortality rate in sepsis patients, especially for critically ill patients [3, 4]. Previous studies have shown that the risk of death in sepsis patients complicated with ARDS was as high as 20–$50\%$ [5, 6]. Therefore, it is essential to pay attention to the risk of ARDS for sepsis patients.
Several researches have indicated that biomarkers, sociodemographic, clinical characteristics were related to the ARDS risk of patients with sepsis [4, 6–8]. In the study of Wang Q et al., they found that microRNA 103 (MIR103) and microRNA 107 (MIR107) were predictive biomarkers for ARDS risks in sepsis patients [6]. A retrospective cohort study found that oral glucocorticoids before admission were associated with a lower incidence of early ARDS among ICU sepsis patients [7]. Nam and colleagues also reported that pneumonia, coagulation score and the central nervous system score were associated with the risk of ARDS in Korean patients with sepsis, and these also were considered as risk factor for 28-day mortality [8]. *In* general, the risk of developing ARDS in patients with sepsis may be influenced by multiple factors, and the development of predictive models is of great importance for risk assessment [9]. Currently, ARDS risk prediction models for different populations have been proposed [10, 11]. The lung injury prediction score (LIPS) was considered to identify patients at a high risk of ARDS in non-emergency department hospitalized patients [10], as well as patients at high risk for acute lung injury early in the course of their illness and before intensive care unit (ICU) admission [12]. In addition, Lin F, et al. successfully constructed a model combining partial pressure of oxygen: fraction of inspired oxygen (PaO2:FiO2), platelet count, lactate dehydrogenase, creatinine, and procalcitonin levels to predict the ARDS risk among patients with severe acute pancreatitis [11]. Nevertheless, to the best of our knowledge, there were few studies have established a predictive model by combining multiple predictors to predict the risk of ARDS in sepsis patients.
Herein, the purpose of this study was to develop and validate a prediction model for prediction of ARDS risk in patients with sepsis based on the Medical Information Mart for Intensive Care (MIMIC) IV database.
## Source of data
We conducted a retrospective cohort study based on the MIMIC-IV database, as a single-center and freely accessible database, which contains a comprehensive and high-quality data of 53,130 patients in ICU at the Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2019 [13]. This study used de-identified data and was approved by the Massachusetts Institute of Technology and Institutional Review Board of BIDMC [14]. Patient’s informed consent has been obtained from all participants. All methods were carried out in accordance with relevant guidelines and regulations (declaration of Helsinki).
## Selection of participants
Sepsis was defined as a suspected infection combined with an acute increase in SOFA score ≥ 2 according to the Sepsis-3 criteria [1]. All information of participants derived from the MIMIC-IV database. Participants were included in the study if they met the definition of sepsis, were older than 18 years old and did not develop ARDS on admission and within 2 days of admission. The exclusion criteria were as follows: [1] patients who stayed in the ICU less than 24 h; [2] patients who had abnormal data records (height ≤ 50 cm or weight ≤ 1 kg). If patients were admitted repeatedly between 2008 and 2019, we adopted only the record of patient's first admission to the ICU. After implementation of inclusion and exclusion criteria, a total of 16,523 patients with sepsis were included in this study (Fig. 1).Fig. 1The flow-chart for population selection
## Data collection
We extracted the following variables from the MIMIC-IV database, including the demographic data [age, gender, ethnicity, marital status, insurance status, admission type, body mass index (BMI, kg/m2) and patients’ comorbidity]; the vital signs and laboratory data within 48 h after ICU admission [respiratory rate (times/min), systolic blood pressure (SBP, mmHg), diastolic blood pressure (DBP, mmHg), heart rate (times/min), temperature (℃), urine output (mL), partial pressure of carbon dioxide (PCO2, mmHg), FiO2, mmHg, bicarbonate (HCO3−), hemoglobin (g/dL), neutrophil (NEUT), lymphocyte (LYM), platelet (PLT, K/L), white blood cell (WBC, K/L), albumin (ALB), alanine aminotransferase (ALT, U/L), aspartate aminotransferase (AST, U/L), creatinine (mg/dL), blood urea nitrogen (BUN, mg/dL), glucose (mg/dL), C-reactive protein (CRP, mg/L), total cholesterol (TC, mg/dL), triglycerides (TG, mg/dL), low density lipoprotein cholesterol (LDL-C, mg/dL), high density lipoprotein cholesterol (HDL-C, mg/dL)]; severity scoring system [Sequential Organ Failure Assessment (SOFA) score, Simplified Acute Physiology Score (SAPS II)]; medications (heparin, aspirin, antibiotics and vasopressors); treatment [continuous renal replacement therapy (CRRT), mechanical ventilation (MV), red blood cell (RBC) transfusion, PLT transfusion, frozen plasma]. If patients received a laboratory test more than one time during their hospitalization, only the initial test results were included in this study. The diagnosis of ARDS met the *Berlin criteria* for patients in the MIMIC-IV database [15]. The *Berlin criteria* include: acute onset, PaO2/FiO2 ≤ 300 mmHg, positive end-expiratory pressure (PEEP) ≥ 5 cm H2O on the first day of ICU admission, bilateral infiltrates on chest radiograph, and absence of heart failure [16].
## Outcomes and follow-up
In this retrospective cohort study, the outcomes were defined as the occurrence of ARDS for ICU patients with sepsis. The start date of follow-up was considered as the date of the patient’s admission, and the median follow-up time was 8.47 (5.20, 16.20) days.
## Development and validation of prediction model
All eligible sepsis patients ($$n = 16$$,523) were randomly divided into the training ($$n = 11$$,566) set and testing set ($$n = 4957$$) in a ratio of 7:3. The prediction model was developed in the training set, and validated in the testing set. In the training set, univariate logistic regression analysis was used to screen the factors with $P \leq 0.05$, combining with factors associated with the risk of ARDS in septic patients in the literature, which were put into a multivariate model for stepwise regression to select some possible predictors. These predictors were used to construct prediction model for predicting the ARDS risk of sepsis patients. The area under the curve (AUC) of receiver operator characteristic curve (ROC) were adopted to compare the predicting performance between constructed prediction model and SOFA, SAPS II scoring system. Calibration curves were used to assess the predicting performance of prediction model in the training set and testing set.
## Statistical analysis
For the present study, mean ± standard deviation (Mean ± SD) and median and quartiles [M (Q1, Q3)] were adopted to described the normally-distributed and nonnormally-distributed of measurement data, respectively. The differences of the groups were compared by the t-test and Mann–Whitney U test. And the categorical data were presented by the number of cases and the constituent ratio [N (%)], and the χ2 test performed the comparisons of groups.
We conducted a difference analysis between the training set and testing set. In the training set ($$n = 11$$,566), patients with sepsis were divided into ARDS group ($$n = 2422$$) and non-ARDS group ($$n = 9144$$) according to whether ARDS occurred, and we also did a difference analysis between the ARDS group and non-ARDS group. Lastly, we developed and validated the predicting performance of developed model by ROC and calibration curves. The relative risk (RR) and $95\%$ confidence interval (CI) were calculated. In addition, we deleted the variables with more than $20\%$ missing values (ALT, ALB, TG, LDL-C, HDL-C, AST, NEUT, SaO2, TC and CRP), and the multiple filling method was used to fill the variables less than $20\%$ missing values. All analyses were conducted by using SAS 9.4 software (SAS Institute Inc., Cary, NC, USA). $P \leq 0.05$ was considered to be statistically significant.
## Baseline characteristics
The incidence of ARDS was $20.66\%$ among total population. No differences were noted between the training set ($$n = 11$$,566) and the testing set ($$n = 4$$,957) ($P \leq 0.05$) with respect to baseline information (Additional file 1: Table S1), suggesting that the division of data was balanced and comparable. The characteristics of 11,566 patients with sepsis in the training set were displayed in Table 1, of which 2422 ($20.94\%$) developed ARDS. The sepsis patients developing ARDS had significantly higher heart rate, respiratory rate, BUN level, PCO2 level and urine output than sepsis patients with non-ARDS. Additionally, compared to sepsis patients with non-ARDS, those with ARDS were more likely to have chronic pulmonary disease, vasopressin, red blood cell transfusion, liver disease and CRRT ($P \leq 0.05$).Table 1Baseline characteristics of 11,566 sepsis patients in the training setVariablesTotal ($$n = 11$$,566)Non-ARDS group ($$n = 9144$$)ARDS group ($$n = 2422$$)StatisticsPAge, years, Mean ± SD65.45 ± 15.6166.09 ± 15.5363.00 ± 15.65t = 8.69 < 0.001Gender, n (%)χ2 = 0.1150.735 Female4607 (39.83)3635 (39.75)972 (40.13) Male6959 (60.17)5509 (60.25)1450 (59.87)BMI, kg/m2, n (%)χ2 = 78.013 < 0.001 < 18.5334 (2.89)253 (2.77)81 (3.34) ≥ 18.5 and < 253322 (28.72)2733 (29.89)589 (24.32) ≥ 25 and < 303749 (32.41)3044 (33.29)705 (29.11) ≥ 304161 (35.98)3114 (34.06)1047 (43.23)Ethnicity, n (%)χ2 = 32.109 < 0.001 Black896 (7.75)676 (7.39)220 (9.08) Other/unknown2831 (24.48)2155 (23.57)676 (27.91) White7839 (67.78)6313 (69.04)1526 (63.01)Marital status, n (%)χ2 = 33.155 < 0.001 Married5448 (47.10)4418 (48.32)1030 (42.53) Other/unknown3227 (27.90)2536 (27.73)691 (28.53) Single2891 (25.00)2190 (23.95)701 (28.94)Insurance, n (%)χ2 = 16.907 < 0.001 Medicaid789 (6.82)579 (6.33)210 (8.67) Medicare5238 (45.29)4149 (45.37)1089 (44.96) Other5539 (47.89)4416 (48.29)1123 (46.37)Admission type, n (%)χ2 = 223.075 < 0.001 Elective739 (6.39)692 (7.57)47 (1.94) Emergency5588 (48.31)4223 (46.18)1365 (56.36) Other2491 (21.54)2141 (23.41)350 (14.45) Urgent2748 (23.76)2088 (22.83)660 (27.25)SBP, mmHg, M (Q1, Q3)118.00 (104.00, 135.00)118.00 (104.00, 135.00)119.00 (103.00, 137.00)$Z = 0.6580.511$DBP, mmHg, M (Q1, Q3)64.00 (54.00, 76.00)63.00 (54.00, 75.00)66.00 (55.00, 79.00)$Z = 5.887$ < 0.001Temperature, ℃, Mean ± SD36.63 ± 0.9536.60 ± 0.9236.74 ± 1.06t = − 6.13 < 0.001Heart rate, times/min, Mean ± SD89.37 ± 20.0287.83 ± 19.1895.20 ± 21.98t = − 15.07 < 0.001Respiratory rate, times/min, M (Q1, Q3)18.00 (15.00, 22.00)17.00 (14.00, 22.00)21.00 (17.00, 25.00)$Z = 23.702$ < 0.001Urine output, mL, M (Q1, Q3)2990.00 (1885.00, 4400.00)2810.00 (1835.00, 4020.00)4110.00 (2335.00, 5590.00)$Z = 21.158$ < 0.001PCO2, mmHg, Mean ± SD41.00 (36.00, 46.00)40.00 (36.00, 46.00)42.00 (35.00, 50.00)$Z = 6.887$ < 0.001FiO2, mmHg, M (Q1, Q3)100.00 (50.00, 100.00)100.00 (50.00, 100.00)80.00 (50.00, 100.00)Z = − 2.2780.023HCO3−, Mean ± SD22.52 ± 4.6722.64 ± 4.3722.09 ± 5.64t = 4.41 < 0.001Hemoglobin, g/dL, Mean ± SD11.38 ± 2.2811.46 ± 2.2411.09 ± 2.40t = 6.81 < 0.001PLT, K/L, M (Q1, Q3)189.00 (135.00, 252.00)188.00 (137.00, 250.00)191.00 (129.00, 259.00)Z = − 0.1260.900WBC, K/L, M (Q1, Q3)10.60 (7.40, 15.10)10.40 (7.30, 14.70)11.50 (7.80, 16.80)$Z = 7.740$ < 0.001Creatinine, mg/dL, M (Q1, Q3)1.00 (0.80, 1.50)1.00 (0.80, 1.40)1.10 (0.80, 1.80)$Z = 9.149$ < 0.001BUN, mg/dL, M (Q1, Q3)20.00 (14.00, 32.00)19.00 (14.00, 30.00)23.00 (15.00, 39.00)$Z = 9.392$ < 0.001Glucose, mg/dL, M (Q1, Q3)123.00 (101.00, 162.00)120.00 (100.00, 158.00)132.00 (105.00, 176.00)$Z = 9.068$ < 0.001SOFA, M (Q1, Q3)39.00 (31.00,49.00)37.00 (30.00,47.00)44.00 (35.00,55.00)$Z = 19.036$ < 0.001SAPS II, M (Q1, Q3)2.00 (0.00,4.00)2.00 (0.00,4.00)2.00 (0.00,4.00)$Z = 3.294$ < 0.001Vasopressin, n (%)χ2 = 687.639 < 0.001 No9989 (86.37)8291 (90.67)1698 (70.11) Yes1577 (13.63)853 (9.33)724 (29.89)CRRT, n (%)χ2 = 638.980 < 0.001 No10,682 (92.36)8739 (95.57)1943 (80.22) Yes884 (7.64)405 (4.43)479 (19.78)Ventilation status, n (%)χ2 = 675.707 < 0.001 High flow557 (4.82)532 (5.82)25 (1.03) Invasive vent114 (0.99)73 (0.80)41 (1.69) Non-invasive vent2115 (18.29)1258 (13.76)857 (35.38) Oxygen73 (0.63)61 (0.67)12 (0.50) Trach8707 (75.28)7220 (78.96)1487 (61.40)RBC-transfusion, n (%)χ2 = 229.181 < 0.001 No6855 (59.27)5745 (62.83)1110 (45.83) Yes4711 (40.73)3399 (37.17)1312 (54.17)PLT-transfusion, n (%)χ2 = 91.898 < 0.001 No9860 (85.25)7944 (86.88)1916 (79.11) Yes1706 (14.75)1200 (13.12)506 (20.89)Frozen plasma, n (%)χ2 = 235.718 <.001 No10,069 (87.06)8186 (89.52)1883 (77.75) Yes1497 (12.94)958 (10.48)539 (22.25)Diabetes, n (%)χ2 = 0.5430.461 No7965 (68.87)6312 (69.03)1653 (68.25) Yes3601 (31.13)2832 (30.97)769 (31.75)Chronic pulmonary disease, n (%)χ2 = 57.827 < 0.001 No8464 (73.18)6839 (74.79)1625 (67.09) Yes3102 (26.82)2305 (25.21)797 (32.91)Renal disease, n (%)χ2 = 2.4070.121 No9115 (78.81)7234 (79.11)1881 (77.66) Yes2451 (21.19)1910 (20.89)541 (22.34)Malignant cancer, n (%)χ2 = 11.189 < 0.001 No10,179 (88.01)8095 (88.53)2084 (86.04) Yes1387 (11.99)1049 (11.47)338 (13.96)Liver disease, n (%)χ2 = 179.186 < 0.001 No9829 (84.98)7980 (87.27)1849 (76.34) Yes1737 (15.02)1164 (12.73)573 (23.66)Myocardial infarct, n (%)χ2 = 0.3490.555 No9282 (80.25)7328 (80.14)1954 (80.68) Yes2284 (19.75)1816 (19.86)468 (19.32)Leukemia, n (%)χ2 = 3.8590.049 No11,379 (98.38)9007 (98.50)2372 (97.94) Yes187 (1.62)137 (1.50)50 (2.06)Septic shock, n (%)χ2 = 109.800 < 0.001 No10,922 (94.43)8740 (95.58)2182 (90.09) Yes644 (5.57)404 (4.42)240 (9.91)Pancreatitis, n (%)χ2 = 120.732 < 0.001 No11,311 (97.80)9013 (98.57)2298 (94.88) Yes255 (2.20)131 (1.43)124 (5.12)BMI Body mass index; SBP Systolic blood pressure; DBP Diastolic blood pressure; SPO2 Pulse oxygen saturation; PCO2 Partial pressure of carbon dioxide; PO2 Oxygen partial pressure; FiO2 Fraction of inspired oxygen; HCO3− Bicarbonate; PLT Platelet; WBC White blood cell; BUN *Blood urea* nitrogen; SOFA Sequential organ failure assessment; SAPS II Simplified acute physiology score II; CRRT Continuous renal replacement therapy; RBC Red blood cell; PLT Platelets; ARDS Acute respiratory distress syndrome
## Construction of the prediction model
The multivariate logistic regression analysis in the training set found that BMI, respiratory rate, urine output, PCO2, BUN, vasopressin, CRRT, ventilation status, chronic pulmonary disease, malignant cancer, liver disease, septic shock and pancreatitis might be predictors (Table 2). A prognostic prediction model, containing thirteen prognostic factors, to predict the ARDS risk in sepsis patients was established. For visualizing the prediction model, we plotted a nomogram (Fig. 2). For instance, a patient with sepsis had a septic shock (No), malignant cancer (No), BMI ≥ 30 kg/m2, BUN = 12 mg/dL, pancreatitis (No), chronic pulmonary disease (No), vasopressin (Yes), liver disease (Yes), PCO2 = 53 mmHg, respiratory rate = 32 times/min, CRRT (No), ventilation status = non-invasive vent, urine output = 1120 mL, the total score was 151 points and meant a predicted the risk of ARDS of 0.481, which was consistent with the actual outcome of this patient with sepsis (Fig. 3). Additionally, we also have developed an online prediction nomogram for easy clinical application: https://xuchi777.shinyapps.io/DynNomapp/Table 2The prognostic factors associated with the risk of ARDS for patients with sepsisVariablesUnivariate logistic regression modelMultivariate logistic regression modelRR ($95\%$ CI)PRR ($95\%$ CI)PAge0.988 (0.985–0.990) < 0.001––Gender FemaleRef–– Male0.984 (0.898–1.079)0.734––BMI < 18.5RefRef ≥ 18.5 and < 250.673 (0.516–0.878)0.0030.732 (0.542–0.987)0.041 ≥ 25 and < 300.723 (0.556–0.941)0.0160.748 (0.555–1.008)0.057 ≥ 301.050 (0.810–1.362)0.7120.863 (0.642–1.159)0.326Ethnicity WhiteRef–– Black1.346 (1.145–1.583) < 0.001–– Other/unknown1.298 (1.171–1.438) < 0.001––*Marital status* SingleRef–– Married0.728 (0.653–0.812) < 0.001–– Other/unknown0.851 (0.755–0.959)0.008––SBP0.988 (0.927–1.053)0.715––DBP0.990 (0.933–1.051)0.748––Respiratory rate1.075 (1.067–1.082) < 0.0011.049 (1.041–1.058) < 0.001Urine output1.635 (1.563–1.710) < 0.0011.000 (1.000–1.000) < 0.001PCO21.019 (1.015–1.022) < 0.0011.017 (1.013–1.021) < 0.001FiO20.998 (0.996–1.000)0.016––HCO3 −0.975 (0.966–0.985) < 0.001––Hemoglobin0.931 (0.913–0.950) < 0.001––PLT1.017 (0.973–1.063)0.459––WBC1.015 (1.010–1.019) < 0.001––Creatinine1.076 (1.048–1.104) < 0.001––BUN1.008 (1.006–1.010) < 0.0011.005 (1.002–1.007) < 0.001Glucose1.002 (1.001–1.002) < 0.001––Vasopressin NoRefRef Yes4.144 (3.705–4.635) < 0.0011.711 (1.491–1.964) < 0.001CRRT NoRefRef Yes5.319 (4.619–6.126) < 0.0014.870 (4.054–5.851) < 0.001Ventilation status High flowRefRef Invasive vent11.951 (6.866–20.801) < 0.0015.617 (3.122–10.107) < 0.001 Non-invasive vent14.495 (9.616–21.850) < 0.0017.387 (4.825–11.308) < 0.001 Oxygen4.186 (2.002–8.752) < 0.0011.681 (0.751–3.760)0.206 Trach4.382 (2.923–6.570) < 0.0012.906 (1.914–4.412) < 0.001PLT-transfusion NoRef–– Yes1.748 (1.558–1.962) < 0.001––Diabetes NoRef–– Yes1.037 (0.942–1.142)0.460––Chronic pulmonary disease NoRefRef Yes1.455 (1.321–1.604) < 0.0011.353 (1.208–1.514) < 0.001Renal disease NoRef–– Yes1.089 (0.978–1.214)0.121––Malignant cancer NoRefRef Yes1.252 (1.097–1.428) < 0.0011.278 (1.097–1.488)0.002Liver disease NoRefRef Yes2.125 (1.899–2.377) < 0.0011.720 (1.505–1.967) < 0.001Leukemia NoRef–– Yes1.386 (1.000–1.922)0.050––Myocardial infarct NoRef–– Yes0.967 (0.863–1.083)0.557––Septic shock NoRefRef Yes2.380 (2.015–2.811) < 0.0011.268 (1.044–1.541)0.017Pancreatitis NoRefRef Yes3.713 (2.892–4.766) < 0.0012.273 (1.693–3.053) < 0.001BMI Body mass index; SBP Systolic blood pressure; DBP Diastolic blood pressure; SPO2 Pulse oxygen saturation; PCO2 Partial pressure of carbon dioxide; PO2 Oxygen partial pressure; FiO2 Fraction of inspired oxygen; HCO3− Bicarbonate; PLT Platelet; WBC White blood cell; BUN *Blood urea* nitrogen; CRRT Continuous renal replacement therapy; RBC Red blood cell; PLT Platelets; ARDS Acute respiratory distress syndrome; RR Relative risk; CI confidence intervalFig. 2The nomogram for predicting the ARDS risk in ICU patients with sepsisFig. 3An example for the application of the nomogram
## Validation of the prediction model
To assess the predictive ability of developed prediction model, the ROC curves and calibration curves were applied in this study. As presented in Table 3, the accuracy, sensitivity, specificity, PPV and NPV of prediction model was 0.732 ($95\%$ CI 0.724–0.740), 0.762 ($95\%$ CI 0.745–0.779), 0.724 ($95\%$ CI 0.714–0.733), 0.422 ($95\%$ CI 0.407–0.437) and 0.920 ($95\%$ CI 0.914–0.926) respectively, in the training set. Similarly, Table 3 displays that the established model had a 0.705 ($95\%$ CI 0.692–0.718) of accuracy, 0.798 ($95\%$ CI 0.773–0.823) of sensitivity, 0.682 ($95\%$ CI 0.668–0.697) of specificity, 0.385 ($95\%$ CI 0.364–0.407) of PPV and 0.931 ($95\%$ CI 0.922–0.940) of NPV in the testing set. Moreover, Table 3 also showed that the area under the curve (AUC) of the constructed prediction model was 0.811 ($95\%$ CI 0.802–0.820) in the training set (Fig. 4a), corresponding to 0.812 ($95\%$ CI 0.798–0.826) in the testing set (Fig. 4b). We also compared the predicting value of constructed prediction model and SOFA, SAPS II scoring systems for predicting the ARDS risk for sepsis patients (Table 3). The AUC of SOFA score and SAPS II score was 0.539 ($95\%$ CI 0.518–0.559) and 0.609 ($95\%$ CI 0.589–0.629) in the testing set (Fig. 4c and d), separately, which was obviously lower than constructed prediction model ($P \leq 0.001$). The result implied that the constructed prediction model had favorable discriminatory ability for the prediction of ARDS risk in patients with sepsis. In addition, the calibration curve also showed a good concordance between the predicted and observed risk of ARDS in both training and testing sets (Fig. 5a and b).Table 3The predictive performance of prediction model, SOFA and SAPSIIModelsSetsAccuracy ($95\%$ CI)AUC ($95\%$ CI)Sensitivity ($95\%$ CI)Specificity ($95\%$ CI)PPV ($95\%$ CI)NPV ($95\%$ CI)Established modelTesting set0.705 (0.692–0.718)0.812 (0.798–0.826)0.798 (0.773–0.823)0.682 (0.668–0.697)0.385 (0.364–0.407)0.931 (0.922–0.940)Training set0.732 (0.724–0.740)***0.811 (0.802–0.820)0.762 (0.745–0.779)*0.724 (0.714–0.733)***0.422 (0.407–0.437)**0.920 (0.914–0.926) *SOFATesting set0.723 (0.711–0.736)0.539 (0.518–0.559)***0.232 (0.206–0.258)***0.846 (0.835–0.857)***0.273 (0.243–0.304)***0.815 (0.803–0.827)***SAPSIITesting set0.609 (0.595–0.623)***0.609 (0.589–0.629)***0.555 (0.524–0.586)***0.623 (0.607–0.638)***0.269 (0.249–0.288)***0.848 (0.835–0.861)***AUC *The area* under of curve; CI Confidence interval; SOFA Sequential organ failure assessment; SAPS II Simplified acute physiology score II; PPV Positive predictive value; NPV Negative predictive valueTaking established model-testing set as reference, the predictive performance of established model training set, SOFA-testing set and SAPSII-testing set was compared;*represents $P \leq 0.05$; **represents $P \leq 0.01$; ***represents $P \leq 0.001$Fig. 4ROC curves of a established model in the training set; b established model in the testing set; c SOFA in the testing set; d SAPSII in the testing setFig. 5Calibration curves of a the training set and b testing set
## Discussion
In this retrospective cohort study, a prediction model for predicting the ARDS risk in sepsis patients admitted to ICU was developed. Through verification, this model had a good predictive ability as well as discrimination.
ARDS is considered to be a serious and acute inflammatory lung injury, and could increase the severity of illness and brought a worse outcome for patients with sepsis [17]. Zhao J, et al. pointed out that sepsis-associated ARDS has a higher disease severity and worse clinical outcomes than non-sepsis-associated ARDS [18]. Therefore, early identification of patients with sepsis who are at high risk of developing ARDS is very important. Previous research has found that some prediction model for predicting the ARDS risk were developed and validated in traumatic brain injury (TBI) patients [19], non-emergency department hospitalized patients [10], patients undergoing cardiac surgery [20], and patients with coronavirus disease (COVID-19) [21]. However, these prediction models were not focused on patients with sepsis so far. In this study, we developed a model based on several clinical indicators to predict the development of ARDS in sepsis patients admitted to ICU. The developed prediction model in this study contains thirteen predictors: BMI, respiratory rate, urine output, PCO2, BUN, vasopressin, CRRT, ventilation status, chronic pulmonary disease, malignant cancer, liver disease, septic shock and pancreatitis. Liver disease was regarded to be a predictor of developing ARDS in this study, which were consistent with prior studies [22, 23]. A study has expounded that liver disease was an important predictor for the in-hospital mortality of patients with sepsis and lung infection [22]. *In* general, the liver could prevent sepsis from aggravating tissue and organ damage by removing bacteria and regulating the metabolism of inflammatory factors. However, when the liver occurs injury, it might increase the inflammatory response of the lung to septic bacterial infection, which leading to an increased risk of ARDS [22, 23]. In the study of Li X, et al., respiratory rate in the non-survival group was significantly higher than that of the survival group among sepsis patients with developing ARDS, which also indicated that respiratory rate was associated with the prognosis for sepsis patients with developing ARDS [24].
Nowadays, nomogram has proven to be an effective tool in predicting an individual’s probability of a clinical event, and it is consistent with the requirements of integrated model [25]. Moreover, the nomogram is also simple, intuitive and convenient for clinicians to use on prognostic prediction of disease [26]. In the present study, for visualizing the developed prediction model, we plotted a nomogram. Additionally, the ROC curves indicated that this established model had a predictive ability compared with SOFA score and SAPS II score. It is worth noting that, we have also developed an online prediction system, which may be more convenient for clinical application (https://xuchi777.shinyapps.io/DynNomapp/). The developed predictive model may also be a potential tool to guide clinicians in predicting the risk of ARDS in septic patients in the ICU, which help take early interventions to prevent ARDS progression in sepsis patients admitted to ICU and improved clinical outcomes.
The present study had some strengths. Firstly, the relatively large sample size of this study makes the results convincing. Secondly, we developed a model with an intuitive and easy to use based on some clinical indicators to predict the ARDS risk of ICU patients with sepsis. Simultaneously, the result of internal validation showed that the prediction model had a good discrimination and accuracy in predicting the risk of ARDS for sepsis patients. Nevertheless, we also acknowledged that there were some limitations in this study. Firstly, due to all patients from MIMIC-IV database and only septic patients in ICU were considered, we were unable to confirm whether this developed prediction model was applicable to patients with sepsis who were not admitted to the ICU. More prospective studies are needed to validate this result. Secondly, this is a retrospective cohort study, some variables with more than $20\%$ missing values (ALT, ALB, TG, LDL-C, HDL-C, AST, NEUT, SaO2, TC and CRP) were deleted, which may affect the result. Thirdly, MIMIC-IV is a single-center database, so the results of this study should be prudently interpreted when involving other populations. Lastly, an external validation should be still required in the future.
## Conclusion
In conclusion, we developed a prediction model incorporating thirteen clinical features to effectively predict the ARDS risk in ICU patients with sepsis. Additionally, the prediction model showed a good predictive ability as well as discrimination by internal validation. Nevertheless, further prospective studies are warranted to validate the effectiveness and applicability of this prediction model.
## Supplementary Information
Additional file 1: Table S1. The difference analysis between the training set and testing set
## References
1. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M. **The third international consensus definitions for sepsis and septic shock (Sepsis-3)**. *JAMA* (2016) **315** 801-810. DOI: 10.1001/jama.2016.0287
2. Prescott HC, Angus DC. **Enhancing recovery from sepsis: a review**. *JAMA* (2018) **319** 62-75. DOI: 10.1001/jama.2017.17687
3. Wang H, Huang J, Liao W, Xu J, He Z, Liu Y. **Prognostic value of the red cell distribution width in patients with sepsis-induced acute respiratory distress syndrome: a retrospective cohort study**. *Dis Markers* (2021) **2021** 5543822. PMID: 34122669
4. Li S, Zhao D, Cui J, Wang L, Ma X, Li Y. **Correlation of microRNA-125a/b with acute respiratory distress syndrome risk and prognosis in sepsis patients**. *J Clin Lab Anal* (2020) **34** e23098. PMID: 31967348
5. Mikkelsen ME, Shah CV, Meyer NJ, Gaieski DF, Lyon S, Miltiades AN. **The epidemiology of acute respiratory distress syndrome in patients presenting to the emergency department with severe sepsis**. *Shock* (2013) **40** 375-381. DOI: 10.1097/SHK.0b013e3182a64682
6. Wang Q, Feng Q, Zhang Y, Zhou S, Chen H. **Decreased microRNA 103 and microRNA 107 predict increased risks of acute respiratory distress syndrome and 28-day mortality in sepsis patients**. *Medicine* (2020) **99** e20729. DOI: 10.1097/MD.0000000000020729
7. McKown AC, McGuinn EM, Ware LB, Wang L, Janz DR, Rice TW. **Preadmission oral corticosteroids are associated with reduced risk of acute respiratory distress syndrome in critically Ill adults with sepsis**. *Crit Care Med* (2017) **45** 774-780. DOI: 10.1097/CCM.0000000000002286
8. Nam H, Jang SH, Hwang YI, Kim JH, Park JY, Park S. **Nonpulmonary risk factors of acute respiratory distress syndrome in patients with septic bacteraemia**. *Korean J Intern Med* (2019) **34** 116-124. DOI: 10.3904/kjim.2017.204
9. Shi Y, Wang L, Yu S, Ma X, Li X. **Risk factors for acute respiratory distress syndrome in sepsis patients: a retrospective study from a tertiary hospital in China**. *BMC Pulm Med* (2022) **22** 238. DOI: 10.1186/s12890-022-02015-w
10. Soto GJ, Kor DJ, Park PK, Hou PC, Kaufman DA, Kim M. **Lung injury prediction score in hospitalized patients at risk of acute respiratory distress syndrome**. *Crit Care Med* (2016) **44** 2182-2191. DOI: 10.1097/CCM.0000000000002001
11. Lin F, Lu R, Han D, Fan Y, Zhang Y, Pan P. **A prediction model for acute respiratory distress syndrome among patients with severe acute pancreatitis: a retrospective analysis**. *Ther Adv Respir Dis* (2022) **16** 17534666221122592. DOI: 10.1177/17534666221122592
12. Gajic O, Dabbagh O, Park PK, Adesanya A, Chang SY, Hou P. **Early identification of patients at risk of acute lung injury: evaluation of lung injury prediction score in a multicenter cohort study**. *Am J Respir Crit Care Med* (2011) **183** 462-470. DOI: 10.1164/rccm.201004-0549OC
13. Zhao QY, Wang H, Luo JC, Luo MH, Liu LP, Yu SJ. **Development and validation of a machine-learning model for prediction of extubation failure in intensive care units**. *Front Med* (2021) **8** 676343. DOI: 10.3389/fmed.2021.676343
14. Jhou HJ, Chen PH, Yang LY, Chang SH, Lee CH. **Plasma anion gap and risk of in-hospital mortality in patients with acute ischemic stroke: analysis from the MIMIC-IV database**. *J Pers Med* (2021) **11** 1004. DOI: 10.3390/jpm11101004
15. Zhang W, Wang Y, Li W, Wang J. **Association between obesity and short-and long-term mortality in patients with acute respiratory distress syndrome based on the Berlin definition**. *Front Endocrinol* (2020) **11** 611435. DOI: 10.3389/fendo.2020.611435
16. Ranieri VM, Rubenfeld GD, Thompson BT, Ferguson ND, Caldwell E, Fan E, Camporota L, Slutsky AS. **Acute respiratory distress syndrome: the Berlin definition**. *JAMA* (2012) **307** 2526-2533. PMID: 22797452
17. Zhang Z, Han N, Shen Y. **S100A12 promotes inflammation and cell apoptosis in sepsis-induced ARDS via activation of NLRP3 inflammasome signaling**. *Mol Immunol* (2020) **122** 38-48. DOI: 10.1016/j.molimm.2020.03.022
18. Zhao J, Tan Y, Wang L, Shi Y. **Discriminatory ability and prognostic evaluation of presepsin for sepsis-related acute respiratory distress syndrome**. *Sci Rep* (2020) **10** 9114. DOI: 10.1038/s41598-020-66121-7
19. Wang R, Cai L, Zhang J, He M, Xu J. **Prediction of acute respiratory distress syndrome in traumatic brain injury patients based on machine learning algorithms**. *Medicina* (2023) **59** 171. DOI: 10.3390/medicina59010171
20. Liu Y, Song M, Huang L, Zhu G. **A nomogram to predict acute respiratory distress syndrome after cardiac surgery**. *Heart Surg Forum* (2021) **24** E445-E450. DOI: 10.1532/hsf.3809
21. Zhang L, Xu J, Qi X, Tao Z, Yang Z, Chen W, Wang X, Pan T, Dai Y, Tian R, Chen Y, Tang B, Liu Z, Tan R, Qu H, Yu Y, Liu J. **Development and validation of a nomogram for predicting the risk of coronavirus-associated acute respiratory distress syndrome: a retrospective cohort study**. *Infect Drug Resist* (2022) **15** 2371-2381. DOI: 10.2147/IDR.S348278
22. Ren Y, Zhang L, Xu F, Han D, Zheng S, Zhang F. **Risk factor analysis and nomogram for predicting in-hospital mortality in ICU patients with sepsis and lung infection**. *BMC Pulm Med* (2022) **22** 17. DOI: 10.1186/s12890-021-01809-8
23. Dhainaut JF, Marin N, Mignon A, Vinsonneau C. **Hepatic response to sepsis: interaction between coagulation and inflammatory processes**. *Crit Care Med* (2001) **29** S42-S47. DOI: 10.1097/00003246-200107001-00016
24. Li X, Shen H, Zhou T, Cao X, Chen Y, Liang Y. **Does an increase in serum FGF21 level predict 28-day mortality of critical patients with sepsis and ARDS?**. *Respir Res* (2021) **22** 182. DOI: 10.1186/s12931-021-01778-w
25. Zhang W, Fang M, Dong D, Wang X, Ke X, Zhang L. **Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer**. *Radiother Oncol* (2020) **145** 13-20. DOI: 10.1016/j.radonc.2019.11.023
26. Park SY. **Nomogram: an analogue tool to deliver digital knowledge**. *J Thorac Cardiovasc Surg* (2018) **155** 1793. DOI: 10.1016/j.jtcvs.2017.12.107
|
---
title: 'Effectiveness of digital care platform CMyLife for patients with chronic myeloid
leukemia: results of a patient-preference trial'
authors:
- Lynn Verweij
- Geneviève I. C. G. Ector
- Yolba Smit
- Bas van Vlijmen
- Bert A. van der Reijden
- Rosella P. M. G. Hermens
- Nicole M. A. Blijlevens
journal: BMC Health Services Research
year: 2023
pmcid: PMC9994406
doi: 10.1186/s12913-023-09153-9
license: CC BY 4.0
---
# Effectiveness of digital care platform CMyLife for patients with chronic myeloid leukemia: results of a patient-preference trial
## Abstract
### Background
Two most important factors determining treatment success in chronic myeloid leukemia (CML) are adequate medication compliance and molecular monitoring albeit still being suboptimal. The CMyLife platform is an eHealth innovation, co-created with and for CML patients, aiming to improve their care, leading to an increased quality of life and the opportunity of hospital-free care.
### Objective
To explore the effectiveness of CMyLife in terms of information provision, patient empowerment, medication compliance, molecular monitoring, and quality of life.
### Methods
Effectiveness of CMyLife was explored using a patient-preference trial. Upon completion of the baseline questionnaire, participants actively used (intervention group) or did not actively use (questionnaire group) the CMyLife platform for at least 6 months, after which they completed the post-intervention questionnaire. Scores between the intervention group and the questionnaire group were compared with regard to the within-subject change between baseline and post-measurement using Generalized Estimating Equation models.
### Results
At baseline, 33 patients were enrolled in the questionnaire group and 75 in the intervention group. Online health information knowledge improved significantly when actively using CMyLife and patients felt more empowered. No significant improvements were found regarding medication compliance and molecular monitoring, which were already outstanding. Self-reported effectiveness showed that patients experienced that using CMyLife improved their medication compliance and helped them to oversee their molecular monitoring. Patients using CMyLife reported more symptoms but were better able to manage these.
### Conclusions
Since hospital-free care has shown to be feasible in time of the COVID-19 pandemic, eHealth-based innovations such as CMyLife could be a solution to maintain the quality of care and make current oncological health care services more sustainable.
### Trial registration
ClinicalTrials.gov NCT04595955, $\frac{22}{10}$/2020.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12913-023-09153-9.
## Introduction
To meet the growing burden of chronic diseases, the World Health Organization reported that health care systems must reorganize and that innovative approaches are essential [1]. Cancer has become a notable proportion of these chronic diseases, as life expectancy of oncology patients has increased substantially due to new technologies, earlier diagnosis and better treatments [2, 3]. An example of such an oncologic chronic disease is chronic myeloid leukemia (CML). CML is characterized by translocation of chromosome 9 and 22, leading to the BCR-ABL1 mutation, which encodes for a constitutively active protein e.g. tyrosine kinase [4, 5]. Since tyrosine kinase inhibitors (TKIs) have been used as treatment the life expectancy of CML patients has increased. It approaches that of the general population, if patients have optimal responses to the treatment [6, 7].
The two most important factors determining treatment success are patients’ medication compliance and adequate molecular monitoring of BCR-ABL1 values according to evidence-based guidelines [8]. The BCR-ABL1 value is a disease-specific biomarker that accurately reflects the course of the disease and treatment response [8]. This monitoring of disease activity in accordance with guidelines is associated with a reduced risk of progression and mortality [9], improved TKI adherence [10], and lower health care costs [11]. Treatment-free remission has become an additional treatment goal, where TKI treatment can be safely discontinued without relapsing [12]. However, guideline adherence is still suboptimal and tends to be lower in hospitals, which are less experienced in treating CML patients [9, 13]. Hence, patients could play an important role in guarding their own molecular values.
This shift to more involved and empowered patients, and ultimately to hospital-free care, needs a patient-centered approach, based on eHealth [14–18]. eHealth could provide patients with tools to oversee and guard the progress and accuracy of their treatment. If they have any questions they could get in touch with their health care provider (HCP) to monitor their treatment. Therefore, eHealth could be a promising medium for patients to easily monitor their molecular levels from home. Besides, eHealth enables patients to monitor their medication compliance, provides patients with access to care efficiently, enables interactive patient-provider communication, and helps them to access and understand medical information more readily [17, 18]. Hence, eHealth seems to have considerable potential and added value, as it may give patients the chance to get sufficient information and care from home and only visit the hospital when necessary. Feasibility of eHealth platforms is extensively investigated, but insight into their effectiveness in haematological malignancies is scarce.
CMyLife is an example of such an eHealth innovation and was co-created with and for CML patients [19]. CMyLife aims to provide CML patients with tools and knowledge to control their care process and improve their medication compliance and molecular monitoring. This could eventually lead to increased quality of life and opportunity of hospital-free care. The aim of this study is to explore the effectiveness of the CMyLife platform in terms of information provision, patient empowerment, medication compliance, molecular monitoring, and quality of life of CML patients.
## Design
A patient-preference trial was performed in order to gain insights into the effectiveness of CMyLife in terms of information provision, patient empowerment, medication compliance, molecular monitoring of BCR-ABL1 values, and quality of life of CML patients. Patients could choose between actively or not actively using CMyLife, including a pre- and post-measurement. The study period lasted from July 2019 to October 2020. Given the nature of this study and the low impact on participants, the Medical Research Involving Human Subjects Act (Dutch: WMO) does not apply, as confirmed by the institutional Medical Ethical Committee ‘CMO Regio Arnhem-Nijmegen’. The trail was registered at ClinicalTrials.gov at $\frac{22}{10}$/2020 (registration number: NCT04595955).
## Study population
HCPs approached their patients (who visited them for an appointment within the study period) for participation in this study in five academic and seven large non-academic hospitals, spread over the Netherlands. In addition, participants were recruited through the website of the Dutch patient advocate association, called Hematon, and via the CMyLife website [20]. Upon participation in the study, all participants signed informed consent. The study population consisted of patients in chronic phase CML who were treated with first or second line TKIs. Patients treated with second line TKIs were only allowed to participate if they had switched TKIs as a result of intolerance, not because of treatment failure. Patients were excluded if they were in treatment-free remission, in acceleration phase, in blast crisis, or if pregnancy was planned in the study period.
Patient preference was leading in determining group enrolment. Patients who agreed to actively use CMyLife for at least 6 months were enrolled in the treatment group; patients who did not were enrolled in the questionnaire group. Active use in the intervention group comprised receival of adequate support from the CMyLife-team. The support consisted of an instruction package and a kick-off workshop to inform patients about how, when, and why they could and should use CMyLife. The CMyLife-team was reachable for questions during the day and offered all users to help them install the platform. Also, the CMyLife-team gathered feedback from users about if their support was adequate. Patients in the questionnaire group were aware of the existence of CMyLife and the website remained accessible for them. However, they received no support. After enrolment participants received a written baseline questionnaire by mail. Upon completion of the baseline questionnaire, participants actively used (intervention group) or did not actively use (questionnaire group) CMyLife for at least 6 months, after which they completed the post-intervention questionnaire.
## Intervention
Development and features of the CMyLife platform were described in detail elsewhere [19]. CMyLife facilitates CML patients with a website, medication app, guideline app, and a personal health environment. All data were secured and conform the General Data Protection Regulation. In accordance with the Dutch security guideline (NEN7510), features containing data from the hospital’s electronic health record (for example, laboratory results) were secured with a two-step validation with a token received via an SMS text message.
The website provides accurate and easy to understand information about the disease, medication, guidelines, side effects, and the effect on daily life (work, sports, mortgage etc.). It is provided with a question and answer functionality, where patients can receive answers from specialized haematologists and pharmacists. In addition, patients (in the intervention group) can communicate with other patients (in the intervention group), to share information or ask questions (via a password protected forum).
The medication app [21] is used to optimize medication compliance and supports patients in preparing the consultation with their HCP by allowing patients to set medication alarms, register their medication intake, request for repeat medication prescriptions and read the information leaflet of the medication they are taking. In addition, they can log side effects they experience, which can be shared with their HCPs through their personal health environment. The medication app also includes an option that enables patients to record the consultation with their HCPs.
The guideline app [22] displays scheduled blood checks and appointments with haematologists, sends monitoring reminders, and shows an understandable explanation of patients’ BCR-ABL1 values and of the Dutch treatment guidelines. This enables patients to compare their values to the guideline’s targets, and when they differ, act accordingly.
Patients can save their own medical records in their personal health environment, consisting of a Patient Knows Best portal [23]. They can determine which information they store and with whom they share it (adjustable by turning off or on their informed consent). For example, they can share side effects with their HCPs in order to discuss them.
## Data collection
At baseline, all participants completed a survey containing patient characteristics (i.e. sociodemographics, CML related information) and Dutch translated and validated questionnaires (Additional file 1) exploring the effectiveness of CMyLife in terms of information provision, patient empowerment, medication compliance, molecular monitoring and quality of life. Six months later, patients completed the post-intervention questionnaire containing the same validated questionnaires. The post-intervention questionnaire of patients in the intervention group included additional questions about self-reported effectiveness. Regarding information provision, familiarity with CML related concepts (Philadelphia chromosome, BCR-ABL1, tyrosine kinase inhibitor, remission, log reduction (for BCR-ABL1), hematological response/remission, cytogenetic response/remission, molecular response/remission, major molecular remission, complete cytogenetic remission, treatment-free remission, Hematon) was rated on a scale from 1 to 6. Information provision, eHealth literacy (patients’ ability to find and evaluate online health information), patient empowerment and medication compliance were measured using the validated questionnaires EORTC QLQ-INFO25 [24], eHealth Literacy Scale (eHEALS) [25], Patient Activation Measure (PAM) [26–28], and the Medication Adherence Rating Scale (MARS) [29, 30], respectively. The MARS was supplemented with patients’ self-reported effectiveness on medication compliance in the post-measurement of patients in the intervention group. Adequate molecular monitoring was measured using the frequency of hospital visits in the past 12 months and was supplemented with patients’ self-reported effectiveness on their molecular monitoring in the post-measurement of patients in the intervention group. Additional file 2 describes questions and interpretation of the self-reported effectiveness on medication compliance and molecular monitoring. Quality of life and disease-specific quality of life were measured using the EORTC QLQ-C30 [31], and the EORTC QLQ-CML24 [32], respectively. Table 1 shows an overview of outcomes with corresponding measures used in this study. Table 1Overview of outcomes and corresponding measures usedOutcomeMeasureInformation provisionFamiliarity with CML related conceptsEORTC QLQ-INFO25a [24]eHealth literacyeHealth Literacy Scale [25]Patient empowermentPatient Activation Measure [26–28]Medication complianceMedication Adherence Rating Scale [29, 30]Self-reported effectiveness on medication compliance bMolecular monitoringFrequency of hospital visits in the past 12 monthsSelf-reported effectiveness on molecular monitoring cQuality of lifeEORTC QLQ-C30d [31]EORTC QLQ-CML24e [32]aEuropean Organization for Research and Treatment of Cancer Quality of Life Questionnaire Information 25-item, bQuestions and interpretation of self-reported effectiveness on medication compliance is described in Additional file 2, cQuestions and interpretation of self-reported effectiveness on molecular monitoring is described in Additional file 2. dEuropean Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30-item, eEuropean Organization for Research and Treatment of Cancer Quality of Life Questionnaire Chronic Myeloid Leukemia 24-item
## Analysis
Measurements were processed anonymously, and analyses were performed with SPSS version 25 (IBM Corp). Descriptive statistics were used to describe basic features of participants. Attrition bias was evaluated by comparing patient characteristics between baseline and post-measurements. The EORTC QLQ-INFO25, eHEALS, PAM, MARS, EORTC QLQ-C30, and EORTC QLQ-CML24 were scored according to their manuals. In addition, all individual items on EORTC QLQ-INFO25 and EORTC QLQ-CML24 were analysed to get better insight in information gaps in CMyLife and in symptoms and problems of CML patients to improve CMyLife. Effectiveness of CMyLife was evaluated by comparing scores between the intervention and questionnaire group with regard to within-subject change between baseline and post-measurement using Generalized Estimating Equation models. If basic characteristics differed significantly between groups, P-values were corrected. Except for self-reported effectiveness, all differences shown in the results are referring to differences between intervention and questionnaire groups with regard to within-subject change between baseline and post-measurement.
## Patient characteristics
In total, 116 CML patients applied to participate in this study. Four patients dropped out, and four patients were excluded (stopped or never used TKIs). At baseline, 33 patients were enrolled in the questionnaire group and 75 in the intervention group. After 6 months, 29 patients filled in the post-intervention questionnaire and four patients did not return their answers ($88\%$ response) in the questionnaire group. During the six-month study period, eight patients in the intervention group stopped their participation. After 6 months, 57 patients filled in the post-intervention questionnaire and ten patients did not return their answers ($85\%$ response). Figure 1 shows the flowchart of the study. Attrition bias between pre- and post-measurements was evaluated and not detected. Table 2 summarises patient characteristics. In the questionnaire group, $64\%$ were female, and $64\%$ were aged ≥65. In the intervention group, $38\%$ were female, and $40\%$ were aged ≥65. The percentage of patients that used CMyLife before participating in this study was comparable between groups. Gender, age, education, and whether patients had previously been treated with TKIs differed significantly between groups (Table 2). Therefore, the P-values were corrected for these characteristics. Fig. 1Flowchart study populationTable 2Baseline characteristics for CML patientsBaseline characteristicsQuestionnaire group ($$n = 33$$)Intervention group ($$n = 75$$)P-valueGender, n (%).013 Male12 (36.4)46 (62.2) Female21 (63.6)28 (37.8)Age (years), n (%).022 18–6412 (36.4)44 (60.3) 65 and older21 (63.6)29 (39.7)Education level, n (%).000 Higha10 (30.3)39 (52.7) Middleb7 (21.2)26 (35.1) Lowc16 (48.5)9 (12.2)Years since diagnosis, (mean ± SD)6.7 ± 5.26.3 ± 6.2.724Previously used CMyLife, n (%).915 Yes, website12 (36.4)25 (33.3) Yes, medication app/ guideline app/otherwise6 (18.2)16 (21.3) No, not yet14 (42.4)31 (41.3)Currently used TKI, n (%).327 Yes, imatinib16 (45.7)44 (60.3) Yes, Otherwised16 (45.7)29 (39.7)Previously treated with TKI, n (%).007 Yes20 (62.5)25 (34.2) No11 (37.5)48 (65.8)Hospital visits in the past 12 months, n (%).186 Zero0 (0.0)2 (2.7) 1 time3 (9.1)2 (2.7) 2 times0 (0.0)6 (8.1) 3 times or more30 (90.8)64 (86.5)ahigh education level: higher vocational education, academic education. bmiddle education level: secondary vocational education, higher general secondary education, pre-university education. clow education level: primary education, lower general secondary education, preparatory secondary vocational education. dMostly dasatinib and nilotinib
## Information provision
During the study period, patients in the intervention group became significantly more familiar with patient association Hematon compared to patients in the questionnaire group ($$P \leq .023$$). For the other concepts, no statistically significant differences were found between the groups. However, in the intervention group, a clear trend towards better familiarity with disease related concepts was seen (Additional file 3).
Table 3 shows the percentage of patients who reported to receive no or little information on all individual information items. After the study period, information on “additional help outside the hospital” scored significantly better in the intervention group ($$P \leq .025$$). Several items showed a trend towards better information provision in the intervention group when compared to the questionnaire group (e.g. diagnosis, extent spread, possible causes of the disease, and expected effects and possible side effects of treatment). Table 3 also shows scores on perceived information provision on different EORTC-scales. There was no significant difference between the two groups although all information scales show a trend towards better information provision in the intervention group. Table 3Percentage of CML patients who perceived to receive no or little information per item of the EORTC QLQ-INFO25 and mean scores (SD) on information provision scales of the EORTC QLQ-INFO25, questionnaire vs intervention groupQuestionnaire groupIntervention groupPercentage of cml patients who perceived to receive no or little information about:Pre ($$n = 33$$) %Post ($$n = 29$$) %ΔPre ($$n = 75$$) %Post ($$n = 57$$) %ΔCorrected P-valueThe diagnosis of your disease30.331.00.726.417.9−8.5.248The extent (spread) of your disease42.455.212.841.933.9−8.133The possible causes of your disease68.865.5−3.364.955.4−9.5.505Whether the disease is under control18.217.2−117.612.5−5.1.281The purpose of any medical tests you have had or may undergo27.331.03.720.316.1−4.2.448The procedures of the medical tests24.224.1−0.123.016.4−6.6.608The results of the medical tests you have already received18.210.3−7.912.29.1−3.1.622The medical treatment18.213.8−4.413.59.1− 4.4.391The expected benefit of the treatment30.227.6−2.618.99.1−9.8.106The possible side-effects of your treatment54.535.7−18.851.427.3−24.1.688The expected effects of the treatment on disease symptoms48.534.5−1441.927.3−14.6.877The effects of the treatment on social and family life87.972.4−15.581.172.7−8.4.529The effect of the treatment on sexual activity83.982.8−1.190.588.9−1.6.940Additional help outside the hospital90.993.12.297.285.5−11.7.025Rehabilitation services90.992.9294.587.3−7.2.155Aspects of managing your illness at home93.986.2−7.787.785.5−2.2.357Possible professional psychological support93.896.62.884.978.6−6.3.163Different locations of care84.493.18.789.085.5−3.5.155Things that you can do to help yourself get well75.078.63.678.178.20.1.402EORTC-QLQ-INFO25 scalesaQuestionnaire groupIntervention groupPre ($$n = 33$$)Mean (SD)Post ($$n = 29$$)Mean (SD)ΔPre ($$n = 75$$)Mean (SD)Post ($$n = 57$$)Mean (SD)ΔCorrected P-valueInformation about Disease58.8 (24.1)57.2 (22.5)−1.657.1 (21.1)63.2 (20.6)6.1.115 Medical tests71.7 (24.2)67.8 (20.6)−3.969.7 (22.5)70.0 (22.2)0.3.442 Treatments48.1 (18.4)53.8 (21.7)5.750.2 (19.1)55.4 (18.1)5.2.854 Other services12.4 (18.1)13.2 (19.7)0.816.4 (18.6)22.3 (25.6)5.9.252 Different location of care facilities18.7 (25.3)10.3 (20.1)−8.416.9 (24.3)20.0 (30.5)3.1.052 How to help yourself29.2 (31.4)29.8 (30.5)0.630.6 (26.5)33.3 (30.1)2.7.513Received written information68.8 (47.1)67.9 (47.6)−0.970.3 (46.0)71.4 (45.6)1.1.884Received cd/video6.06 (24.2)10.3 (31.0)4.245.48 (22.9)7.14 (26.0)1.66.538Satisfaction with amount of information43.4 (25.7)41.4 (21.2)−238.4 (23.4)36.4 (20.6)−2.865Wish to receive more info33.3 (47.9)31.0 (47.1)−2.345.9 (50.2)22.8 (42.3)−23.1.106Wish to receive less info66.7 (47.9)69.0 (47.1)2.354.1 (50.2)77.8 (42.0)23.7.105Helpfulness of information31.3 (26.3)34.5 (26.4)3.231.1 (21.8)32.1 (22.2)1.222Global score46.3 (16.1)46.7 (17.0)0.447.1 (15.9)51.2 (16.5)4.1.161P-value shows the difference between intervention and questionnaire with regard to the within-subject change between baseline and post-measurementΔ = difference between pre- and post-measurementsaScores between 0 and 100, higher scores indicate more/better information and satisfaction
## eHealth literacy
Table 4 shows mean scores on eHealth literacy. Results from the eHEALS showed that both groups had comparable competences before the study period. After the study period, patients’ knowledge on where to find helpful health resources on the internet was significantly improved in the intervention group ($$P \leq .032$$). Furthermore, the intervention group showed a trend towards better eHealth literacy after the study period. Table 4Mean scores (SD) on eHealth Literacy (eHEALS)a per item questionnaire vs intervention groupQuestionnaire groupIntervention groupItemPre ($$n = 33$$)Mean (SD)Post ($$n = 29$$) Mean (SD)ΔPre ($$n = 75$$) Mean (SD)Post ($$n = 57$$) Mean (SD)ΔCorrected P-valueI know what health resources are available on the Internet4.03 (0.67)3.86 (0.79)−0.173.73 (0.97)3.95 (0.77)0.22.076I know where to find helpful health resources on the Internet4.03 (0.73)3.79 (0.83)−0.243.77 (1.04)4.04 (0.69)0.27.032I know how to find helpful health resources on the Internet3.88 (0.82)3.83 (0.85)−0.053.85 (0.92)4.04 (0.69)0.19.424I know how to use the Internet to answer my health questions3.67 (0.82)3.79 (0.68)0.123.69 (0.98)3.95 (0.70)0.26.543I know how to use the health information I find on the Internet to help me3.48 (0.94)3.59 (0.73)0.113.46 (1.00)3.77 (0.76)0.31.464I have the skills I need to evaluate the health resources I find on the Internet3.69 (1.03)3.86 (0.79)0.173.58 (0.97)3.96 (0.76)0.38.437I can tell high-quality from low-quality health resources on the Internet3.47 (1.05)3.69 (0.89)0.223.46 (1.05)3.84 (0.89)0.38.703I feel confident in using information from the Internet to make health decisions3.31 (1.15)3.36 (0.91)0.053.26 (1.10)3.71 (0.89)0.45.110P-value shows the difference between intervention and questionnaire with regard to the within-subject change between baseline and post-measurementΔ = difference between pre- and post-measurementsaScores between 0 and 5 with higher scores indicating better eHealth literacy
## Patient empowerment
Figure 2 shows scores on patient empowerment. At baseline, $40\%$ of patients in the questionnaire group did not believe in their own role in treating their CML or did not have trust or knowledge to take action (level 1–2). After the study period, $35\%$ of them was level 1 or 2. For the intervention group, these rates were 43 and $26\%$, respectively. Fig. 2Patient empowerment levels (PAM-13 NL) questionnaire vs intervention group Moreover, there was no significant difference in the standardized activation score between the intervention and the questionnaire group ($$P \leq .139$$). In the intervention group, a trend towards increased patient empowerment was observed. The mean PAM score of the questionnaire group increased from 60.5 (SD = 14.6) to 62.6 (SD = 13.2) whereas the intervention group increased from 58.5 (SD = 13.7) to 64.0 (SD = 12.4).
## Medication compliance
No statistical difference was found in mean total score of patients’ medication compliance between groups ($$P \leq .922$$). Mean total score of the questionnaire group increased from 5.3 (SD = 0.92) to 5.4 (SD = 1.00) whereas the intervention group increased from 5.4 (SD = 0.54) to 5.5 (SD = 0.82) after using CMyLife. Patients’ self-reported effectiveness showed that $34.2\%$ of patients in the intervention group reported that due to using the medication app they knew better why taking their medication properly is important. Additionally, $47.4\%$ felt more motivated to take medication properly and $28.9\%$ had started to think differently about how important it is. Using the medication app stimulated $21.0\%$ of patients to search for help if necessary, it improved medication compliance of $42.1\%$ of patients, and $52.7\%$ of them felt less insecure about it.
## Molecular monitoring according to guidelines
In the questionnaire group, $90.8\%$ of patients visited the hospital three times or more at baseline versus $86.2\%$ in the post-measurement. In the intervention group, these rates decreased from 86.2 to $78.9\%$. No statistical differences were found between the groups ($$P \leq .685$$). In the self-reported effectiveness questionnaire, $44.0\%$ of patients in the intervention group indicated that they were more aware of when they needed to get their BCR-ABL1 values checked, and $20.0\%$ felt less insecure about it after using the guideline app. In addition, $20.0\%$ of patients felt more motivated to get their BCR-ABL1 values checked timely when using the guideline app, and it stimulated $32.0\%$ to contact their HCP if their response to treatment was not sufficient according to guidelines. Forty percent of patients knew when treatment response was good and $20.0\%$ of patients started to think differently about how important it is to get their BCR-ABL1 values checked in time. Lastly, $32.0\%$ of patients reported to have more insight into their BCR-ABL1 values and its course.
## Quality of life
Table 5 shows scores of the EORTC-QLQ-C30 and EORTC-QLQ-CML24 questionnaires. On the scales of the EORTC-QLQ-C30 no significant differences between the pre- and post-measurement of the groups were reported. Regarding the EORTC-QLQ-CML24, during the study period, individual items showed that patients in the intervention group had a significant increase in experiencing treatment as burdensome, compared to patients in the questionnaire group ($$P \leq .043$$) (Table 5). Also, on the scales of the EORTC-QLQ-CML24, a significant decrease was reported on impact on daily life ($$P \leq .043$$) and an increase in symptom burden ($$P \leq .013$$) in the intervention group compared to the questionnaire group (Table 5).Table 5Mean scores (SD) from the EORTC-QLQ-C30 and the EORTC-QLQ-CML24 scales and percentage of patients experiencing moderate to severe burden of disease specific complaints (EORTC QLQ-CML24): questionnaire vs intervention groupQuestionnaire groupIntervention groupScalesPre ($$n = 33$$)Mean (SD)Post ($$n = 29$$)Mean (SD)ΔPre ($$n = 75$$)Mean (SD)Post ($$n = 57$$)Mean (SD)ΔCorrected P-valueEORTC QLQ-C30 a Global health status/ QoL75.0 (15.5)69.8 (19.0)−5.275.3 (18.9)75.7 (19.6)0.4.317Functioning Physical functioning82.2 (15.9)84.8 (15.2)2.686.8 (16.4)87.3 (15.4)0.5.482 Role functioning76.3 (23.9)81.6 (23.7)5.375.5 (28.4)79.8 (26.1)4.3.968 Emotional functioning81.3 (17.4)79.3 (20.6)−283.6 (18.2)82.7 (19.8)−0.9.700 Cognitive functioning87.9 (16.8)89.1 (16.8)1.285.6 (18.6)83.9 (20.4)−1.7.417 Social functioning82.8 (25.5)87.4 (15.8)4.683.1 (23.0)86.0 (20.1)2.9.626Symptoms Fatigue33.3 (19.4)28.7 (22.7)−4.633.3 (28.4)30.4 (26.4)−2.9.377 Pain20.7 (25.4)19.0 (23.9)− 1.714.4 (24.4)14.0 (24.2)−0.4.516 Nausea and vomiting9.09 (17.2)9.20 (15.2)0.117.21 (13.0)9.06 (14.8)1.85.537 Insomnia28.3 (30.2)31.0 (33.3)2.719.4 (26.5)23.4 (25.9)4.690 Dyspnoea21.9 (23.4)19.5 (24.4)−2.414.4 (24.7)14.0 (23.5)−0.4.589 Loss of appetite16.2 (29.0)16.1 (26.2)−0.16.31 (18.9)5.26 (15.2)−1.05.940 Constipation16.7 (22.4)9.52 (17.8)−7.184.05 (13.5)7.02 (18.6)2.97.064 Diarrhoea20.2 (31.1)18.5 (29.7)−1.720.1 (25.3)21.1 (28.6)1.332 Financial difficulties8.08 (16.7)5.75 (12.8)−2.336.31 (20.4)5.26 (15.2)−1.05.720EORTC QLQ-CML24a Impact on daily life16.0 (15.2)18.4 (19.1)2.419.2 (17.1)15.0 (15.8)−4.2.043 Symptom burden25.6 (15.2)22.5 (16.2)−3.120.6 (12.4)21.6 (13.2)1.013 Impact on worry/mood21.1 (20.8)25.9 (22.5)4.815.4 (13.9)20.0 (18.5)4.6.671 Body image problems28.1 (26.9)20.7 (24.3)−7.420.1 (27.2)22.8 (29.7)2.7.052 Satisfaction with care and information77.4 (24.9)77.8 (17.3)0.472.4 (32.0)74.2 (30.9)1.8.871 Satisfaction with social life67.7 (28.7)64.4 (25.1)−3.363.9 (33.2)59.6 (33.2)−4.3.844Questionnaire groupIntervention groupModerate to severe burden of following complaints (EORTC QLQ-CML24):Pre ($$n = 33$$) %Post ($$n = 29$$) %ΔPre ($$n = 75$$) %Post ($$n = 57$$) %ΔCorrected P-valueAbdominal pain or cramps9.43.4−68.110.52.4*Dry mouth21.927.65.712.219.37.1.538Worry about change in weight12.56.9−5.65.47.11.7.365Skin problems31.324.1−7.214.919.34.4.170Headache3.16.93.84.17.02.9.910Aches or pains in muscles or joints37.532.1−5.425.733.98.2.080Hair loss15.63.4−12.25.43.5−1.9.254Excessive sweating19.410.3−9.114.910.5−4.4.527Acid indigestion or heartburn9.413.84.48.18.90.8.805Drowsiness15.63.4−12.212.212.50.3.072Oedema21.917.2−4.716.212.3−3.9.946Frequent urination28.117.2−10.929.719.3−10.4.639Problem with eyes28.120.7−7.418.919.60.7.479Muscle cramps37.534.5−324.324.60.3.433Emotional ups and downs12.513.81.39.614.34.7.259Worry about future health28.120.7−7.412.714.01.3.137Difficulty living a normal life because I got tired quickly21.913.8−8.117.815.8−2.529Worry about infection12.527.615.14.119.615.5.373Dissatisfied with my body as a result of illness or treatment25.013.8−11.215.517.52.073Burdensome treatment16.120.74.619.25.3−13.9.043Social support was needed0.03.43.45.55.3−0.2*Satisfied with the care89.396.3780.683.02.4.278Satisfied with the information90.386.2−4.176.883.66.8.280Satisfied about the quality of my social life78.169.0−9.168.563.2−5.3.668P-value shows the difference between intervention and questionnaire with regard to the within-subject change between baseline and post-measurementΔ = difference between pre- and post-measurements*P-value could not be determined due to limited number in participantsaScores between 0 and 100; higher scores on the functioning scales indicate better quality of life, higher scores on the symptom scales indicate more/more severe symptoms. Higher scores on daily life, symptom burden, worry/mood and body image problems, indicates higher impact, higher scores on satisfaction indicate more satisfaction
## Principal results and comparison with prior work
This is the first patient directed preference trial, including a before-after measurement, to explore the effectiveness of CMyLife on information provision, patient empowerment, medication compliance, molecular monitoring, and quality of life of CML patients. Overall, active use of CMyLife showed a clear trend towards improvement of all above-mentioned components except quality of life. The quality of life questionnaire showed a slight increase in patients’ burden of symptoms but simultaneously a decrease on impact on daily life.
Both familiarity with CML related concepts and perceived information provision showed positive trends in patients that actively used CMyLife and information on additional support outside the hospital improved significantly. Especially information provision on direct disease related concepts, such as diagnosis, treatment, tests, and side effects, showed a positive trend towards better information provision, although the percentage of sufficiently informed patients stayed unchanged low in other items. Content of CMyLife should be adjusted according to patients’ information needs to improve information provision. Previous literature emphasized the beneficial effects of adequate information provision in cancer patients [33–37]. Perceived information provision in patients with other malignancies varied although it is roughly comparable [38–40].
In addition to information provision, CMyLife significantly improved patients’ knowledge on where to find online health information. Literature shows that only half of cancer patients have knowledge and capability to access and differentiate the high amount of web-based information [41]. Results showed that active use of CMyLife improved eHealth literacy among CML patients and helped them become a well-informed partner in health care. Especially during the COVID-19 pandemic for instance, it is important for CML patients to stay accurately informed. Accurate and reliable information should be provided rapidly to these patients since almost $40\%$ of them reported to be substantially concerned about the coronavirus [42]. CMyLife had been a great medium to provide patients with this timely information [43].
Patient empowerment was increased in our study, albeit not significant. Patients who are more empowered in taking control of their own disease could believe in their abilities to optimize their medication compliance and oversee their molecular monitoring. Other interactive patient portals, such as one for breast cancer survivors and one for lung cancer patients, showed a decrease in patient activation, although not directly comparable due to a difference in study population, content and features [44, 45]. Perhaps no significant increase in patient empowerment was found because of limited sample size, because the outcome measure is not responsive enough to detect an effect, or because actual usage was not strong enough. Therefore, future studies should take actual usage into account when investigating patient empowerment.
Our study showed extremely high medication compliance rates. Pre-intervention, these rates were reported as almost $100\%$ and did not significantly change post-intervention. In literature, medication compliance rates vary from 19 to $98\%$, probably due to heterogeneity in adherence measurement methods and study populations [46, 47]. Only small proportions of patients were perfectly adherent. Patients in our study were very strict in taking their medication because they are very motivated for taking care of their disease, and therefore, participated in this patient preference study. As MARS is a rather general measure it is not possible to determine whether patients take their medication on all prescribed moments. Therefore, a more objective and specific tool to measure medication compliance should be used in the future. The well-known medication event monitoring system is an example of such an adequate measure, it provides an objective and reliable measure of medication compliance [48, 49]. Nevertheless, self-reported effectiveness shows patients to be more motivated and medication compliant when using the medication app.
Our study results show that frequency of hospital visits was not significantly affected by CMyLife. Remarkably, hospital visits decreased in both groups, probably due to the COVID-19 pandemic, during which routine care was deliberately postponed and digital care was stimulated. CMyLife enabled CML patients to continuate their care digitally without any delay or interruption. CMyLife gave patients opportunity to replace in-person visits by video consultations, which decreased the risk of exposing them to COVID-19 [50]. Self-reported effectiveness showed that by using the guideline app, patients felt more motivated to undergo testing on scheduled time, and they gained more insights in their obtained results and disease course. Importance of patient empowerment is further emphasized by suboptimal guideline adherence rates by HCPs [51]. CMyLife could be a great medium for accurate surveillance of treatment-free remission in CML patients.
None of the EORTC-QLQ-C30 scales showed significant differences in quality of life between the questionnaire and intervention group. Scores in our study are roughly comparable to cancer patients in general [52]. The EORTC-QLQ-CML24 scales showed a significant decrease of impact on daily life and a significant increase in symptom burden. Using CMyLife with logging their side effects could have made patients more aware of their symptoms. On the other side the concurrent decrease in impact suggests that patients were better able to self-manage their symptoms. In CML, adverse events are the main reason of intentional non-adherence, so timely recognition and management by HCPs is important [53–56]. Yet, HCPs tend to underestimate severity of patients’ symptoms [57]. Patient reported outcomes (PROs) are essential in patient-centered care, and its advantages as well as the necessity for routine PRO assessment in clinical practice are well described, not only in CML, but other malignancies too [57–61]. CMyLife provides a platform for this assessment.
## Future improvements
Despite promising potential of CMyLife, there is still room for improvement. In the future, content of CMyLife should be adjusted according to changing patients’ information needs. This requires a dynamic improvement process, where patients’ needs are measured routinely and content of CMyLife is adjusted accordingly. In addition to the information gap, PRO assessment combined with personalized feedback should be properly integrated in the CMyLife platform together with proper monitoring of molecular levels and medication adherence to enhance self-management aiming at the ultimate goal of true patient-centered care. HCPs will not become entirely redundant as monitoring and follow-up for adverse events and late effects of TKI treatment, albeit from a distance, is still required but their role is changing in coaching the patient.
## Strengths, limitations and future research
In our study a number of strengths and limitations are recognized. First, our results are strengthened by the presence of a questionnaire group. Despite the limited sample size and the sample being a convenience sample, it encompasses an adequate, nationwide, representation of Dutch CML patients. Our preference-based design has advantages compared to randomised controlled trials (RCTs). EHealth users should be motivated in order to actually use the innovation. Patients with a strong preference for one of the arms who refuse randomisation may be absent in RCTs. Also, literature shows high dropout and nonresponse rates in studies evaluating the effectiveness of eHealth using RCTs. When randomising patients to use or not use an eHealth innovation, bias will be introduced. Not all patients in the intervention group wanted to use the innovation and patients in the control group could have wanted to use the intervention but were not allowed to. Absence, dropout, and nonresponse of patients affects generalisability of study results, which is not the case in a preference based design [62–64]. Allocation of patients to use or not use eHealth randomly is therefore not ethical and should always be done in agreement with patients. However, when using a non-randomised design, unknown and uncontrolled confounders could be present [65]. Access to the CMyLife website for patients in the questionnaire group was not denied, this could have caused underestimation of the effects of the platform. Although as many covariates as possible were taken into account in our analysis, some factors should be focus of future studies in order to minimize confounding effects. For example, no data of CMyLife utilization were collected, nor how patients’ use other information and communication resources aside from CMyLife. Lastly, communication between patients could not have been prevented since patients in the intervention group met in small groups during workshops. Also, part of the platform was that patients in the intervention group were enabled to communicate with other patients in the intervention group, to share information or ask questions via a password protected forum. However, communication between the intervention group and the questionnaire group was very unlikely since the groups did not meet each other and had no contact information of each other. Therefore, we do not think this could have affected results.
## Conclusion
Aim of CMyLife is to provide hospital-free virtual care, by giving CML patients tools and know-how to self-manage their disease. Results of our study suggest that although more symptoms were reported, patient were better able to manage their symptoms. Knowledge on where to find online health information was improved significantly and, albeit not significant, they felt more empowered. Beneficial effects on medication compliance and molecular monitoring were experienced by patients, which is paramount in patient-centered care. CMyLife provides patients with information, tailored in amount and content and in a timely manner. However, we found room for improvement in content of information provision by expanding topics, adjusted to patients’ information needs. Personalized information based on PROs assessed in CMyLife such as symptoms experienced, and quality of life could further enhance self-management. An iterative process of assessing patients’ needs and further adjustment of CMyLife is required to keep care patient-centered, fit CMyLife into future perspectives, and put patients in lead of their disease process. Since hospital-free care has shown to be feasible in time of the COVID-19 pandemic, eHealth-based innovations such as CMyLife could be a solution to maintain quality of care and make current oncological health care services more sustainable.
## Supplementary Information
Additional file 1. Validated questionnaires with scores and internal consistency. Additional file 2. Self-reported effectiveness on medication compliance and molecular monitoring questions and interpretation. Additional file 3. Patients’ familiarity with CML related concepts questionnaire vs intervention group.
## References
1. Pruitt S, Epping-Jordan J. *Innovative care for chronic conditions: building blocks for action: global report* (2002.0)
2. 2.Zhao K, Short M, Harding J. Cancer survival and prevalence in Australia: cancers diagnosed from 1982 to 2004. Australian Institute of Health and Welfare; 2008. p. 102.
3. Wang H, Naghavi M, Allen C, Barber RM, Bhutta ZA, Carter A. **Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015**. *Lancet* (2016.0) **388** 1459-1544. DOI: 10.1016/S0140-6736(16)31012-1
4. Rowley JD. **A new consistent chromosomal abnormality in chronic myelogenous leukaemia identified by quinacrine fluorescence and Giemsa staining**. *Nature.* (1973.0) **243** 290. DOI: 10.1038/243290a0
5. Druker BJ. **Current treatment approaches for chronic myelogenous leukemia**. *Cancer J (Sudbury, Mass)* (2001.0) **7** S14-S18
6. Hughes T, Deininger M, Hochhaus A, Branford S, Radich J, Kaeda J. **Monitoring CML patients responding to treatment with tyrosine kinase inhibitors: review and recommendations for harmonizing current methodology for detecting BCR-ABL transcripts and kinase domain mutations and for expressing results**. *Blood.* (2006.0) **108** 28-37. DOI: 10.1182/blood-2006-01-0092
7. Pfirrmann M, Baccarani M, Saussele S, Guilhot J, Cervantes F, Ossenkoppele G. **Prognosis of long-term survival considering disease-specific death in patients with chronic myeloid leukemia**. *Leukemia.* (2016.0) **30** 48-56. DOI: 10.1038/leu.2015.261
8. Ossenkoppele G, Janssen J, Posthuma E, Falkenburg J, Biemond B, Bos G. **Aanbevelingen voor de behande-ling van chronische myeloïde leukemie 2011**. *Ned Tijdschr Hematol* (2011.0) **8** 237-247
9. Goldberg SL, Chen L, Guerin A, Macalalad AR, Liu N, Kaminsky M. **Association between molecular monitoring and long-term outcomes in chronic myelogenous leukemia patients treated with first line imatinib**. *Curr Med Res Opin* (2013.0) **29** 1075-1082. DOI: 10.1185/03007995.2013.812034
10. Guérin A, Chen L, Dea K, Wu EQ, Goldberg SL. **Association between regular molecular monitoring and tyrosine kinase inhibitor therapy adherence in chronic myelogenous leukemia in the chronic phase**. *Curr Med Res Opin* (2014.0) **30** 1345-1352. DOI: 10.1185/03007995.2014.904281
11. Guérin A, Chen L, Dea K, Wu EQ, Goldberg SL. **Economic benefits of adequate molecular monitoring in patients with chronic myelogenous leukemia**. *J Med Econ* (2014.0) **17** 89-98. DOI: 10.3111/13696998.2013.862251
12. Cortes J, Rea D, Lipton JH. **Treatment-free remission with first-and second-generation tyrosine kinase inhibitors**. *Am J Hematol* (2019.0) **94** 346-357. PMID: 30394563
13. Geelen IG, Thielen N, Janssen JJ, Hoogendoorn M, Roosma TJ, Willemsen SP. **Impact of hospital experience on the quality of tyrosine kinase inhibitor response monitoring and consequence for chronic myeloid leukemia patient survival**. *Haematologica.* (2017.0) **102** e486. DOI: 10.3324/haematol.2017.175265
14. 14.Health DoExpert patient: a new approach to chronic disease management for the 21st century2001LondonDoH. *Expert patient: a new approach to chronic disease management for the 21st century* (2001.0)
15. Schildmann J, Grunke M, Kalden J, Vollmann J. **Information and participation in decision-making about treatment: a qualitative study of the perceptions and preferences of patients with rheumatoid arthritis**. *J Med Ethics* (2008.0) **34** 775-779. DOI: 10.1136/jme.2007.023705
16. Gray CS, Miller D, Kuluski K, Cott C. **Tying eHealth tools to patient needs: exploring the use of eHealth for community-dwelling patients with complex chronic disease and disability**. *JMIR Res Protoc* (2014.0) **3** e67. DOI: 10.2196/resprot.3500
17. Mosa ASM, Yoo I, Sheets L. **A systematic review of healthcare applications for smartphones**. *BMC Med Inform Decis Mak* (2012.0) **12** 67. DOI: 10.1186/1472-6947-12-67
18. Kim H, Goldsmith JV, Sengupta S, Mahmood A, Powell MP, Bhatt J. **Mobile health application and e-health literacy: opportunities and concerns for cancer patients and caregivers**. *J Cancer Educ* (2019.0) **34** 3-8. DOI: 10.1007/s13187-017-1293-5
19. Ector GI, Westerweel PE, Hermens RP, Braspenning KA, Heeren BC, Vinck OM. **The development of a web-based, patient-centered intervention for patients with chronic myeloid leukemia (CMyLife): design thinking development approach**. *J Med Internet Res* (2020.0) **22** e15895. DOI: 10.2196/15895
20. 20.CMyLife. URL:
www.CMyLife.nl. Accessed 18 Aug 2021.
21. 21.MedApp. URL: https://www.medapp.nu/en/. Accessed 18 Aug 2021.
22. 22.RICHTLIJN CHRONISCHE MYELOIDE LEUKEMIE (GUIDELINE CHRONIC MYELOID LEUKEMIA). URL: https://hovon.nl/en/treatment-guidelines/leukemia. Accessed 18 Aug 2021. HOVON MPN werkgroep; [Available from: https://hovon.nl/_asset/_public/TreatmentGuidelines/TreatmentGuidelines_Leukemia/hovon-cml-richtlijn-14-04-2018_geautoriseerd.pdf].
23. Greenhalgh T, Shaw S, Wherton J, Vijayaraghavan S, Morris J, Bhattacharya S. **Real-world implementation of video outpatient consultations at macro, meso, and micro levels: mixed-method study**. *J Med Internet Res* (2018.0) **20** e150. DOI: 10.2196/jmir.9897
24. Arraras JI, Greimel E, Sezer O, Chie W-C, Bergenmar M, Costantini A. **An international validation study of the EORTC QLQ-INFO25 questionnaire: an instrument to assess the information given to cancer patients**. *Eur J Cancer* (2010.0) **46** 2726-2738. DOI: 10.1016/j.ejca.2010.06.118
25. Van der Vaart R, van Deursen AJ, Drossaert CH, Taal E, van Dijk JA, van de Laar MA. **Does the eHealth literacy scale (eHEALS) measure what it intends to measure? Validation of a Dutch version of the eHEALS in two adult populations**. *J Med Internet Res* (2011.0) **13** e86. DOI: 10.2196/jmir.1840
26. Hibbard JH, Mahoney ER, Stockard J, Tusler M. **Development and testing of a short form of the patient activation measure**. *Health Serv Res* (2005.0) **40** 1918-1930. DOI: 10.1111/j.1475-6773.2005.00438.x
27. Hibbard JH, Stockard J, Mahoney ER, Tusler M. **Development of the patient activation measure (PAM): conceptualizing and measuring activation in patients and consumers**. *Health Serv Res* (2004.0) **39** 1005-1026. DOI: 10.1111/j.1475-6773.2004.00269.x
28. Rademakers J, Nijman J, van der Hoek L, Heijmans M, Rijken M. **Measuring patient activation in the Netherlands: translation and validation of the American short form patient activation measure (PAM13)**. *BMC Public Health* (2012.0) **12** 577. DOI: 10.1186/1471-2458-12-577
29. Thompson K, Kulkarni J, Sergejew A. **Reliability and validity of a new medication adherence rating scale (MARS) for the psychoses**. *Schizophr Res* (2000.0) **42** 241-247. DOI: 10.1016/S0920-9964(99)00130-9
30. Owie GO, Olotu SO, James BO. **Reliability and validity of the medication adherence rating scale in a cohort of patients with schizophrenia from Nigeria**. *Trends Psychiatry Psychother* (2018.0) **40** 85-92. DOI: 10.1590/2237-6089-2017-0077
31. Aaronson NK, Ahmedzai S, Bergman B, Bullinger M, Cull A, Duez NJ. **The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology**. *JNCI* (1993.0) **85** 365-376. DOI: 10.1093/jnci/85.5.365
32. 32.Efficace F, Iurlo A, Patriarca A, Stagno F, Bee P-C, Ector G, et al. Validation and reference values of the EORTC QLQ-CML24 questionnaire to assess health-related quality of life in patients with chronic myeloid leukemia. Leuk Lymphoma. 2021;62(3):669–78.
33. Husson O, Mols F, van de Poll-Franse LV. **The relation between information provision and health-related quality of life, anxiety and depression among cancer survivors: a systematic review**. *Ann Oncol* (2011.0) **22** 761-772. DOI: 10.1093/annonc/mdq413
34. Luker KA, Beaver K, Leinster SJ, Owens RG, Degner LF, Sloan JA. **The information needs of women newly diagnosed with breast cancer**. *J Adv Nurs* (1995.0) **22** 134-141. DOI: 10.1046/j.1365-2648.1995.22010134.x
35. Mistry A, Wilson S, Priestman T, Damery S, Haque M. **How do the information needs of cancer patients differ at different stages of the cancer journey? A cross-sectional survey**. *JRSM Short Rep* (2010.0) **1** 30. DOI: 10.1258/shorts.2010.010032
36. Geissler J, Sharf G, Bombaci F, Daban M, De Jong J, Gavin T. **Factors influencing adherence in CML and ways to improvement: results of a patient-driven survey of 2546 patients in 63 countries**. *J Cancer Res Clin Oncol* (2017.0) **143** 1167-1176. DOI: 10.1007/s00432-017-2372-z
37. Noens L, van Lierde MA, De Bock R, Verhoef G, Zachee P, Berneman Z. **Prevalence, determinants, and outcomes of nonadherence to imatinib therapy in patients with chronic myeloid leukemia: the ADAGIO study**. *Blood.* (2009.0) **113** 5401-5411. DOI: 10.1182/blood-2008-12-196543
38. Husson O, Thong MS, Mols F, Oerlemans S, Kaptein AA, van de Poll-Franse LV. **Illness perceptions in cancer survivors: what is the role of information provision?**. *Psycho-Oncology.* (2013.0) **22** 490-498. DOI: 10.1002/pon.3042
39. Bozec A, Schultz P, Gal J, Chamorey E, Chateau Y, Dassonville O. **Evaluation of the information given to patients undergoing head and neck cancer surgery using the EORTC QLQ-INFO25 questionnaire: a prospective multicentric study**. *Eur J Cancer* (2016.0) **67** 73-82. DOI: 10.1016/j.ejca.2016.08.005
40. Bouma G, de Hosson LD, van Woerkom CE, van Essen H, de Bock GH, Admiraal JM. **Web-based information and support for patients with a newly diagnosed neuroendocrine tumor: a feasibility study**. *Support Care Cancer* (2017.0) **25** 2075-2083. DOI: 10.1007/s00520-017-3598-7
41. Heiman H, Keinki C, Huebner J. **EHealth literacy in patients with cancer and their usage of web-based information**. *J Cancer Res Clin Oncol* (2018.0) **144** 1843-1850. DOI: 10.1007/s00432-018-2703-8
42. Ector GI, Huijskens EG, Blijlevens NM, Westerweel PE. **Prevalence of COVID-19 diagnosis in Dutch CML patients during the 2020 SARS-CoV2 pandemic. A prospective cohort study**. *Leukemia* (2020.0) **34** 2533-2535. DOI: 10.1038/s41375-020-0964-0
43. Ector GI, Verweij L, Hermens RP, Blijlevens NM. **Filling the gaps of patient information needs and information perception in chronic myeloid leukemia with the patient-physician co-produced web-based platform CMyLife**. *Patient Educ Couns* (2022.0) **105** 686-694. DOI: 10.1016/j.pec.2021.06.025
44. Kuijpers W, Groen WG, Oldenburg HS, Wouters MW, Aaronson NK, van Harten WH. **eHealth for breast cancer survivors: use, feasibility and impact of an interactive portal**. *JMIR Cancer* (2016.0) **2** e3. DOI: 10.2196/cancer.5456
45. Groen WG, Kuijpers W, Oldenburg HS, Wouters MW, Aaronson NK, van Harten WH. **Supporting lung cancer patients with an interactive patient portal: feasibility study**. *JMIR cancer* (2017.0) **3** e10. DOI: 10.2196/cancer.7443
46. Hall AE, Paul C, Bryant J, Lynagh MC, Rowlings P, Enjeti A. **To adhere or not to adhere: rates and reasons of medication adherence in hematological cancer patients**. *Crit Rev Oncol Hematol* (2016.0) **97** 247-262. DOI: 10.1016/j.critrevonc.2015.08.025
47. Kekale M, Talvensaari K, Koskenvesa P, Porkka K, Airaksinen M. **Chronic myeloid leukemia patients’ adherence to peroral tyrosine kinase inhibitors compared with adherence as estimated by their physicians**. *Patient Prefer Adherence* (2014.0) **8** 1619-1627. DOI: 10.2147/PPA.S70712
48. Vervloet M, van Dijk L, Santen-Reestman J, van Vlijmen B, Bouvy ML, de Bakker DH. **Improving medication adherence in diabetes type 2 patients through real time medication monitoring: a randomised controlled trial to evaluate the effect of monitoring patients’ medication use combined with short message service (SMS) reminders**. *BMC Health Serv Res* (2011.0) **11** 1-8. DOI: 10.1186/1472-6963-11-5
49. Riekert KA, Rand CS. **Electronic monitoring of medication adherence: when is high-tech best?**. *J Clin Psychol Med Settings* (2002.0) **9** 25-34. DOI: 10.1023/A:1014131928789
50. 50.Nekhlyudov L, Duijts S, Hudson SV, Jones JM, Keogh J, Love B, et al. Addressing the needs of cancer survivors during the COVID-19 pandemic. Springer; 2020. p. 601–6.
51. Geelen IG, Thielen N, Janssen JJ, Hoogendoorn M, Roosma TJ, Willemsen SP. **Treatment outcome in a population-based,‘real-world’cohort of patients with chronic myeloid leukemia**. *Haematologica.* (2017.0) **102** 1842-1849. DOI: 10.3324/haematol.2017.174953
52. Hjermstad MJ, Fayers PM, Bjordal K, Kaasa S. **Health-related quality of life in the general Norwegian population assessed by the European Organization for Research and Treatment of Cancer Core quality-of-life questionnaire: the QLQ= C30 (+ 3)**. *J Clin Oncol* (1998.0) **16** 1188-1196. DOI: 10.1200/JCO.1998.16.3.1188
53. Breccia M, Efficace F, Alimena G. **Adherence to treatment is a complex and multifaceted issue that can substantially alter the outcome of chronic myeloid leukemia patients treated with tyrosine kinase inhibitors**. *Leuk Res* (2012.0) **36** 804-805. DOI: 10.1016/j.leukres.2012.02.032
54. Breccia M, Efficace F, Sica S, Abruzzese E, Cedrone M, Turri D. **Adherence and future discontinuation of tyrosine kinase inhibitors in chronic phase chronic myeloid leukemia. A patient-based survey on 1133 patients**. *Leuk Res* (2015.0) **39** 1055-1059. DOI: 10.1016/j.leukres.2015.07.004
55. Efficace F, Baccarani M, Rosti G, Cottone F, Castagnetti F, Breccia M. **Investigating factors associated with adherence behaviour in patients with chronic myeloid leukemia: an observational patient-centered outcome study**. *Br J Cancer* (2012.0) **107** 904-909. DOI: 10.1038/bjc.2012.348
56. Eliasson L, Clifford S, Barber N, Marin D. **Exploring chronic myeloid leukemia patients’ reasons for not adhering to the oral anticancer drug imatinib as prescribed**. *Leuk Res* (2011.0) **35** 626-630. DOI: 10.1016/j.leukres.2010.10.017
57. Efficace F, Rosti G, Aaronson N, Cottone F, Angelucci E, Molica S. **Patient- versus physician-reporting of symptoms and health status in chronic myeloid leukemia**. *Haematologica.* (2014.0) **99** 788-793. DOI: 10.3324/haematol.2013.093724
58. De Marchi F, Medeot M, Fanin R, Tiribelli M. **How could patient reported outcomes improve patient management in chronic myeloid leukemia?**. *Expert Rev Hematol* (2017.0) **10** 9-14. DOI: 10.1080/17474086.2017.1262758
59. Efficace F, Cottone F. **Time for patient reported outcomes assessment in routine hematology practice: the case of chronic myeloid leukemia**. *Expert Rev Hematol* (2018.0) **12** 1-3. DOI: 10.1080/17474086.2019.1547189
60. Basch E, Pugh SL, Dueck AC, Mitchell SA, Berk L, Fogh S. **Feasibility of patient reporting of symptomatic adverse events via the patient-reported outcomes version of the common terminology criteria for adverse events (PRO-CTCAE) in a chemoradiotherapy cooperative group multicenter clinical trial**. *Int J Radiat Oncol Biol Phys* (2017.0) **98** 409-418. DOI: 10.1016/j.ijrobp.2017.02.002
61. Velikova G, Booth L, Smith AB, Brown PM, Lynch P, Brown JM. **Measuring quality of life in routine oncology practice improves communication and patient well-being: a randomized controlled trial**. *J Clin Oncol* (2004.0) **22** 714-724. DOI: 10.1200/JCO.2004.06.078
62. Fairhurst K, Dowrick C. **Problems with recruitment in a randomized controlled trial of counselling in general practice: causes and implications**. *J Health Serv Res Policy* (1996.0) **1** 77-80. DOI: 10.1177/135581969600100205
63. Christensen H, Mackinnon A. **The law of attrition revisited**. *J Med Internet Res* (2006.0) **8** e558. DOI: 10.2196/jmir.8.3.e20
64. Eysenbach G. **The law of attrition**. *J Med Internet Res* (2005.0) **7** e402. DOI: 10.2196/jmir.7.1.e11
65. Brewin T, Thornton H, Bradley C, McPherson K, Gore S, Silverman W. **Patients’ preferences and randomised trials**. *Lancet* (1996.0) **347** 1118-1119. DOI: 10.1016/S0140-6736(96)90317-7
|
---
title: An agent-based approach for modelling and simulation of glycoprotein VI receptor
diffusion, localisation and dimerisation in platelet lipid rafts
authors:
- Chukiat Tantiwong
- Joanne L. Dunster
- Rachel Cavill
- Michael G. Tomlinson
- Christoph Wierling
- Johan W. M. Heemskerk
- Jonathan M. Gibbins
journal: Scientific Reports
year: 2023
pmcid: PMC9994409
doi: 10.1038/s41598-023-30884-6
license: CC BY 4.0
---
# An agent-based approach for modelling and simulation of glycoprotein VI receptor diffusion, localisation and dimerisation in platelet lipid rafts
## Abstract
Receptor diffusion plays an essential role in cellular signalling via the plasma membrane microenvironment and receptor interactions, but the regulation is not well understood. To aid in understanding of the key determinants of receptor diffusion and signalling, we developed agent-based models (ABMs) to explore the extent of dimerisation of the platelet- and megakaryocyte-specific receptor for collagen glycoprotein VI (GPVI). This approach assessed the importance of glycolipid enriched raft-like domains within the plasma membrane that lower receptor diffusivity. Our model simulations demonstrated that GPVI dimers preferentially concentrate in confined domains and, if diffusivity within domains is decreased relative to outside of domains, dimerisation rates are increased. While an increased amount of confined domains resulted in further dimerisation, merging of domains, which may occur upon membrane rearrangements, was without effect. Modelling of the proportion of the cell membrane which constitutes lipid rafts indicated that dimerisation levels could not be explained by these alone. Crowding of receptors by other membrane proteins was also an important determinant of GPVI dimerisation. Together, these results demonstrate the value of ABM approaches in exploring the interactions on a cell surface, guiding the experimentation for new therapeutic avenues.
## Introduction
The plasma membrane of eukaryotic cells provides a physical and biochemical interface1 that allows the precise control of cell functions, facilitates shape change and movement2, attachment to the extracellular matrix or other cells, the controlled transfer of solutes outside-in and inside-out3, and the onset of the signalling mechanisms that regulate a cell4. Through the basic structure of its phospholipid bilayer, the plasma membrane provides a specialised environment in which cell surface receptors engage with extracellular ligands to trigger the transduction of signals in the cytosol. These signals are then propagated and amplified through enzyme cascades culminating in a controlled change in cell behaviour, for instance in gene expression, migration, secretion, proliferation, survival and apoptosis5–7.
Transmembrane receptors may move laterally within the phospholipid plane of the plasma membrane, although there are movement restraints due to the presence of and linkage to other surface proteins, as well as due to the presence of intracellular proteins, such as the membrane actin-myosin and tubular cytoskeletons8,9. The receptors may also be restricted in their movements due to the position of ligands, for instance, in the extracellular matrix10, or due to ligand-induced dimerisation or clustering, as in cases of the insulin and antibody receptors11,12. Interactions of plasma membrane receptors with other proteins inside the cell are furthermore controlled via biochemical processes such as post-translational modifications of proteins (phosphorylation, acetylation, ubiquitination, sumoylation, glycosylation, lipidation), ultimately leading to precisely regulated temporal and spatial control of cell signalling mechanisms13–16.
The ability of receptors to initiate cell signalling is influenced by the membrane phospholipid composition and distribution7. Small and transient nanodomains of the membrane enriched in cholesterol and glycolipids, known as lipid rafts, present unique physicochemical properties, enabling a highly localised enrichment of cholesterol and other lipid molecular species to influence membrane fluidity and the ability of proteins to move within17. The concept of intra-membrane heterogeneities and lipid rafts has thereby facilitated our understanding of the spatiotemporal orchestration of receptor signalling mechanisms.
Limited efforts have been made so far to develop theoretical models that combine the effects of intra-membrane constraints and ligand-induced actions with for understanding of the critical elements of receptor localisation and movement. One approach that can be used to study the dynamics of a particle on a membrane is agent-based modelling (ABM). Previous work has utilised this approach to investigate the formation of generalised molecular clusters18, finding that protein diffusion is influenced by its neighbourhood, or to investigate more specific questions about particular receptor classes (such as integrins19) without recourse to data. Das and coworkers18 have developed an in-house code to link data and agent-based models to answer specific questions centring on the activation of trafficking of EGFR-HER2 receptors.
For this study we constructed a simple and effective model, based on experimental evidence, for predicting receptor movements on the anucleate platelets using the ABM approach. Our chosen target was the receptor for collagen, glycoprotein (GP) VI, which is uniquely expressed on blood platelets and megakaryocytes20,21. The binding of collagen to this receptor leads to GPVI dimerisation and clustering, and to a signalling response that culminates in rapid thrombus formation, which contributes to haemostasis22. The monomeric GPVI receptor has a weak affinity for collagen and is non-covalently associated with the Fc receptor γ-chain, through which it transmits signals23. Receptor dimerisation results in the formation of a complex with a higher affinity for collagen (Fig. 1A), thus facilitating ligand binding and signalling responses24–26.Figure 1Structure and dimerisation of platelet GPVI receptors. ( A) The extracellular domain of monomeric GPVI on platelets comprises of two IgG domains and a connection to the transmembrane domain (blue). The GPVI protein is stably connected to two chains of the FcR γ-chain, forming ITAM-containing signalling domains. Monomers of GPVI can dimerise with other monomers (dimerisation), a process that is reversible (dissociation). Adapted from Induruwa et al. [ 2016]27. ( B) Crystal structure of human platelet GPVI. Image taken from RCSB PDB (rcsb.org), annotation PDB ID 2GI723. ( C) Projected illustration of GPVI as a transmembrane protein with an assigned effective area in two dimensions.
Developing an ABM with distinct regions of membrane lipid composition—here referred to as confined domains that are proxy entities for lipid rafts28,29—we studied how GPVI receptors on the platelet plasma membrane can switch between monomer and dimeric entities. Our modelling studies support the preferential enrichment for GPVI in lipid rafts, in agreement with experimental observations30. Through simulation of multiple facets of the plasma membrane and membrane proteins, we thus provide a basis for understanding how receptor complexes form and function, and can impact altered receptor signalling processes in disease.
## Application of agent-based modelling (ABM)
An ABM approach was used to simulate agents (receptors and lipids) on the cell surface31. This approach has been used in different fields of physical science, biological science, social science, and finances32. For example, several recently published works used ABM to study the spreading of the COVID-19 pandemic33–35. There are several ways to implement ABM, either by coding the model from scratch or using existing software. A commonly used ABM software package is NetLogo, which is multi-purpose, computationally efficient and easy to use, offering the advantage of being easily implemented and modified by non-theoretical experimentalists36. Using NetLogo, we simulated the diffusion of receptors in a two-dimensional plasma membrane. The implementation of this is demonstrated in Fig. 2A-D, and a flowchart is provided in the Supplement. *The* generated models can be easily modified to model different kinds of receptors and transmembrane proteins, by adjusting properties such as size, mass and diffusivity. To ease this modification, the code to run simulations is made available, and details on how to install and implement it are given in the Supplement. In our ABM approach, receptors are able to move with an assigned behaviour, which is either deterministic or stochastic as modelled. Certain areas of the plasma membrane were considered as confined areas with reduced diffusivity. By default, components in the system were studied in a two-dimensional box with periodic boundary conditions to imitate an infinite membrane37.Figure 2Overview of ABM simulation procedure. ( A) The target system, i.e. the platelet membrane. The simplified version of a membrane consists of two areas, i.e. parts where molecules are confined in movements (confined domains), and the remaining part where they move freely (Brownian motion). In addition to inert proteins, the receptors of interest are indicated as transmembrane proteins. ( B) Application of ABM to target receptor dimerisation. The membrane in the simulation box consists of agents (receptor molecules) in monomeric or dimeric forms and inert proteins. The confined domains are considered to represent lipid rafts. All agents are treated as independent, of which mathematical rules determine their properties and interactions. ( C) Assignment of agent parameters. The simulation parameters included diffusivity, particle size and step size. ( D) Rules for agent movements. Each simulation step consists of a randomly placed agent with random walk (rejected in case of overlapping), dimerisation and dissociation. Steps are repeated until all agents are selected, after which movements follow.
## Brownian motion
Agents (receptor molecules and other membrane proteins) were considered to move freely in the two-dimensional surface in random directions. By applying a mean square distance (MSD) of Brownian motion on a two-dimensional surface as time (t) dependent38:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ MSD = 4Dt, $$\end{document}MSD=4Dt,a given step size (dS ~ MSD½) was taken, depending on the agent’s diffusivity (D) as dS ~ D½. Herein, the constant of variation was a function of the applied scaling. Agents in the simulation were modelled as circular discs, which never overlapped. It was assumed that the area occupied by one receptor is conserved during dimerisation, and that the space occupied by two monomers is equal to that occupied by one dimer, πR2dimer = 2πR2monomer. The sizes (radii) of dimer Rdimer and monomer were then related as Rdimer = √2 Rmonomer (Fig. 2C). The relationship of diffusivity and particle size was retrieved from the Stokes–Einstein relation39:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ R = {\text{k}}_{{\text{B}}} {\text{T}}/6\pi \eta D\sim1/D, $$\end{document}R=kBT/6πηD∼1/D,where kB, T, and η are Boltzmann constant, temperature, and viscosity, respectively. Although this formula is modelled in a 3-dimensional case, we presumed that the inversely proportional relationship between R and D was retained in 2-dimensions, the coefficient being absorbed in the scaling process. Combining these assumptions, the relationship between step size of monomer and dimer was:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ dS_{{{\text{monomer}}}} = 2^{$\frac{1}{4}$} dS_{{{\text{dimer}}}} $$\end{document}dSmonomer=$\frac{21}{4}$dSdimer Note that the step size of an agent (receptor) in each time step may not be equal. In the calculation above, the maximum step size was set, but the actual step size in each movement could be generated according to a Wiener’s process, dSactual ~|N[0,1]|dSmaximum. Herein N[0,1] forms a random variable with a standard normal distribution (Fig. 2D).
Experimentally, using single-particle tracking, it has been seen that the diffusivity of GPVI molecules on mouse platelets decreased by approximately ten times, when present in regions with confined membrane properties40, with the receptor’s mode of motion changing from Brownian movement to restricted movement. In our ABM implementation, the mode of motion of the receptor inside and outside the confined domain remained the same; the only difference being the diffusivity. While the presence of this domain confined the movement of the receptor, we assumed that the receptor was effectively moved slower, with a smaller diffusivity within the domain.
## Receptor dimerisation
The effects of dimerisation and dissociation of receptors were captured by the probabilities kb and kd,, respectively. Herein, dimerisation was defined as the conversion from two monomers to one dimer. The threshold of conversion was arbitrarily set at $10\%$ of the monomer’s diameter. For calculating the conversion, a random number R[0,1] ∈ [0,1] was generated. Dimerisation occurred if this number met the condition of R[0,1] < kb. Conversely, dissociation was imputed as the change from one dimer to two monomers. For dimer movements, also a random number R[0,1] was generated, and dissociation occurred when R[0,1] < kd (Fig. 2D).
## Parameterisation and scaling analysis
The following section explains how values were assigned to parameters. Note that when precise values for parameters were not available, order of magnitude estimates needed to be made, applicable to the platelet surface and the collagen receptor GPVI. The simulation conversion parameters estimated in the following session are summarised in Table 1.Table 1List of real-world and simulation parameters. See estimates in “Methodology” section. ParameterReal-world scaleSimulation scaleSize of simulation box3.0 × 10–7 m30GPVI effective size114 Å$3.8\%$ of 30 ~ 1.14GPVI diffusivity0.091 × 10–12 m2/s1Expected step size of GPVI in a time-step12.5 nm(π/2)½ ~ 1.25Time-step0.43 ms1
## Platelet surface area
The platelet volume based on previous work41 was taken to be Vp ≈ 7.4 × 10–18 m3, allowing us to determine (by assuming that platelets are perfect spheres) the radius R and the surface area A:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ R \approx 1.2 \times 10^{{{-}6}} \;{\text{m}}, $$\end{document}R≈1.2×10-6m,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ A \approx 1.8 \times 10^{{{-}11}} \;{\text{m}}^{2}. $$\end{document}A≈1.8×10-11m2.
Some assumptions needed to be made in considering the shape and volume of platelets, since their activation results in changes in morphology and membrane organisations. We reasoned that with the open canicular system exposed, following activation, the morphology of a platelet is closer to a sphere than a discoid. If an average discoid platelet is considered to have an average diameter of ~ 3 μm, the thickness of the cell can be determined as ~ 1 μm. Thus, the surface area of a platelet would be ~ 2.4 × 10–11 m2 (~ $33\%$ more than a spherical shape). If we account for the contribution of the open canicular system (estimated to be ~ $25\%$ of the plasma membrane surface)42, the total surface area will increase to 3.2 × 10–11 m2. However, since the open canalicular system is continuous with the plasma membrane, we assumed that the volume of a platelet remains constant during shape change. The consequences of a different receptor surface density is addressed in section 9 of the results. For the remaining simulations, we maintained a platelet surface area of 1.8 × 10–11 m2, consistent with spherical shape with a diameter of 2.4 μm.
## Simulation box size
The (transient) confined domain diameter for a lipid raft of d ≈ 100 – 300 nm was obtained from an earlier publication28. For convenience, we used a raft size of 200 nm. Note that the size did not affect the model outcomes (see “Results” section). A model limitation is the assumption of the confined domain as a single circular area in the centre of a periodic box, implying that a too-small box can result in simulation artefacts. In other words, if a raft size is smaller than 30 nm, less than one receptor molecule will be present inside a box. Too-small number of receptors per box could also lead to high fluctuations in the simulation results. We further assumed lipid rafts occupy about $35\%$ of the plasma membrane surface area43. The total area occupied by lipid rafts was then calculated as:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ A_{{{\text{raft}}}} \approx 35\% \times A \approx 6.4 \times 10^{{{-}12}} \;{\text{m}}^{2}. $$\end{document}Araft≈$35\%$×A≈6.4×10-12m2.
Considering this as the total area of confined domains, with d ≈ 200 nm, the count of domains was:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text{box}}_{{\text{per platelet}}} = A_{{{\text{raft}}}} /\left({\pi \left({d/2} \right)^{2} } \right) \approx 205. $$\end{document}boxper platelet=Araft/πd/22≈205.
In the simulation, the box area (consisting of one confined domain per box) was:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ A_{{{\text{box}}}} = A/box_{{\text{per platelet}}} \approx 9.0 \times 10^{{{-}14}} \;{\text{m}}^{2}, $$\end{document}Abox=A/boxper platelet≈9.0×10-14m2,with a box length of:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ L = \left({A_{{{\text{box}}}} } \right)^{$\frac{1}{2}$} \approx 3.0 \times 10^{{{-}7}} \;{\text{m}}. $$\end{document}L=Abox$\frac{1}{2}$≈3.0×10-7m.
## Receptor count per box
The number of GPVI molecules in a single platelet was estimated as ≈ 9600 copies44. This gave as a number of GPVI monomers per simulation box:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text{GPVI}}_{{\text{per box}}} = {\text{GPVI}}_{{\text{per platelet}}} /{\text{box}}_{{\text{per platelet}}} \approx $\frac{9600}{117}$ \approx 47 $$\end{document}GPVIper box=GPVIper platelet/boxper platelet≈$\frac{9600}{117}$≈47
## GPVI receptor molecule dimensions
The molecular dimensions of GPVI were taken from its crystal structure23: 114 Å × 45 Å × 75 Å. Considering the extremum case that its longest side is the projected diameter of the GPVI on the platelet surface (Fig. 1B,C), we choose a dGPVI ≈ 114 Å. The size of a GPVI monomer scaled to the box size then was:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ d_{{{\text{GPVI}}}}^{{{\text{scaled}}}} = d_{{{\text{GPVI}}}} /L \approx (114 \times 10^{{{-}10}})/(3.0 \times 10^{{{-}7}}) \approx 3.8\%. $$\end{document}dGPVIscaled=dGPVI/L≈(114×10-10)/(3.0×10-7)≈$3.8\%$.
## Step size and time scale of modelling
The diffusivity of a single GPVI molecule in the membrane has been measured before40, Dexp ≈ 0.091 × 10–12 m2 s–1. From the mean square distance of particle on a two-dimensional surface moving in Brownian motion, the step size can be scaled as:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ dS^{2} \approx 4Ddt. $$\end{document}dS2≈4Ddt.
According to this equation, we could either pick dS and determine the scale of dt from dS or vice versa. To simplify the simulation, we scaled the step size to order O[1] by setting D ~ 1, dt = 1, and dS = D½|N[0,1]|. Note that the constant 4 was absorbed in the D scaling and that random Brownian motion was assumed to have a random standard normal distribution, N[0,1]. With these definitions, the expected step size was calculated at:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ dS_{{{\text{e}} {\text{xpected}}}} \approx \left({\pi /2} \right)^{$\frac{1}{2}$} \times \left({L/30} \right) \approx 12.5 \times 10^{{{-}9}} \;{\text{m}}, $$\end{document}dSexpected≈π/$\frac{21}{2}$×L/30≈12.5×10-9m,where rexpected = (π/2)½ is the expected distance determined by a standard normal distribution function, and 30 comes from the defined scaled box size. Hence, the time scale was set as:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ dt \approx dS^{2} /4D \approx \left({12.5 \times 10^{{{-}9}} } \right)^{2} /\left({4 \times 0.091 \times 10^{{{-}12}} } \right) \approx 0.43\;{\text{ms}}. $$\end{document}dt≈dS$\frac{2}{4}$D≈12.5×10-$\frac{92}{4}$×0.091×10-12≈0.43ms.
This time scale was small enough to capture the confined behaviour of particles, which occurs in seconds28.
## Inert proteins
The modelling further included an unknown number of transmembrane proteins that have no interaction with the receptor of interest. The effect of a collision between proteins was already incorporated in the diffusion simulation via Brownian motion. The motion direction and step size changed randomly due to random encounters, implying that the presence of inert proteins was included by default. Additional parameters such as additional inert proteins (in arbitrary numbers) were used to check for effects on receptor dimerisation.
## Standard setup of ABM simulations
Simulations were performed in NetLogo 6.2.2 (Supplementary Figure S1), using the algorithm illustrated in Fig. 2 (for details see Supplementary Figure S2). A list of simulation parameters per research question is provided in Table 2. The default start setting was 47 receptor monomers that were uniformly distributed in a box representing the plasma membrane. Of note, this default did not take into account the heterogeneities caused by membrane cytoskeletal connections and receptor complexes, although the model may reach a non-uniform equilibrium after the simulation. Based on the calculations above, the diameter of GPVI was approximated as $3.8\%$ of the length of the simulation box (scaled as 30 × 30 pixels). The movement speed of monomers was set to D½. Each simulation was run for ≥ 200,000 steps to ensure equilibrium. An average of the last 50,000 steps was used for the analysis. Table 2List of parameters used in each simulation. CD, confined domain. Please note in this context, kd and kb are implemented as a rate in unit time as described in the “Methodology” section. Simulation typeBinding rate (kb) (per molecule per unit time)Dissociation rate (kd) (per unit time)Diffusivity outside CD (Do) (unit lenght2 per unit time)Diffusivity inside CD (Di) (unit lenght2 per unit time)CD occupied area (%)Number of added inert proteins (molecule)Number of inert protein packs (dimensionless)Fold number of CD merging (dimensionless)GPVI number per platelet (% of 9600)1. Receptors in CD area vs Di0011,2−1…,2−9,2−10350011002. Dimerisation (with CD) vs Di0.050.0111,2−1…,2−9,2−10350011003. Dimerisation vs %CD & Di0.050.0111,2−1…,2−4,2−50,5…,75,800011004. Dimerisation vs CD merging0.050.0110.135000.5,1…,7.5,81005. Added inert proteins0.050.011100,25…,175,2000,25…,175,20011006. Disintegrated inert proteins0.050.011101001,21…,27,2811007. Dimerisation (w/o CD) vs D0.050.012−5,2−4…,24,25100011008. Receptors in CD area vs %CD0, 0.01, 0.005, …, 0.000625kb/510.115,16,…,220011009. Dimerisation vs receptor number0.050.0110.13500125, 50, …, 150, 175, 20010. Dimerisation vs kb & kd60, 70, …, 130, $140\%$ of 0.0560, 70, …, 130, $140\%$ of 0.0110.135001100 Except where indicated otherwise, binding and dissociating probabilities were arbitrarily defined as kb = 0.05 molecule–1 per unit time and kd = 0.01 per unit time (or per timestep, dt ~ 0.43 ms as calculated above). These numbers were chosen to ensure balancing of the time scale of dimerisation and dissociation, i.e., to prevent an equilibrium without dimers or monomers. This also ensured that the number of GPVI molecules in dimeric form in the simulations were broadly consistent with the dimeric levels measured experimentally26. The impact of variation of these parameters is shown in the results (see “Simulation of ligand binding increases GPVI dimerisation”). All simulations were repeated three times. The code for this model, together with the setup for each simulation, is available in the Supplement.
## Results
In the present study, we aimed to understand how a fluid-mosaic plasma membrane influences receptor diffusion, interaction or dimerisation, and the initiation of cell signalling. According to the mosaic model, the phospholipids and proteins are not uniformly distributed. Lipid patches (rafts) are considered to concentrate signalling proteins, including receptors, thereby permitting or enhancing cell signalling processes17,30. Precisely how this occurs has not yet been resolved. The agent-based modelling (ABM) approach allowed us to explore the impact of confined lipid domains within the plasma membrane on the enrichment and clustering of the collagen receptor GPVI. The model can easily be applied to other receptors and cell types of interest, with adapted parameters as in the methods section.
To address ABM simulations, we designed a receptor-containing simulation box, representing a defined square part of the plasma membrane with mobile GPVI molecules and initially a single confined domain (“raft”). With the chosen parameters, we assumed that GPVI monomers have no inherent tendency to form dimers or clusters.
## Simulated receptors preferentially localise to confined domain areas
Differential diffusivity in the lipid domains of a membrane may result in an uneven distribution of transmembrane proteins. In the present ABM, we assumed that the confined domains contain a higher level of proteins that are free to move inside or outside45,46. In a series of simulations, we tested this idea.
The proportion occupied by the confined domain, as assumed in rafts, was estimated as $35\%$ by Prior et al.39. The size of the confined domain was fixed as a circle, which represented a domain of lower protein diffusion. The diffusivity ratio outside and inside the confined domain was varied to simulate effects on receptor diffusion.
If the receptor localisation is not affected by diffusivity, the relative numbers of receptors located inside or outside the confined domain will be similar for all diffusivity ratios. In Fig. 3A, the ratio of diffusivity of receptors between the outside and inside of a confined domain, expressed as Dout:Din, was taken as an independent variable and then changed from 20, 21, 22, … to 210 (i.e., 1024). The actual ratio can be estimated to be ~ 10, according to single particle tracking results of GPVI molecules in mouse platelets40. In our studies we varied this ratio from 1 to 1024 to explore the extreme relationships between diffusivity and location preference of GPVI. The number of receptors located inside the domain, as a dependent variable, was found to asymptotically reach $100\%$, with $50\%$ at a Dout:Din of in the range of 8 to 16 (Fig. 3B). Note that if a different diffusivity ratio does not affect the receptor localisation, this number should not deviate from the starting value of $35\%$. Based on the obtained changes at default model settings, we concluded that GPVI receptors will preferentially localise to the confined domains, i.e., the areas with lower diffusivity. Figure 3Preferential localisation of single receptors in the confined domain. ( A) Snapshots of 11 simulations of 47 receptors (red dots) moving on the simulated membrane with confined domain (yellow circle). Note the sub-micrometer size of the simulation box of 0.3 × 0.3 μm, and the initial random distribution of GPVI receptors. Simulations were run for ≥ 200,000 steps, with an average of the last 50,000 steps shown. The diffusivity ratio between outside and inside confined domains, Dout:Din, was changed from 1 [20] to 1024 [210]. The snapshots shown are for Dout:Din = 1, 4, 16, 64, 256 and 1024. ( B) Effect of an altered ratio Dout:Din on number of receptors inside the confined domain. Each simulation was repeated three times, means ± SD.
Previous studies have demonstrated that GPVI is present in cholesterol-rich lipid rafts. GPVI recruitment occurs upon platelet adhesion to collagen47, a process which can lead to GPVI clustering48. While these membrane structures concentrate specific signalling proteins within, recent studies reveal that lipid rafts also cage or restrict protein and receptors diffusion49,50, which may be a prerequisite for GPVI clustering. Indeed, a heterotypic interaction of GPVI with PECAM1 is increased in lipid rafts51. Considering that lipid rafts can orchestrate the GPVI signalling52, we hypothesized that lowered diffusivity in rafts compared to non-raft domains results in an increased GPVI dimerisation within.
## Decreasing diffusivity in the confined domain increases receptor dimerisation
We then explored how the confined domain affected the likeliness of receptor dimerisation, a process that is known to enhance GPVI ligand-binding properties24–26. For simplicity in the ABM simulation, we assumed that dimerisation is not modulated by other proteins in the plasma membrane or actin cytoskeleton. We thus assumed that the fraction of receptors in dimeric form remains the same for all Dout:Din ratios.
As illustrated in simulation snapshots (Fig. 4A), we found that an increase in the diffusivity ratio (i.e., lower diffusivity in the confined domain with Dout:Din set from 20 to 210) yielded a higher number of receptor dimers. Herein, the ratio of diffusivity of receptors outside or inside the confined domains was taken as an independent variable. The dimeric receptors increased non-linearly with the diffusivity ratio to reach a saturation level of $80\%$ (Fig. 4B). The simulation thus pointed to a main effect of intra-membrane differences in receptor diffusivity for promoting receptor dimerisation. Figure 4Higher diffusivity ratio enhances receptor confinement and dimerisation. ( A) Snapshots of simulation of receptors in monomeric (red) or dimeric (orange) forms in the presence of a confined domain (yellow circle). Initially, 47 monomeric receptors were randomly distributed without dimeric form. Simulations were run for ≥ 200,000 steps, with an average of the last 50,000 steps shown. The diffusivity ratio between outside and inside confined domains, Dout:Din, was varied from 1 [20] to 1024 [210]. Snapshots are shown for Dout:Din = 1, 4, 16, 64, 256 and 1024. ( B) Effect of altering the ratio of Dout:Din on the number of receptors in dimeric form. Simulations were repeated three times, means ± SD.
## Total area of the confined domain influences receptor dimerisation
To explore whether the relative size of a confined domain affected dimerisation, this domain was again set as a circular area, of which the relative radius was altered to make up an increasing part of the membrane box size (Fig. 5A). The area occupied by the confined domain was then modelled from 0–$75\%$, i.e., up to twice the estimated area of lipid rafts, while the diffusivity ratio Dout:Din was varied from 1 to 32. We found that both the area occupied by confined domains and the diffusivity ratio greatly affected the average number of dimers. Interestingly, the number of receptor dimers increased substantially from $10\%$ to plateau to $40\%$, when the Dout:Din increased (Fig. 5B). The highest dimer levels were reached at the two highest Dout:Din ratios of 16 and 32. In addition, a larger area occupied by the confined domains was needed to plateau at lower Dout:Din ratios. In other words, the level of dimerisation increased with the diffusivity ratio, with curves reaching a saturation point at the lower domain area in case of a higher diffusivity ratio. Translated to receptor biology, this suggested that both the attraction strength and the size of raft-like structures can determine the extent of receptor dimerisation. Figure 5Increasing confined domain area induces more receptor dimerisation. Effect of increasing the confined domain area at different diffusivity ratios. ( A) Snapshots of the occupied area of the confined domain (yellow circle) from upper left at $20\%$, $40\%$, $60\%$ and $75\%$. Red and orange dots represent monomeric and dimeric receptors, respectively. Note that at higher area percentages, the number of receptors per box reduces, when the number of boxes per cell increases. The size of the confined domain was kept constant. ( B) Results of simulation for receptor fractions in dimeric form. Simulations were run for ≥ 200,000 steps, with an average of the last 50,000 steps shown.
## Merging of confined domains does not influence receptor dimerisation
Since membrane rafts are temporary structures that can reversibly merge53, we hypothesised that the merging could affect receptor dimerisation. To assess this, we varied the number of confined domains while fixing the total area occupied, and then simulated the receptor organisation. Herein, we set the ratio of outside/inside diffusivity of receptors Dout:Din to 10, knowing that about half of the GPVI receptors on mouse platelets have a diffusivity approximately ten times lower than the other half of receptors with Brownian motion40. The simulation is visualized by snapshots in Fig. 6A. When extending this domain number to higher fold merging, we observed no change in dimer formation (Fig. 6B). Translating to real life, for platelets this suggests that the mere merging of membrane rafts does not impact receptor dimerisation. Figure 6Merging of confined domains has no effect on receptor dimerisation. Simulated was the effect of merging two confined domains while fixing the total occupied area size. ( A) Snapshots of two confined domains merged into one (yellow circles). The red and orange dots represent monomeric and dimeric receptors, respectively. ( B) Simulation for determining dimeric receptors as a function of the fold merging of confined domains. Simulations were repeated three times, mean ± SD; Pearson correlation of 0.40 indicates a weak positive correlation between confined domain folds and dimerisation.
## Inert protein crowding in the membrane increases receptor dimerisation
As the platelet membrane contains other moving transmembrane proteins without interaction with the GPVI receptor, we also added free-moving membrane proteins to the ABM, acting as obstacles to receptor diffusion. In our simulation, the number of inert proteins per box varied from 0, 25, 50, … to 200 (Fig. 7A). The size of inert proteins was arbitrarily set at 0.05 of the box size, and their speed was set at 0.5D½. The average number of receptors in dimeric form, as an outcome variable, almost linearly increased from 25 to $45\%$, while the number of inert proteins increased from 0 to 200 (Fig. 7B). This is explained by the space occupied by the inert proteins, thus tightening the diffusion room of monomeric receptors, which then leads to a higher encounter rate between receptors. Figure 7Increasing inert protein crowding induces more receptor dimerisation. Simulation of added inert proteins on the receptor dimerisation. Red and orange dot represents monomeric and dimeric receptors, respectively; green dots represent inert proteins. Simulations were run for ≥ 100,000 steps, with an average of the last 50,000 steps shown. ( A) Snapshots from the left top with inert proteins of 50, 100, 150 and 200. ( B) Plot of dimer counts versus number of inert proteins. Note the more abundant dimeric receptors, when protein crowdedness increases. Each simulation was repeated three times, mean ± SD.
## Disintegration of inert proteins has a minor impact on receptor dimerisation
We then considered that inert proteins could differ upon platelet activation, i.e., the proteins can become aggregated or disintegrated54. This was simulated by splitting the space size into smaller components while not changing the total space occupied by inert proteins. Inert proteins were placed randomly in the simulation box, and the inert protein size was initially set as one large circle with a diameter half of the box size. Then the protein number was increased from 1 to 256 (20 to 28), while the size was proportionally decreased with a total conserved area (Fig. 8A). According to the Stokes–Einstein relation39, diffusivity may be expected to increase since smaller particles move faster. Yet, our ABM simulations showed a minor increase from 30 to $36\%$ of dimeric receptors, when the inert protein disintegrated from 1 to 256 pieces (Fig. 8B). To verify that this was not statistical noise, we determined a Pearson correlation coefficient of + 0.94. Accordingly, it appears that the disintegration of inert proteins exhibits only a minimal effect on receptor dimerisation. Figure 8Disintegration of inert protein slightly affects receptor dimerisation. Simulation of the effect of size of inert proteins on receptor dimerisation. The total area occupied by inert proteins was kept constant, while subareas of smaller size were created. See further Fig. 5. ( A) Snapshots for 1, 4, 16 and 64 splits of inert proteins. ( B) Plot of receptor dimer counts versus the number of disintegrated inert proteins. Each simulation was repeated three times, mean ± SD (Pearson correlation + 0.96).
## Decreasing receptor diffusivity increases the level of receptor dimerisation
According to work by Haining et al.40, the activation of GPVI decreased in Tspan9 knock-out mice, while also the overall diffusivity of GPVI decreased. This suggested that a reduced diffusivity per se can lead to reduced dimer formation. To test this, we simulated the variation of receptor diffusivities from 2–5, 2–6, … to 25; and then measured dimerisation, taken as a proxy for receptor activation. It appeared that the number of dimeric receptors, as a dependent variable decreased substantially from 85 to $25\%$, when the diffusivity increased (Fig. 9). In other words, a slower-moving agent has a higher chance of encountering other agents. This suggests that the phenotype of reduced GPVI signalling of Tspan9-knock-out platelet is unlikely to be explained by changes other than membrane diffusion alone. Figure 9Decreasing receptor diffusivity increases dimerisation. Simulated testing of altered receptor diffusivity to assess receptor dimerisation, with otherwise fixed parameters. For convenience, receptor velocity was taken as v = D½. Diffusivity varied from 2–5, 2–3, …, to 25. The simulation shows a decrease in dimer number at an increased diffusivity. Data are shown in a semi-log2 scale; mean ± SD ($$n = 3$$ simulations).
## Estimation of the plasma membrane area of confined domains
Haining et al.40 deduced the proportion of the plasma membrane that constitutes confined domains using a single particle tracking microscopy, noting that GPVI exhibited distinctive Brownian and confined movement without and within confined domains, respectively. The number of GPVI molecules in Brownian and restricted movement mode was approximately equal40. A temporal change in the proportion of the membrane confined domains may also impact the localisation of a receptor. Using electron microscopy and spatial point pattern analysis, previously lipid rafts were estimated to comprise approximately $35\%$ of the total membrane surface43.
We used ABM to ask what proportion range of the plasma membrane should comprise a confined domain, such in accordance with the $50\%$ of GPVI receptors with restricted movements40. To answer this, we fixed the diffusivity ratio to Dout:Din = 10:1 and the size of the domain to 200 nm, and then varied the percentage of the plasma membrane occupied by a confined domain. The number 10:1 was obtained from the diffusion coefficients of two pools of GPVI40. A first run of the simulation gave 20–$21\%$ of confined domains, which is below the estimation of lipid rafts of $35\%$43. Subsequently, the effects of enforced dimerisation were added (Fig. 10). The adding of dimerisation somewhat decreased the corresponding domain area (with GPVIinside ~ $50\%$), meaning that the area occupied by the confined domain would not exceed $21\%$, based on the model prediction. We therefore concluded that, while confined domains govern the receptor dimerisation rate, the physicochemical properties of these do not alone control receptor function. Other constraining features such as more complex receptor interactions, including the actin-based membrane skeleton within lipid rafts55 and receptor crowding, are also important. Figure 10Enforced GPVI dimerisation reduces the confined domain area for a given GPVI localisation ratio. Plot of simulation of GPVI localisation in the presence of a confined domain with variable occupied area. Note the near linear increase of receptors inside the confined domain when this area increases. The reported value of GPVI with restricted movements40 is about $50\%$, pointing to a confined domain size of 20–$21\%$. In the presence of dimerisation, this area slightly decreases to 19–$20\%$, with a kb = 0.000625 and kd = 0.000125 (least square regression analysis).
## Increased receptor surface density results in higher predicted dimerisation levels
Several estimates may affect the number of receptors on the cell surface used in the current model. The first variable is the number of GPVI receptor copies. We set this number at 9,600 per platelet, following Burkhart’s work44, which was obtained by quantitative mass spectrometry. Other studies using flow cytometry reported different figures ranging from 3,000 to 9,00056,57, while also different GP6 alleles lead to altered membrane-expressed GPVI levels58. Furthermore, even within a given subject, platelet sub-populations exist with > tenfold differences in GPVI level, related to ageing cells59, differential cell size, receptor internalisation and shedding60.
Parameter estimation in this model assumed the platelet to be a perfect sphere; in reality, the disc-like shape of platelet leads to a higher surface area given the same volume. Also human and mouse platelets differ in this respect. For mice, the GPVI density can be estimated as 575 molecules per μm2 (mouse platelet volume of ~ 4.7 fl61, with GPVI ~ 7800 molecules per platelet62). Considering that the dimerisation rate depends on receptor density, inter-species differences can also be captured by the current simulation.
While setting for human the GPVI density as 9,600 per platelet surface area ≈ 1.8 × 10–11 m2 (σ0 = $\frac{9600}{1.8}$ × 10–11 m2 = 533 molecules per μm2) as a reference, we varied this density from −75 to + $100\%$ from σ0 and measured the percentage of receptors in dimeric form (Fig. 11). The simulation predicted that an increased surface density of GPVI elevates the dimeric GPVI from 50 to $75\%$ (over a range of −75 to + $100\%$ of reference levels). This imply that in the model dimerisation does not increase proportionately with the ratio of density. Figure 11Increasing GPVI surface density increases dimerisation. ( A) Simulation setups with various surface densities of GPVI: from left to right, top to bottom $50\%$, $100\%$, $150\%$, and $200\%$ of σ0. ( B) The plot shows that receptor dimerisation (as proportional to total GPVI, in %) increases with the GPVI surface density (as proportional to σ0 in %). Reference density σ0 was set as 533 molecules per μm2 (9600 receptors divided by platelet surface area ≈ 1.8 × 10–11 m2; spherical assumption).
## Simulation of ligand binding increases GPVI dimerisation
A way to simulate the effect of ligand binding is to increase the GPVI binding rate and/or dissociation rate. In real life, we expect GPVI in dimeric form to increase and to remain dimeric on collagen-adhered platelets26. To simulate this, we varied the kb and kd from the initial values (kb = 0.05 molecule–1 unit time–1 and kd = 0.01 unit time–1) by ± $10\%$, ± $20\%$, ± $30\%$, and ± $40\%$. The percentage of receptors in dimeric form was then simulated, as displayed in Fig. 12. In this case, the dimerisation rate to increased when the binding rate was increased and/or dissociation rate decreased – both may illustrate the effect of ligand binding. A decrease in dissociation rate means that a formed dimer is more stable (e.g. stabilised by a multimeric ligand), while an increase in binding rate allows monomers to form into dimers with greater probability (induced by receptor-associated proteins).Figure 12Increase in binding rate and decrease in dissociation rate increase GPVI dimerisation. Simulation varying the binding and dissociation rate value from −$40\%$, −$30\%$, …, + $30\%$, + $40\%$, deviating from the initial values of kb = 0.05 molecule–1 unit time–1 and kd = 0.01 unit time–1. Values represent percentages of GPVI in dimeric form, as a proportion of total GPVI. Red colour represents higher dimerisation, blue lower dimerisation.
## Concluding remarks
In this study, we have demonstrated the abilities of a simple ABM technique to understand the constraints of receptor localisation and movement in the plasma membrane. Receptor dimerisation and subsequent clustering upon ligation are key initiators of signal transduction by many receptors that regulate cell function, including cell adhesion, migration and activation, for instance in the context of haemostasis and immunity. The ABM illustrates the presence of different lipid domains with distinctive properties (as confined domains), the space that these occupy on the cell surface, and the importance of the plethora of additional proteins on the cell surface, that form crowds and influence a given receptor’s ability to interact with partner proteins. The relative contributions of the functionally relevant parameters tested in the ABM to GPVI dimerisation levels are summarised in Fig. 13.Figure 13Relative contributions of various modelled factors to GPVI dimer formation. Arrows show the direction of increasing the indicated factor. Other conditions were assumed to be fixed, when varying the parameter of interest. The level of dimerisation (in %) displayed in colour scale: from $20\%$ (red) to $90\%$ (right).Numbers indicate parameter ranges tested with units or dimension indicated in parentheses.
Due to its simplicity, computational efficiency and ease of use, ABM has the potential to be developed and generalised also to other cell types and more complex systems of receptor/protein or cell membranes. It can be applied to various studies by adapting the properties of agents (e.g., mass, size, environment), and how these affect agent diffusivity and interaction rules. Moreover, the present still simple ABM can be further developed into a more complex system with more agents and conditions. Useful additions such as receptor interactions with the cytoskeleton can be added in by utilising a computational cluster63,64.
Taken together, this study forms an initial step to model and define membrane properties and their influences on receptor function. This will highlight specific processes that may be targeted therapeutically to increase or decrease receptor function and may be used for teaching, enabling the impact of modulation of various model components to be tested or demonstrated in silico.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-30884-6.
## References
1. Lombard J. **Once upon a time the cell membranes: 175 years of cell boundary research**. *Biol. Direct.* (2014.0) **9** 1-35. DOI: 10.1186/s13062-014-0032-7
2. Radhakrishnan K, Halász Á, Vlachos D, Edwards JS. **Quantitative understanding of cell signaling: The importance of membrane organization**. *Curr. Opin. Biotechnol.* (2010.0) **21** 677-682. DOI: 10.1016/j.copbio.2010.08.006
3. Stein WD. *The Movement of Molecules across Cell Membranes* (1967.0)
4. Klammt C, Lillemeier BF. **How membrane structures control T cell signaling**. *Front. Immunol.* (2012.0) **3** 1-9. PMID: 22679445
5. Juliano RL, Haskill S. **Signal transduction from the extracellular matrix**. *J. Cell Biol.* (1993.0) **120** 577-585. DOI: 10.1083/jcb.120.3.577
6. Goñi FM. **The basic structure and dynamics of cell membranes: An update of the Singer-Nicolson model**. *Biochim. Biophys. Acta.* (2014.0) **1838** 1467-1476. DOI: 10.1016/j.bbamem.2014.01.006
7. Sunshine H, Iruela-Arispe ML. **Membrane lipids and cell signaling**. *Curr. Opin. Lipidol.* (2017.0) **28** 408-413. DOI: 10.1097/MOL.0000000000000443
8. Owen DM, Williamson D, Rentero C, Gaus K. **Quantitative microscopy: Protein dynamics and membrane organisation**. *Traffic* (2009.0) **10** 962-971. DOI: 10.1111/j.1600-0854.2009.00908.x
9. Trimble WS, Grinstein S. **Barriers to the free diffusion of proteins and lipids in the plasma membrane**. *J. Cell Biol.* (2015.0) **208** 259-271. DOI: 10.1083/jcb.201410071
10. Dityatev A, Seidenbecher CI, Schachner M. **Compartmentalization from the outside: The extracellular matrix and functional microdomains in the brain**. *Trends Neurosci.* (2010.0) **33** 503-512. DOI: 10.1016/j.tins.2010.08.003
11. Ward C. **Structural insights into ligand-induced activation of the insulin receptor**. *Acta Physiol.* (2008.0) **192** 3-9. DOI: 10.1111/j.1748-1716.2007.01781.x
12. Lemmon MA. **Ligand-induced ErbB receptor dimerization**. *Exp. Cell Res.* (2009.0) **315** 638-648. DOI: 10.1016/j.yexcr.2008.10.024
13. Ardito F, Giuliani M, Perrone D, Troiano G, Muzio L. **The crucial role of protein phosphorylation in cell signalingand its use as targeted therapy**. *Int. J. Mol. Med.* (2017.0) **40** 271-280. DOI: 10.3892/ijmm.2017.3036
14. Foot N, Henshall T, Kumar S. **Ubiquitination and the regulation of membrane proteins**. *Physiol. Rev.* (2017.0) **97** 253-281. DOI: 10.1152/physrev.00012.2016
15. Torres M. **The implications for cells of the lipid switches driven by protein–membrane interactions and the development of membrane lipid therapy**. *Int. J. Mol. Sci.* (2020.0) **21** 2377. DOI: 10.3390/ijms21072322
16. Okada AK. **Lysine acetylation regulates the interaction between proteins and membranes**. *Nat. Commun.* (2021.0) **12** 1-12. DOI: 10.1038/s41467-021-26657-2
17. Sezgin E, Levental I, Mayor S, Eggeling C. **The mystery of membrane organization: Composition, regulation and roles of lipid rafts**. *Nat. Rev. Mol. Cell Biol.* (2017.0) **18** 361-374. DOI: 10.1038/nrm.2017.16
18. Das AA, Darsana TA, Jacob E. **Agent-based re-engineering of ErbB signaling: A modeling pipeline for integrative systems biology**. *Bioinformatics* (2017.0) **33** 726-732. DOI: 10.1093/bioinformatics/btw709
19. Juliette G, Peters R, Owen DM. **An agent-based model of molecular aggregation at the cell membrane**. *PLoS ONE* (2020.0) **15** 1-17
20. Watson S. **Platelet activation by extracellular matrix proteins in haemostasis and thrombosis**. *Curr. Pharm. Des.* (2009.0) **15** 1358-1372. DOI: 10.2174/138161209787846702
21. Mammadova-Bach E, Nagy M, Heemskerk JW, Nieswandt B, Braun A. **Store-operated calcium entry in thrombosis and thrombo-inflammation**. *Cell Calcium* (2019.0) **77** 39-48. DOI: 10.1016/j.ceca.2018.11.005
22. Rayes J, Watson SP, Nieswandt B. **Functional significance of the platelet immune receptors GPVI and CLEC-2**. *J. Clin. Invest.* (2019.0) **129** 12-23. DOI: 10.1172/JCI122955
23. Horii K, Kahn ML, Herr AB. **Structural basis for platelet collagen responses by the immune-type receptor glycoprotein VI**. *Blood* (2006.0) **108** 936-942. DOI: 10.1182/blood-2006-01-010215
24. Miura Y, Takahashi T, Jung SM, Moroi M. **Analysis of the interaction of platelet collagen receptor glycoprotein VI (GPVI) with collagen: A dimeric form of GPVI, but not the monomeric form, shows affinity to fibrous collagen**. *J. Biol. Chem.* (2002.0) **277** 46197-46204. DOI: 10.1074/jbc.M204029200
25. Jung SM, Tsuji K, Moroi M. **Glycoprotein (GP) VI dimer as a major collagen-binding site of native platelets: Direct evidence obtained with dimeric GPVI-specific Fabs**. *J. Thromb. Haemost.* (2009.0) **7** 1347-1355. DOI: 10.1111/j.1538-7836.2009.03496.x
26. Jung SM. **Constitutive dimerization of glycoprotein VI (GPVI) in resting platelets is essential for binding to collagen and activation in flowing blood**. *J. Biol. Chem.* (2012.0) **287** 30000-30013. DOI: 10.1074/jbc.M112.359125
27. Induruwa I, Jung SM, Warburton EA. **Beyond antiplatelets: The role of glycoprotein VI in ischemic stroke**. *Int. J. Stroke* (2016.0) **11** 618-625. DOI: 10.1177/1747493016654532
28. Dietrich C, Yang B, Fujiwara T, Kusumi A, Jacobson K. **Relationship of lipid rafts to transient confinement zones detected by single particle tracking**. *Biophys. J.* (2002.0) **82** 274-284. DOI: 10.1016/S0006-3495(02)75393-9
29. Triantafilou M, Morath S, Mackie A, Hartung T, Triantafilou K. **Lateral diffusion of Toll-like receptors reveals that they are transiently confined within lipid rafts on the plasma membrane**. *J. Cell Sci.* (2004.0) **117** 4007-4014. DOI: 10.1242/jcs.01270
30. Bodin S, Tronchère H, Payrastre B. **Lipid rafts are critical membrane domains in blood platelet activation processes**. *Biochim. Biophys. Acta.* (2003.0) **1610** 247-257. DOI: 10.1016/S0005-2736(03)00022-1
31. Bonabeau E. **Agent-based modeling: Methods and techniques for simulating human systems**. *Proc. Natl. Acad. Sci. USA* (2002.0) **99** 7280-7287. DOI: 10.1073/pnas.082080899
32. Niazi M, Hussain A. **Agent-based computing from multi-agent systems to agent-based models: A visual survey**. *Scientometrics* (2011.0) **89** 479-499. DOI: 10.1007/s11192-011-0468-9
33. Cuevas E. **An agent-based model to evaluate the COVID-19 transmission risks in facilities**. *Comput. Biol. Med.* (2020.0) **121** 103827. DOI: 10.1016/j.compbiomed.2020.103827
34. 34.Truszkowska A., Behring B., Hasanyan J., Zino L., Butail S., Caroppo E., Jiang Z.P., Rizzo A., & Porfiri P, M. High‐resolution agent‐based modeling of COVID‐19 spreading in a small town. (2021). ArXiv q-bio. 10.1002/adts.202000277
35. Shamil MS, Farheen F, Ibtehaz N, Khan IM, Rahman MS. **An agent-based modeling of COVID-19: Validation, analysis, and recommendations**. *Cognit. Comput.* (2021.0). DOI: 10.1007/s12559-020-09801-w
36. 36.Wilensky, U. NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1999). http://ccl.northwestern.edu/netlogo/.
37. Pratt LR, Haan SW, Pratt LR, Haan SW. **Effects of periodic boundary conditions on equilibrium properties of computer simulated fluids**. *J. Chem. Phys.* (1981.0) **74** 1864. DOI: 10.1063/1.441276
38. Michalet X. **Brownian motion in an isotropic medium**. *Phys. Rev. E. Interdiscip. Top.* (2010.0) **82** 1-13. DOI: 10.1103/PhysRevE.82.041914
39. Einstein A. **On the motion of small particles suspended in liquids at rest required by the molecular-kinetic theory of heat**. *Annu. Phys.* (1905.0) **17** 208
40. Haining EJ. **Tetraspanin Tspan9 regulates platelet collagen receptor GPVI lateral diffusion and activation**. *Platelets* (2017.0) **28** 629-642. DOI: 10.1080/09537104.2016.1254175
41. Dunster JL, Mazet F, Fry MJ, Gibbins JM, Tindall MJ. **Regulation of early steps of GPVI signal transduction by phosphatases: A systems biology approach**. *PLoS Comput. Biol.* (2015.0) **11** 1-26. DOI: 10.1371/journal.pcbi.1004589
42. Frojmovic MM, Milton JG. **Human platelet size, shape, and related functions in health and disease**. *Physiol. Rev.* (1982.0) **62** 185-261. DOI: 10.1152/physrev.1982.62.1.185
43. Prior IA, Muncke C, Parton RG, Hancock JF. **Direct visualization of Ras proteins in spatially distinct cell surface microdomains**. *J. Cell Biol.* (2003.0) **160** 165-170. DOI: 10.1083/jcb.200209091
44. Burkhart JM. **The first comprehensive and quantitative analysis of human platelet protein composition allows the comparative analysis of structural and functional pathways**. *Blood* (2012.0) **120** 73-82. DOI: 10.1182/blood-2012-04-416594
45. Honigmann A, Pralle A. **Compartmentalization of the cell membrane**. *J. Mol. Biol.* (2016.0) **428** 4739-4748. DOI: 10.1016/j.jmb.2016.09.022
46. Krapf D. **Compartmentalization of the plasma membrane**. *Curr. Opin. Cell Biol.* (2018.0) **53** 15-21. DOI: 10.1016/j.ceb.2018.04.002
47. Komatsuya K, Kaneko K, Kasahara K. **Function of platelet glycosphingolipid microdomains/lipid rafts**. *Int. J. Mol. Sci.* (2020.0) **21** 1-18. DOI: 10.3390/ijms21155539
48. Jooss NJ. **Anti-GPVI nanobody blocks collagen- and atherosclerotic plaque-induced GPVI clustering, signaling, and thrombus formation**. *J. Thromb. Haemost.* (2022.0) **20** 2617-2631. DOI: 10.1111/jth.15836
49. Goiko M, De Bruyn JR, Heit B. **Short-lived cages restrict protein diffusion in the plasma membrane**. *Sci. Rep.* (2016.0) **6** 1-13. DOI: 10.1038/srep34987
50. Mosqueira A, Camino PA, Barrantes FJ. **Cholesterol modulates acetylcholine receptor diffusion by tuning confinement sojourns and nanocluster stability**. *Sci. Rep.* (2018.0) **8** 1-11. DOI: 10.1038/s41598-018-30384-y
51. Lee FA. **Lipid rafts facilitate the interaction of PECAM-1 with the glycoprotein VI-FcR γ-chain complex in human platelets**. *J. Biol. Chem.* (2006.0) **281** 39330-39338. DOI: 10.1074/jbc.M607930200
52. Locke D, Chen H, Liu Y, Liu C, Kahn ML. **Lipid rafts orchestrate signaling by the platelet receptor glycoprotein VI**. *J. Biol. Chem.* (2002.0) **277** 18801-18809. DOI: 10.1074/jbc.M111520200
53. Saitov A. **Determinants of lipid domain size**. *Int. J. Mol. Sci.* (2022.0) **23** 3502. DOI: 10.3390/ijms23073502
54. Rönnlund D, Yang Y, Blom H, Auer G, Widengren J. **Fluorescence nanoscopy of platelets resolves platelet-state specific storage, release and uptake of proteins, opening up future diagnostic applications**. *Adv. Healthc. Mater.* (2012.0) **1** 707-713. DOI: 10.1002/adhm.201200172
55. Fujiwara T, Ritchie K, Murakoshi H, Jacobson K, Kusumi A. **Phospholipids undergo hop diffusion in compartmentalized cell membrane**. *J. Cell Biol.* (2002.0) **157** 1071-1081. DOI: 10.1083/jcb.200202050
56. Best D. **GPVI levels in platelets: Relationship to platelet function at high shear**. *Blood* (2003.0) **102** 2811-2818. DOI: 10.1182/blood-2003-01-0231
57. Barrachina MN. **GPVI surface expression and signalling pathway activation are increased in platelets from obese patients: Elucidating potential anti-atherothrombotic targets in obesity**. *Atherosclerosis* (2019.0) **281** 62-70. DOI: 10.1016/j.atherosclerosis.2018.12.023
58. Joutsi-Korhonen L. **The low-frequency allele of the platelet collagen signaling receptor glycoprotein VI is associated with reduced functional responses and expression**. *Blood* (2003.0) **101** 4372-4379. DOI: 10.1182/blood-2002-08-2591
59. Veninga A. **GPVI expression is linked to platelet size, age, and reactivity**. *Blood Adv.* (2022.0) **6** 4162-4173. DOI: 10.1182/bloodadvances.2021006904
60. Selvadurai MV, Hamilton JR. **Structure and function of the open canalicular system–the platelet’s specialized internal membrane network**. *Platelets* (2018.0) **29** 319-325. DOI: 10.1080/09537104.2018.1431388
61. Emeis JJ. **A guide to murine coagulation factor structure, function, assays, and genetic alterations**. *J. Thromb. Haemost.* (2007.0) **5** 670-679. DOI: 10.1111/j.1538-7836.2007.02408.x
62. Huang J. **Assessment of a complete and classified platelet proteome from genome-wide transcripts of human platelets and megakaryocytes covering platelet functions**. *Sci. Rep.* (2021.0) **11** 1-18. PMID: 33414495
63. Fachada N, Lopes VV, Martins RC, Rosa AC. **Parallelization strategies for spatial agent-based models**. *Int. J. Parallel Program.* (2017.0) **45** 449-481. DOI: 10.1007/s10766-015-0399-9
64. Kabiri Chimeh M, Heywood P, Pennisi M, Pappalardo F, Richmond P. **Parallelisation strategies for agent based simulation of immune systems**. *BMC Bioinformatics* (2019.0) **20** 1-14. DOI: 10.1186/s12859-019-3181-y
|
---
title: 'Enhancing Mental and Physical Health of Women through Engagement and Retention
(EMPOWER) 2.0 QUERI: study protocol for a cluster-randomized hybrid type 3 effectiveness-implementation
trial'
authors:
- Alison B. Hamilton
- Erin P. Finley
- Bevanne Bean-Mayberry
- Ariel Lang
- Sally G. Haskell
- Tannaz Moin
- Melissa M. Farmer
journal: Implementation Science Communications
year: 2023
pmcid: PMC9994412
doi: 10.1186/s43058-022-00389-w
license: CC BY 4.0
---
# Enhancing Mental and Physical Health of Women through Engagement and Retention (EMPOWER) 2.0 QUERI: study protocol for a cluster-randomized hybrid type 3 effectiveness-implementation trial
## Abstract
### Background
Women Veterans are the fastest-growing segment of Veterans Health Administration (VA) users. The VA has invested heavily in delivering care for women Veterans that is effective, comprehensive, and gender-tailored. However, gender disparities persist in cardiovascular (CV) and diabetes risk factor control, and the rate of perinatal depression among women *Veterans is* higher than that among civilian women. Challenges such as distance, rurality, negative perception of VA, discrimination (e.g., toward sexual and/or gender minority individuals), and harassment on VA grounds can further impede women’s regular use of VA care. Enhancing Mental and Physical Health of Women through Engagement and Retention (EMPOWER) 2.0 builds on work to date by expanding access to evidence-based, telehealth preventive and mental health services for women Veterans with high-priority health conditions in rural and urban-isolation areas.
### Methods
EMPOWER 2.0 will evaluate two implementation strategies, Replicating Effective Practices (REP) and Evidence-Based Quality Improvement (EBQI), in supporting the implementation and sustainment of three evidence-based interventions (Virtual Diabetes Prevention Program; Telephone Lifestyle Coaching Program; and Reach Out, Stay Strong Essentials) focused on preventive and mental health care for women Veterans. We will conduct a mixed-methods implementation evaluation using a cluster-randomized hybrid type 3 effectiveness-implementation trial design to compare the effectiveness of REP and EBQI on improved access to and rates of engagement in telehealth preventive lifestyle and mental health services. Other outcomes of interest include (a) VA performance metrics for telehealth care delivery and related clinical outcomes; (b) progression along the Stages of Implementation Completion; (c) adaptation, sensemaking, and experiences of implementation among multilevel stakeholders; and (d) cost and return on investment. We will also generate implementation playbooks for program partners to support scale-up and spread of these and future evidence-based women’s health programs and policies.
### Discussion
EMPOWER 2.0 provides a model for mixed-methods hybrid type 3 effectiveness-implementation trial design incorporating evaluation of performance metrics, implementation progress, stakeholder experience, and cost and return on investment, with the ultimate goal of improving access to evidence-based preventive and mental telehealth services for women Veterans with high-priority health conditions.
### Trial registration
ClinicalTrials.gov, NCT05050266. Registered on 20 September 2021.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s43058-022-00389-w.
## Background
As the fastest-growing segment of users in the Veterans Health Administration (VA) [1], the number of women Veterans receiving care in the VA is expected to increase by $73\%$, from 9.3 to $16.4\%$, between 2015 and 2043 [2]. In anticipation of this increase, the VA has invested heavily in providing effective, comprehensive, and gender-tailored care [3, 4] to address women Veteran patients’ unique and complex healthcare needs [5–7]. However, gender disparities persist in cardiovascular (CV) and diabetes risk factor control in and out of the VA [8, 9]. Also, the prevalence of many mental and physical comorbidities, including perinatal depression, obesity [10], and cardiovascular disease [11], remains higher among women Veterans than among civilian women [12]. Historically, women Veterans have had a high rate of attrition from VA care due to distance and low ratings of VA care quality [13]. Negative perceptions of VA care [14], comorbid mental health issues [15], discrimination based on sexual and/or gender minority identities [16], harassment on VA grounds [17], and distance and rurality [18, 19] are cited barriers in women’s regular use of VA care. Thus, improvements are still needed to increase women Veterans’ access to and engagement in convenient, safe, evidence-based, patient-centered care that achieves the VA “lane of effort” of Veterans’ “lifelong health, well-being, and resilience” [20].
Telehealth, or the delivery of healthcare via remote technologies including telephone, video calls, and online, is ideal for addressing existing gaps in care for women Veterans. Barriers to gender-specific care include the low numbers of women Veterans at any single VA location, access barriers due to work, travel times, and school and caregiving responsibilities, particularly for women Veterans living in rural areas. Telehealth can address these barriers by providing flexible, individualized care and reducing travel burden. The evidence suggests that women Veterans stand to benefit from telehealth approaches, particularly in healthy lifestyle change for disease prevention (including increasing physical activity, weight management, smoking cessation, and diet) and reproductive health (including contraception, prenatal care, maternal mental health, lactation, and postpartum care) [21].
Since its inception in 2015, the Enhancing Mental and Physical health of Women through Engagement and Retention (EMPOWER) QUERI 1.0 team has focused on implementing gender-tailored, preference-based care models for women Veteran patients with high-priority health conditions: [1] type 2 diabetes risk, [2] cardiovascular risk, and [3] anxiety and/or depression [22]. In EMPOWER 1.0 studies, women Veterans expressed preferences for gender-specific (women only) care and for telehealth care options [23]. Therefore, we are extending our overarching commitment to improving women Veterans’ engagement and retention in care with our EMPOWER 2.0 Impact Goal to expand access to evidence-based, preventive lifestyle and mental health services delivered by telehealth for women Veterans with high-priority health conditions in rural and urban-isolation areas. To achieve this impact goal, we propose a coordinated program with the following aims:Using two implementation strategies (Replicating Effective Practices [REP] [24] and Evidence-Based Quality Improvement [EBQI] [25]), support implementation and sustainment of three evidence-based practices (EBPs) focused on preventive lifestyle and mental health care for women Veterans across 20 VA facilities (10 with REP, 10 with EBQI), several of which are rural, low-performing in women’s health care, and/or lead sites for high-reliability organization [20].*Conduct a* mixed methods implementation evaluation using a cluster randomized hybrid type 3 effectiveness-implementation trial design [26]. We will compare the effectiveness of REP and EBQI in improved access to and rates of engagement in preventive lifestyle and mental telehealth services. Other outcomes include (a) VA performance metrics for telehealth care delivery and related clinical outcomes for women Veterans; (b) progression along the Stages of Implementation Completion [27]; (c) adaptation, sensemaking [28], and experiences of EBP implementation among multilevel stakeholders [29]; and (d) cost and return on investment [30].Generate implementation “playbooks” [22] for program partners that are scalable and serve as guidance for future implementation of a broader array of evidence-based women’s health programs and policies.
These conceptually and methodologically linked aims will be realized by an experienced multidisciplinary team with a strong track record of collaboration and guided by a Technical Expert Panel of operational and research partners and subject matter experts. We will implement three EBPs: [1] Virtual Diabetes Prevention Program (Virtual DPP) [31], an evidence-based lifestyle intervention, emphasizing moderate weight loss, diet, and physical activity, shown to prevent and/or delay progression to type 2 diabetes; [2] Telephone Lifestyle Coaching Program (TLC) [32], developed by one of our partners (National Center for Disease Prevention and Health Promotion), which provides evidence-based, individual-level, personalized health coaching focused on wellness and cardiovascular disease prevention; and [3] Reach Out, stay Strong, Essentials (ROSE) [33], an evidence-based intervention for prevention of perinatal depression that can be delivered via telehealth. ( For detailed background on these EBPs and related health conditions among women Veterans, see Additional file 1.)
## Overview of study design
In this 5-year study, we will conduct a cluster randomized hybrid [26] type 3 effectiveness-implementation trial to evaluate the effectiveness of two implementation strategies (REP and EBQI) in supporting the implementation of three evidence-based practices (virtual DPP, TLC, and ROSE) to increase use of prevention-focused telehealth services among women Veterans (Aim 1). Below we describe our three Aims in detail.
## Implementation strategies
EMPOWER 2.0 focuses on comparing two evidence-based implementation strategies described below (Table 1).Table 1Implementation strategies: key activities by phaseYears 1-2.5Years 2.5-4Year 5QUERI RoadmapPre-implementationIdentify problem & solutionEngage stakeholdersDevelop measures and dataImplementationImplement an interventionActivate implementation teamsMonitor implementation progressSustainmentSustain an interventionTransition ownership to stakeholdersOngoing evaluation and reflectionREPPre-conditionsPre-implementationImplementationMaintenance and evolutionEnsure fit, identify barriers, draft intervention packageDevelop package, pilot test, identify champion, hold orientation meetingsTrain staff, provide TA, conduct evaluation, measure fidelity, measure outcomes, share results, discuss sustainabilityChange practice to facilitate long-term adoption, prepare package for dissemination, recustomize deliveryEBQIRegional stakeholder planning meetings, formative evaluationTrain local QI champions and team membersProvide practice facilitation, expert review and feedback, monthly across-site calls, technical workgroups, formative evaluationFoster regional spread, summative evaluation
## Implementation strategy 1—Replicating Effective Programs (REP)
REP has a strong evidence base in VA health services and implementation research—including in EMPOWER QUERI 1.0 [22, 34]—in promoting uptake of evidence-based pratices [24]. Informed by theories of Diffusion of Innovation and Social Learning, the REP framework consists of four phases: pre-conditions, pre-implementation, implementation, and maintenance/evolution [24]. Careful attention is paid to intervention packaging during pre-conditions and pre-implementation; training, technical assistance, and fidelity assessment during implementation; and re-customizing during maintenance/evolution. During each phase, local context is paramount, with varying deployment of the intervention depending on local priorities, needs, and resources. Our team has comprehensive expertise in REP grounded in implementation of three EBPs across diverse sites in EMPOWER 1.0.
## Implementation strategy 2—Evidence-Based Quality Improvement (EBQI)
Tested in several VA implementation trials (e.g., of collaborative care for depression, VA’s patient-centered medical home (PACT), Women’s Health PACT), EBQI is a systematic QI method for engaging frontline primary care practices in improvement that introduces “best science” and evidence in the service of operational goals and is supported by a partnership between multi-level, interdisciplinary operations stakeholders and a research team [29]. EBQI is aimed at developing learning organizations through multi-level, cross-discipline engagement with science and data. It draws upon explicit QI support by scientific teams to enable context-tailored evidence-based practices; social science theory on provider/team behavior; and improved use of implementation and QI methods. Application of EBQI in the EMPOWER QUERI 2.0 will focus on identifying (with multilevel stakeholders) context-specific design priorities for the three EBPs. Notably, both EMPOWER 2.0 implementation strategies reflect phased approaches consistent with recommendations of the QUERI Implementation Roadmap (Table 1) [35].
We have selected EBQI and REP because they provide differing approaches to supporting sites with implementation of EBPs: EBQI is a higher-intensity strategy that entails multilevel stakeholder engagement, while REP is a lower-intensity strategy with an explicit process framework for local tailoring. Both strategies have strong evidence of effectiveness and are well-established in VA. As noted in the QUERI Roadmap [35], health systems have already invested in EBQI “to scale up and spread their practices and policies nationally,” but we have yet to understand the conditions in which this investment in a high-intensity strategy is warranted, compared to a low-intensity strategy. Comparing these strategies across multiple sites for three different EBPs (virtual DPP, TLC, and ROSE) will provide data on implementation strategy effectiveness and cost, which will be of direct relevance to leadership in decision-making and investment. Findings will allow a detailed examination of how each implementation strategy supported the implementation of each EBP across varying settings, thereby elucidating a blueprint for matching strategies with EBPs in differing organizational contexts.
Setting and site selection: We recruited sites (Fig. 2 below) from the Women’s Health Practice-Based Research Network (PBRN), a network that provides a research infrastructure for investigators seeking to increase inclusion of women in VHA research or conduct multi-site women’s focused research in VHA. Comprised of approximately 70 VA sites that see one-half of women Veteran VA users, the PBRN helps investigators overcome the challenges of multi-site studies through the engagement of Site Leads with established working relationships with local clinicians and facility leadership. Sites were also recruited in collaboration with regional leadership in four regional Veterans Integrated Service Networks (VISNs). Each VISN was selected based on leadership’s interest in the EBPs, priorities for women Veterans’ preventive health, and our history of collaboration.
Low-performing sites: Our team has a long history of working with sites to improve women Veterans’ health, including QI efforts in low-performing sites. For the current project, a number of our sites are designated low-performing sites based on site visit assessment data.
Patient eligibility and selection: Women Veteran VA users are eligible for each EBP as follows: [1] Virtual DPP—overweight/obese women Veterans (BMI≥25 kg/m2 [≥23 if Asian]) with history of prediabetes (defined as either an HbA1c 5.7-$6.5\%$ or fasting blood glucose 100–125 mg/dL or oral glucose tolerance test 140–199 mg/dL) or history of gestational diabetes (GDM) or high risk on diabetes screening questionnaire. Women will receive invitation letters or be referred to the program by a healthcare provider and self-register with a CDC-recognized virtual DPP provider. [ 2] TLC—all women Veterans (who are not pregnant) are eligible to participate in TLC. Women will be referred to the program by a healthcare provider and will receive an outreach call from contracted health coaches to enroll. [ 3] ROSE—All pregnant women Veterans are eligible. Women will be identified and recruited by the site pregnancy/maternity care coordinator or other women’s health staff. As part of each implementation strategy (REP and EBQI, see below), sites will determine their own locally relevant recruitment strategies to engage 40 women Veterans/site in virtual DPP or TLC, and all interested pregnant women in ROSE.
## EMPOWER 2.0 Conceptual Framework
Figure 1 reflects grounding in the Consolidated Framework for Implementation Research (CFIR) [36] and intention to examine implementation and sustainment outcomes that QUERI and operations partners have indicated are of the highest value. CFIR offers a synthesis of theory and constructs from across implementation science. Constructs are organized into five domains (outer setting, inner setting, characteristics of the intervention, characteristics of individuals, and implementation process), which are dynamic and interrelated in producing implementation readiness at the individual and collective (e.g., clinical unit, facility) levels. CFIR is increasingly used to aid in implementation and evaluation planning and is specifically recommended for use in examining predictive factors associated with implementation outcomes [37]. Because it accounts for individual- and setting-level characteristics and acknowledges how interactions between individuals and their larger environment(s) may support behavior change, CFIR provides an ideal framework for delineating multilevel factors in complex health interventions. Fig. 1Conceptual model
## Cluster-randomized trial of the implementation strategies
Randomization: *For this* cluster-randomized trial, we will randomize sites clustered within four VISNs to either REP or EBQI (Fig. 2). Two VISNs will implement virtual DPP and two VISNs will implement TLC. To ensure adequate number of pregnant women for ROSE, all four VISNs will have the option to implement ROSE for their pregnant patients. With our focus on increasing access to preventive services for women living in rural areas, sites that serve rural Veterans will be selectively recruited. Rural sites tend to be community-based outpatient clinics (CBOCs) as opposed to large VA medical centers (VAMCs), so at least one additional CBOC in each VISN will invited to participate. Because the same leadership team may oversee VAMC and CBOC operations, any VAMC and CBOC that share a leadership team will be randomized as a unit to avoid contamination across implementation strategies. Therefore, a total of 20 individual VA sites (VAMCs or CBOCs) will be included from four VISNs. With the balanced design, half of the sites ($$n = 10$$) will receive REP and half will receive EBQI ($$n = 10$$).Fig. 2Site-level randomization Aim 2a: Data sources—We will use VA administrative data in the Corporate Data Warehouse (CDW) via the VA Informatics and Computing Infrastructure (VINCI) platform to examine EBP access and engagement and will monitor changes and trends in VA performance measures using the Reporting, Analytics, Performance, Improvement & Deployment’s Electronic Quality Measurement (eQM) portal. Since many of the interactive algorithms available on the eQM portal are not available by site and by gender, we will use the algorithms and data available on the portal and merge patient-level data in CDW to create gender-specific performance measures focusing on diabetes, hypertension, and depression/mental health. Additional engagement and outcome data will also be collected from each EBP delivery including number of sessions/modules completed, and program-specific measures.
Aim 2a: Measures—These fall into four basic categories shown in Table 2. The primary outcomes of interest are access to and engagement in telehealth care for preventive services, measured by referrals and enrollment. We will create composite measures of the proportion of women who participate in preventive telehealth care services for each EBP (# participate/# eligible). The primary outcomes for virtual DPP include the number of women who self-register for DPP (i.e., enroll) and the number who participate in virtual DPP (i.e., complete at least one online DPP module). For TLC and ROSE, the primary outcomes include the number of provider referrals for the programs and number of women Veterans with enrollment encounters. Parallel encounter coding will be developed for ROSE with the guidance of our Office of Primary Care. Participation and engagement outcomes collected by EBP are based on the average number of sessions completed and behavior changes documented during the program (e.g., weight change, increase physical activity, depression screens). For virtual DPP, we will assess rates of participation and engagement according to CDC standards for DPP delivery (i.e., average number of sessions attended, proportion who completed ≥9 and ≥16 sessions, etc.). For TLC, rates of participation and engagement are at ≥3 calls and ≥8 calls based on guidance provided by VA national offices regarding meaningful levels of contact with this EBP. For ROSE, maternity care coordinators (or comparable role) and ROSE facilitators will collect data on the number of sessions completed and proportion of completion of postnatal depression screens among participants. The primary impact performance goals for the EBPs will be based on performance metrics for telehealth care. For virtual DPP, we will monitor site-level performance measures for participation in weight management programs (e.g., MOVE!). For TLC, we will monitor site-level changes in telehealth encounters for prevention (e.g., Whole Health). For ROSE, we will monitor site-level changes in the use of telemental health and site-level changes in number of prenatal referrals sent out of the VA. Across all EBPs, we will also monitor metrics for use of telehealth services by women Veterans living in rural areas. Secondary outcomes of interest will include clinical outcomes and other VA performance measures related to each EBP. For virtual DPP and TLC, we will examine trends in clinical outcomes including percent weight change over 12 and 24 months, prevalence of hypertension, pre-diabetes, and diabetes. We will also examine trends in incident type 2 diabetes (≥1 inpatient diagnosis or any combination of ≥2 within 24 months: A1C≥$6.5\%$, or fasting glucose≥126, or random glucose ≥ 200 or 2-h 75-g oral glucose tolerance test ≥ 200, or outpatient diagnosis code, or anti-hyperglycemic medication claim except metformin) [38, 39]. The VA (HEDIS) performance measures for diabetes and hypertension focus on proportion with guideline-recommended level of control among patients with the condition. Although our project is focused on prevention, we will track these measures because EBPs focused on lifestyle behavior change could impact level of control among diabetics and patients with hypertension. For ROSE, we will examine trends in postpartum attrition from VA among pregnant women through utilization of VA health services in the 12 months post-partum. Table 2Measures for Aim 2aEBPPrimary outcomeParticipation and engagementPrimary impact performance goalsClinical outcomes and other VA performance measuresVirtual DPP• # of eligible patients who register at the DPP site (i.e., enroll)• # of eligible patients who participate in virtual DPP and attend at least one session• Average # of DPP sessions attendeda• Proportion who completed ≥3, ≥9 and ≥16 DPP sessionsa• Change in reported physical activitya• % weight change at 12 and 24 monthsIncrease participation in weight management program (e.g., MOVE)• Overweight/obesity/weight management• Prevalence of prediabetes, diabetes, and incident type 2 diabetes• HEDIS: Hypertension and DiabetesTLC• # of provider referrals for TLC• # of patients w/ TLC encounter (enrolled)• Average # of TLC sessions completeda• % with SMART goala• % with behavioral change (physical activity; diet: fruit, vegetable, and sugary beverage intake; stress and coping; and weight)aIncrease in telehealth encounters for prevention (e.g., Whole Health)• Overweight/obesity/weight management• Prevalence of prediabetes, diabetes, and incident type 2 diabetes• HEDIS: Hypertension and DiabetesROSE [4]• # of provider referrals for ROSE• # of patients w/ ROSE encounter (enrolled)• Average # of ROSE sessions completeda• Depression screeningIncrease in telehealth care for mental health• # of patients that return to VA post-partum (utilization of VA health services in the 12 months post-partum)• Mental health/depressionaThis data will be collected as part of the EBP. All other data will come from CDW We will include data on-site-, provider-, and patient-level variables from VA administrative data or our operations partners. For the sites, we will include facility type (VAMC or CBOC) and size, women panel size, proportion of women at site, type of comprehensive primary care model for women Veterans [4], number of designated women’s health providers, and urban/rurality. Provider characteristics will include provider type (MD or NP) and whether the provider is a designated women’s health provider. Patient-level controls will include sociodemographic characteristics, comorbidity including diabetes and cardiovascular risk, service-connected disability status, utilization, and the most common ICD-10 codes related to pregnancy status.
Data analysis plan for Aim 2a: We will use generalized linear models to evaluate the effectiveness of the intervention strategies for implementation of the EBPs in increasing women’s participation in telehealth-based preventive care. The models will account for clustering at the site level as well as incorporating site- and patient-level covariates outlined above. The main outcomes will include the composite measures of the proportion of women referred and enrolled in preventive telehealth care services, and the primary independent variable of interest is implementation using REP versus EBQI. We will conduct subgroup analyses by EBP to examine if the effects of the implementation strategies on proportion referred and enrolled vary by EBP (TLC or virtual DPP) as well as evaluate the effect on the primary impact performance metrics for telehealth care (weight management programs, Whole Health, and telemental health). In each model, we will examine the potential moderating effects of site-level characteristics (e.g., facility type, size, women’s health care program model, etc.). Adjustment for clustering will be performed using Stata v15, and we will evaluate goodness-of-fit using Mallows statistic (Cp). For DPP and TLC, we will use appropriate generalized linear models to assess weight and behavior change and examine differences by levels of participation (number of sessions completed). For ROSE, we will assess depression scores by levels of participation.
Power calculations: We based power calculations on the study’s basic single level of clustering sample design, where sites are randomized to receive either REP or EBQI to implement the EBPs. To detect a moderate effect size in terms of Cohen’s D (.26 standard deviations) between REP and EBQI, with 20 sites, and cluster adjustment (ICC=.01) with $80\%$ power we will recruit 40 women per site (total of 800 women). We have considered all 20 sites to be separate clusters in terms of these calculations.
Aims 2b–2d are summarized in Table 3.Table 3Overview of Aims 2b–2dMethodTiming (per QUERI Roadmap)Intended analysesPre-implementationImplementationSustainmentSite tracking log• Meeting minutes• Templated reflections• Stages of Implementation Completion (SIC)2b: SIC2c: Adaptations, experiences of EBP implementation2d: costSemi-structured interviews• CFIR-based pre-implementation interview• CFIR-based post-implementation interview2b: SIC2c: Adaptations, experiences of EBP implementationPeriodic reflections• Conducted approximately monthly to document: stakeholder engagement; EBP and/or implementation strategy adaptations; inner and outer setting influences; and planning, processes, and key events.2b: SIC2c: Adaptations, experiences of EBP implementation Aim 2b: Data sources—The Stages of Implementation Completion (SIC) [27] is an assessment tool developed in a large randomized trial for the purpose of comparing “progress and milestones toward successful implementation…regardless of the implementation strategy utilized” [40]. SIC allows for quantitative scoring of sites’ implementation progress, reflecting multiple components of overall implementation success. The REP and EBQI teams will gather the information necessary to populate the assessment tool for each site.
Aim 2b: Measures—The SIC delineates a set of eight stages and activities across three phases (Pre-Implementation, Implementation, and Sustainment, consistent with the QUERI Roadmap [35]) that are largely universal in implementation efforts (e.g., readiness planning, adherence monitoring) [41]. The SIC may be unique among implementation measures for its utility in capturing standardized yet flexible assessments suitable for comparison across sites, EBPs, and implementation strategies.
Aim 2b: Analysis—SIC allows for calculation of the following scores: each site’s stage score, which describes the ultimate stage achieved at a given site; proportion score, which describes the proportion of activities completed by a site within a given stage; and duration score, which describes the amount of time a site spends in each stage. For additional detail on mixed-method analysis of SIC data, see Aim 2c: Analysis.
Aim 2c: Data sources—Adaptations of an EBP in practice [42], how individuals and teams engage in sensemaking around the EBP and implementation process [43], and stakeholders’ observations and experiences of implementation [28, 44] are all recognized for their importance in understanding implementation and sustainment outcomes [45]. We will evaluate adaptation, sensemaking, and experiences of implementation through interviews and brief structured data collection with multilevel key stakeholders (KS), defined as individuals who are “responsible for…healthcare-related decisions that can be informed by research evidence” [46]. Consistent with the Women’s Health PACT cluster randomized trial of EBQI [29], eligible roles include regional leadership, facility leadership, facility-level clinical leaders such as WH Medical Directors, facility-level Women Veteran Program Managers, and PBRN Site Leads. Expanding beyond these roles for our prevention focus will allow the inclusion of primary care, primary care mental health integration (or mental health), and Whole Health/Health Promotion Disease Prevention leaders, program managers, and providers. Individuals in these roles will be identified using publicly available information as well as lists provided by the Site Leads. Using a snowball sampling approach, individuals in the eligible roles will be asked to recommend other KS due to their women’s health expertise. Based on our prior study, we estimate 6 KS per site (120 total) and 5 KS per VISN (20 total) for a total sample at pre-implementation of $$n = 140$.$ A similar sample size is anticipated for post-implementation/pre-sustainment KS interviews. Periodic reflections (see below) will be completed with members of the implementation team for each EBP and site (e.g., PIs, project managers, site leads, EBQI and REP leads, etc.).
Aim 2c: Measures—Adaptation, sensemaking, and experiences of EBP implementation will be assessed using KS interviews, structured data collection, and periodic reflections. Semi-structured qualitative interviews will be conducted with KS during pre-implementation and post-implementation/pre-sustainment phases. Pre-implementation interviews (see Additional file 2 for a draft interview guide) will examine usual care for the relevant care condition as well as CFIR domains including inner and outer setting, perceived characteristics of the intervention, characteristics of individuals (e.g., prior training), and implementation process using semi-structured interview guides informed by CFIR online resources and our work in EMPOWER QUERI 1.0 [22] and the Women’s Health PACT trial [25, 29]. Post-implementation/pre-sustainment interviews will also assess [1] CFIR domains (e.g., perceived characteristics of the EBP) and subdomains (e.g., relative advantages, complexity, etc.) and [2] recommendations for adaptations to and/or spread of the EBP to which KS were exposed.
We have developed a site tracking log that allows facilitators and other QI leads to document site contacts and meetings; the log format includes open-ended, templated written reflections on implementation progress from the external team member’s perspective. Logs will be used to document SIC progression and date of stage completion; capture information on dose and intensity of contacts across the two implementation strategies; support development of cost estimates per strategy, per EBP, and per site; and provide qualitative data from templated reflections for analysis as described below.
Periodic reflections [28] allow for consistent documentation of key activities and other implementation phenomena and have been shown to support timely and accurate documentation of adaptations, changes to inner and outer setting, team sensemaking, and dynamic experiences of EBP implementation. Periodic reflections will be conducted approximately monthly via telephone by a member of the Implementation Core with members of the implementation team for each EBP and site, will follow the template developed for use in EMPOWER 1.0 (Additional file 3), and will last 15–60 min, depending on the amount of current activity and number of team members participating in the call (range 1–4).
Aim 2c: Analysis plan—All KS interviews will be digitally recorded and securely transmitted to an approved transcriptionist for verbatim transcription. Transcripts will be reviewed, edited for accuracy, and summarized by the qualitative team. Consistent with our team’s approach across multiple projects, matrix analysis methods [47] will be used for rapid turn-around of the results to inform implementation processes. In-depth analysis of the qualitative data will be conducted using ATLAS.ti, a qualitative data analysis software program that allows for fluid interaction of data across types and sources. Initially, a top-level codebook will be developed for the pre-implementation interviews based on CFIR constructs and the semi-structured interview guide. This codebook will be elaborated upon based on emergent themes. Interviews will be compared within each site, across sites (e.g., to compare urban and rural sites), across implementation strategies, and over time. Periodic reflections and other sources of qualitative data (i.e., meeting minutes, templated reflections in site tracking logs, and archival information) will also be included in the data set and will be coded separately and in relation to the interview data, with particular attention to adaptations, individual and team sensemaking, and changes over time (e.g., within CFIR domains), similar to the processes previously used by members of this team [28]. Specifically, in the pre-implementation transcripts, we will identify commonly shared knowledge, attitudes, and beliefs related to the EBPs and their foci, and anticipated barriers to and facilitators of implementation. In post-implementation interview data, we will take a summative approach to characterizing overall experiences of and perspectives on implementation, with a particular focus on expectations for sustainment of the EBPs and the implementation strategies.
It is an innovation and strength of this proposal that we will combine 2b and 2c data sources to link qualitative (via interviews, reflections, etc.) assessments of CFIR constructs with implementation and effectiveness outcomes—including SIC scores for stage, duration, and proportion—for each EBP (virtual DPP, TLC, ROSE) and implementation strategy (REP, EBQI). With the exception of recent work by Palinkas et al., relatively few studies have previously investigated associations between CFIR domains and the stages and timing of implementation milestones achieved [40]. This will enable us to investigate, for example, whether EBQI, a strategy that requires more intensive activity in the initial stages and may thus be slower to reach implementation launch, is associated with improved sustainment, given that sites will be equipped with QI tools beyond the life of the project.
Aim 2d: Data sources—In line with current recommendations for budget impact analysis (BIA) for single payers [48] and within VA [49], we will compare costs associated with delivery of usual care with costs for implementation and delivery of our prevention-focused telehealth interventions (virtual DPP, TLC, ROSE) in terms of impact on short-term use of downstream health care resources. For example, we will evaluate whether the cost of implementing the ROSE intervention is associated with cost savings related to a reduction in need for specialty mental health care among women Veterans during the postpartum period.
Aim 2d: Measures—Core requirements for BIA include estimates of eligible population, current treatment mix and associated costs, expected treatment mix post-implementation and associated costs, and estimated changes in condition-related downstream costs [50]. Cost estimates for implementation strategies will be informed by site tracking log data (e.g., implementation dose in #hours/per site) as described in Aim 2c: Data sources. Healthcare utilization data will be drawn from CDW, as described for Aim 2a: Data sources, above.
Aim 2d: Analysis—Analyses will be structured to examine the comparative cost and outcomes of our two implementation strategies, REP and EBQI, as follows [30]: (CostIntervention + CostREP) − (CostIntervention + CostEBQI) vs. OutcomeInterventionW/REP − OutcomeInterventionW/EBQI. Qualitative data collected for Aim 2c will also be examined to understand the “qualitative residual” that often remains underexplored in traditional quantitative economic evaluations, aiding in improved cost estimates and understanding of how stakeholders, staff, providers, and implementation teams make sense of implementation need, impact, and cost [30]. For example, cost estimates for an EBP may need to be updated based on adaptations in routine practice reported by providers in interviews. Similarly, leadership across sites may have differing perspectives on whether a higher-intensity strategy, like EBQI, is worth investing in given observed outcomes, with implications for future sustainment and spread efforts.
EMPOWER 2.0 will evaluate the business case for REP and EBQI in implementing DPP, TLC, and ROSE by first drawing upon the Cost of Implementing New Strategies (COINS) method for identifying resources invested in implementation [51]. COINS provides a systematic way of assessing costs associated with each Stage of Implementation Completion (SIC), which provides the blueprint for the business plan [27]. In establishing our cost estimates, we will integrate data from stakeholder interviews, site tracking logs, and reflections to assess costs as they occurred: (a) at each site, (b) for each EBP, (c) for both REP and EBQI, and (d) according to each stage of the implementation effort. As a measure of implementation process, the SIC will be cross-walked with the COINS to develop template plans and timelines for future adopters. Knowing, for instance, that they need to budget for 3 months of pre-launch preparation will allow new sites to plan against their fiscal years, e.g., knowing what to budget when, and what can be spread across different fiscal cycles.
## Aim 3: Generate implementation “playbooks” for program partners that are scalable and serve as guidance for future implementation of a broader array of evidence-based women’s health programs and policies
Implementation evaluation of process and outcomes across EBPs, actively supported by the Implementation Core (see below), will contribute to the development of implementation playbooks—brief, user-friendly summations of implementation targets, processes, outcomes, and recommendations for sustainment, scale up, and spread [52]. EMPOWER 1.0 was an early QUERI champion for playbooks and has provided a laboratory for examining what kinds of information playbooks should contain for optimum utility and impact. For EMPOWER 2.0, we will work collaboratively with sites and partners to develop operations playbooks, brief “tip sheets”, and other operations-focused implementation support products as needed.
## Discussion
EMPOWER 2.0 provides a model for the conduct of a hybrid type 3 effectiveness-implementation trial comparing two implementation strategies in a large healthcare system. It is a limitation of this study that it is being conducted entirely within the VA healthcare system and thus results may not generalize; however, site- and regional-level conditions within the VA vary considerably, and we anticipate that findings will speak to common challenges in addressing the unique needs and resources of local settings.
Although this proposal was developed and submitted for funding prior to the COVID-19 pandemic, recent events have only highlighted the need for increased telehealth services and significantly expanded remote care technology integration within and outside of the VA. Pre-existing gaps faced by many healthcare systems in the uptake and reach of preventive care services have been amplified during the pandemic, exacerbated by the deferral of care for non-COVID-19 conditions [53–55]. The emphasis of this study on the implementation of preventive care services is thus an important strength, particularly as this has traditionally been an underdeveloped area of implementation science [56, 57].
As relatively few studies have compared the effectiveness of implementation strategies for achieving rollout of evidence-based interventions across large healthcare systems, the proposed rigorous comparison of clinical benefit, cost and return on investment, and time to achieving implementation associated with REP and EBQI will be of value in directly supporting evidence-based policy and resource allocation decisions. Our program will not only enhance care for women Veterans across 20 VA sites, but will also shed fundamental insights on implementation strategies to help sustain these improvements and inform broader dissemination across and beyond VA.
## Supplementary Information
Additional file 1. EMPOWER 2.0 Key Health Conditions and Evidence-Based Practices (EBPs).Additional file 2. Sample Key Stakeholder Interview Guide. Additional file 3. EMPOWER 2.0 Periodic Reflections Template.
## References
1. Frayne SM, Phibbs CS, Saechao F. *Sourcebook: Women veterans in the Veterans Health Administration. Volume 3. Sociodemographics, utilization, costs of care, and health profile* (2014.0)
2. 2.Statistics NCfVAaWomen Veterans Report: the past, present, and future of women veterans2017. *Women Veterans Report: the past, present, and future of women veterans* (2017.0)
3. deKleijn M, Lagro-Janssen AL, Canelo I, Yano EM. **Creating a roadmap for delivering gender-sensitive comprehensive care for women Veterans: results of a national expert panel**. *Med Care* (2015.0) **53** S156-S164. DOI: 10.1097/mlr.0000000000000307
4. Yano EM, Haskell S, Hayes P. **Delivery of gender-sensitive comprehensive primary care to women veterans: implications for VA Patient Aligned Care Teams**. *J Gen Intern Med* (2014.0) **29** S703-S707. DOI: 10.1007/s11606-013-2699-3
5. Bergman AA, Frankel RM, Hamilton AB, Yano EM. **Challenges with delivering gender-specific and comprehensive primary care to women veterans**. *Womens Health Issues* (2015.0) **25** 28-34. DOI: 10.1016/j.whi.2014.10.004
6. Chuang E, Brunner J, Mak S. **Challenges with implementing a patient-centered medical home model for women veterans**. *Womens Health Issues* (2017.0) **27** 214-220. DOI: 10.1016/j.whi.2016.11.005
7. Haskell SG, Mattocks K, Goulet JL. **The burden of illness in the first year home: do male and female VA users differ in health conditions and healthcare utilization**. *Womens Health Issues* (2011.0) **21** 92-97. DOI: 10.1016/j.whi.2010.08.001
8. Goldstein KM, Melnyk SD, Zullig LL. **Heart matters: gender and racial differences cardiovascular disease risk factor control among veterans**. *Womens Health Issues* (2014.0) **24** 477-483. DOI: 10.1016/j.whi.2014.05.005
9. Vimalananda VG, Biggs ML, Rosenzweig JL. **The influence of sex on cardiovascular outcomes associated with diabetes among older black and white adults**. *J Diabetes Complications* (2014.0) **28** 316-322. DOI: 10.1016/j.jdiacomp.2013.12.004
10. Breland JY, Phibbs CS, Hoggatt KJ. **The obesity epidemic in the Veterans Health Administration: prevalence among key populations of women and men veterans**. *J Gen Intern Med* (2017.0) **32** 11-17. DOI: 10.1007/s11606-016-3962-1
11. Han JK, Yano EM, Watson KE, Ebrahimi R. **Cardiovascular care in women veterans**. *Circulation* (2019.0) **139** 1102-1109. DOI: 10.1161/circulationaha.118.037748
12. Creech SK, Pulverman CS, Crawford JN. **Clinical complexity in women veterans: a systematic review of the recent evidence on mental health and physical health comorbidities**. *Behav Med* (2021.0) **47** 69-87. DOI: 10.1080/08964289.2019.1644283
13. Hamilton AB, Frayne SM, Cordasco KM, Washington DL. **Factors related to attrition from VA healthcare use: findings from the National Survey of Women Veterans**. *J Gen Intern Med* (2013.0) **28** S510-S516. DOI: 10.1007/s11606-013-2347-y
14. Wagner C, Dichter ME, Mattocks K. **Women veterans’ pathways to and perspectives on Veterans Affairs health care**. *Womens Health Issues* (2015.0) **25** 658-665. DOI: 10.1016/j.whi.2015.06.009
15. Lehavot K, Der-Martirosian C, Simpson TL, Sadler AG, Washington DL. **Barriers to care for women veterans with posttraumatic stress disorder and depressive symptoms**. *Psychol Serv* (2013.0) **10** 203-212. DOI: 10.1037/a0031596
16. Shipherd JC, Darling JE, Klap RS, Rose D, Yano EM. **Experiences in the Veterans Health Administration and impact on healthcare utilization: comparisons between LGBT and non-LGBT women veterans**. *LGBT Health* (2018.0) **5** 303-311. DOI: 10.1089/lgbt.2017.0179
17. Klap R, Darling JE, Hamilton AB. **Prevalence of stranger harassment of women veterans at Veterans Affairs Medical Centers and impacts on delayed and missed care**. *Womens Health Issues* (2019.0) **29** 107-115. DOI: 10.1016/j.whi.2018.12.002
18. Friedman SA, Frayne SM, Berg E. **Travel time and attrition from VHA care among women veterans: how far is too far?**. *Med Care* (2015.0) **53** S15-S22. DOI: 10.1097/mlr.0000000000000296
19. Gawron LM, Pettey WBP, Redd AM, Suo Y, Turok DK, Gundlapalli AV. **Distance matters: geographic barriers to long acting reversible and permanent contraception for homeless women Veterans**. *J Soc Distress Homeless* (2019.0) **28** 139-148. DOI: 10.1080/10530789.2019.1619242
20. 20.Administration VHVeterans Health Administration modernization campaign plan2019. *Veterans Health Administration modernization campaign plan* (2019.0)
21. Weinert C, Cudney S, Hill WG. **Rural women, technology, and self-management of chronic illness**. *Can J Nurs Res* (2008.0) **40** 114-134. PMID: 18947095
22. Hamilton AB, Farmer MM, Moin T. **Enhancing Mental and Physical Health of Women through Engagement and Retention (EMPOWER): a protocol for a program of research**. *Implement Sci* (2017.0) **12** 127. DOI: 10.1186/s13012-017-0658-9
23. Dyer KE, Moreau JL, Finley E. **Tailoring an evidence-based lifestyle intervention to meet the needs of women Veterans with prediabetes**. *Women Health* (2020.0) **60** 748-762. DOI: 10.1080/03630242.2019.1710892
24. Kilbourne AM, Neumann MS, Pincus HA, Bauer MS, Stall R. **Implementing evidence-based interventions in health care: application of the replicating effective programs framework**. *Implement Sci* (2007.0) **2** 42. DOI: 10.1186/1748-5908-2-42
25. Yano EM, Darling JE, Hamilton AB. **Cluster randomized trial of a multilevel evidence-based quality improvement approach to tailoring VA Patient Aligned Care Teams to the needs of women Veterans**. *Implement Sci* (2016.0) **11** 101. DOI: 10.1186/s13012-016-0461-z
26. Landes SJ, McBain SA, Curran GM. **An introduction to effectiveness-implementation hybrid designs**. *Psychiatry Res* (2019.0) **280** 112513. DOI: 10.1016/j.psychres.2019.112513
27. Saldana L. **The stages of implementation completion for evidence-based practice: protocol for a mixed methods study**. *Implement Sci* (2014.0) **9** 43. DOI: 10.1186/1748-5908-9-43
28. Finley EP, Huynh AK, Farmer MM. **Periodic reflections: a method of guided discussions for documenting implementation phenomena**. *BMC Med Res Methodol* (2018.0) **18** 153. DOI: 10.1186/s12874-018-0610-y
29. Hamilton AB, Brunner J, Cain C. **Engaging multilevel stakeholders in an implementation trial of evidence-based quality improvement in VA women’s health primary care**. *Transl Behav Med* (2017.0) **7** 478-485. DOI: 10.1007/s13142-017-0501-5
30. Dopp AR, Mundey P, Beasley LO, Silovsky JF, Eisenberg D. **Mixed-method approaches to strengthen economic evaluations in implementation research**. *Implement Sci* (2019.0) **14** 2. DOI: 10.1186/s13012-018-0850-6
31. Moin T, Damschroder LJ, AuYoung M. **Results from a trial of an online diabetes prevention program intervention**. *Am J Prev Med* (2018.0) **55** 583-591. DOI: 10.1016/j.amepre.2018.06.028
32. Damschroder LJ, Reardon CM, Sperber N, Robinson CH, Fickel JJ, Oddone EZ. **Implementation evaluation of the Telephone Lifestyle Coaching (TLC) program: organizational factors associated with successful implementation**. *Transl Behav Med* (2017.0) **7** 233-241. DOI: 10.1007/s13142-016-0424-6
33. Zlotnick C, Tzilos G, Miller I, Seifer R, Stout R. **Randomized controlled trial to prevent postpartum depression in mothers on public assistance**. *J Affect Disord* (2016.0) **189** 263-268. DOI: 10.1016/j.jad.2015.09.059
34. Huynh AK, Hamilton AB, Farmer MM. **A pragmatic approach to guide implementation evaluation research: strategy mapping for complex interventions**. *Front Public Health* (2018.0) **6** 134. DOI: 10.3389/fpubh.2018.00134
35. Kilbourne AM, Goodrich DE, Miake-Lye I, Braganza MZ, Bowersox NW. **Quality enhancement research initiative implementation roadmap: toward sustainability of evidence-based practices in a learning health system**. *Med Care* (2019.0) **57 Suppl** S286-S293. DOI: 10.1097/mlr.0000000000001144
36. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. **Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science**. *Implement Sci* (2009.0) **4** 50. DOI: 10.1186/1748-5908-4-50
37. Kirk MA, Kelley C, Yankey N, Birken SA, Abadie B, Damschroder L. **A systematic review of the use of the Consolidated Framework for Implementation Research**. *Implement Sci* (2016.0) **11** 72. DOI: 10.1186/s13012-016-0437-z
38. Nichols GA, Schroeder EB, Karter AJ. **Trends in diabetes incidence among 7 million insured adults, 2006-2011: the SUPREME-DM project**. *Am J Epidemiol* (2015.0) **181** 32-39. DOI: 10.1093/aje/kwu255
39. Lipscombe LL, Hwee J, Webster L, Shah BR, Booth GL, Tu K. **Identifying diabetes cases from administrative data: a population-based validation study**. *BMC Health Serv Res* (2018.0) **18** 316. DOI: 10.1186/s12913-018-3148-0
40. Palinkas LA, Campbell M, Saldana L. **Agency leaders’ assessments of feasibility and desirability of implementation of evidence-based practices in youth-serving organizations using the stages of implementation completion**. *Front Public Health* (2018.0) **6** 161. DOI: 10.3389/fpubh.2018.00161
41. Chamberlain P, Brown CH, Saldana L. **Observational measure of implementation progress in community based settings: the Stages of Implementation Completion (SIC)**. *Implement Sci* (2011.0) **6** 116. DOI: 10.1186/1748-5908-6-116
42. Stirman SW, Baumann AA, Miller CJ. **The FRAME: an expanded framework for reporting adaptations and modifications to evidence-based interventions**. *Implement Sci* (2019.0) **14** 58. DOI: 10.1186/s13012-019-0898-y
43. Stensaker I, Falkenberg J, Grønhaug K. **Implementation activities and organizational sensemaking**. *J Appl Behav Sci* (2008.0) **44** 162-185. DOI: 10.1177/0021886307313794
44. George AS, Branchini C. **Principles and processes behind promoting awareness of rights for quality maternal care services: a synthesis of stakeholder experiences and implementation factors**. *BMC Pregnancy Childbirth* (2017.0) **17** 264. DOI: 10.1186/s12884-017-1446-x
45. Proctor E, Silmere H, Raghavan R. **Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda**. *Adm Policy Ment Health* (2011.0) **38** 65-76. DOI: 10.1007/s10488-010-0319-7
46. Concannon TW, Meissner P, Grunbaum JA. **A new taxonomy for stakeholder engagement in patient-centered outcomes research**. *J Gen Intern Med* (2012.0) **27** 985-991. DOI: 10.1007/s11606-012-2037-1
47. Averill JB. **Matrix analysis as a complementary analytic strategy in qualitative inquiry**. *Qual Health Res* (2002.0) **12** 855-866. DOI: 10.1177/104973230201200611
48. 48.Eisman AB, Kilbourne AM, Dopp AR, Saldana L, Eisenberg D. Economic evaluation in implementation science: making the business case for implementation strategies. Psychiatry Res. 2019;283.
49. Wagner TaS A. *Economic and budget impact analysis* (2019.0)
50. Sullivan SD, Mauskopf JA, Augustovski F. **Budget impact analysis-principles of good practice: report of the ISPOR 2012 Budget Impact Analysis Good Practice II Task Force**. *Value Health* (2014.0) **17** 5-14. DOI: 10.1016/j.jval.2013.08.2291
51. Saldana L, Chamberlain P, Bradford WD, Campbell M, Landsverk J. **The Cost of Implementing New Strategies (COINS): a method for mapping implementation resources using the stages of implementation completion**. *Child Youth Serv Rev* (2014.0) **39** 177-182. DOI: 10.1016/j.childyouth.2013.10.006
52. Sharp A, Nguyen H, Hahn E. *The just do it playbook for implementation science* (2014.0)
53. DeJong C, Katz MH, Covinsky K. **Deferral of care for serious non-COVID-19 conditions: a hidden harm of COVID-19**. *JAMA Intern Med* (2021.0) **181** 274. DOI: 10.1001/jamainternmed.2020.4016
54. Blecker S, Jones SA, Petrilli CM. **Hospitalizations for chronic disease and acute conditions in the time of COVID-19**. *JAMA Intern Med* (2021.0) **181** 269-271. DOI: 10.1001/jamainternmed.2020.3978
55. Wright A, Salazar A, Mirica M, Volk LA, Schiff GD. **The invisible epidemic: neglected chronic disease management during COVID-19**. *J Gen Intern Med* (2020.0) **35** 2816-2817. DOI: 10.1007/s11606-020-06025-4
56. Wiggers J, McElwaine K, Freund M. **Increasing the provision of preventive care by community healthcare services: a stepped wedge implementation trial**. *Implement Sci* (2017.0) **12** 105. DOI: 10.1186/s13012-017-0636-2
57. McElwaine KM, Freund M, Campbell EM, Bartlem KM, Wye PM, Wiggers JH. **Systematic review of interventions to increase the delivery of preventive care by primary care nurses and allied health clinicians**. *Implement Sci* (2016.0) **11** 50. DOI: 10.1186/s13012-016-0409-3
|
---
title: 'Protection Motivation Perspective Regarding the Use of COVID-19 Mobile Tracing
Apps Among Public Users: Empirical Study'
journal: JMIR Formative Research
year: 2023
pmcid: PMC9994426
doi: 10.2196/36608
license: CC BY 4.0
---
# Protection Motivation Perspective Regarding the Use of COVID-19 Mobile Tracing Apps Among Public Users: Empirical Study
## Abstract
### Background
Access to data is crucial for decision-making; this fact has become more evident during the pandemic. Data collected using mobile apps can positively influence diagnosis and treatment, the supply chain, and the staffing resources of health care facilities. Developers and health care professionals have worked to create apps that can track a person’s COVID-19 status. For example, these apps can monitor positive COVID-19 test results and vaccination status. Regrettably, people may be concerned about sharing their data with government or private sector organizations that are developing apps. Understanding user perceptions is essential; without substantial user adoption and the use of mobile tracing apps, benefits cannot be achieved.
### Objective
This study aimed to assess the factors that positively and negatively affect the use of COVID-19 tracing apps by examining individuals’ perceptions about sharing data on mobile apps, such as testing regularity, infection, and immunization status.
### Methods
The hypothesized research model was tested using a cross-sectional survey instrument. The survey contained 5 reflective constructs and 4 control variables selected after reviewing the literature and interviewing health care professionals. A digital copy of the survey was created using Qualtrics. After receiving approval, data were collected from 367 participants through Amazon Mechanical Turk (MTurk). Participants of any gender who were 18 years or older were considered for inclusion to complete the anonymized survey. We then analyzed the theoretical model using structural equation modeling.
### Results
After analyzing the quality of responses, 325 participants were included. Of these 325 participants, 216 ($66.5\%$) were male and 109 ($33.5\%$) were female. Among the participants in the final data set, $72.6\%$ ($\frac{236}{325}$) were employed. The results of structural equation modeling showed that perceived vulnerability (β=0.688; $P \leq .001$), self-efficacy (β=0.292; $P \leq .001$), and an individual’s prior infection with COVID-19 (β=0.194; $$P \leq .002$$) had statistically significant positive impacts on the intention to use mobile tracing apps. Privacy concerns (β=−0.360; $P \leq .001$), risk aversion (β=−0.150; $$P \leq .09$$), and a family member’s prior infection with COVID-19 (β=−0.139; $$P \leq .02$$) had statistically significant negative influences on a person’s intention to use mobile tracing apps.
### Conclusions
This study illustrates that various user perceptions affect whether individuals use COVID-19 tracing apps. By working collaboratively on legislation and the messaging provided to potential users before releasing an app, developers, health care professionals, and policymakers can improve the use of tracking apps. Health care professionals need to emphasize disease vulnerability to motivate people to use mobile tracing apps, which can help reduce the spread of viruses and diseases. In addition, more work is needed at the policy-making level to protect the privacy of users, which in return can increase user engagement.
## Background
The BA.5, Omicron, and Delta variants of COVID-19 continue to fuel the global health crisis [1]. Federal, state, and local agencies, and many private sector organizations have taken steps to move the United States to the “new normal.” People often report positive COVID-19 test results and vaccination status to achieve this normalcy. For example, the Occupational Safety and Health Administration (OSHA) adopted a Healthcare Emergency Temporary Standard compelling all health care workers to be vaccinated or submit to frequent testing [2]. The Biden administration signed an executive order requiring COVID-19 testing and vaccination reporting for federal employees and companies receiving federal funding [3]. Major cities like Los Angles and New York also have mandates to report vaccination status for attending classes in grades K-12 and universities, indoor dining, or making purchases in shopping malls [4,5]. However, there is a general distrust about sharing COVID-19–related information, which has motivated personal and political legal challenges to reporting mandates [6].
This study examined individuals’ perceptions about sharing data on COVID-19–related metrics, such as testing frequency, diagnosis, and vaccination status, on mobile apps. Technologies like mobile apps are used to improve outcomes, increase patient participation in their care [7], and reduce the strain on limited health care resources. Further, since treatment regimens are developed slowly, information gathered and disseminated using mobile apps can be pivotal for stemming the spread of the disease. The use of COVID-19 tracing apps can also provide the public, businesses, and health care professionals with data to make informed decisions about the risks of infection. The findings of our study will fill an important gap in the literature, considering that more technologies are being created to mitigate the spread of COVID-19. A systematic review of COVID-19 mobile apps indicated that leveraging technology can be vital for combating the disease [8]; however, researchers have not examined the salient factors that support their use.
Using a modified version of the protection motivation theory (PMT), we explored individuals’ privacy concerns, risk aversion, COVID-19 infection status, vulnerability, and self-efficacy perception on their likelihood to use COVID-19 tracing apps. This research is one of the first to apply the PMT in the COVID-19 data–sharing context. Our theoretical contribution is 2-fold. First, we adapted the PMT by operationalizing response costs as privacy concerns. Second, we adapted threat appraisal by examining risk aversion and assessing an individual’s or their family members’ COVID-19 infection status. The rest of the paper is organized as follows: background literature on mobile apps used to fight COVID-19 and the PMT hypotheses, followed by a summary of the data collection and measurement model in the Methods section. The discussion and conclusion are in the final section.
## Mobile Technology as an Intervention Tool for Combating COVID-19
Early studies reviewing the use of mobile apps in health care showed promise. Now that mobile apps have been used for over a decade in health care, they are increasingly seen as necessary tools to promote evidence-based medicine. To achieve that aim, apps are being developed to educate patients and help health care professionals treat and diagnose various diseases [7]. Although mobile apps represent a small percentage of health care technology developed for use, they are the primary interface for Internet of Things (IoT) devices designed to improve patient experiences, reduce costs, and improve outcomes [9]. Improvements in IoT have contributed to the possibility of rapidly deploying health apps in a crisis like COVID-19 [10,11]. The expansion of mobile technology in the health care industry allows COVID-19 apps to be leveraged for risk assessment, self-management of symptoms, home monitoring, contact tracing, information sharing, training, and decision-making [8]. In this study, we examined the perceptions that may impact using mobile apps for the abovementioned purposes.
A health risk assessment is a tool used to collect information on disease status and risk; it is preemptive and can be used to manage the spread of a disease. Risk assessments can be completed by a health care professional or patient participating in self-management [12,13]. Mobile apps can be used to self-administer risk assessments, allowing individuals to identify the magnitude of their susceptibility to COVID-19. Two groups requiring risk assessments are health care workers and the general population. Health care workers are at the highest risk of contracting the disease; therefore, monitoring apps can be an effective strategy for collecting data. Researchers have used an agile methodology to develop a mobile app that identified symptomatic team members who could have posed a risk to the entire team [14], thereby establishing an effective way to assess risk. *The* general population can also benefit from an app-based risk assessment instrument. Researchers have found that they could predict a user’s likelihood of COVID-19 [15] after examining the data collected from a mobile app used by approximately 2.6 million users. The result aligns with our study’s aim, as it shows that apps can improve people’s awareness of their vulnerability to COVID-19.
Another way that COVID-19–related apps are helpful is that they bridge the gap in health care resources. The pandemic exacerbated the lack of health care resources. With many health care facilities at maximum capacity and staff shortages, apps helped patients with self-management [16] by facilitating the diagnosis of mild symptoms or assisting individuals in deciding when medical intervention is necessary. A French research team developed an app to track the loss of smell, a COVID-19 symptom that an affected individual can quickly identify. The data collected from the app were used to prevent the spread of COVID-19 and predict new outbreaks [17]. Another research team identified the top 5 strongest predictors for COVID-19 infection using data from a mobile app. The predictors included chills, fever, smell loss, nausea, vomiting, and shortness of breath [18]. With these predictors identified, people can be proactive in seeking care. According to the Centers for Disease Control and Prevention (CDC), except for cases of shortness of breath, individuals can use telehealth services or over-the-counter treatments [19], thereby limiting the strain on in-person clinics, hospitals, and urgent care facilities [20]. This ability of apps to limit the strain on resources is supported by a study of 3 COVID-19 apps in Thailand showing that the apps helped expand the reach of health care resources and improved the community’s health [21].
COVID-19 tracing app use can be affected by an individual’s ability to use apps effectively. The key factors that impact efficacy are app design and usability, wherein the elements can impact use and reduce the desired benefits of implementation. Research on the usability and inclusivity of COVID-19 mobile apps found that the grade level for readability exceeded the US national average. The study also found that most apps were developed for English speakers, and only a fraction of the features represented a broad cross-section of users [22]. An individual’s inability to understand instructions in an app due to high readability levels or limited language options may impact efficacy and decrease COVID-19 app use. Unfortunately, digital health equity issues, such as those mentioned above, have limited the treatment options for many vulnerable populations during the pandemic [23]. Few studies have examined digital health equity characteristics that may promote using tools like mobile apps [24]. Usability and inclusivity are salient factors that should be discussed in future studies, as feature selection may affect the use of mobile apps for infectious disease mitigation.
In a study of 12 apps used during the pandemic, including Mawid, Tabaud, Tawakkalna, Sehha, Aarogya Setu, TraceTogether, COVID safe, Immuni, COVID symptom study, COVID watch, NHS COVID-19, and PathCheck, the following features were identified: health tools, learning options, communication tools, networking tools, and safety and security options. Of note was the lack of built-in social media features in many apps [25]. In our society, where it is customary to share one’s day-to-day activities, it is unexpected that more developers would not include these features. If implemented, one use of the social media feature could be contact tracing, which identifies individuals who have been near someone newly diagnosed with COVID-19. Research shows that contact tracing apps are practical, and people will download them [26]. People must be willing to share their information for contact tracing to work. In a nationally representative survey of chronically ill individuals, only $21.8\%$ of respondents were highly likely to share their information on a COVID-19 mobile app [27]. The authors did not examine why less than $50\%$ of all the respondents would share their data with the mobile app developers, but it may be associated with privacy concerns.
Although addressing user concerns is essential, researchers examining data protection in contact tracing mobile apps found a need to balance protecting the society and the rights of individual patients to privacy. The government’s efforts to protect the society are substantial, as a review of 115 mobile apps showed that government agencies created the majority of apps [28]. Another study of 63 mobile apps found that $39\%$ (the highest percentage) were developed by federal agencies [29]. Interestingly, government agencies created most apps, although private sector organizations are usually the first movers on new initiatives. Does the fact that government agencies create most COVID-19 apps impact privacy concerns? Our study is interested in information sharing; the results may elucidate why people are hesitant about sharing their data with public health officials.
With the distributed development of mobile apps across Android and iOS platforms, more individuals have access to apps; therefore, it may be easier for health care providers to implement COVID-19 tracking apps based on an individual’s preferences. Studies have shown that apps have helped people engage in self-monitoring; we will examine if patients are more likely to use COVID-19 apps if they can do so effectively. It is also evident from the studies reviewed that developers have leveraged multiple features to fight COVID-19. One of our goals was to assess whether an individual’s evaluation of risk and vulnerability to COVID-19 might induce the use of these apps. The developer’s exclusion of features that may impact privacy is noteworthy, highlighting the reason we aimed to evaluate the impact of privacy concerns on app use.
## Willingness to Use Mobile Technology
User willingness to use mobile technology has been investigated in various contexts, such as lifestyle [30], learning and education [31,32], entertainment [33], financial services [34], and health care [35]. Research in the context of mobile health care technology found that, in general, users are willing to use mobile apps when they find substantial benefits, even when they have concerns. For example, Ahadzadeh et al [36] found that patients with chronic diseases continue to use mobile health services to manage their chronic conditions when they see that the apps are helpful and satisfactory. Likewise, Zhang et al [37] found that patients are willing to use diabetes management apps to manage their conditions when they find the apps beneficial. Studies focused on fitness and wearable device apps found that most users are willing to share their information with health care providers [38]. Although prior research has shown some willingness to share data, studies have yet to examine user willingness to share COVID-19–related data. Most studies examined apps that users may see as personally beneficial. Evaluating the willingness to use COVID-19 tracing apps is critical since it also has public health ramifications.
## PMT
Rogers posited the PMT; it is used to examine situations where individuals try to cope with or avoid noxious events to reduce a perceived threat [39]. COVID-19 is a global threat; individuals must find mechanisms to face this peril. Researchers have adapted the PMT in different contexts to evaluate how people are motivated to perform a specific behavior for hazard reduction. The use of data collection and dissemination as a method for reducing the risks of COVID-19 is appropriately evaluated using the PMT as it requires determining why people would be motivated to use the underlying mobile apps. Similarly, the PMT is used to examine protective health behaviors related to several other diseases, including schistosomiasis, HIV, and cancer, and prevention/detection behaviors, such as dieting, tobacco cessation, and exercise [40-43].
Protection motivation has 3 central stimuli: the event’s magnitude, the probability, and the availability and effectiveness of a copying response [44]. In the seminal PMT model, Rogers equated the event’s magnitude to perceived severity, the probability to a perceived vulnerability, and the response mechanism to perceived efficacy. These stimuli can be classified into threat appraisal or copying pathways. The threat appraisal pathway includes severity and vulnerability, and the copying pathway includes perceived self-efficacy and perceived cost [41]. Figure 1 depicts this study’s modified PMT model; it includes the new variables of COVID-19 infection status and risk aversion that determine a person’s perception of vulnerability in the threat appraisal pathway. The figure also illustrates the adaptation of the model to include privacy concerns as a perceived cost in the coping appraisal pathway.
**Figure 1:** *Modified protection motivation theory model. Bold text indicates modifications to the original model.*
## Threat Appraisal Pathway
The threat appraisal pathway evaluates a person’s perception of a threat [41]. This appraisal is measured by the person’s belief that a disease is a threat in health care. To assess people’s threat levels, we assessed perceived vulnerability, infection status, and risk aversion. Table 1 presents a summary of the definitions of the latent variables.
**Table 1**
| Variable | Definition |
| --- | --- |
| Perceived vulnerability | Perceived vulnerability assesses how personally susceptible an individual feels to the communicated threat. Do individuals feel prone to contract COVID-19? |
| Risk aversion | Risk aversion refers to an individual’s reluctance to take risks and accept potential losses unless significant rewards compensate for this. In this study, risk aversion relates to the potential loss of information control by sharing data in the COVID-19 app. |
| Self-efficacy | Self-efficacy concerns an individual’s beliefs about whether he or she can perform the recommended coping response (related to COVID-19). |
| Privacy concern | Privacy concern is the apprehension over the loss of privacy and the need for protection against unwarranted communication and use of personal information. Will the wrong people gain access to my COVID-19 data? |
## Perceived Vulnerability
Perceived vulnerability assesses how personally susceptible an individual feels to the communicated threat [44]. Do individuals feel prone to contract COVID-19? *In a* study of Belgian nationals, researchers found that individuals who were unable to telecommute, elderly individuals, female individuals, and those with lower educational attainment felt more susceptive to COVID-19 [45]. These results are supported by the infection rates and deaths nationally and internationally. A German study characterized perceived vulnerability to COVID-19 on a continuum from high to low. Researchers found that participants across all the groups exhibited similar behaviors. Those who perceived themselves as vulnerable were more likely to practice preventive adaptive behaviors and less likely to practice risky behaviors [46]. Individuals who perceive themselves as susceptible have also shown positive protective responses to increases in the number of cases in their communities [47]. We hypothesized that perceive vulnerability will positively affect an individual’s likelihood of using a COVID-19 monitoring app (hypothesis 1).
## COVID-19 Infection of the Individual or Family
COVID-19 infection is considered a positive test result in the polymerase chain reaction (PCR) test or an in-home rapid test [48]. This study measured an individual’s infection or the infection of a family member. This operationalization reflects the societal impact of COVID-19. People are fatigued and will likely experience psychological and emotional strain if their family members are affected [45]. Some individuals may comply with COVID-19 protocols to protect themselves or their families owing to positive COVID-19 test results. We hypothesized that an individual’s infection with COVID-19 will positively affect their likelihood of using a COVID-19 monitoring app (hypothesis 2a) and that a family member’s infection with COVID-19 will affect their likelihood of using a COVID-19 monitoring app (hypothesis 2b).
## Risk Aversion
Risk aversion is studied extensively in health. It refers to individuals’ reluctance to take risks and accept potential losses unless significant rewards compensate for this. In our study, risk aversion was related to the potential loss of information control by sharing data in the COVID-19 app; we surmise this is a threat appraisal. One common form of risk aversion is health insurance to mitigate the risk of high costs from an unforeseen illness [49,50]. Depending on a patient’s risk aversion, they may choose plans with high or low deductibles. For example, healthy individuals (low risk aversion) choose plans with high deductibles. Similarly, individuals with high risk tolerance are found to participate in less preventative and detective behaviors, and participate in risky behaviors like lack of exercise, smoking, or alcohol abuse [51,52]. We hypothesized that risk aversion will negatively affect an individual’s likelihood of using a COVID-19 monitoring app (hypothesis 3).
## Coping Appraisal Pathway
The coping appraisal pathway evaluates a person’s ability to cope with a threat [41]. To assess people’s ability to manage threats, we assessed self-efficacy and response cost/privacy concerns.
## Self-efficacy
Self-efficacy concerns an individual’s beliefs about whether he or she can perform the recommended coping response [44]. To help individuals cope with COVID-19, we proposed using a mobile tracing app to better face the pandemic. One study evaluated both the technological and health care impact of self-efficacy; to do so, the researchers developed a new construct called health care technology self-efficacy (HTSE). The assessment indicated that HTSE positively influenced the attitude toward using health technologies [53]. We hypothesized that perceived self-efficacy will positively affect an individual’s likelihood of using a COVID-19 monitoring app (hypothesis 4).
## Response Costs: Privacy Concerns
Response costs concern beliefs about how costly performing the recommended response will be to the individual [44]. Response costs can include the money, time, or effort associated with taking the adaptive coping response [54]. If I adopt the COVID-19 monitoring app, how would it impact me? Privacy concern is the apprehension over the loss of privacy and the need for protection against unwarranted communication and use of personal information [55]. Will the wrong people gain access to my COVID-19 data? Loss of privacy may be considered a response cost for using a COVID-19 monitoring app. *In* general, concerns over the security and privacy of protected health information have significantly impacted whether patients disclose medical information [56]. More specifically, researchers have found that patients struggle with adopting new technology due to privacy concerns [56-58]. We hypothesized that privacy concerns will negatively affect an individual’s likelihood of using a COVID-19 monitoring app (hypothesis 5).
## Ethical Considerations
We initially applied to the institutional review board (IRB) for approval. Participants aged at least 18 years were considered for inclusion, and the population included all genders. We then collected data after receiving IRB approval (approval number: 1764316-1). The first page of the survey included the consent to participate in the study. Participants were informed that their participation is voluntary and that they may withdraw from participation at any time without adverse consequences. If they wish to withdraw, they can simply discard the survey. The form also highlighted that the survey was anonymous and participants cannot be identified. Participation in the study was completely voluntary. The following statement was present: “By taking this survey, you are consenting to participation.” Participants who completed the survey received US $1 in compensation.
## Data Collection and Summary
We collected data from 367 participants through Amazon Mechanical Turk (MTurk). Many studies have used MTurk for data collection in the health care domain [59,60]. MTurk is an online platform that connects requesters and workers (MTurk users). The features of MTurk include data collection and survey distribution. Of the 367 participants, 5 had missing responses and were removed from the data set. In addition, 37 participants did not pass the quality check question and were also removed from the data set. The final data set contained 325 valid responses from MTurk users. Of the 325 final participants, 216 were male and 109 were female. Additionally, 236 participants were employed and 89 were unemployed. Most participants ($$n = 271$$) had a 4-year college degree.
Participants who chose to participate in the study were redirected to a Qualtrics link. The survey questions and responses were collected through Qualtrics. After participants finished the survey, they were required to copy a personalized survey code and input it into the MTurk survey code textbox. The first page of the survey was the “consent to participate in research” form; it explained (among other things) that participation was voluntary. The survey included multiple latent variables (privacy concerns, risk aversion, perceived vulnerability, self-efficacy, and prior privacy invasion) and some categorical variables, such as gender, education, and income. The dependent variable in the study was the intention to use a contact tracing mobile app. We also measured the participants’ experience with the disease using the following binary variables: COVID-19 infected (1=the participant has been infected with COVID-19) and COVID-19 infected-family (1=a family member has been infected with COVID-19). Table 2 shows the average Likert scale response for each latent variable.
To maintain the quality of the responses, we added a quality check question in the middle of the survey. The item asked participants to “please choose option number 3 (neither agree nor disagree).” Participants who did not select option 3 were eliminated from the study because they were not reading the questions carefully.
**Table 2**
| Variable | Minimum score | Maximum score | Mean score |
| --- | --- | --- | --- |
| Intention to use a contact tracing mobile app | 1.0 | 5.0 | 3.881 |
| Privacy concerns | 1.0 | 5.0 | 3.9754 |
| Risk aversion | 1.0 | 5.0 | 3.7685 |
| Self-efficacy | 1.0 | 5.0 | 4.0708 |
| Perceived vulnerability | 1.0 | 5.0 | 3.6031 |
| Prior privacy invasion | 1.0 | 5.0 | 3.319 |
## Study Objective
This study assessed whether individuals would use a mobile app to give others access to data on their COVID-19 status, including infection, testing, and vaccination status. We characterized 5 constructs into 2 mediational processes to elucidate the factors that change behavioral intentions. Following the model by Roger [39], the threat appraisal pathway included perceived vulnerability. In our model, we contextualized threat appraisal by assessing the impact of COVID-19 infection on both the individual and their family. Further, to evaluate one’s sensitivity to how threatened they feel, we measured their risk aversion. We used 2 constructs to measure the coping appraisal pathway (perceived efficacy and perceived cost). Because this study assessed the use of a mobile app, we operationalized perceived cost in terms of privacy concerns. The control variables included gender, income, education, employment, and prior experience with privacy invasion.
## Measurement Model
We cleaned the data using SAS Enterprise Guide version 8.1 (SAS Institute Inc). Then, we exported the data and analyzed the measurement model using IBM SPSS AMOS version 27 (IBM Corp). Table 3 shows the results of the measurement model. The factor loading for all items was significant. The factor loading ranged from 0.588 (the lowest) to 0.923 (the highest). The results also showed that latent variables were reliable [61]. The construct reliability for the latent variables ranged from 0.754 (the lowest) to 0.914 (the highest). In addition, all latent variables satisfied the validity assessment. First, the average variance extracted (AVE) was above 0.5 for all factors (above the minimum cutoff). Moreover, the AVE scores for all factors were above the squared multiple correlations with other factors (Table 4). Finally, the overall measurement model met the guideline for a good fit model (comparative fit index=0.933, Tucker-Lewis index=0.918, root mean square error of approximation=0.065, and χ2/df=2.387).
## Conceptual Model Results
We analyzed the theoretical model using structural equation modeling (SEM). We used AMOS version 27 (IBM Corp) to run the analysis. Table 5 shows the SEM results of the theoretical model. The results showed good model fit indices (comparative fit index=0.939, Tucker-Lewis index=0.917, root mean square error of approximation=0.054, and χ2/df=1.935).
First, perceived vulnerability was hypothesized to have a positive influence on the intention to use mobile tracing apps. The results also provide support. This variable showed the highest impact compared to any other variable in the model, and the impact was almost twice the impact of the second highest variable (privacy concerns). This shows that people feel vulnerable to getting infected when in close contact with others. Individuals may feel they have control over what preventive measures they take to avoid getting infected; however, they have no control over what others do. Thus, vulnerability is a big concern for individuals. Therefore, using a mobile tracing app can help people mitigate this vulnerability and increase their sense of security.
Second, we hypothesized that prior experience with the disease (COVID-19 infection of the individual or family) would show a significant influence on the intention to use a mobile tracing app. However, prior infection of a family member showed a negative impact, while prior infection of the participant showed a positive impact. This is an interesting result that perhaps needs further investigation.
**Table 5**
| Variable | Variable.1 | Variable.2 | Estimate | Estimate.1 | P value |
| --- | --- | --- | --- | --- | --- |
| Effect | Effect | Effect | | | |
| | INTa → Perceived vulnerability | 0.688 | 0.688 | <.001 | <.001 |
| | INT → COVID infection of the individual | 0.194 | 0.194 | .002 | .002 |
| | INT → COVID infection of the family | −0.139 | −0.139 | .02 | .02 |
| | INT → Risk aversion | −0.150 | −0.150 | .09 | .09 |
| | INT → Self-efficacy | 0.292 | 0.292 | <.001 | <.001 |
| | INT → Privacy concerns | −0.360 | −0.360 | <.001 | <.001 |
| Control variables | Control variables | Control variables | | | |
| | INT → Prior privacy invasion | −0.034 | −0.034 | .61 | .61 |
| | INT → Male | −0.010 | −0.010 | .83 | .83 |
| | INT → Income | 0.005 | 0.005 | .91 | .91 |
| | INT → Employed | 0.015 | 0.015 | .75 | .75 |
| | INT → College degree | 0.126 | 0.126 | .04 | .04 |
Third, we argued that risk aversion would have a negative effect on the intention to use mobile tracing apps. This hypothesis was marginally significant, with a P value of.09 and an estimate of −0.15. Thus, the negative influence of risk aversion and privacy concerns on sharing shows that individuals seek to avoid risk when it comes to information sharing and privacy-related issues.
Fourth, we hypothesized that self-efficacy would positively influence the intention to use mobile tracing apps. This hypothesis is also supported by the results shown in Table 5. The estimate of self-efficacy was positive at 0.292, and the P value was significant at <.001. This indicates that people feel confident that they can take self-preventive measures to avoid getting infected by COVID-19.
Finally, we hypothesized that privacy concerns would negatively influence the intention to use mobile tracing apps. The results of the SEM model support hypothesis 5 as the estimate was negative and significant (−0.36; $P \leq .001$). Thus, privacy concerns continue to be a barrier to using technology to protect the health of individuals and the public. Even with a pandemic, privacy is still important to individuals.
## Principal Findings
Technology is integral to patient care, as it gives health care professionals an increased capacity to communicate with patients, collect data, and diagnose and treat illnesses. Unfortunately, these benefits cannot be realized without patient adoption and the use of technology. The objective of this study was to explicate the factors that may limit or improve the adoption of technology to aid in the fight against COVID-19. This study examined individuals’ perceptions about using mobile apps that gather and monitor COVID-19–related information. The PMT was used to assess how user perception can help app development, improve adoption, and foster the use of mobile tracing apps. The results showed that an individual’s perceived vulnerability, self-efficacy, and infection with COVID-19 positively impacted the willingness to share information. Conversely, factors that negatively impacted the intention to share data on tracing apps included privacy concerns and risk aversion.
This study determined that perceived vulnerability had the highest positive impact on a person’s likelihood of using a mobile app that tracks COVID-19–related data like testing frequency, diagnosis, and vaccination status. This finding suggests that providing people with methods for assessing vulnerability may improve the adoption of mobile apps. Therefore, it is imperative to provide people with accurate information as early as possible, as prior research shows that perceived vulnerability could be manipulated based on the information people consume [46,47,62]. The knowledge gained from this study can enhance future pandemic preparation. For example, implementing tracing apps can aid individuals in determining their level of vulnerability because they can monitor infections among close contacts and use geomapping to view and possibly avoid locations with high infection rates. With the knowledge gained, consumers should be informed of potential vulnerabilities early to adopt mitigation techniques.
Efforts to implement mobile tracing apps should have specialized messaging for people who are perceived as vulnerable. Researchers at Kaiser Permanente, for example, have gathered and analyzed a wealth of data about vulnerable people; developers and implementation specialists should carefully consider the characteristics of users when creating the app and marketing material. For instance, the vulnerable population may include elderly people; therefore, the design should consider suitable features for this demographic. Organizations may consider marketing tracing apps as a way to enhance self-efficacy and assure consumers that use will not increase the risk of uncertain outcomes. Social media, videos, or even testing influencer marketing techniques can be used to achieve this aim.
In terms of self-efficacy, the results indicated that an individual’s ability to control exposure to the disease positively impacts the individual’s likelihood of using a COVID-19 tracing app. Respondents who felt they had control over activities that would expose them to COVID-19 were likely to use a mobile app. This confirms earlier findings of a positive relationship between task execution and the user’s self-efficacy [63,64]. A future study could analyze whether mitigation techniques, such as mask-wearing, social distancing, the closure of recreational facilities, and remote work significantly impact people’s perceptions of self-efficacy. This finding has implications for health care professionals and developers. Health care professionals and mobile app developers can increase the use of tracing apps and the likelihood of sharing data by providing interventions that improve app users’ self-efficacy.
Another factor with a positive impact was whether the participant had a college degree. Education was one of the control variables in our study; however, it is essential to discuss it since COVID-19 impacts the entire population. Only $42\%$ of Americans have a college degree, so this positive finding may indicate a lower number of people who want to use tracking apps. It is not surprising since prior research has highlighted a positive relationship between patient technology use and education [65]. To ensure the equitable use of tracking apps, developers can adjust the content in the apps to meet the literacy needs of most users by applying standardized measures like the Flesch Kincaid to approximate the educational level a person requires to read the content.
The results also showed that individuals with COVID-19 infection were more likely to use mobile tracing apps. Specifically, people who contracted the disease were more willing to share data. The increased likelihood may be attributed to the experiences gained from the infection, for example, not wanting others to experience the same thing or wanting to reduce reinfection. A remarkable finding was that a family member’s infection status negatively impacted a person’s intention to use a mobile tracing app. Participants in the study may have had negative feelings about such an app since they may have believed it is too late to help a family member, that is, use would not be beneficial. Additional research is required to understand this finding fully. For example, researchers may collect data on the culture, structure, and composition of a person’s household or the type of relationship with a family member.
As hypothesized, people are less inclined to use mobile apps if they are concerned about the privacy of their information. The perception of intentional or unintentional disclosure of information to unauthorized actors may also lessen the likelihood of using tracing apps. The reasons for privacy concerns may vary; one factor may be the growing mistrust in established bodies, such as the CDC, which may monitor or collect COVID-19 data [66]. Concerns may also stem from perceptions that others could use the COVID-19 status to harm or discriminate. Further, COVID-19 has been highly politicized; therefore, privacy concerns may arise if an individual does not support the current government [67].
A multitiered governmental approach is required to protect patient data and reduce privacy concerns. In the United States, the primary regulation governing the use of patient data is the Health Insurance Portability and Accountability Act (HIPAA). Unfortunately, it is limited in its scope to protect patients’ rights to their data [68]. Attempts have been made at the federal level to improve patient protections with The 2020 Cures Act Final Rule; however, it is still limited. A more robust rule, for example, the California Consumer Privacy Act of 2018 (CCPA), provides greater protection to information. The CCPA gives consumers more control over the personal information that businesses collect about them, giving them the right to opt-out, the right to know, the right to delete, and the right to nondiscrimination. This policy is similar to the General Data Protection Regulation (GDPR) in the European Union for data protection and privacy [69]. If federation legislation can be enacted similar to the CCPA or GDPR and patients are informed of its implementation, it may go a long way in reducing privacy concerns. There are caveats to enacting new legislation alone, as studies have shown that lack of regulatory clarity and sophisticated digital infrastructure can impede the likelihood of enforcing these rules [70].
Risk aversion was another factor that had a negative impact on the likelihood of using mobile tracing apps, as indicated by the study results. Risk-averse individuals want a guaranteed outcome; for example, using a tracing app will prevent COVID-19. If the perceived benefit of using mobile tracing apps is no greater than other mitigation techniques like social distancing and vaccination, individuals will be less likely to adopt and use these apps. This finding is supported by prior research, which showed that risk-averse laypersons were overcautious when deciding whether they needed medical care [71]. To improve the adoption and use of tracing apps, health care professionals may consider reiterating that an app is a supplement to other mitigation methods, improving the odds of a guaranteed outcome. Risk aversion is also impacted by multiple social determinants of health, such as education and income [71,72]; therefore, developers and health care professionals should consider these factors to improve the adoption and use of tracing apps.
## Limitations
There are several limitations of this study. First, as noted throughout this research, the pandemic poses a significant public health threat; therefore, mitigation efforts should help a large cross-section of people. Respondents in this study were only from the United States. Therefore, the results can be applied to nations with well-developed health care infrastructure or countries with universal health care where tracing app development can be centralized. Conversely, the results may not be generalizable to countries with cultural nuances, limited health care infrastructure, or socioeconomic and political constraints. Second, COVID-19 knowledge, perceptions, and statistics change frequently. Although this study captured perceptions cross-sectionally, the factors influencing mobile tracing app use will change over time. Future research should explore additional factors that may improve the use of COVID-19 tracing apps. Third, the sample included many educated and employed participants, which may result in bias. Finally, the sample for this study was recruited using an online tool, which limited the population to those who have internet access and use online platforms regularly.
## Conclusion
Multiple factors positively and negatively influence the use of COVID-19 tracing apps. This research is salient as mobile apps can aid in information collection, dissemination, and analysis. As new variants of COVID-19 are identified and the likelihood of future pandemics lurks, access to credible information can allow individuals and health care officials to make quick decisions that will prevent the spread of highly contagious diseases. We found that perceived vulnerability to COVID-19 and privacy concerns were the 2 main factors that impacted the use of tracing apps. Accordingly, after identifying potential disease threats, health care officials should inform users of their vulnerability to diseases like COVID-19 by delivering fact-based content to improve the use of tracing apps. Significant work is required to implement and enforce health care laws protecting privacy at all government levels. Self-efficacy and one’s COVID-19 infection were associated with positive impacts on the use of tracing apps. Future disease control and prevention initiatives may benefit from using tracing apps to increase self-efficacy as it may influence one’s perception of their ability to prevent infection. Including features in apps that improve disease prevention and detection may influence risk-averse individuals, thereby reducing the negative influence on tracking app use.
This study makes various theoretical contributions. First, we adapted the PMT by operationalizing response costs as privacy concerns. Second, we adopted 2 new threat appraisals by examining risk aversion and assessing an individual’s or their family members’ COVID-19 infection status. Further, the practical implications inherent in this research are relevant to policymakers, health care practitioners, and developers. To improve the use of COVID-19 tracking apps, federal, state, local, and private sector agencies and businesses should collaborate, as the current approach lacks coordination.
## Data Availability
The data sets generated during and/or analyzed during this study are available from the corresponding author on reasonable request.
## References
1. **COVID-19: Variants of the Virus**. *Centers for Disease Control and Prevention*
2. **COVID-19 Healthcare ETS**. *Occupational Safety and Health Administration*
3. **Executive Order on Requiring Coronavirus Disease 2019 Vaccination for Federal Employees**. *The White House*
4. **New York City expands COVID vaccine mandates for children, private sector**. *Reuters*
5. **Stricter COVID-19 protocols to get into large events in LA County start today**. *ABC7 Los Angeles*
6. **Legal Challenges To OSHA’s Vaccination And Testing Emergency Temporary Standard**. *Forbes*
7. Mosa ASM, Yoo I, Sheets L. **A systematic review of healthcare applications for smartphones**. *BMC Med Inform Decis Mak* (2012.0) **12** 67. DOI: 10.1186/1472-6947-12-67
8. Kondylakis H, Katehakis DG, Kouroubali A, Logothetidis F, Triantafyllidis A, Kalamaras I, Votis K, Tzovaras D. **COVID-19 Mobile Apps: A Systematic Review of the Literature**. *J Med Internet Res* (2020.0) **22** e23170. DOI: 10.2196/23170
9. Riazul Islam SM, Kwak D, Humaun Kabir M, Hossain M, Kwak SK. **The Internet of Things for Health Care: A Comprehensive Survey**. *IEEE Access* (2015.0) **3** 678-708. DOI: 10.1109/access.2015.2437951
10. Drew D, Nguyen LH, Steves CJ, Menni C, Freydin M, Varsavsky T, Sudre CH, Cardoso MJ, Ourselin S, Wolf J, Spector TD, Chan AT. **Rapid implementation of mobile technology for real-time epidemiology of COVID-19**. *Science* (2020.0) **368** 1362-1367. DOI: 10.1126/science.abc0473
11. Singh KR, Dash S, Deka B, Biswas S, Al-Turjman F, Devi A, Nayyar A. **Mobile Technology Solutions for COVID-19**. *Emerging Technologies for Battling Covid-19. Studies in Systems, Decision and Control, vol 324* (2021.0) 271-294
12. de Carvalho TM, Noels E, Wakkee M, Udrea A, Nijsten T. **Development of Smartphone Apps for Skin Cancer Risk Assessment: Progress and Promise**. *JMIR Dermatol* (2019.0) **2** e13376. DOI: 10.2196/13376
13. O'Keefe JB, Tong EJ, Taylor TH, O'Keefe GAD, Tong DC. **Use of a Telemedicine Risk Assessment Tool to Predict the Risk of Hospitalization of 496 Outpatients With COVID-19: Retrospective Analysis**. *JMIR Public Health Surveill* (2021.0) **7** e25075. DOI: 10.2196/25075
14. Zhang H, Dimitrov D, Simpson L, Plaks N, Singh B, Penney S, Charles J, Sheehan R, Flammini S, Murphy S, Landman A. **A Web-Based, Mobile-Responsive Application to Screen Health Care Workers for COVID-19 Symptoms: Rapid Design, Deployment, and Usage**. *JMIR Form Res* (2020.0) **4** e19533. DOI: 10.2196/19533
15. Menni C, Valdes AM, Freidin MB, Sudre CH, Nguyen LH, Drew DA, Ganesh S, Varsavsky T, Cardoso MJ, El-Sayed Moustafa JS, Visconti A, Hysi P, Bowyer RCE, Mangino M, Falchi M, Wolf J, Ourselin S, Chan AT, Steves CJ, Spector TD. **Real-time tracking of self-reported symptoms to predict potential COVID-19**. *Nat Med* (2020.0) **26** 1037-1040. DOI: 10.1038/s41591-020-0916-2
16. Bodenheimer T, Lorig K, Holman H, Grumbach K. **Patient self-management of chronic disease in primary care**. *JAMA* (2002.0) **288** 2469-75. DOI: 10.1001/jama.288.19.2469
17. Denis F, Fontanet A, Le Douarin Y, Le Goff F, Jeanneau S, Lescure F. **A Self-Assessment Web-Based App to Assess Trends of the COVID-19 Pandemic in France: Observational Study**. *J Med Internet Res* (2021.0) **23** e26182. DOI: 10.2196/26182
18. Zens M, Brammertz A, Herpich J, Südkamp N, Hinterseer M. **App-Based Tracking of Self-Reported COVID-19 Symptoms: Analysis of Questionnaire Data**. *J Med Internet Res* (2020.0) **22** e21956. DOI: 10.2196/21956
19. **If You Are Sick or Caring for Someone**. *Centers for Disease Control and Prevention*
20. Lukas H, Xu C, Yu Y, Gao W. **Emerging Telemedicine Tools for Remote COVID-19 Diagnosis, Monitoring, and Management**. *ACS Nano* (2020.0) **14** 16180-16193. DOI: 10.1021/acsnano.0c08494
21. Intawong K, Olson D, Chariyalertsak S. **Application technology to fight the COVID-19 pandemic: Lessons learned in Thailand**. *Biochem Biophys Res Commun* (2021.0) **534** 830-836. DOI: 10.1016/j.bbrc.2020.10.097
22. Blacklow S, Lisker S, Ng MY, Sarkar U, Lyles C. **Usability, inclusivity, and content evaluation of COVID-19 contact tracing apps in the United States**. *J Am Med Inform Assoc* (2021.0) **28** 1982-1989. DOI: 10.1093/jamia/ocab093
23. Lyles CR, Wachter RM, Sarkar U. **Focusing on Digital Health Equity**. *JAMA* (2021.0) **326** 1795-1796. DOI: 10.1001/jama.2021.18459
24. Nouri S, Adler-Milstein J, Thao C, Acharya P, Barr-Walker J, Sarkar U, Lyles C. **Patient characteristics associated with objective measures of digital health tool use in the United States: A literature review**. *J Am Med Inform Assoc* (2020.0) **27** 834-841. DOI: 10.1093/jamia/ocaa024
25. Alanzi T. **A Review of Mobile Applications Available in the App and Google Play Stores Used During the COVID-19 Outbreak**. *JMDH* (2021.0) **Volume 14** 45-57. DOI: 10.2147/jmdh.s285014
26. Altmann S, Milsom L, Zillessen H, Blasone R, Gerdon F, Bach R, Kreuter F, Nosenzo D, Toussaert S, Abeler J. **Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Study**. *JMIR Mhealth Uhealth* (2020.0) **8** e19857. DOI: 10.2196/19857
27. Camacho-Rivera M, Islam JY, Rivera A, Vidot DC. **Attitudes Toward Using COVID-19 mHealth Tools Among Adults With Chronic Health Conditions: Secondary Data Analysis of the COVID-19 Impact Survey**. *JMIR Mhealth Uhealth* (2020.0) **8** e24693. DOI: 10.2196/24693
28. Almalki M, Giannicchi A. **Health Apps for Combating COVID-19: Descriptive Review and Taxonomy**. *JMIR Mhealth Uhealth* (2021.0) **9** e24322. DOI: 10.2196/24322
29. Davalbhakta S, Advani S, Kumar S, Agarwal V, Bhoyar S, Fedirko E, Misra DP, Goel A, Gupta L, Agarwal V. **A Systematic Review of Smartphone Applications Available for Corona Virus Disease 2019 (COVID19) and the Assessment of their Quality Using the Mobile Application Rating Scale (MARS)**. *J Med Syst* (2020.0) **44** 164. DOI: 10.1007/s10916-020-01633-3
30. Lee K, Bejerano IL, Han M, Choi HS. **Willingness to use smartphone apps for lifestyle management among patients with schizophrenia**. *Arch Psychiatr Nurs* (2019.0) **33** 329-336. DOI: 10.1016/j.apnu.2019.01.002
31. Sun Y, Gao F. **An investigation of the influence of intrinsic motivation on students’ intention to use mobile devices in language learning**. *Education Tech Research Dev* (2019.0) **68** 1181-1198. DOI: 10.1007/s11423-019-09733-9
32. Buabeng-Andoh C. **Exploring University students’ intention to use mobile learning: A research model approach**. *Educ Inf Technol* (2020.0) **26** 241-256. DOI: 10.1007/s10639-020-10267-4
33. Hung S, Chang C, Ma Y. **A new reality: Exploring continuance intention to use mobile augmented reality for entertainment purposes**. *Technology in Society* (2021.0) **67** 101757. DOI: 10.1016/j.techsoc.2021.101757
34. Sunny P, George A. **Determinants of behavioral intention to use mobile wallets--a conceptual model**. *Journal of Management (JOM)* (2018.0) **5** 52-62
35. Balapour A, Reychav I, Sabherwal R, Azuri J. **Mobile technology identity and self-efficacy: Implications for the adoption of clinically supported mobile health apps**. *International Journal of Information Management* (2019.0) **49** 58-68. DOI: 10.1016/j.ijinfomgt.2019.03.005
36. Ahadzadeh AS, Wu SL, Ong FS, Deng R. **The Mediating Influence of the Unified Theory of Acceptance and Use of Technology on the Relationship Between Internal Health Locus of Control and Mobile Health Adoption: Cross-sectional Study**. *J Med Internet Res* (2021.0) **23** e28086. DOI: 10.2196/28086
37. Zhang Y, Liu C, Luo S, Xie Y, Liu F, Li X, Zhou Z. **Factors Influencing Patients' Intentions to Use Diabetes Management Apps Based on an Extended Unified Theory of Acceptance and Use of Technology Model: Web-Based Survey**. *J Med Internet Res* (2019.0) **21** e15023. DOI: 10.2196/15023
38. Chandrasekaran R, Katthula V, Moustakas E. **Patterns of Use and Key Predictors for the Use of Wearable Health Care Devices by US Adults: Insights from a National Survey**. *J Med Internet Res* (2020.0) **22** e22443. DOI: 10.2196/22443
39. Rogers RW. **A Protection Motivation Theory of Fear Appeals and Attitude Change1**. *J Psychol* (1975.0) **91** 93-114. DOI: 10.1080/00223980.1975.9915803
40. Milne S, Orbell S, Sheeran P. **Combining motivational and volitional interventions to promote exercise participation: protection motivation theory and implementation intentions**. *Br J Health Psychol* (2002.0) **7** 163-84. DOI: 10.1348/135910702169420
41. Xiao H, Li S, Chen X, Yu B, Gao M, Yan H, Okafor CN. **Protection motivation theory in predicting intention to engage in protective behaviors against schistosomiasis among middle school students in rural China**. *PLoS Negl Trop Dis* (2014.0) **8** e3246. DOI: 10.1371/journal.pntd.0003246
42. Xiao N, Sharman R, Rao H, Upadhyaya S. **Factors influencing online health information search: An empirical analysis of a national cancer-related survey**. *Decision Support Systems* (2014.0) **57** 417-427. DOI: 10.1016/j.dss.2012.10.047
43. Macdonell K, Chen X, Yan Y, Li F, Gong J, Sun H, Li X, Stanton B. **A Protection Motivation Theory-Based Scale for Tobacco Research among Chinese Youth**. *J Addict Res Ther* (2013.0) **4** 154. DOI: 10.4172/2155-6105.1000154
44. Milne S, Sheeran P, Orbell S. **Prediction and Intervention in Health-Related Behavior: A Meta-Analytic Review of Protection Motivation Theory**. *J Appl Social Pyschol* (2000.0) **30** 106-143. DOI: 10.1111/j.1559-1816.2000.tb02308.x
45. De Coninck D, d'Haenens L, Matthijs K. **Perceived vulnerability to disease and attitudes towards public health measures: COVID-19 in Flanders, Belgium**. *Pers Individ Dif* (2020.0) **166** 110220. DOI: 10.1016/j.paid.2020.110220
46. Stangier U, Kananian S, Schüller J. **Perceived vulnerability to disease, knowledge about COVID-19, and changes in preventive behavior during lockdown in a German convenience sample**. *Curr Psychol* (2022.0) **41** 7362-7370. DOI: 10.1007/s12144-021-01456-6
47. Safra L, Sijilmassi A, Chevallier C. **Disease, perceived infectability and threat reactivity: A COVID-19 study**. *Pers Individ Dif* (2021.0) **180** 110945. DOI: 10.1016/j.paid.2021.110945
48. **COVID-19 and PCR Testing**. *Cleveland Clinic*
49. Outreville JF. **Risk Aversion, Risk Behavior, and Demand for Insurance: A Survey**. *Journal of Insurance Issues* (2014.0) **37** 158-186
50. Schmitz H, Winkler S. **Information, Risk Aversion, and Healthcare Economics**. *Oxford Research Encyclopedia of Economics and Finance* (2018.0)
51. Riddel M, Hales D. **Predicting Cancer-Prevention Behavior: Disentangling the Effects of Risk Aversion and Risk Perceptions**. *Risk Anal* (2018.0) **38** 2161-2177. DOI: 10.1111/risa.13111
52. Dohmen T, Falk A, Huffman D, Sunde U, Schupp J, Wagner GG. **Individual risk attitudes: Measurement, determinants, and behavioral consequences**. *Journal of the European Economic Association* (2011.0) **9** 522-550. DOI: 10.1111/j.1542-4774.2011.01015.x
53. Rahman MS, Ko M, Warren J, Carpenter D. **Healthcare Technology Self-Efficacy (HTSE) and its influence on individual attitude: An empirical study**. *Computers in Human Behavior* (2016.0) **58** 12-24. DOI: 10.1016/j.chb.2015.12.016
54. Floyd DL, Prentice-Dunn S, Rogers RW. **A Meta-Analysis of Research on Protection Motivation Theory**. *J Appl Social Pyschol* (2000.0) **30** 407-429. DOI: 10.1111/j.1559-1816.2000.tb02323.x
55. Bansal G, Zahedi FM, Gefen D. **The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online**. *Decision Support Systems* (2010.0) **49** 138-150. DOI: 10.1016/j.dss.2010.01.010
56. Agaku IT, Adisa AO, Ayo-Yusuf OA, Connolly GN. **Concern about security and privacy, and perceived control over collection and use of health information are related to withholding of health information from healthcare providers**. *J Am Med Inform Assoc* (2014.0) **21** 374-8. DOI: 10.1136/amiajnl-2013-002079
57. Appari A, Johnson ME. **Information security and privacy in healthcare: current state of research**. *IJIEM* (2010.0) **6** 279. DOI: 10.1504/ijiem.2010.035624
58. Balapour A, Nikkhah HR, Sabherwal R. **Mobile application security: Role of perceived privacy as the predictor of security perceptions**. *International Journal of Information Management* (2020.0) **52** 102063. DOI: 10.1016/j.ijinfomgt.2019.102063
59. Brinton JE, Keating MD, Ortiz AM, Evenson KR, Furberg RD. **Establishing Linkages Between Distributed Survey Responses and Consumer Wearable Device Datasets: A Pilot Protocol**. *JMIR Res Protoc* (2017.0) **6** e66. DOI: 10.2196/resprot.6513
60. Taylor CO, Flaks-Manov N, Ramesh S, Choe EK. **Willingness to Share Wearable Device Data for Research Among Mechanical Turk Workers: Web-Based Survey Study**. *J Med Internet Res* (2021.0) **23** e19789. DOI: 10.2196/19789
61. Hu L, Bentler PM. **Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives**. *Structural Equation Modeling: A Multidisciplinary Journal* (1999.0) **6** 1-55. DOI: 10.1080/10705519909540118
62. Kananian S, Al-Sari S, Stangier U. **Perceived Vulnerability to Disease, Knowledge and Preventive Behavior Related to COVID-19 in Farsi and Arabic Speaking Refugees**. *J Immigr Minor Health* (2022.0) **24** 1245-1250. DOI: 10.1007/s10903-021-01322-4
63. Anderson-Bill ES, Winett RA, Wojcik JR. **Social cognitive determinants of nutrition and physical activity among web-health users enrolling in an online intervention: the influence of social support, self-efficacy, outcome expectations, and self-regulation**. *J Med Internet Res* (2011.0) **13** e28. DOI: 10.2196/jmir.1551
64. Litman L, Rosen Z, Spierer D, Weinberger-Litman S, Goldschein A, Robinson J. **Mobile Exercise Apps and Increased Leisure Time Exercise Activity: A Moderated Mediation Analysis of the Role of Self-Efficacy and Barriers**. *J Med Internet Res* (2015.0) **17** e195. DOI: 10.2196/jmir.4142
65. Gordon NP, Crouch E. **Digital Information Technology Use and Patient Preferences for Internet-Based Health Education Modalities: Cross-Sectional Survey Study of Middle-Aged and Older Adults With Chronic Health Conditions**. *JMIR Aging* (2019.0) **2** e12243. DOI: 10.2196/12243
66. **Poll Finds Public Health Has A Trust Problem**. *NPR*
67. **Public opinion about coronavirus is more politically divided in U.S. than in other advanced economies**. *Pew Research Center*
68. Bhuyan SS, Kim H, Isehunwa OO, Kumar N, Bhatt J, Wyant DK, Kedia S, Chang CF, Dasgupta D. **Privacy and security issues in mobile health: Current research and future directions**. *Health Policy and Technology* (2017.0) **6** 188-191. DOI: 10.1016/j.hlpt.2017.01.004
69. Iyengar A, Kundu A, Pallis G. **Healthcare Informatics and Privacy**. *IEEE Internet Comput* (2018.0) **22** 29-31. DOI: 10.1109/mic.2018.022021660
70. Mulgund P, Mulgund BP, Sharman R, Singh R. **The implications of the California Consumer Privacy Act (CCPA) on healthcare organizations: Lessons learned from early compliance experiences**. *Health Policy and Technology* (2021.0) **10** 100543. DOI: 10.1016/j.hlpt.2021.100543
71. Kopka M, Feufel MA, Balzer F, Schmieding ML. **The Triage Capability of Laypersons: Retrospective Exploratory Analysis**. *JMIR Form Res* (2022.0) **6** e38977. DOI: 10.2196/38977
72. Rosen AB, Tsai JS, Downs SM. **Variations in Risk Attitude across Race, Gender, and Education**. *Med Decis Making* (2016.0) **23** 511-517. DOI: 10.1177/0272989x03258431
|
---
title: Social support as a mediator between anxiety and quality of sleep among Chinese
parents of special children
authors:
- Junda Xu
- Jiliang Tang
journal: Frontiers in Psychology
year: 2023
pmcid: PMC9994432
doi: 10.3389/fpsyg.2023.1077596
license: CC BY 4.0
---
# Social support as a mediator between anxiety and quality of sleep among Chinese parents of special children
## Abstract
### Objective
The psychological problems among Chinese parents of special children (mental retardation, limb disorder, hearing impairment, autism, cerebral palsy and other types) should be paid more attention. The aim of this study was to investigate the association between anxiety, social support, coping style and sleep quality among Chinese parents of special children during the early COVID-19 epidemic, so as to provide more help for the mental health of parents of special children scientifically and effectively.
### Method
A total of 305 Chinese parents of special children were invited to accomplish four questionnaires. Anxiety was measured by the Self-Rating Anxiety Scale, social support was evaluated by the Perceived Social Support Scale, sleep quality was assessed by the Pittsburgh Sleep Quality Index, and coping style was measured by the Simplified Coping Style Questionnaire.
### Results
This study revealed that anxiety was positively correlated with sleep quality ($p \leq 0.01$) and negatively correlated with social support ($p \leq 0.01$) and coping style ($p \leq 0.01$). Sleep quality was negatively correlated with social support ($p \leq 0.01$), but not significantly correlated with coping style ($p \leq 0.05$). Social support was positively correlated with coping style ($p \leq 0.01$). The study confirmed that social support had a partial mediating effect on the relationship between anxiety and sleep quality.
### Conclusion
The anxiety of parents of special children not only directly affects sleep quality, but also indirectly affects sleep quality through social support. Social support can alleviate the impact of anxiety on sleep quality through the mediating role.
## Introduction
Sleep quality which is always recognized as a very important physical and mental health index has been widely discussed (American Psychiatric Association, 2013). For example, studies found that lower sleep quality and sleep duration increase the risk of being overweight and obese (Parvaneh et al., 2016). Sleep disturbance is strongly associated with periodontitis, cardiovascular diseases and some cancers (Alqaderi et al., 2020). Lack of sleep leads to decreased work efficiency (Morgan, 1974), and poor sleep quality impairs emotion regulation and contributes depression symptoms (O'Leary et al., 2016). There are intertwined relationships discussed among sleep quality, physiological and psychological factors (Mohammadkhani et al., 2019; Taheri et al., 2019). Many studies have shown that parents of special children (mentally disabled children) have sleep problems. Parents of children with ASDs (autism spectrum disorders) reported poorer sleep quality compared to the TD (typically developing) group (Meltzer, 2008). Parents of children with developmental disabilities reported poorer sleep quality than parents of normal children (Gallagher et al., 2010). Children with neurodevelopmental disorders and their parents reported more severe sleep disturbances (sleep quality, insomnia symptoms and sleep efficiency) than typically developing children and their parents (Varma et al., 2021). Therefore, the quality of sleep has a big impact on body and mental health, and it is necessary to carry out further study for clarifying sleep quality related to mental health among parents of special children.
Which psychological factors affect sleep quality? Studies have shown that anxiety affects sleep quality. Most of studies have found a significant correlation between anxiety and sleep quality, and anxiety is a significant predictor of sleep quality (Augner, 2011; Liu et al., 2021; Simonetti et al., 2021; Yin et al., 2021). Many studies also have shown that social support is related to sleep quality (Brummett et al., 2006; Nordin et al., 2012; Guo et al., 2014; Marini et al., 2020; Mitchell et al., 2022). Some Studies have found positive coping style was negatively correlated with sleep quality (Kong et al., 2021) or not (Zhao et al., 2006), while negative coping style was positively correlated with sleep quality (Kong et al., 2021; Xue et al., 2012) or not (Zhou et al., 2013). In addition, studies have shown a correlation between anxiety and social support, coping style (Wu, 2010; Ma, 2013; Chen et al., 2016; Taş, 2019).
So, previous studies have found that anxiety, social support, coping style and sleep quality are related to each other. In addition, many studies have shown that the COVID-19 epidemic will affect individual sleep, anxiety and other mental health (Stanton et al., 2020; Qiu et al., 2021; Chatterjee, 2022). Studies have explored the psychometric relationship among these related variables, but the mediating role of social support and coping style between anxiety and sleep quality has been explored very rarely. How social support and coping style mediate the association between anxiety and sleep quality? Besides, selecting subjects of previous relevant studies mainly focus on college students.
Based on existing theories and researches, this study speculated that social support and coping style mediate the relationship between anxiety and sleep quality, and selected Chinese parents of special children as the psychological measurement subjects. The purpose of this study was to investigate the deep relationship among anxiety, social support, sleep quality and coping style of parents with special children, so as to provide some theoretical and practical proofs for the mental health of parents with special children during the COVID-19 epidemic period.
## Participants
The Participants of this survey study were Chinese parents of special children refer to mental retardation, limb disorder, hearing impairment, autism, cerebral palsy and other types. This study conducted an online questionnaire to parents of special children in Hangzhou Green Apple Kindergarten in March 2020. A total of 305 questionnaires were distributed. After excluding missing data, a total of 283 questionnaires were included in this study. Two hundred eighty-three participants information see Table 1, and the study flow chart see Figure 1.
## Self-rating anxiety scale
Anxiety was assessed with a 20-item Self-Rating Anxiety Scale by Zung (the scale derived from handbook: Wang et al., 1999). Ratings were made a 4-point Scale. Cronbach’s α of the scale in this study was 0.817. Confirmatory factor analysis indicated that the validity of this scale was good (χ2 =376.244; χ2/df = 2.650; $p \leq 0.001$; GFI=0.874; NFI=0.803; IFI=0.867; TLI = 0.817; CFI = 0.864; RMSEA = 0.076).
## Perceived social support scale
Social support was assessed with a Chinese revised version of Perceived Social Support Scale by Qianjin Jiang (the scale derived from handbook: Wang et al., 1999). The 12 items were rated on a 7-point Likert scale, ranging from 1 for strongly disagree to 7 for strongly agree with higher scores indicating greater social support. The Perceived Social Support Scale consists of three subscales: family support, friend support and significant others support. Cronbach’s α of the scale in this study was 0.932. Confirmatory factor analysis indicated that the validity of this scale was good (χ2 =122.376; χ2/df = 2.781; $p \leq 0.001$; GFI=0.935; NFI=0.949; IFI=0.966; TLI = 0.949; CFI = 0.966; RMSEA = 0.079).
## Simplified coping style questionnaire
Coping style in this study was assessed with a 20-item Simplified Coping Style Questionnaire developed by Yaning Xie (the scale derived from handbook: Wang et al., 1999). Ratings were made on a 4-point scale. The Simplified Coping Style Questionnaire consists of two subscales: positive and negative coping style. Cronbach’s α of the scale in this study was 0.875. Confirmatory factor analysis indicated that the validity of this scale was good (χ2 =396.742; χ2 /df = 2.681; $p \leq 0.001$; GFI=0.875; NFI=0.828; IFI=0.885; TLI = 0.849; CFI = 0.883; RMSEA = 0.077).
## Pittsburgh sleep quality index
Sleep quality was assessed with a Chinese revised version of Pittsburgh Sleep Quality Index by Xianchen Liu (the scale derived from handbook: Wang et al., 1999). This Scale had 24 items. The total score (without item 19 and 5 other evaluation items) ranged from 0 to 21, with higher scores indicating poorer sleep quality. Cronbach’s α of the scale in this study was 0.742. Confirmatory factor analysis indicated that the validity of this scale was good (χ2 =16.824; χ2/df = 2.103; $p \leq 0.05$; GFI=0.984; NFI=0.984; IFI=0.992; TLI = 0.978; CFI = 0.992; RMSEA = 0.063).
## Test of common method bias
Harman’s one-factor test was used to check Common Method Bias by exploratory factor analysis. As the results showed that characteristic values of 14 factors were greater than 1. The variation explained for the first factor of 14 factors was $19.015\%$, indicating that the variance interpretation of the maximum factor was below the upper limit standard of $40\%$ (Podsakoff et al., 2003). Thus, there were no serious Common Method Bias of data in this study and further analysis can be used.
## Statistical method
Data statistics were implemented by SPSS (version 22.0). Applicable statistical methods were chosen to analyze the relationship among anxiety, social support, coping style and sleep quality in parents of special children.
## Descriptive analysis
Descriptive analysis results based on the questionnaire of 283 parents of special children are presented in Table 2. The average score of anxiety (SAS) was 32.10 (standard deviation: 6.92). For social support (PSSS), the average score was 57.61(standard deviation: 13.04). For sleep quality (PSQI), the average score was 4.84(standard deviation: 3.11). For coping style (SCSQ), the average score was 28.14(standard deviation: 9.68). Subscale average scores of the four variables are also presented in Table 2.
**Table 2**
| Variables | M ± SD |
| --- | --- |
| Anxiety (SAS) | 32.10 ± 6.92 |
| Social support (PSSS) | 57.61 ± 13.04 |
| Family support | 20.66 ± 5.02 |
| Friend support | 18.52 ± 4.94 |
| Significant others support | 18.44 ± 4.68 |
| Coping style (SCSQ) | 28.14 ± 9.68 |
| Positive coping style | 20.77 ± 7.66 |
| Negative coping style | 7.37 ± 3.90 |
| Sleep quality (PSQI) | 4.84 ± 3.11 |
## Correlation analysis
The results of correlation analysis (Pearson) are presented in Table 3. Sleep quality had significant positive correlation with anxiety ($p \leq 0.01$) and significant negative correlation with social support (included its three subscales “family, friend and significant others support”) ($p \leq 0.01$). There was nonsignificant correlation between sleep quality and coping style (included its subscale “positive coping style”) ($p \leq 0.05$), but the subscale “negative coping style” was positively correlated with sleep quality ($p \leq 0.01$). Anxiety had significant negative correlation with social support and coping style ($p \leq 0.01$). Social support and coping style had significant positive correlation ($p \leq 0.01$).
**Table 3**
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1. Anxiety (SAS) | 1 | | | | | | | | |
| 2. Social support (PSSS) | −0.343** | 1 | | | | | | | |
| 3. Family support | −0.319** | 0.856** | 1 | | | | | | |
| 4. Friend support | −0.330** | 0.910** | 0.639** | 1 | | | | | |
| 5. Significant others support | −0.266** | 0.906** | 0.639** | 0.795** | 1 | | | | |
| 6. Coping style (SCSQ) | −0.261** | 0.298** | 0.265** | 0.237** | 0.296** | 1 | | | |
| 7. Positive coping style | −0.358** | 0.356** | 0.325** | 0.302** | 0.325** | 0.925** | 1 | | |
| 8. Negative coping style | 0.057 | 0.041 | 0.020 | −0.005 | 0.097 | 0.665** | 0.331** | 1 | |
| 9. Sleep quality (PSQI) | 0.470** | −0.297** | −0.285** | −0.320** | −0.183** | −0.026 | −0.114 | 0.160** | 1.0 |
## Stepwise analysis method
The mediation effect was analyzed by using the stepwise analysis method (Baron and Kenny, 1986; Wen and Ye, 2014). In Figure 2, results indicated that anxiety played significant prediction on sleep quality (total effect coefficient “c”, $t = 8.924$, $p \leq 0.001$) and social support (indirect effect coefficient “a,” t = −6.128, $p \leq 0.001$); After bring into the mediation variable (social support), social support played significant prediction on sleep quality (indirect effect coefficient “b,” t = −2.769, $p \leq 0.01$), anxiety also played significant prediction on sleep quality (direct effect coefficient “cʹ”, $t = 7.529$, $p \leq 0.001$). Therefore, social support partially mediated the relationship between anxiety and sleep quality.
**Figure 2:** *Mediating effect model.*
Table 4 showed that in the stepwise regression equation, the direct effect (cʹ) had allocated $88.72\%$ explanatory effect, and the indirect effect (a × b) had allocated $11.17\%$ explanatory effect. Obviously, social support as the mediating role leaded to this indirect effect producing.
**Table 4**
| Effect style | Effect size | Explanatory percentage |
| --- | --- | --- |
| Direct effect (c΄) | 0.417 | 88.72% (Direct/Total) |
| Indirect effect (a × b) | −0.343 × −0.153 = 0.052479 | 11.17% (Indirect/Total) |
| Total effect (c) | 0.470 | |
## Bootstrap analysis method
Beside the method of Stepwise, Bootstrap method (Hayes, 2013) was also made to test the mediating effect. According to the mediating effect test procedure proposed by Zhao et al. [ 2010], indirect effect “a × b” (confidence interval: LLCI = 0.0059, ULCI = 0.0503) was significant, and the direct effect “cʹ,” (confidence interval: LLCI = 0.1382, ULCI = 0.2361) was also significant. So, bootstrap method suggested that social support partially mediated the relationship between anxiety and sleep quality as same as the stepwise method.
## Discussion
The aim of this study was to investigate the association between anxiety, social support, coping style and sleep quality among Chinese parents of special children during the early COVID-19 epidemic. The result found anxiety was significantly positively correlated with sleep quality, which was consistent with previous studies (Augner, 2011; Liu et al., 2021; Simonetti et al., 2021; Yin et al., 2021), and showed that social support also related to sleep quality closely, which was consistent with previous studies (Brummett et al., 2006; Nordin et al., 2012; Marini et al., 2020; Mitchell et al., 2022). This study found that coping style and its subscale “positive coping style” were not correlated with sleep quality, but the subscale “negative coping style” was positively correlated with sleep quality, which was partially consistent with previous studies (Zhao et al., 2006; Xue et al., 2012; Gao and Hu, 2021; Luo et al., 2022; Yu et al., 2022). So, we did not consider the coping style as the mediating role in the following analysis.
In this study, both stepwise and bootstrap analysis methods result suggested that social support partially mediated the relationship between anxiety and sleep quality. That means if parents of special children who have more anxiety will get worse sleep quality, while more social support can alleviate the impact of anxiety on sleep quality (Pittsburgh Sleep Quality Index with higher scores indicating poorer sleep quality) between anxiety and sleep quality. Like other studies with different subjects, the more social support was received or perceived, the better sleep quality was, and the social support could improve sleep quality (Guo et al., 2014; Wang et al., 2021; Xu et al., 2022). On the other hand, this study results suggest that the anxiety of parents of special children not only directly affects sleep quality, but also indirectly affects sleep quality through social support. The mediating role of social support can alleviate the impact of anxiety on sleep quality, and play a buffer role.
During early COVID-19 epidemic period (March 2020, also the time when parents answered the questionnaires), the Hangzhou Green Apple Kindergarten had delayed the start of school for a while. For parents of special children, they had to take care and train children at home themselves, without professional help of rehabilitation institute (the kindergarten) and without adequate outdoor physical activity. In this situation, as common mental health influence factors, anxiety and sleep problems may occur more obviously (Qiu et al., 2021; Blasco-Lafarga et al., 2022). We have seen that social support will be the key point for solving anxiety and sleep disorders through their logical relationship in this study. Therefore, increasing social support of parents of special children for reducing the impact of anxiety on their sleep quality is very necessary during COVID-19 epidemic with the prevention and control policy of school. Relevant organizations and charities should offer social psychological service and lead the society to give more understanding and support to these parents of special children.
## Conclusion
[1] Sleep quality had a significant positive correlation with anxiety and significant negative correlation with social support, but nonsignificant correlation with coping style; [2] Social support had a partial mediating effect on the relationship between anxiety and sleep quality. The anxiety of parents of special children not only directly affects sleep quality, but also indirectly affects sleep quality through social support. The mediating role of social support can alleviate the impact of anxiety on sleep quality through a buffer role.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Ethics Committee of Zhejiang Rehabilitation Medical Center. All participants gave informed consent before be investigated.
## Author contributions
JT and JX designed the study. JX recruited the participants, distributed and collected the questionnaire. JX and JT analyzed the data, wrote the manuscript. JT and JX revised the manuscript. JT replied the office. JX submit for publication. All authors approved the final manuscript for publication.
## Funding
This work was supported by Taizhou Philosophy and Social Science Planning Project (22GHB24), and General Project of Zhejiang Provincial Education Department (Y201941753).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## References
1. Alqaderi H., Goodson J. M., Agaku I.. **Association between sleep and severe periodontitis in a nationally representative adult US population**. *J. Periodontol.* (2020) **91** 767-774. DOI: 10.1002/JPER.19-0105
2. American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders: DSM-5, Arlington: American Psychiatric Association, 361–422.. *Diagnostic and Statistical Manual of Mental Disorders: DSM-5* (2013) 361-422
3. Augner C.. **Associations of subjective sleep quality with depression score, anxiety, physical symptoms and sleep onset latency in students**. *Cent. Eur. J. Public Health.* (2011) **19** 115-117. DOI: 10.21101/cejph.a3647
4. Baron R. M., Kenny D. A.. **The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations**. *J. Pers. Soc. Psychol.* (1986) **51** 1173-1182. DOI: 10.1037/0022-3514.51.6.1173
5. Blasco-Lafarga C., Roldán A., Cordellat A., Monteagudo P.. **Acute high-intensity exercise reduces performance anxiety: a pilot study in wind musicians**. *Ann. Appl. Sport Sci.* (2022) **10** e1094. DOI: 10.52547/aassjournal.1094
6. Brummett B. H., Babyak M. A., Siegler I. C., Vitaliano P. P., Ballard E. L., Gwyther L. P.. **Associations among perceptions of social support, negative affect, and quality of sleep in caregivers and noncaregivers**. *Health Psychol.* (2006) **25** 220-225. DOI: 10.1037/0278-6133.25.2.220
7. Chatterjee A. B.. **COVID-19, critical illness, and sleep**. *Crit. Care Med.* (2022) **50** 1021-1023. DOI: 10.1097/CCM.0000000000005519
8. Chen C. C., Du X. X., Su S. H., Yang Y. R.. **The relationship between mindfulness and anxiety in parents of special children: the mediating role of coping style**. *Chin. J. Spec. Educ.* (2016) **07** 70-75. DOI: 10.3969/j.issn.1007-3728.2016.07.011
9. Gallagher S., Phillips A. C., Carroll D.. **Parental stress is associated with poor sleep quality in parents caring for children with developmental disabilities**. *J. Pediatr. Psychol.* (2010) **35** 728-737. DOI: 10.1093/jpepsy/jsp093
10. Gao J., Hu L. J.. **Study on the relationship between sleep quality, coping style and social support of pediatric medical staff in 5 hospitals in Jiangxi Province**. *Occup. Health* (2021) **16** 2211-2215. DOI: 10.13329/j.cnki.zyyjk.2021.0539
11. Guo S. R., Wu S. W., Feng X. W.. **The effect of social support on sleep quality in college students: a multiple mediation model test**. *Psychol. Sci.* (2014) **6** 1404-1408. DOI: 10.16719/j.cnki.1671-6981.2014.06.020
12. Hayes A. F.. *An Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-based Approach* (2013)
13. Kong H., Huang H. X., Wang M. H., Cheng F., Song Y. P.. **Mediation between coping style and sleep quality of vocational high school students with anxiety**. *The Modern Chinese Physician* (2021) **14** 160-163
14. Liu B. L., Qiao K., Lu Y. F.. **The relationship between perceived stress, state-trait anxiety, and sleep quality among university graduates in China during the COVID-19 pandemic**. *Front. Psychol.* (2021) **12** 664780. DOI: 10.3389/fpsyg.2021.664780
15. Luo Y. F., Lu G. Y., Zhang H. Y., Li Q. H.. **The correlation analysis of alert fatigue, coping style and sleep quality among medical staff in the critical care department of 5 third class first class hospitals in Sichuan Province**. *Occup. Health.* (2022) **9** 1237-1242. DOI: 10.13329/j.cnki.zyyjk.2022.0230
16. Ma Y.. **A study on the relationship among locus of control, anxiety and coping style of college students**. *J. Henan Univ. Sci. Technol.* (2013) **6** 78-80. DOI: 10.3969/j.issn.1673-6060.2013.06.025
17. Marini C., Wilson S., Nah S., Martire L., Sliwinski M.. **Links between rumination and sleep quality among older adults: an examination of the role of social support**. *Innov. Aging.* (2020) **4** 562. DOI: 10.1093/geroni/igaa057.1853
18. Meltzer L. J.. **Brief report: sleep in parents of children with autism spectrum disorders**. *J. Pediatr. Psychol.* (2008) **33** 380-386. DOI: 10.1093/jpesy/jsn005
19. Mitchell A. M., Kowalsky J. M., Christian L. M., Belury M. A., Cole R. M.. **Perceived social support predicts self-reported and objective health and health behaviors among pregnant women**. *J. Behav. Med.* (2022) **45** 589-602. DOI: 10.1007/s10865-022-00306-5
20. Mohammadkhani P. G., Irandoust K., Taheri M., Mirmoezzi M., Baić M.. **Effects of eight weeks of aerobic exercise and taking caraway supplement on C-reactive protein and sleep quality in obese women**. *Biol. Rhythm. Res.* (2019) **52** 218-226. DOI: 10.1080/09291016.2019.1587837
21. Morgan B. B.. **Effects of continuous work and sleep loss in the reduction and recovery of work efficiency**. *Am. Ind. Hyg. Assoc. J.* (1974) **35** 13-20. DOI: 10.1080/0002889748507001
22. Nordin M., Westerholm P., Alfredsson L., Akerstedt T.. **Social support and sleep. Longitudinal relationships from the wolf-study**. *Psychology.* (2012) **03** 1223-1230. DOI: 10.4236/psych.2012.312A181
23. O'Leary K., Bylsma L. M., Rottenberg J.. **Why might poor sleep quality lead to depression? A role for emotion regulation**. *Cognit. Emot.* (2016) **31** 1698-1706. DOI: 10.1080/02699931.2016.1247035
24. Parvaneh K., Poh B., Hajifaraji M., Ismail M.. **Less sleep duration and poor sleep quality Lead to obesity**. *Proc. Nutr. Soc.* (2016) **75** E19. DOI: 10.1017/S0029665115004528
25. Podsakoff P. M., Mackenzie S. B., Lee J. Y., Podsakoff N. P.. **Common method biases in behavioral research: a critical review of the literature and recommended remedies**. *J. Appl. Psychol.* (2003) **88** 879-903. DOI: 10.1037/0021-9010.88.5.879
26. Qiu Y., Huang J., Sun J. H., Zhao J. X., Chen A., Chen J. D.. **Prevalence of risk factors associated with mental health symptoms among the outpatient psychiatric patients and their family members in China during the coronavirus disease 2019 pandemic**. *Front. Psychol.* (2021) **12** 622339. DOI: 10.3389/fpsyg.2021.622339
27. Simonetti V., Durante A., Ambrosca R., Arcadi P., Cicolini G.. **Anxiety, sleep disorders and self-efficacy among nurses during COVID-19 pandemic: a large cross-sectional study**. *J. Clin. Nurs.* (2021) **30** 1360-1371. DOI: 10.1111/jocn.15685
28. Stanton R., To Q. G., Khalesi S., Williams S. L., Alley S. J., Thwaite T. L.. **Depression, anxiety and stress during COVID-19: associations with changes in physical activity, sleep, tobacco and alcohol use in Australian adults**. *Int. J. Environ. Res. Public Health.* (2020) **17** 4065. DOI: 10.3390/ijerph17114065
29. Taheri M., Irandost K., Mirmoezzi M., Ramshini M.. **Effect of aerobic exercise and Omega-3 supplementation on psychological aspects and sleep quality in Prediabetes elderly women**. *Sleep Hypnosis.* (2019) **21** 170-174. DOI: 10.5350/Sleep.Hypn.2019.21.0185
30. Taş I.. **Association between depression, anxiety, stress, social support, resilience and internet addiction: a structural equation modelling**. *Malays. Online J. Educ. Technol.* (2019) **7** 1-10. DOI: 10.17220/mojet.2019.03.001
31. Varma P., Conduit R., Junge M., Lee V. V., Jackson M. L.. **A systematic review of sleep associations in parents and children**. *J. Child Fam. Stud.* (2021) **30** 2276-2288. DOI: 10.1007/s10826-021-02002-5
32. Wang Q., Mu Y. L., Qiu R. Y.. **Social support mediates stress and sleep quality**. *Chin. Contemp. Med.* (2021) **28** 208-211. DOI: 10.3969/j.issn.1674-4721.202.25.057
33. Wang X. D., Wang X. L., Ma H.. *Handbook of Mental Health Rating Scale* (1999)
34. Wen Z. L., Ye B. J.. **Mediation effect analysis: method and model development**. *Adv. Psychol. Sci.* (2014) **22** 731-745. DOI: 10.3724/SP.J.1042.2014.00731
35. Wu M.. **A study on the relationship between social support and anxiety of parents of autistic children**. *J. Gannan Norm. Univ.* (2010) **2** 100-102. DOI: 10.13698/j.cnki.cn36-1037/c.2010.02.013
36. Xu R. Z., Ma Y., Dai Q.. **The relationship between expectant sadness and sleep disorder in parents of children with acute leukemia -- mediated by social support**. *Chin. J. Pract. Nurs.* (2022) **34** 2691-2697. DOI: 10.3760/cma.j.cn211501-20220217-00429
37. Xue F., Zhang Q. X., Wang J. N., Luo S. F., Yang L. H.. **The relationship between life events, coping styles and sleep quality of college students**. *Neuropathy and Mental Health* (2012) **12** 275-278. DOI: 10.3969/j.issn.1009-6574.2012.03.019
38. Yin H. Z., Zhang L., Li D., Xiao L., Cheng M.. **The gray matter volume of the right insula mediates the relationship between symptoms of depression/anxiety and sleep quality among college students**. *J. Health Psychol.* (2021) **26** 1073-1084. DOI: 10.1177/1359105319869977
39. Yu H., Li X. Y., Wen F., Li H. M.. **The influence of social support and coping style on sleep quality of medical staff in grade a hospitals**. *Occup. Health.* (2022) **13** 1812-1816. DOI: 10.13329/j.cnki.zyyjk.2022.0373
40. Zhao J. B., Xie Y. N., Ren Z. W., Shi M. C.. **Sleep quality of Plateau soldiers and its influencing factors**. *Chinese J. Neuropsychiatric Disorders* (2006) **2** 154-156. DOI: 10.3969/j.issn.1002-0152.2006.02.016
41. Zhao X., Lynch J. G., Chen Q.. **Reconsidering Baron and Kenny: myths and truths about mediation analysis**. *J. Consum. Res.* (2010) **37** 197-206. DOI: 10.1086/651257
42. Zhou X. N., Zhao W. Y., Li N. N., Ji F.. **The relationship between sleep quality, job burnout and coping style of medical staffs**. *Chin. J. Health Psychol.* (2013) **3** 353-355. DOI: 10.13342/j.cnki.cjhp.2013.03.040
|
---
title: Infancy‐onset diabetes caused by de‐regulated AMPylation of the human endoplasmic
reticulum chaperone BiP
authors:
- Luke A Perera
- Andrew T Hattersley
- Heather P Harding
- Matthew N Wakeling
- Sarah E Flanagan
- Ibrahim Mohsina
- Jamal Raza
- Alice Gardham
- David Ron
- Elisa De Franco
journal: EMBO Molecular Medicine
year: 2023
pmcid: PMC9994480
doi: 10.15252/emmm.202216491
license: CC BY 4.0
---
# Infancy‐onset diabetes caused by de‐regulated AMPylation of the human endoplasmic reticulum chaperone BiP
## Abstract
Dysfunction of the endoplasmic reticulum (ER) in insulin‐producing beta cells results in cell loss and diabetes mellitus. Here we report on five individuals from three different consanguineous families with infancy‐onset diabetes mellitus and severe neurodevelopmental delay caused by a homozygous p.(Arg371Ser) mutation in FICD. The FICD gene encodes a bifunctional Fic domain‐containing enzyme that regulates the ER Hsp70 chaperone, BiP, via catalysis of two antagonistic reactions: inhibitory AMPylation and stimulatory deAMPylation of BiP. Arg371 is a conserved residue in the Fic domain active site. The FICDR371S mutation partially compromises BiP AMPylation in vitro but eliminates all detectable deAMPylation activity. Overexpression of FICDR371S or knock‐in of the mutation at the FICD locus of stressed CHO cells results in inappropriately elevated levels of AMPylated BiP and compromised secretion. These findings, guided by human genetics, highlight the destructive consequences of de‐regulated BiP AMPylation and raise the prospect of tuning FICD's antagonistic activities towards therapeutic ends.
Modification of the endoplasmic reticulum (ER) chaperone BiP by FICD, an enzyme that adds or removes an adenosine monophosphate (AMP), regulates protein folding homeostasis. Secretory cells that produce the hormone insulin or promote neurodevelopment depend on intact ER function.
## Introduction
Translocation channels, chaperones, protein modifying enzymes and membrane trafficking components all maintain protein folding homeostasis in the endoplasmic reticulum and contribute to the integrity of secretion in eukaryotes (reviewed in: Braakman & Hebert, 2013). Whilst components of this machinery are common to most cells, its dysfunction in animals often manifests prominently as failure of the insulin‐producing pancreatic beta cells to keep up with the needs of the organism. Although the basis for this apparent hypersensitivity of beta cells to ER stress is incompletely understood, rare mutations in 10 genes encoding components of this general ER proteostasis network are known to cause monogenic forms of diabetes (reviewed in: Shrestha et al, 2021; Yong et al, 2021). Given the broad role of protein secretion in animal physiology, these monogenic diseases are enriched in extra‐pancreatic manifestations, often resulting in syndromic forms of diabetes mellitus associated with developmental abnormalities (reviewed in: Sanchez Caballero et al, 2021).
The Hsp70 chaperone BiP is an essential cog of the ER proteostasis network (reviewed in: Pobre et al, 2019). BiP is encoded by an essential gene, HSPA5, whose inactivation leads to early embryonic lethality in mice (Luo et al, 2006) but loss‐of‐function alleles in several BiP co‐factors are associated with beta cell dysfunction (Synofzik et al, 2014; Danilova et al, 2019). Thus, beta cells are sensitive to disruption of BiP's chaperone network.
Unique amongst Hsp70 chaperones, BiP is regulated by AMPylation, a reversible covalent modification by which an AMP moiety from ATP is transferred onto a protein's hydroxylated side chain. AMPylation (also known as adenylylation) is extensively exploited by bacteria to regulate endogenous processes (Kingdon et al, 1967) or host proteins in the context of infection (reviewed in: Woolery et al, 2010). Animal genomes encode a single ortholog of the widely disseminated family of Fic domain proteins that carry out AMPylation in bacteria (Worby et al, 2009; Yarbrough et al, 2009). Animal FICD is an ER‐localised type II transmembrane protein whose active site faces the lumen. There, it single‐handedly carries out two functionally antagonistic reactions: BiP AMPylation (Ham et al, 2014; Sanyal et al, 2015; Preissler et al, 2015b) and deAMPylation (Casey et al, 2017; Preissler et al, 2017a).
AMPylation, which occurs on Thr518 (Preissler et al, 2015b), locks BiP in a conformation with low substrate affinity and impaired responsiveness to the stimulatory effect of J‐domain co‐factors, effectively neutralising the chaperone (Preissler et al, 2015b, 2017b). This biochemical feature is consistent with earlier observations that levels of modified (now known to be AMPylated) BiP are regulated physiologically: decreasing with mounting ER stress and increasing with stress resolution (fig 1 in Hendershot et al, 1988; figs 1 and 3 in Laitusis et al, 1999). This balancing act is critically dependent on FICD (Casey et al, 2017; Preissler et al, 2017a) and arises from fine tuning of the enzyme's active site to reciprocally regulate its two antagonistic functions (Perera et al, 2019, 2021).
Despite advances in the biochemical analysis of BiP AMPylation, its consequences in terms of organismal physiology are less well understood. Photosensitivity has been reported in flies lacking ficd (Rahman et al, 2012; Moehlman et al, 2018), but in knockout mice the phenotype is limited to weak immunological and learning deficits (McCaul et al, 2021) or mild hypersensitivity to experimental stress conditions (Casey et al, 2022). Here, we identify a rare missense, recessive mutation within FICD's active site in five individuals diagnosed with infancy‐onset diabetes mellitus and neurodevelopmental abnormalities. This finding provides insight into the functional consequences of deregulated BiP AMPylation.
## The homozygous FICD p.(Arg371Ser) mutation lead to infancy‐onset diabetes with neurodevelopmental abnormalities
Two siblings, born to related parents, were referred to the Exeter genomics laboratory for genetic testing on suspicion of Wolcott Rallison syndrome, a disorder caused by recessive variants in the EIF2AK3 gene that encodes a regulator of ER proteostasis, PERK (Delepine et al, 2000). The proband (Fig 1A) was diagnosed with diabetes at the age of 23 weeks. They also had severe developmental delay and skeletal abnormalities were suspected when they were 5 years old. Their older sibling was similarly affected and died aged 7 (cause unknown). Analysis of known neonatal diabetes genes, including EIF2AK3, did not identify a likely causative variant. In search of novel genetic causes of monogenic diabetes, whole genome sequencing analysis of the two siblings was carried out. The two shared 13 rare homozygous or hemizygous coding variants (Table EV1). None of these variants were located within genes previously linked to diabetes. Next, we investigated whether any of these 13 genes had been previously found to harbour homozygous or hemizygous variants in a cohort of 126 individuals diagnosed with diabetes before the age of 12 months, whose genome had been sequenced but no causative mutation discovered. This analysis revealed that one of the variants identified in the two siblings, the NM_007076.2:c.1113G>C, p.(Arg371Ser) in the FICD gene, had been previously detected in three additional individuals from two families, taking the total number of individuals homozygous for this variant to 5, all diagnosed with neonatal/infancy‐onset diabetes (Fig 1A and Table 1).
**Figure 1:** *A homozygous p.(Arg371Ser) mutation within the human
FICD
gene results in infant‐onset diabetes and severe neurodevelopmental delay
APartial pedigrees of three families found to bear this rare genetic variant. Where available, information pertaining to the FICD genotype and the phenotype of affected individuals are shown. The first individual identified to carry this homozygous mutation (the proband, family member 1ii) is indicated with an arrow.B, CArg371 is located within the catalytic Fic motif and is highly conserved both across metazoan FICD orthologues (B) and across the wider Fic and Doc protein superfamily (C). The Fic motif is highlighted in grey, and residue numbering corresponds to human FICD.* TABLE_PLACEHOLDER:Table 1 The p.(Arg371Ser) variant is very rare [minor allele frequency = 3.98 × 10−6 in the GnomADv2 database (Karczewski et al, 2020)] and it was not found in the homozygous state in either the ‘100,000 genomes’, ‘Gene and health’, ‘GnomAD’, ‘BioBank’, or ‘Decipher’ data sets. The p.(Arg371Ser) variant affects a residue located within the catalytic Fic domain motif and, as such, it is highly conserved both across metazoan FICD protein homologues (Fig 1B) and across the wider Fic and Doc (Fido) protein superfamily (Fig 1C).
Since the five individuals were homozygous for the same FICD variant, we investigated whether they shared a haplotype consistent with them being related and having inherited the variant from a common ancestor. Analysis of the whole genome sequencing data revealed that the four affected individuals in Family 1 and Family 3 shared a 4.5 Mb haplotype that includes the FICD locus, suggesting that their FICD p.(Arg371Ser) variant was inherited from a common distant ancestor (Fig EV1). Within the haplotype, they shared three rare variants, of which FICD p.(Arg371Ser) was the only one predicted to affect the encoded protein. Importantly, the individual in Family 2 did not share a haplotype with the other two families, suggesting that the FICD variant is likely to have arisen independently in that family.
**Figure EV1:** *Analysis of shared haplotypes amongst affected individuals homozygous for the p.(Arg371Ser) mutation in FICD
AGraphical representation of genome‐wide shared genomic segments larger than 3 Mbp in the five individuals with FICD homozygous mutations. Each colour bar represents a haplotype shared by two individuals (across all four copies of the same chromosome), labelled according to Fig 1A. The four rectangles on the long arm of chromosome 12 (inset) show that the four individuals in Families 1 and 3 share a haplotype [approximate coordinates chr12(hg19):108512514–113023258] including the FICD gene [chr12(hg19):108909051–108913380], but none of them shares a haplotype with the patient in Family 2 (individual 2i) —hence the lack of annotated purple or dark blue bars. Note, genotype information was not available for family member 2ii.BGenotype calls for 2,735 single nucleotide, not multiallelic variants, located on chr12:(hg19):108500000–113100000 in the five individuals with the FICD homozygous p.(Arg371Ser) mutation. Only variants where at least one of the five patients carries the alternative allele are shown. Variants with a coverage of < 15 reads and an allele balance for heterozygous calls < 0.25 were removed. The position of the three variants in the FICD locus (including the pathogenic p.(Arg371Ser) variant) is indicated at the bottom of the graph. Green = homozygous for the reference allele, Yellow = heterozygous, Red = homozygous for the alternative allele. Note the similarity in variant pattern in affected individuals from families 1 & 3 and the dissimilarity with the affected member of family 2.*
The clinical features of the five individuals homozygous for the FICD p.(Arg371Ser) mutation are summarised in Table 1. All were diagnosed with diabetes before the age of 12 months and needed replacement insulin therapy. The proband in Family 2 had a sibling who was also diagnosed with diabetes but died in the neonatal period (no DNA was available). The birth weight in this cohort was within the normal range (mean 3,063 g, range 2,800–3,460 g), consistent with these patients not having a severe reduction in foetal insulin secretion in utero. None of the parents in the three families (who are obligate heterozygous carriers of the FICD mutation) had diabetes.
Severe developmental delay was diagnosed in all the affected children except for one (the proband in Family 2 who could not be re‐assessed after insulin‐dependent diabetes was discovered in infancy). 3 of the 5 affected children developed bilateral cataracts. Additional features such as short stature, skeletal abnormalities, microcephaly and deafness were also observed but were not seen in more than one family; therefore, it is unclear whether they are features of the FICD‐related disease.
The identification of the same rare variant in three families, across two separate haplotypes, with similar clinical features, supports the FICD p.(Arg371Ser) mutation being responsible for the observed phenotype.
## The Arg371Ser mutation compromises both enzymatic activities of FICD
in vitro but leads to dominance of BiP AMPylation over deAMPylation
Arg371 is an active site residue conserved in FICD and in the very large family of bacterial Fic‐domain proteins and the wider Fido superfamily (Fig 1B and C) (Kinch et al, 2009; Khater & Mohanty, 2015). In AMPylating FICD, Arg371 contributes to the binding of the β‐phosphate of ATP, a co‐substrate in the reaction, and presumably in AMPylation transition‐state stabilisation (Fig 2A). In the deAMPylating enzyme, Arg371 engages the side chain of Glu234 in the conformation necessary for Glu234 to position a catalytic water molecule in‐line for nucleophilic attack into the phosphodiester bond linking BiP(Thr518) and the AMP moiety (Fig 2B). Thus, the Arg371Ser mutation is poised to compromise both reactions.
**Figure 2:** *Mutation of Arg371 is predicted to perturb both FICD‐catalysed AMPylation and deAMPylation
AStructures of monomeric (FICDL258D) and dimeric FICD proteins, crystallised in the presence of a large excess of magnesium cations and ATP, are superposed (PDB 6I7K and 6I7G, respectively). The former engages MgATP in an AMPylation competent conformation and the latter engages (only) ATP in an AMPylation incompetent conformation (Perera et al, 2019). Arg371 forms hydrogen bonds with both nucleotides (pink dashed lines). Arg371's interaction with the β‐phosphate of MgATP is also likely to help stabilise the AMPylation transition state. A potential long range ionic interaction between Arg371 and Glu234 in the non‐competent conformation is also annotated.BTwo alternative states of FICD's active site engaged with its deAMPylation substrate, BiP‐AMP. Glu234 can exist in at least two conformations. In the deAMPylation competent conformation, Glu234 correctly orientates a catalytic water molecule (*) for in‐line nucleophilic attack into the backside of the BiP‐AMP phosphodiester bond (PDB 7B7Z). In the deAMPylation incompetent conformation Glu234 points away from the position of this putative catalytic water (PDB 7B80) (Perera et al, 2021). Hydrogen bonds formed by Glu234 are annotated in both cases (pink dashed lines). Arg371 interacts with Glu234 only when it resides in a deAMPylation competent conformation. Insets, reduced view of the superposed active sites, shown in orthogonal views, additionally overlaid with the catalytic His363 residues from PDB 6I7K (cyan).*
The antagonistic nature of the two reactions implies that the phenotype arising from the mutation is likely to depend on the extent to which each is compromised. To address this issue, we expressed the structured luminal domains of wild‐type and Arg371Ser mutant FICD in E. coli and evaluated the purified enzymes in vitro. Nucleotide‐free FICDR371S possesses a modestly lower melting temperature than the wild‐type enzyme, consistent with the stabilising effect of an ionic interaction between the side chains of Arg371 and Asp367 noted in the crystal structure of the apo wild‐type enzyme (PDB 4U04). Importantly, whereas the thermal stability of the wild‐type enzyme increased significantly upon binding ATP and even more so ADP, the stability of the FICDR371S mutant remained unchanged (Figs 3A and EV2). This finding is consistent with an important role for the Arg371 sidechain in contacting the β‐phosphate of ADP or ATP bound in an AMPylation competent conformation or the γ‐phosphate of ATP bound in an AMPylation incompetent conformation (Fig 2A; Perera et al, 2019). Thus, the Arg371Ser mutation is likely to both lower the affinity of the FICD for nucleotide and reduce the protein‐stabilising effect of any bound nucleotide di‐ or tri‐phosphate. The FICDR371S mutant enzyme nonetheless retains some ability to bind nucleotide, as evidenced by the marked protein‐stabilising effect of exogenous ATP when the Fic domain gatekeeper glutamate residue, that normally limits ATP affinity and inhibits AMPylation (Engel et al, 2012; Bunney et al, 2014), is also mutated (FICDE234G‐R371S; Figs 3A and EV2). Together these finding point to an important role of the Arg371Ser mutation in modifying the active site and leaves open the possibility of some residual enzymatic activity.
**Figure 3:** *The FICD Arg371Ser mutation compromises BiP AMPylation and eliminates all detectable BiP deAMPylation
in vitro
APlot of the principal melting temperatures (T
ms) of wild‐type FICD (WT) and FICDR371S in the presence or absence of nucleotide ligands and/or FICD's gatekeeper Glu234 residue, derived from differential scanning fluorimetry (DSF). The mean T
m ± 95% confidence interval (CI) is displayed, from n = 3 independent experiments. Arg371Ser mutation slightly destabilises the apo enzyme and significantly reduces the ability of ATP and ADP to stabilise FICD. Glu234Gly mutation partially restores ATP (but not ADP) stabilisation of FICDE234G‐R371S (see Fig EV2A).BSize exclusion chromatography confirms that the oligomerisation tendency of FICDR371S is unperturbed relative to wild‐type FICD. Across a range of assessable enzyme concentrations both FICDs elute as predominantly dimeric species. The elution profile of a monomeric FICDL258D‐H363A mutant is also shown for reference, alongside the elution times of molecular weight standards.CRepresentative immunoblot comparing the ability of wild‐type and Arg371Ser FICD (at a wide range of enzyme concentrations) to catalyse the accumulation of BiP‐AMP in the presence of physiological ATP concentrations (5 mM). At low enzyme concentrations (and early time points) the AMPylation defect imposed by Arg371Ser is manifest. At higher enzyme concentrations where the deAMPylation activity of the wild‐type enzyme begins to dominate, the residual AMPylation activity of FICDR371S results in a greater accumulation of BiP‐AMP (relative to the same concentration of wild‐type enzyme).DQuantification of the AMPylated BiP signals, relative to total BiP, from experiments as in (C) (mean values ± SEM, n = 4, biological replicates). Figure EV2B displays the values from all four repetitions.EA fluorescence polarisation‐based deAMPylation assay highlighting the lack of activity catalysed by FICDR371S. Enzyme‐saturating concentrations of BiP‐AMP were provided as deAMPylation substrate. No enzymatic activity of 10 μM (grey trace, shown) or 20 μM enzyme FICDR371S was detectable. The estimated fluorescence anisotropy value of a fully deAMPylated BiP sample is derived from a heuristic fitting of a single exponential decay curve. The k
cat value for wild‐type FICD was calculated from n = 3 biological replicates (mean ± SEM).FRepresentative immunoblot comparing the accumulation of BiP‐AMP in reactions constituted with the indicated concentrations of wild‐type and Arg371Ser FICD, performed as in (C) (upper left panel). The lower left panel is a Coomassie‐stained gel of the reaction constituents. An N‐terminally extended version of the wild‐type enzyme (residues 45–458 was used in lanes 2 and 5–7, to distinguish its migration on the gel from the mutant (residues 104–445). The bar diagram provides quantification of the AMPylated BiP signals relative to total BiP (from n = 3, biological replicates, mean values ± SEM).* **Figure EV2:** *Effect of the R371S mutation on stability and activity of FICD
ARepresentative (of experiments reproduced three time), normalised differential scanning fluorimetry (DSF) melt curves of the indicated FICD proteins (1 μM) in presence and absence of nucleotide (2.5 mM), shown above their corresponding negative first‐derivatives. Note, FICDE234G‐R371S displays a non‐uniform T
m shift in response to ATP.BQuantification of the AMPylated BiP signals relative to total BiP of experiments displaying the same data in Fig 3C (mean values ± SEM, n = 4, biological replicates), but split into the two time points of the experiment and presenting the values from all four replicates.*
It was previously found that the oligomeric state of wild‐type FICD reciprocally regulates its functionally opposed enzymatic activities (Perera et al, 2019). However, at the concentrations tested, both wild‐type and mutant FICD appear principally dimeric (as assessed by size exclusion chromatography, Fig 3B), consistent with previous findings pertaining to wild‐type FICD (Bunney et al, 2014; Perera et al, 2019). This suggests that differences in oligomeric state are unlikely to account for disparities between their relative enzymatic activities.
The antagonistic activities of wild‐type FICD are subordinate to its oligomeric state: the dimer is a better deAMPylase and the monomer a better AMPylase (Perera et al, 2019). Given a K d of FICD dimerisation in the nanomolar range, the relationship between the amount of AMPylated BiP and the FICD enzyme concentration, produced by reactions not otherwise limited by ATP, is biphasic—FICD's AMPylation activity dominates at low (nanomolar) enzyme concentrations whilst its deAMPylation activity dominates at higher enzyme concentrations (Perera et al, 2019). This biphasic relationship is lost in reactions catalysed with FICDR371S. Moreover, at both early and late time points, more AMPylated BiP is recovered in reactions performed with the wild‐type enzyme at low FICD concentrations, but this difference is reversed at higher FICD concentrations (Figs 3C and D and EV2B).
This situation is consistent with a defect in AMPylation activity of the mutant enzyme, which is perhaps most clearly demonstrated by the difference in BiP‐AMP accumulation catalysed by low concentrations of FICD at early time points (conditions which are likely to report on the initial AMPylation rate of each enzyme). Nevertheless, the greater accumulation of AMPylated BiP catalysed by higher concentrations of FICDR371S, relative to FICDWT, is indicative of an even more significant impact of the mutation on FICD‐mediated deAMPylation. Indeed, comparing the rate of BiP deAMPylation under substrate saturating conditions, by the wild‐type and FICDR371S enzyme, reveals robust activity of the former and no measurable deAMPylation activity of the latter (Fig 3E).
The absence of measurable deAMPylation activity coupled with weak AMPylation activity of the FICDR371S enzyme explains the accumulation of BiP‐AMP over time. These features are also consistent with the observation that even sub‐stoichiometric concentrations of wild‐type FICD can limit the accumulation of AMPylated BiP in reactions that contain the mutant enzyme (Fig 3F). The implications of these in vitro observations to the mode of inheritance of the functional deficits are discussed below.
## FICDR371S
de‐regulates BiP‐AMP levels and compromises secretion
Cells lacking FICD are unable to deAMPylate BiP (Casey et al, 2017; Preissler et al, 2017a). Therefore, the in vitro experiments above suggest that in cells expressing FICDR371S as their only source of BiP‐modifying enzymatic activity, the deAMPylation‐unopposed, weak AMPylation function of the mutant FICD might induce a hyperAMPylated pool of BiP. To test this idea, we transiently introduced wild‐type and Arg371Ser mutant FICD into CHO cells lacking endogenous FICD and measured the effects on BiP AMPylation, using a recently described monoclonal antibody reactive with AMPylated proteins (MoAb 17G6, Hopfner et al, 2020).
Expression of wild‐type FICD led to no detectable BiP‐AMP signal, reflecting the dominance of its deAMPylating activity (Preissler et al, 2017a). By contrast, the Arg371Ser mutant FICD promoted a conspicuous pool of AMPylated BiP (Fig 4A, compare lanes 2 & 4). The AMPylating activity of the Arg371Ser mutant was dependent on the integrity of its active site, as no BiP‐AMP signal was detectable in cells expressing the double Arg371Ser‐His363Ala mutant. As expected, levels of AMPylated BiP were even higher in cells expressing the deregulated Glu234Gly mutant FICD, which possesses both enhanced AMPylating activity and no detectable deAMPylating activity (Preissler et al, 2017a). The promiscuous features of FICDE234G likely account for its auto‐AMPylation and lower expression compared to the other FICD variants (Fig 4A, lane 3). The FICD immunoblot also reveals a minor, lower‐mobility species in cells expressing FICDR371S (Fig 4A, compare lane 2 with lanes 4 & 5, marked by an asterisk) likely reflecting cryptic glycosylation of Asn369, exposed by the mutation (see Discussion). Only the FICDE234G cells manifested significant activation of their CHOP::GFP and XBP1::Turquoise unfolded protein response (UPR) reporters (Fig 4B and C), an observation consistent with a requirement for high levels of AMPylation to yield basal activation of the UPR (Perera et al, 2019).
**Figure 4:** *FICDR371S
promotes accumulation of AMPylated BiP in CHO cells lacking endogenous FICD
AImmunoblots of AMPylated proteins detected with a pan‐AMP antibody, BiP, FICD and eIF2α (a loading control) in lysates of FICD
∆
CHO cells transiently transfected with plasmids encoding the indicated derivatives of FICD. Signals corresponding to AMPylated BiP, auto‐AMPylated FICDE234G, total BiP, FICD and eIF2α are indicated. The novel species reactive with the anti‐FICD antiserum in lysates of cells transfected with plasmids expressing FICDR371S (*) likely reflects a glycosylated isoform arising from the creation of a new glycosylation site at Asn369.BHistograms of Integrated Stress Response reporter CHOP::GFP and XBP::Turquoise (XBP::Turq, a reporter of the IRE1 branch of the UPR) fluorescence in FICD
∆
CHO dual reporter cells transfected with expression vectors as in (A). Transfected cells were gated for mCherry positivity, a marker carried on the expression plasmid in trans to the indicated FICD effector.CQuantification of the data from repeats of the experiment as shown in (B). The upper two graphs show the percent of cells with high CHOP::GFP or high XBP::Turq for each transfection. The lower panel provides quantitation of the median fluorescence intensity for each reporter. Mean ± SD, n = 4, biological replicates.*
To explore the consequences of more physiological expression levels of FICDR371S, we used CRISPR/Cas9 mediated homologous recombination to replace the endogenous wild‐type FICD of CHO cells with an Arg371Ser mutant (Fig 5A).
**Figure 5:** *Expressed at the endogenous
FICD
locus, the p.(Arg371Ser) mutation leads to defective clearance of AMPylated BiP in ER stressed cells
AOn the left is a schema of the Chinese Hamster Ovary cell FICD locus. Exons depicted as blue boxes, introns as lines. Forward (F) and reverse (R) primers used to genotype the locus and the guide RNA that directs the Cas9 nuclease (CRISPR guide) are depicted by arrows. A schema of the repair template is provided below, with the Arg371Ser mutation, the silent BamHI site and silent PAM‐destroying features noted. To the right is the sequence of the locus before and after recombination and a sequencing trace of the locus in the mutant FICD
R371S
derivative cell line.BOn the left are immunoblots of AMPylated proteins detected with pan‐AMP, BiP and eIF2α (a loading control) reactive antibodies in lysates of cells of the indicated genotype. The cells were untreated or exposed to the protein synthesis inhibitor, cycloheximide (Cx, 100 μg/ml) for the indicated time. In the panel on the right are immunoblots of samples immunoprecipitated with a BiP‐specific antiserum from the lysates of the samples shown on the left.CImmunoblots as in (B) in lysates of cells of the indicated genotype. The cells were untreated or exposed to the protein synthesis inhibitor, cycloheximide (Cx, 100 μg/ml) and/or thapsigargin (Tg) for the indicated periods prior to harvest.DQuantification of ratio of the BiP‐AMP to total BiP signal (expressed in arbitrary units) of repeats of the experiment shown in (C). Mean ± SD of the BiP‐AMP signal normalised to total BiP signal from the same samples are depicted (P values by two tailed t‐test, n = 6, biological replicates).*
The monoclonal antibody, reactive with AMPylated proteins, revealed a strong signal of 75 kDa in immunoblot of lysate from cycloheximide‐treated wild‐type CHO cells and weaker signal in untreated cells (Fig 5B, left panel) a pattern consistent with previous observations (Laitusis et al, 1999; Preissler et al, 2015b). The identity of the signal with AMPylated BiP was confirmed by performing the same assay on BiP immunopurified from cells with a BiP‐specific antibody (Fig 5B, right panel). FICD R371S cells had a different BiP AMPYlation profile, with a consistent baseline signal (compared with a variable, often undetectable signal in wild‐type cells), that increased only modestly upon cycloheximide treatment. Conversely, cells lacking FICD activity altogether (FICD ∆ cells) had only a background signal in the immunoblot, consistent with FICD's essential role in BiP AMPylation (Preissler et al, 2015b).
To explore further consequences of the FICD R371S mutation on the regulation of BiP AMPylation, we exposed wild‐type and mutant cells to thapsigargin, an agent that induces ER stress by luminal calcium depletion. In wild‐type cells the imposition of ER stress markedly lowered the BiP‐AMP signal (compare lanes 1 with 3 and 5 with 6, Fig 5C; as previously noted: Laitusis et al, 1999). By contrast, in FICD R371S mutant cells the imposition of ER stress had a much weaker effect on BiP‐AMP levels (compare lanes 7 with 9 and 11 with 12, Fig 5C and D). These observations are consistent with impaired ER stress‐mediated de‐AMPylation in the mutant cells and suggest the potential for defective proteostasis arising from their inability to adjust functional levels of BiP to fluctuating levels of ER stress.
Under certain experimental conditions FICD can AMPylate proteins other than BiP (Truttmann et al, 2016, 2017; Hopfner et al, 2020; McCaul et al, 2021). To further explore the role of BiP hyperAMPYlation in the functional consequences of deregulated FICD, we exploited recent insights into the mode of BiP engagement by FICD: Complementarity between the proximal surface containing the substrate residue BiPT518 and FICD's active site is insufficient for AMPylation or deAMPylation. BiP‐specific enzymatic activity relies on the integrity of FICD's accessory TPR domain, which interacts specifically with domain‐docked BiP. Point mutations in FICD's TPR domain (K124E, H131A) that compromise BiP binding have no effect on the enzyme's active site but abolish its ability to use BiP as a substrate (Fauser et al, 2021; Perera et al, 2021). Therefore, we compared the secretion of a model protein (secreted alkaline phosphatase, SEAP) upon co‐expression of FICD enzymes which possess either wild‐type or compromised TPR domains—mutations K124E and H131A that abolish BiP interaction. These experiments, carried out in FICD ∆ cells, reveal that compromised secretion by deregulated alleles of FICD is sensitive to mutations that weaken their interaction with BiP (Fig 6A–C). The existence of other substrates whose hyperAMPylation might contribute to the genetic disorder cannot be excluded. However, these finding indicate that compromised secretion—a pathomechanism plausibly operative in the patients—relies on features of FICD that endow it with selectivity towards BiP.
**Figure 6:** *Compromised secretion by deregulated FICD depends on residues specifying BiP AMPylation
ARepresentative immunoblot of AMPylated proteins in FICD
∆
CHO cells transiently transfected with expression plasmids encoding the indicated FICD enzymes (replicated three times). Note that the hyperactive E234G mutant bearing the K124E‐H131A mutations that compromise its ability to interact with BiP, is nonetheless consistently auto‐AMPylated.BSchema of the assay used to measure the effect of FICD on secretion.CBar diagram of the amount of alkaline phosphatase (normalised to cytoplasmic luciferase [a transfection marker]) secreted from FICD
∆
CHO cells co‐expressing the indicated FICD enzymes. The K124E‐H131A mutations that interfere with engagement of BiP as a substrate reverse the secretion defect imposed by the hyperactive FICD enzymes (Mean ± SD, P values by ANOVA with Šídák's multiple comparisons test, n = 8–16 [biological replicates]).*
## Discussion
The findings presented here establish a specific recessive missense mutation [FICD p.(Arg371Ser)] in a gene encoding an ER‐localised, protein modifying enzyme, as causing a previously unrecognised genetic syndrome characterised by infancy‐onset diabetes mellitus and neurodevelopmental defects. The disease mechanism likely consists of an intra‐organellar perturbation that compromises both insulin‐producing beta cells and cells relevant to neurological development and/or function. Patients with pathogenic mutations in other genes known to cause diabetes through dysregulation of ER function also have early‐onset insulin‐requiring diabetes and neurodevelopmental features (Shrestha et al, 2021). The identification of the same mutation in three families suggests a mutation‐specific mechanism, as opposed to overall loss of gene function. A biochemical analysis of the effects of the Arg371Ser mutation on FICD's enzymology and the extant understanding of the ER chaperone BiP, a natural FICD substrate, suggest a mechanism for the underlying molecular pathology.
BiP, the only Hsp70 chaperone in the mammalian ER, is essential. Complete inactivation of the encoding gene compromises both unicellular eukaryotes such as budding yeast (Normington et al, 1989) and metazoans at an early developmental stage (Luo et al, 2006). Reduction in BiP levels by exposure to SubA, a bacterial toxin that cleaves the protein intracellularly, is sufficient to compromise the secretory apparatus (Paton et al, 2006). BiP AMPylation on Thr518, locks the chaperone in an inert conformation that precludes productive engagement of substrate (Wieteska et al, 2017; Preissler et al, 2017b) and is thus tantamount to a reduction in the concentration of active BiP in the ER.
Normally, this process is subject to tight regulation—surplus BiP is inactivated by AMPylation as ER stress wanes and the reserve pool of AMPyated BiP is reactivated, through deAMPylation, as stress levels rise (reviewed in: Preissler & Ron, 2019; Perera & Ron, 2022). The Arg371Ser mutation compromises both aspects of BiP regulation: timely BiP AMPylation and deAMPylation. The defect in AMPylation capacity explains the diminished ability of mutant cells to rapidly inactivate excess BiP upon a pharmacological reduction in unfolded protein flux into the ER (Fig 7).
**Figure 7:** *Cartoon contrasting the hypothetical divergent consequences of total absence of FICD (FICD
∆) with the Arg371Ser mutation (FICDR371S
) in a pancreatic islet beta cell experiencing a physiologically‐driven 10‐fold increase in proinsulin biosynthesisIn wild‐type cells (WT) the stress imposed by increased production of proinsulin is met by rapid de‐AMPylation of BiP and a slow, transcriptionally‐mediated increase in BiP synthesis. As the stress wanes, BiP is re‐AMPylated followed by slow decline in BiP protein levels, as its biosynthesis decreases. Note, the changes in concentration of BiP‐AMP depicted here are based on measurements made in fed and fasted mice (Chambers et al, 2012) and assume a total concentration of BiP ~ 500 μM. In FICD
∆
cells protein folding homeostasis is presumably maintained by transcriptional mechanisms that are adequate to sustain glycaemic control in knockout mice (McCaul et al, 2021). The FICD
R371S
cells are burdened with a residual pool of AMPylated BiP, even when stressed (depicted by stippled green rectangle) compromising ER function.*
The complete absence of deAMPylation activity of the mutant enzyme creates an intrinsic imbalance that favours AMPylation over deAMPylation to deprive cells of a readily accessible pool of dormant BiP when needed. Such deregulation is suggested by a consistent basal level of AMPylated BiP in FICD R371S cells, whereas the basal signal in wild‐type cells varies. Importantly, under stress conditions wild‐type cells can fully re‐activate pre‐existing pools of AMPylated chaperone, whilst the mutant cells are impaired in this recruitment mechanism (compare the effects of thapsigargin treatment on wild‐type and mutant cells in Fig 5).
This scenario is consistent with the recessive nature of the genetic disease. Whilst the imbalance in antagonistic enzymatic activities is intrinsic to the FICDR371S mutant enzyme, our in vitro observations suggest that the product of a wild‐type FICD allele, a powerful deAMPylase (Preissler et al, 2017a), can buffer the weak AMPylation bias imposed by the product of the mutant allele (Fig 3F). The buffering task of the wild‐type protein in heterozygotes is likely favoured not only by the weak AMPylation activity of FICDR371S but also by the fact that the mutation creates a cryptic glycosylation site in the enzyme's active site. Glycosylation on Asn369 is likely to compromise FICD folding and reduce the burden of AMPylation even further in the heterozygous state. By contrast, in the homozygous state, the weak AMPylation activity of FICDR371S molecules that escape co‐translational glycosylation is unopposed.
High levels of AMPylation can inactivate enough BiP to compromise ER function (Casey et al, 2017; Moehlman et al, 2018) and to trigger a measurable increase in signalling in the ER unfolded protein response (UPR) (Preissler et al, 2015b). By contrast, the activities of fluorescent UPR reporters are unaltered under basal conditions in FICDR371S‐expressing cells. This likely reflects their adaptation to the mutant allele and suggests that the consequences of deregulated AMPylation are not manifest in the basal state but rather as cells experience fluctuations in the burden of ER client proteins. Insulin‐producing beta cells are known to face large (glucose‐driven) excursions in the burden of unfolded pro‐insulin entering their ER (> 10 fold increase over 30 min, Itoh & Okamoto, 1980). A defect in recruiting AMPylated BiP to the chaperone cycle as beta cells cope with physiological glycaemic excursions, may account for their sensitivity to this mutation.
Whilst this paper was under review a report appeared describing five individuals with a slowly progressive neurodegenerative disorder of the motor neurons resulting from homozygosity of a specific, different mutation in FICD's active site—p.(Arg374His) (Rebelo et al, 2022). The p.(Arg374His) mutant protein was not characterised in vitro but patient‐derived fibroblasts exhibited elevated levels of BiP‐AMP, consistent with a defect that favours AMPylation over deAMPylation. Thus, two different mutations that bias FICD towards BiP hyper‐AMPylation compromise the nervous system, albeit with important differences in the phenotype and age of onset. Importantly, the Arg371Ser mutation identified in our patients resulted in a multi‐system disease, including bilateral cataracts, deafness and infancy‐onset diabetes. Whilst the basis for the phenotypic differences resulting from the two mutations is currently not understood, the genetic evidence suggests mutation‐specific mechanisms resulting in distinct FICD‐related diseases.
It is interesting to contrast the consequences of complete loss of FICD function, which has no effect on glycaemic control or gross neurological function in mice (McCaul et al, 2021; Casey et al, 2022), with the homozygous human p.(Arg371Ser) and p.(Arg374His) mutations. From a mechanistic perspective, this suggests that loss of AMPylation/deAMPylation as a post‐translational strand of the UPR can be buffered by parallel regulatory processes, whilst inability to antagonise constitutive AMPylation is poorly tolerated (Fig 7). It also implies that the profound physiological perturbation arising from homozygosity of the FICD R371S mutation could be reversed by complete inactivation of FICD, for example by a small molecule that blocks the enzyme's adenosine‐binding site.
Here, we interpret the consequences of the FICDR371S mutation through the inactivating features of BiP AMPylation: assuming that BiP‐AMP is completely inert, and that the phenotype arises from a deficit of active chaperone. However, it remains possible that BiP‐AMP retains important biochemical activities whose deregulation contributes to the phenotype via biochemical gain‐of‐function features. For example, it is possible that constitutive AMPylation of BiP may result in inappropriate sequestration of BiP co‐chaperones (away from the active/unmodified pool of BiP).
Our focus on BiP is justified by the observation that it is the main protein to undergo detectable incorporation of AMP (fig 1 in Carlsson & Lazarides, 1983 and 17G6 immunoblots here). However, it is noteworthy that the therapeutic strategy articulated above is relevant even if the pathogenic consequences of the mutation were to be realised via deregulated AMPylation of other, yet to be discovered, FICD substrates. Finally, it is intriguing to consider that the severe consequences of a genetic disease, which highlights the cost of an extreme imbalance in BiP AMPylation, may hint at the therapeutic utility of re‐balancing the AMPylation/deAMPylation poise of wild‐type FICD in other extreme circumstances.
## Subjects
Individuals with diabetes diagnosed before the age of 12 months were recruited by their clinicians for molecular genetic analysis in the Exeter Molecular Genetics Laboratory. The study was conducted in accordance with the Declaration of Helsinki, the United States Department of Health and Human Services Belmont Report. All subjects or their parents/guardians gave informed consent for genetic testing.
## Genetic analysis
Genome sequencing was performed on DNA extracted from peripheral blood leukocytes. The samples from individuals 1a and 1b were sequenced on an Illumina HiSeq X10 with a mean read depth of 41.2 and 38.3, respectively. The samples of 126 individuals were analysed by whole‐genome sequencing on an Illumina HiSeq 2,500 ($$n = 8$$), HiSeq X10 ($$n = 71$$) or with BGISeq‐500 ($$n = 47$$). The sequencing data were analysed using an approach based on the GATK best practice guidelines. GATK haplotypecaller was used to identify variants which were annotated using Alamut batch version 1.11 (Sophia Genetics) and variants which failed the QD2 VCF filter or had < 5 reads supporting the variant allele were excluded. Copy number variants were called by SavvyCNV (Laver et al, 2022) which uses read depth to judge copy number states. SavvyVcfHomozygosity (https://github.com/rdemolgen/SavvySuite) was used to identify large (> 3 Mb) homozygous regions in the genome sequencing data. An in‐house software was used to detect shared haplotypes (https://github.com/rdemolgen/SavvySuite).
## Protein purification
The structured regions of human FICD proteins (residues 104–445) and BiPT229A‐V461F (residues 27–635) were expressed in T7 Express lysY/I q (NEB) E. coli cells as N‐terminal His6‐SUMO fusion proteins or as GST fusion proteins. The proteins were expressed and purified (Perera et al, 2019, 2021). In brief, the His6‐SUMO tag was removed from the FICD/BiP fusion partner by Ulp1 cleavage following Ni‐NTA affinity purification or the GST tag by TEV protease cleavage. The cleaved proteins of interest were further purified by anion exchange (using a RESOURCE Q 6 ml column [Cytivia]) and gel filtration (using a S200 Increase $\frac{10}{300}$ GL column [Cytivia]). Proteins were concentrated in a final buffer consisting of 25 mM Tris pH 8.0, 150 mM NaCl and 1 mM TCEP (buffer TNT). The plasmids used to express the FICD and BiP proteins utilised in this study are detailed in Table EV3.
## Differential scanning fluorimetry (DSF)
DSF analyses were conducted as in (Perera et al, 2019, 2021) with minor modifications: final DSF samples contained 1 μM FICD protein ± 2.5 mM nucleotide (as indicated in Fig 3A) in a buffer of HKM supplemented with 1.5× SYPRO Orange protein gel stain (Thermo Fisher Scientific).
## Analytical size exclusion chromatography
Analytical size exclusion chromatography was conducted on an Agilent Bio SEC‐3 HPLC column (300 Å pore size, 3 μm particle size, 4.6 × 300 mm) equilibrated in TNT buffer at 25°C. The FICD proteins were diluted in TNT buffer to the indicated concentration (Fig 3B) and incubated for at least 1 h at room temperature before a 10 μl volume of the protein solution was injected (using an HPLC autosampler) onto the column. Protein was eluted at a flow rate of 0.3 ml/min.
## In vitro
AMPylation assay
5 μM unmodified BiP substrate was incubated at 25°C with FICD proteins at the indicated concentration for the indicated time (Fig 3C and F) in a buffer consisting of HKM (25 mM HEPES‐KOH pH 7.4, 150 mM KCl and 10 mM MgCl2) supplemented with 5 mM ATP. Reactions were quenched by the addition of LDS‐PAGE sample buffer and heating to 70°C. Samples containing 1 μg of BiP were loaded onto and resolved on a 4–$12\%$ SDS–PAGE gel and subsequently wet‐transferred onto a PVDF membrane. Total protein was imaged using Ponceau S stain (Fig 3C) or by use of parallel gels visualised with Coomassie protein stain (Fig 3F). The membrane was blocked with 1× ROTI Block (Roth) diluted in water and then probed for 1 h at 25°C with a mouse monoclonal IgG antibody reactive to AMPylated proteins (MoAb 17G6, Hopfner et al, 2020), diluted $\frac{1}{1000}$ (v/v) in 1 × ROTI Block. The AMPylated BiP signal was imaged using an IRDye 800CW goat anti‐mouse IgG secondary antibody [Li‐Cor], diluted $\frac{1}{2000}$ (v/v) in 1x ROTI‐Block.
## In vitro
deAMPylation assay
The fluorescence anisotropy‐based deAMPylation assay and the k cat calculation (for wild‐type FICD) was conducted as in Perera et al [2019, 2021] with minor modifications. Namely, each deAMPylation reaction was carried out in a 15 μl volume containing 100 μM BiPT229A‐V461F‐AMP (a concentration previously found to be able to saturate wild‐type FICD) and 10 nM BiPT229A‐V461F modified with FAM labelled AMP, in a buffer of HKM supplemented with $0.05\%$ (v/v) Triton X‐100. The assay was conducted in a 384‐well non‐binding, low volume, HiBase, black microplate (greiner bio‐one). Wild‐type and Arg371Ser FICD enzymes were added at $t = 0$ to a final concentration of 10 μM. Note, FICDR371S enzyme at 20 μM also failed to catalyse any discernible deAMPylation.
The fluorescence anisotropy of the FAM signal was recorded on a CLARIOstar Plus plate reader (BMG Labtech) exciting at 482–16 nm and top reading emission at 530–40 nm. A reference well containing only 10 nM N6‐(6‐Amino)hexyl‐ATP‐6‐FAM (Jena Bioscience) was used to set the relative gains in the parallel and perpendicular emission channels (targeting 25 mP units). The deAMPylation time course was conducted with the CLARIOstar maintaining a temperature of 25°C, whilst also mixing the sample plate using a 2 s double orbital shake after each kinetic cycle. The fluorescence anisotropy signal of fully deAMPylated BiP was estimated from the plateau value of a single exponential decay curve heuristically fitted to the deAMPylation trace catalysed by wild‐type FICD.
## Mammalian cell culture
The previously described cell lines CHO‐K1, CHO‐K1‐FICD∆ (Preissler et al, 2015b) and CHO‐K1‐FICDR371S cells (described below and in Key Resource Table) were cultured in Nutrient mixture F‐12 Ham (Sigma) supplemented with $10\%$ (vol/vol) serum (FetalClone II; HyClone), 1× Penicillin–Streptomycin (Sigma) and 2 mM L‐glutamine (Sigma) at 37°C and $5\%$ CO2. The identity of the CHO cell lines has been authenticated using the criteria of: A. successful targeting of essential genes using species‐specific CRISPR whole genome library, and B. sequencing of the wildtype or mutant alleles of the genes studied that confirmed the sequence reported for the corresponding genome. The cell lines tested negative for mycoplasma contamination using a commercial kit (MycoAlert (TM) Mycoplasma Detection Kit, Lonza). None of the cell lines is on the list of commonly misidentified cell lines maintained by the International Cell Line Authentication Committee.
## FICD mutation knock‐in
Plasmid and oligonucleotide reagents referenced herein are listed in Tables EV2 and EV3—plasmid list and Key Resources Tables respectively. A guide targeting Ficd was selected in the region of Exon 2 (previously called exon 3) surrounding the codon for R371S into the CCTop—CRISPR/Cas9 target online predictor (Stemmer et al, 2015) and duplex DNA oligonucleotides (3035_g2_R371_S, 3036_g2_R371_AS) of the sequence was inserted into the pSpCas9(BB)‐2A‐mCherry_V2 plasmid (UK1610) to create guide plasmid UK2959. Cells in $80\%$ confluent 6‐well dishes were transfected 24 h post plating with 0.5 μg guide plasmid (UK2959) and 1.5 μg of a single‐stranded oligonucleotide repair template (3037_FICD_R371S_repair_V2S) that contains base changes to introduce the R371S allele as well as silent mutations introducing a BamHI site and pam destroying mutation using Lipofectamine LTX (Invitrogen). Thirty‐six hours post‐transfection the cells were washed with PBS, resuspended in PBS containing 4 mM EDTA and $0.5\%$ (w/v) BSA, and mCherry‐positive cells were individually sorted by fluorescence‐activated cell sorting (FACS) into 96‐well plates using a Melody Cell sorter (Beckman Coulter). Genomic DNA was isolated from 186 clones of which 171 produced a 296 bp PCR product with primers 3003_cgFICD_F and 3004_cgFICD_R. Upon digestion with BamHI, 5 had the expected size fragments and sequencing of these identified a single clone (R371S‐C74) with a single correct R371S mutant allele.
## Cell treatment, transfection and analysis
Cells ($80\%$ confluent) were transfected 24 h after plating with 2 μg of the indicated expression plasmids per well of a 6‐well (35 cm) dish using Lipofectamine LTX (Life Technologies) according to manufacturer's instructions. The medium was exchanged after 24 h and harvest for Flow cytometry or immunoblot were performed 36 h after transfection (Fig 4). For analysis of CHO wildtype, FICDD or FICDR371S cells, medium on $80\%$ confluent 10 cm dishes of cells was exchanged 1 h prior to drug treatments of cells (100 μg/ml cycloheximide or 0.2 μM thapsigargin) of the indicated genotype for 3 or 6 h prior to harvest as indicated in the figures.
For immunoblotting cells were washed twice in PBS‐2 mM EDTA and collected in the same and then lysed as previously described (Preissler et al, 2015a) in 4 cell volumes HG buffer (50 mM HEPES–KOH pH 7.4, 150 mM NaCl, 2 mM MgCl2, 33 mM D‐glucose, $10\%$ [v/v] glycerol, $1\%$ [v/v]) Triton X‐100 and protease inhibitors (2 mM phenylmethylsulphonyl fluoride [PMSF], 4 μg/ml pepstatin, 4 μg/ml leupeptin, 8 μg/ml aprotinin) with 100 U/ml hexokinase (from *Saccharomyces cerevisiae* Type F‐300; Sigma). Lysates were cleared at 20,000 g and 20 μg of protein was separated by PAGE and transferred to PDVF membranes. For Immunoprecipitation, 430 μg of lysate was precleared with 20 μl UltraLink Hydrazine Resin (Pierce cat. # 53149) blocked with aniline for 1 h at 4C followed by incubation with 20 μl UltraLink Hydrazine Resin on which BiP‐specific chicken IgY antibodies have been covalently immobilised according to the manufacturer's instructions (Preissler et al, 2015a). After washing three times with lysis buffer the proteins were eluted with Laemmli buffer prior to PAGE and transfer to PDVF. The PVDF membranes were subsequently blocked in $\frac{1}{10}$ dilution of ROTI®Block 10×, $5\%$ BSA in TBS (50 mM Tris–Cl, pH 7.5) then sequentially incubated with primary antisera diluted in blocking buffer as follows: anti‐AMP (MoAb 17G6) and eIF2α [mouse anti‐eIF2α (Avezov et al, 2013)] hamster BiP [chicken anti‐hamster BiP (Avezov et al, 2013)], and FICD [chicken anti‐FICD (Preissler et al, 2015b)] were used at a dilutions of $\frac{1}{1}$,000, $\frac{1}{5}$,000 and $\frac{1}{1}$,000, and $\frac{1}{1}$,000 (v/v), respectively. The membranes were then washed 3 times in TBS and incubated with secondary antisera linked to IR800 ($\frac{1}{2}$,000 v/v) or Cy3 ($\frac{1}{1}$,000 v/v) in blocking buffer. The membranes were scanned with an Odyssey near‐infra‐ red imager (LI‐COR) to detect IR800 secondary antisera or Biorad ChemiDocTM MP Imaging system with v3.0.1.14 Image Lab Touch software to detect Cy3. Where applicable, IB band quantification was carried out using NIH‐Image (Fiji) Gel tool.
## Secreted alkaline phosphatase (SEAP) assay
Cells were transfected with 500 ng FICD expression vector, 100 ng SEAP expression vector (UK1014), and 25 ng SV40_Luc_pGL3 expression vector (UK 3075, a transfection marker) per well in 24 well dishes of FICD−/−10 CHO cells. 16 h later the medium was changed and after a further 24 h of culture the medium was collected, heated at 60°C to for 1 h prior to assay for SEAP activity at room temperature by mixing 20 μl of heat‐treated medium to 100 μl 1 M diethanolamine buffer, pH 9.8, 0.5 mM MgCl2 containing 1 mg/ml freshly dissolved phosphatase substrate (4‐Nitrophenyl phosphate disodium salt hexahydrate, Sigma, S0942) and measuring the OD405 and OD 630 every 4.75 min for 20 cycles. The cells were lysed in the dish in 250 μl luciferase lysis buffer (25 mM gly‐gly, 15 mM MgSO4, 4 mM EGTA, 1 mM DTT, $1\%$ Triton X 100) for 20 min on ice and 25 μl was assayed for luciferase activity by addition of 25 μl of luciferase assay reagent (25 mM gly‐gly, 15 mM MgSO4, 4 mM EGTA, 11.7 mM potassium phosphate, 1.6 mM ATP Sigma A2383, 0.2 mg/ml coenzyme A Alfa‐Aesar J65434.MC, 500 μM d‐Luciferin, ABcam ab143655) in a BMG labtech Clariostar plate reader using SMART control v 6.10 acquisition software and MARS v 4.10 data analysis software. ANOVA in Prism software indicated that there was a significant difference amongst means ($P \leq 0.0001$) and Šídák's multiple comparisons test was used to determine the significance between data pairs as indicated in the legend. Parallel 6 well dishes were transfected and harvested for immunoblot as in Figs 4 and 5.
## Statistical analysis
The statistical tools used in the genetic analysis are described above (in Genetic analysis) and listed in Table EV3. Statistical analysis of the biochemical and cellular data was performed using GraphPad Prism 9.4.1 (GraphPad Software Inc.). Statistical significance between groups was calculated using unpaired Student's t‐test with P‐values < 0.05 considered statistically significant.
## Problem
Diabetes mellitus in humans commonly develops in the context of resistance to insulin action (type II) or an immune assault on the insulin‐producing beta cells (type I). By contrast *Diabetes mellitus* presenting soon after birth (neonatal diabetes) is often caused by mutations in genes that compromise the machinery of insulin‐producing beta cells. *The* genetic study of families with these rare subtypes of diabetes provides important insight into pathogenetic mechanisms operating at the level of beta cells. These processes, in less extreme forms, may also contribute to the more common types of diabetes and their understanding may provide insight into potential therapies.
## Results
Genome sequencing led to the discovery of five individuals, from three families, with onset of diabetes in the first year of life, severe neurodevelopmental delay, and homozygosity for the same mutation (p.Arg371Ser) in the gene encoding FICD. Genetic analysis provides strong evidence for the causal relationship between this mutation and the disease. FICD is an enzyme that catalyses two antagonistic reactions: The conjugation of adenosine monophosphate (AMP) to the endoplasmic reticulum (ER) chaperone BiP, to inactivate BiP, and reactivating removal of the AMP moiety. Normally, FICD carries out these activities in a highly regulated manner, responding to a cell's need for active BiP. The Arg371Ser mutation derails this process. Given BiP's important role in the folding of secreted proteins, including insulin, it seems plausible that inappropriately high levels of AMPylated BiP contribute to impaired secretion and dysfunction of beta cells and cells relevant to neurodevelopment.
## Clinical Impact
Identification of human disease caused by deregulated activity of an ER‐localised AMPylase expands the inventory of biochemical processes known to be required for ER homeostasis in health. Importantly, analysis of this rare genetic change also suggests the possibility of a specific remedy. Whilst FICD deregulation by the Arg371Ser mutation is severely pathogenic, complete loss of FICD has only minimal consequences (in mice). Thus, it stands to reason that an inhibitor that blocks all FICD activity may be useful in treating individuals homozygous for the Arg371Ser mutation, as the underlying pathophysiological process relies on deregulated AMPylation.
## Author contributions
Luke A Perera: Conceptualization; investigation; methodology; writing—original draft; writing—review and editing. Andrew T Hattersley: Conceptualization; supervision; funding acquisition; methodology; writing—review and editing. Heather P Harding: Conceptualization; investigation; methodology; writing—review and editing. Matthew N Wakeling: Data curation; formal analysis; methodology. Sarah E Flanagan: Data curation; formal analysis; methodology. Ibrahim Mohsina: Resources. Jamal Raza: Resources. Alice Gardham: Resources. David Ron: Conceptualization; supervision; funding acquisition; investigation; methodology; writing—original draft; project administration; writing—review and editing. Elisa De Franco: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; writing—original draft; project administration; writing—review and editing.
## Disclosure and competing interests statement
The authors declare that they have no conflict of interest.
## For more information
The Ron Lab website <https://ron.cimr.cam.ac.uk>.
Monogenic diabetes research centre at the University of Exeter <https://www.diabetesgenes.org/about‐neonatal‐diabetes/>.
## Data availability
This study includes no data deposited in external repositories.
## References
1. Avezov E, Cross BC, Kaminski Schierle GS, Winters M, Harding HP, Melo EP, Kaminski CF, Ron D. **Lifetime imaging of a fluorescent protein sensor reveals surprising stability of ER thiol redox**. *J Cell Biol* (2013) **201** 337-349. PMID: 23589496
2. Braakman I, Hebert DN. **Protein folding in the endoplasmic reticulum**. *Cold Spring Harb Perspect Biol* (2013) **5**. PMID: 23637286
3. Bunney TD, Cole AR, Broncel M, Esposito D, Tate EW, Katan M. **Crystal structure of the human, FIC‐domain containing protein HYPE and implications for its functions**. *Structure* (2014) **22** 1831-1843. PMID: 25435325
4. Carlsson L, Lazarides E. **ADP‐ribosylation of the Mr 83,000 stress‐inducible and glucose‐regulated protein in avian and mammalian cells: modulation by heat shock and glucose starvation**. *Proc Natl Acad Sci USA* (1983) **80** 4664-4668. PMID: 6576354
5. Casey AK, Moehlman AT, Zhang J, Servage KA, Kramer H, Orth K. **Fic‐mediated deAMPylation is not dependent on homodimerization and rescues toxic AMPylation in flies**. *J Biol Chem* (2017) **292** 21193-21204. PMID: 29089387
6. Casey AK, Gray HF, Chimalapati S, Hernandez G, Moehlman AT, Stewart N, Fields HA, Gulen B, Servage KA, Stefanius K. **Fic‐mediated AMPylation tempers the unfolded protein response during physiological stress**. *Proc Natl Acad Sci USA* (2022) **119**. PMID: 35914137
7. Chambers JE, Petrova K, Tomba G, Vendruscolo M, Ron D. **ADP ribosylation adapts an ER chaperone response to short‐term fluctuations in unfolded protein load**. *J Cell Biol* (2012) **198** 371-385. PMID: 22869598
8. Danilova T, Belevich I, Li H, Palm E, Jokitalo E, Otonkoski T, Lindahl M. **MANF is required for the postnatal expansion and maintenance of pancreatic beta‐cell mass in mice**. *Diabetes* (2019) **68** 66-80. PMID: 30305368
9. Delepine M, Nicolino M, Barrett T, Golamaully M, Lathrop GM, Julier C. **EIF2AK3, encoding translation initiation factor 2‐alpha kinase 3, is mutated in patients with Wolcott‐Rallison syndrome**. *Nat Genet* (2000) **25** 406-409. PMID: 10932183
10. Engel P, Goepfert A, Stanger FV, Harms A, Schmidt A, Schirmer T, Dehio C. **Adenylylation control by intra‐ or intermolecular active‐site obstruction in fic proteins**. *Nature* (2012) **482** 107-110. PMID: 22266942
11. Fauser J, Gulen B, Pogenberg V, Pett C, Pourjafar‐Dehkordi D, Krisp C, Hopfner D, Konig G, Schluter H, Feige MJ. **Specificity of AMPylation of the human chaperone BiP is mediated by TPR motifs of FICD**. *Nat Commun* (2021) **12** 2426. PMID: 33893288
12. Ham H, Woolery AR, Tracy C, Stenesen D, Kramer H, Orth K. **Unfolded protein response‐regulated drosophila fic (dFic) protein reversibly AMPylates BiP chaperone during endoplasmic reticulum homeostasis**. *J Biol Chem* (2014) **289** 36059-36069. PMID: 25395623
13. Hendershot LM, Ting J, Lee AS. **Identity of the immunoglobulin heavy‐chain‐binding protein with the 78,000‐Dalton glucose‐regulated protein and the role of posttranslational modifications in its binding function**. *Mol Cell Biol* (1988) **8** 4250-4256. PMID: 3141786
14. Hopfner D, Fauser J, Kaspers MS, Pett C, Hedberg C, Itzen A. **Monoclonal anti‐AMP antibodies are sensitive and valuable tools for detecting patterns of AMPylation**. *iScience* (2020) **23**. PMID: 33299971
15. Itoh N, Okamoto H. **Translational control of proinsulin synthesis by glucose**. *Nature* (1980) **283** 100-102. PMID: 6985712
16. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP. **The mutational constraint spectrum quantified from variation in 141,456 humans**. *Nature* (2020) **581** 434-443. PMID: 32461654
17. Khater S, Mohanty D. **In silico identification of AMPylating enzymes and study of their divergent evolution**. *Sci Rep* (2015) **5** 10804. PMID: 26039278
18. Kinch LN, Yarbrough ML, Orth K, Grishin NV. **Fido, a novel AMPylation domain common to fic, doc, and AvrB**. *PLoS One* (2009) **4**. PMID: 19503829
19. Kingdon HS, Shapiro BM, Stadtman ER. **Regulation of glutamine synthetase. 8. ATP: glutamine synthetase adenylyltransferase, an enzyme that catalyzes alterations in the regulatory properties of glutamine synthetase**. *Proc Natl Acad Sci USA* (1967) **58** 1703-1710. PMID: 4867671
20. Laitusis AL, Brostrom MA, Brostrom CO. **The dynamic role of GRP78/BiP in the coordination of mRNA translation with protein processing**. *J Biol Chem* (1999) **274** 486-493. PMID: 9867869
21. Laver TW, De Franco E, Johnson MB, Patel KA, Ellard S, Weedon MN, Flanagan SE, Wakeling MN. **SavvyCNV: genome‐wide CNV calling from off‐target reads**. *PLoS Comput Biol* (2022) **18**. PMID: 35294448
22. Luo S, Mao C, Lee B, Lee AS. **GRP78/BiP is required for cell proliferation and protecting the inner cell mass from apoptosis during early mouse embryonic development**. *Mol Cell Biol* (2006) **26** 5688-5697. PMID: 16847323
23. McCaul N, Porter CM, Becker A, Tang CA, Wijne C, Chatterjee B, Bousbaine D, Bilate A, Hu CA, Ploegh H. **Deletion of mFICD AMPylase alters cytokine secretion and affects visual short‐term learning**. *J Biol Chem* (2021) **100991** 100991
24. Moehlman AT, Casey AK, Servage K, Orth K, Kramer H. **Adaptation to constant light requires fic‐mediated AMPylation of BiP to protect against reversible photoreceptor degeneration**. *Elife* (2018) **7**. PMID: 30015618
25. Normington K, Kohno K, Kozutsumi Y, Gething MJ, Sambrook J. *Cell* (1989) **57** 1223-1236. PMID: 2661019
26. Paton AW, Beddoe T, Thorpe CM, Whisstock JC, Wilce MC, Rossjohn J, Talbot UM, Paton JC. **AB5 subtilase cytotoxin inactivates the endoplasmic reticulum chaperone BiP**. *Nature* (2006) **443** 548-552. PMID: 17024087
27. Perera LA, Ron D. **AMPylation and endoplasmic reticulum protein folding homeostasis**. *Cold Spring Harb Perspect Biol* (2022) **30**
28. Perera LA, Rato C, Yan Y, Neidhardt L, McLaughlin SH, Read RJ, Preissler S, Ron D. **An oligomeric state‐dependent switch in the ER enzyme FICD regulates AMPylation and deAMPylation of BiP**. *EMBO J* (2019) **38**. PMID: 31531998
29. Perera LA, Preissler S, Zaccai NR, Prevost S, Devos JM, Haertlein M, Ron D. **Structures of a deAMPylation complex rationalise the switch between antagonistic catalytic activities of FICD**. *Nat Commun* (2021) **12** 5004. PMID: 34408154
30. Pobre KFR, Poet GJ, Hendershot LM. **The endoplasmic reticulum (ER) chaperone BiP is a master regulator of ER functions: getting by with a little help from ERdj friends**. *J Biol Chem* (2019) **294** 2098-2108. PMID: 30563838
31. Preissler S, Ron D. **Early events in the endoplasmic reticulum unfolded protein response**. *Cold Spring Harb Perspect Biol* (2019) **11**. PMID: 30396883
32. Preissler S, Chambers JE, Crespillo‐Casado A, Avezov E, Miranda E, Perez J, Hendershot LM, Harding HP, Ron D. **Physiological modulation of BiP activity by**. *Elife* (2015a) **4**. PMID: 26473973
33. Preissler S, Rato C, Chen R, Antrobus R, Ding S, Fearnley IM, Ron D. **AMPylation matches BiP activity to client protein load in the endoplasmic reticulum**. *Elife* (2015b) **4**. PMID: 26673894
34. Preissler S, Rato C, Perera LA, Saudek V, Ron D. **FICD acts bifunctionally to AMPylate and de‐AMPylate the endoplasmic reticulum chaperone BiP**. *Nat Struct Mol Biol* (2017a) **24** 23-29. PMID: 27918543
35. Preissler S, Rohland L, Yan Y, Chen R, Read RJ, Ron D. **AMPylation targets the rate‐limiting step of BiP's ATPase cycle for its functional inactivation**. *Elife* (2017b) **6**. PMID: 29064368
36. Rahman M, Ham H, Liu X, Sugiura Y, Orth K, Kramer H. **Visual neurotransmission in drosophila requires expression of fic in glial capitate projections**. *Nat Neurosci* (2012) **15** 871-875. PMID: 22544313
37. Rebelo AP, Ruiz A, Dohrn MF, Wayand M, Farooq A, Danzi MC, Beijer D, Aaron B, Vandrovcova J, Houlden H. **BiP inactivation due to loss of the deAMPylation function of FICD causes a motor neuron disease**. *Genet Med* (2022) **24** 2487-2500. PMID: 36136088
38. Sanchez Caballero L, Gorgogietas V, Arroyo MN, Igoillo‐Esteve M. **Molecular mechanisms of beta‐cell dysfunction and death in monogenic forms of diabetes**. *Int Rev Cell Mol Biol* (2021) **359** 139-256. PMID: 33832649
39. Sanyal A, Chen AJ, Nakayasu ES, Lazar CS, Zbornik EA, Worby CA, Koller A, Mattoo S. **A novel link between fic (filamentation induced by cAMP)‐mediated adenylylation/AMPylation and the unfolded protein response**. *J Biol Chem* (2015) **290** 8482-8499. PMID: 25601083
40. Shrestha N, De Franco E, Arvan P, Cnop M. **Pathological beta‐cell endoplasmic reticulum stress in type 2 diabetes: current evidence**. *Front Endocrinol (Lausanne)* (2021) **12**. PMID: 33967960
41. Stemmer M, Thumberger T, Del Sol Keyer M, Wittbrodt J, Mateo JL. **CCTop: an intuitive, flexible and reliable CRISPR/Cas9 target prediction tool**. *PLoS One* (2015) **10**. PMID: 25909470
42. Synofzik M, Haack TB, Kopajtich R, Gorza M, Rapaport D, Greiner M, Schonfeld C, Freiberg C, Schorr S, Holl RW. **Absence of BiP co‐chaperone DNAJC3 causes diabetes mellitus and multisystemic neurodegeneration**. *Am J Hum Genet* (2014) **95** 689-697. PMID: 25466870
43. Truttmann MC, Cruz VE, Guo X, Engert C, Schwartz TU, Ploegh HL. **The Caenorhabditis elegans protein FIC‐1 is an AMPylase that covalently modifies heat‐shock 70 family proteins, translation elongation factors and histones**. *PLoS Genet* (2016) **12**. PMID: 27138431
44. Truttmann MC, Zheng X, Hanke L, Damon JR, Grootveld M, Krakowiak J, Pincus D, Ploegh HL. **Unrestrained AMPylation targets cytosolic chaperones and activates the heat shock response**. *Proc Natl Acad Sci USA* (2017) **114** E152-E160. PMID: 28031489
45. Wieteska L, Shahidi S, Zhuravleva A. **Allosteric fine‐tuning of the conformational equilibrium poises the chaperone BiP for post‐translational regulation**. *Elife* (2017) **6**. PMID: 29064369
46. Woolery AR, Luong P, Broberg CA, Orth K. **AMPylation: something old is new again**. *Front Microbiol* (2010) **1** 113-119. PMID: 21607083
47. Worby CA, Mattoo S, Kruger RP, Corbeil LB, Koller A, Mendez JC, Zekarias B, Lazar C, Dixon JE. **The fic domain: regulation of cell signaling by adenylylation**. *Mol Cell* (2009) **34** 93-103. PMID: 19362538
48. Yarbrough ML, Li Y, Kinch LN, Grishin NV, Ball HL, Orth K. **AMPylation of rho GTPases by vibrio VopS disrupts effector binding and downstream signaling**. *Science* (2009) **323** 269-272. PMID: 19039103
49. Yong J, Johnson JD, Arvan P, Han J, Kaufman RJ. **Therapeutic opportunities for pancreatic beta‐cell ER stress in diabetes mellitus**. *Nat Rev Endocrinol* (2021) **17** 455-467. PMID: 34163039
|
---
title: Targetable Brg1‐CXCL14 axis contributes to alcoholic liver injury by driving
neutrophil trafficking
authors:
- Nan Li
- Hong Liu
- Yujia Xue
- Zheng Xu
- Xiulian Miao
- Yan Guo
- Zilong Li
- Zhiwen Fan
- Yong Xu
journal: EMBO Molecular Medicine
year: 2023
pmcid: PMC9994483
doi: 10.15252/emmm.202216592
license: CC BY 4.0
---
# Targetable Brg1‐CXCL14 axis contributes to alcoholic liver injury by driving neutrophil trafficking
## Abstract
Alcoholic liver disease (ALD) accounts for a large fraction of patients with cirrhosis and hepatocellular carcinoma. In the present study we investigated the involvement of Brahma‐related gene 1 (Brg1) in ALD pathogenesis and implication in ALD intervention. We report that Brg1 expression was elevated in mouse models of ALD, in hepatocyte exposed to alcohol, and in human ALD specimens. Manipulation of Brg1 expression in hepatocytes influenced the development of ALD in mice. Flow cytometry showed that Brg1 deficiency specifically attenuated hepatic infiltration of Ly6G+ neutrophils in the ALD mice. RNA‐seq identified C‐X‐C motif chemokine ligand 14 (CXCL14) as a potential target for Brg1. CXCL14 knockdown alleviated whereas CXCL14 over‐expression enhanced ALD pathogenesis in mice. Importantly, pharmaceutical inhibition of Brg1 with a small‐molecule compound PFI‐3 or administration of an antagonist to the CXCL14 receptor ameliorated ALD pathogenesis in mice. Finally, a positive correlation between Brg1 expression, CXCL14 expression, and neutrophil infiltration was detected in ALD patients. In conclusion, our data provide proof‐of‐concept for targeting the Brg1‐CXCL14 axis in ALD intervention.
Neutrophil homing to the liver constitutes a major pathophysiological process in alcoholic liver disease (ALD) by promoting inflammation, producing ROS, and altering metabolism. The chromatin remodeling protein Brg1 activates transcription of CXCL14 in hepatocytes to drive neutrophil trafficking.
## Introduction
A wide range of sociocultural, neuropsychological, and genetic factors contribute to excessive alcohol consumption (Poikolainen, 2000). It is estimated, based on survey data published by the World Health Organization, that approximately $18\%$ of the adult population is addicted to heavy alcohol use causing over three million premature deaths worldwide (Peacock et al, 2018). Alcohol use disorder (AUD), a.k.a. alcoholism, encompasses a series of severe pathologies that include alcoholic liver disease (ALD), alcoholic digestive disease, alcoholic heart disease, alcoholic‐related diabetic complications, alcoholic immune disorders, and alcoholic neurological complications (Mason & Heyser, 2021). In the US alone, 14.5 million people aged 12 and above are affected by AUD incurring an economic burden of 240 billion dollars each year (Friedmann, 2013). ALD, the most prominent AUD, contributes to more than a quarter of all deaths caused by chronic liver diseases such as cirrhosis and hepatocellular carcinoma (Akinyemiju et al, 2017). In addition, ALD is implicated in more than $30\%$ of all patients with liver failure thus necessitating liver transplantation (Goldberg et al, 2017). Typical pathological characteristics of ALD include steatosis (accumulation of triglycerides and fatty acids in hepatocyte), parenchymal inflammation, and diffuse fibrosis (Celli & Zhang, 2014). Although abstinence is considered the best therapeutic strategy for ALD, the interventional options for those patients with advanced/irreversible liver damages have not evolved in the past decades and therefore are limited (Seitz et al, 2018), suggesting the existence of major gaps in our understanding of ALD pathogenesis.
Hepatic homeostasis, or the lack thereof, is acutely influenced by the immune cell populations (Robinson et al, 2016). Neutrophils, borne out of the bone marrow, are the most abundant circulating leukocytes in the human body (de Oliveira et al, 2016). A cell lineage short lived with rather heterogeneous nature, neutrophils are considered the first line of defense in the innate immune response (Ng et al, 2019). It has long been thought that neutrophils are scarcely present in the liver under physiological conditions, a notion verified recently by single‐cell RNA‐seq studies (Zhao et al, 2020). On the contrary, multiple injurious stimuli promote the trafficking of circulating neutrophils to the liver where they exert strong pro‐inflammatory effects (Ramaiah & Jaeschke, 2007; Gao et al, 2011; Xu et al, 2014). Increased neutrophil infiltration is observed in ALD patients and appears to be associated with poor prognosis (Mookerjee et al, 2007; Das et al, 2017). Typically, neutrophils, under the influence of excessive influx of ethanol, produce a large amount of pro‐inflammatory mediators, hydrolytic proteases, and reactive oxygen species, which in combination cause extensive hepatocellular damages (Lucey et al, 2009). The debris of the necroptotic hepatocytes may act as damage‐associated molecular patterns and fuel the activation of innate immunity inside the liver to aggravate the injuries (Cho & Szabo, 2021). Consistent with the pro‐pathogenic role of neutrophils in ALD, it has been independently demonstrated by the Gao group (Bertola et al, 2013b) and the Szabo group (Bukong et al, 2018) that depletion of neutrophils prior to alcohol drinking markedly attenuated the severity of liver injury in model animals. A host of chemoattractive substances, including complement C5a (Robbins et al, 1987), interleukin 8 (Hill et al, 1993; Joshi‐Barve et al, 2003), and interleukin 17 (Lemmers et al, 2009), have been implicated in neutrophil homing to the liver in the pathogenesis of ALD. However, the transcriptional regulation underlying neutrophil trafficking in this context remains largely underexplored.
Brahma‐related gene 1 (Brg1) is the core component of the SWI/SNF chromatin remodeling complex (Khavari et al, 1993). Although Brg1 is essential for organogenesis as evidenced by the observation that germline deletion of Brg1 leads to embryonic lethality (Bultman et al, 2000, 2005), post‐natal Brg1 deficiency in certain lineages is compatible with a normal life‐span under physiological conditions. We have previously reported that hepatocyte‐restricted Brg1 deletion in mice is associated with amelioration of a range of liver pathologies (Zhang et al, 2019; Fan et al, 2020; Hong et al, 2020; Dong et al, 2021; Li et al, 2021; Kong et al, 2021a). Here we present evidence to implicate Brg1 in ALD pathogenesis through stimulating hepatocyte‐derived chemokine CXCL14 to promote neutrophil infiltration.
## Alcohol exposure up‐regulates Brg1 expression in vivo and in vitro
In order to implicate Brg1 in the pathogenesis of ALD, response of hepatic Brg1 expression to alcohol exposure was tested in several different models. In the first model the C57/BL6 mice were subjected to acute alcohol consumption via two consecutive gavages of ethanol (5 g/kg) separated by 12 h and sacrificed 8 h after the second gavage (Fig 1A). Quantitative PCR (Fig 1B), Western blotting (Fig 1C), and immunohistochemical staining (Fig 1D) clearly indicated an increase of Brg1 expression in the livers of alcohol‐exposed mice compared to the saline‐exposed mice. In the second model, also known as the NIAAA model (Bertola et al, 2013a), the C57/BL6 mice were fed the Lieber–DeCarli ethanol liquid diet for 2 weeks immediately followed by a single dose of alcohol gavage (Fig 1E). Again, hepatic Brg1 expression was up‐regulated as measured by qPCR (Fig 1F), Western blotting (Fig 1G), and immunohistochemical staining (Fig 1H). Next, primary murine hepatocytes and human hepatoma cells (HepG2) were treated with ethanol; exposure to ethanol markedly and transiently stimulated Brg1 expression peaking as early as 1 h after the treatment (Fig 1I and J). Finally, a comparison of Brg1 immunochemical staining performed using paraffin sections of livers from ALD patients and healthy donors revealed that alcohol consumption probably led to up‐regulation of hepatic Brg1 proteins in humans (Fig 1K).
**Figure 1:** *Alcohol exposure up‐regulates Brg1 expression in vivo and in vitro
A–DAlcoholic liver injury was induced in C57/BL6 mice by gavage as described in Methods (A). Brg1 levels were examined by qPCR (B), Western (C), and immunohistochemical staining (D). N = 5 mice for each group. Scale bar, 100 μm.E–HAlcoholic liver injury was induced in C57/BL6 mice by NIAAA feeding as described in Methods (E). Brg1 levels were examined by qPCR (F), Western (G), and immunohistochemical staining (H). N = 5 mice for each group. Scale bar, 100 μm.I, JMouse primary hepatocytes and HepG2 cells were exposed to ethanol (50 mM) and harvested at indicated time points. N = 3 biological replicates.KHuman liver paraffin sections were stained with anti‐BRG1 and quantified by Image Pro. N = 10 for each group. Scale bar, 100 μm.
Data information: Data are expressed as mean ± S.D. *P < 0.05, two‐tailed Student's test.
Source data are available online for this figure.*
We also investigated the potential mechanism whereby Brg1 expression was up‐regulated in hepatocytes by ethanol exposure. The promoter region of Brg1 (encoded by Smarca4) was fused to a reporter gene and transfected into HepG2 cells. Ethanol exposure stimulated the Brg1 promoter activity, indicating that Brg1 might be transcriptionally activated. By comparing the sensitivity of the 1.5 kb reporter construct and the 1 kb reporter construct to ethanol exposure, it was determined that an alcohol‐response element might reside between −1,500 and − 1,000 of the Brg1 promoter relative to the transcription start site (TSS, Appendix Fig S1A). Upon close examination, an E2F1 motif was discovered to locate between −1,076 and − 1,071 of the BRG1 promoter; mutagenesis of this E2F1 motif completely abrogated the response to ethanol treatment (Appendix Fig S1B). Indeed, E2F1 depletion with siRNAs attenuated the up‐regulation of Brg1 expression by ethanol exposure (Appendix Fig S1C). Further, ChIP assay confirmed that E2F1 was recruited to the Brg1 promoter in hepatocytes following ethanol exposure (Appendix Fig S1D). Therefore, we conclude that E2F1 might mediate Brg1 up‐regulation in hepatocytes at the transcriptionally level.
## Manipulation of Brg1 expression influences alcoholic liver injury in mice
In order to translate the correlation between Brg1 expression in hepatocytes and alcohol exposure into causality, the following experiments were performed in mice harboring hepatocyte‐specific Brg1 deletion or Brg1 over‐expression (Appendix Fig S2 for verification of Brg1 expression in different transgenic strains). In the first set of experiments, alcoholic liver injury was induced in hepatocyte‐specific Brg1 knockout (Brg1LKO) mice (Li et al, 2019a) and wild type (WT) littermates using the NIAAA model. It was observed that Brg1 deficiency attenuated the increase in liver weight and liver weight/body weight ratio but did not alter body weight, gonadal white adipose tissue (WAT) tissue weight, or WAT weight/body weight ratio in mice (Appendix Fig S3). Plasma ALT (Fig 2A) and AST (Fig 2B) measurements indicated that liver injury was less severe in the Brg1LKO mice than in the WT mice. In addition, less hepatic accumulation of triglycerides was detected in the Brg1LKO mice than in the WT mice (Fig 2C). Histological evaluation of liver sections (H&E staining and oil red O staining) confirmed that there were fewer lipid droplets in the Brg1LKO livers than in the WT livers (Fig 2D). QPCR analysis showed that hepatic expression levels of pro‐inflammatory mediators, including interleukin 1 beta (Il1b), interleukin 6 (Il6), tumor necrosis factor alpha (Tnfa), and inducible NO synthase (Nos2), and molecules involved in lipid metabolism, including fatty acid synthase (Fasn), stearoyl‐CoA desaturase 1 (Scd1), acetyl‐CoA carboxylase 1 (Acc1), and sterol response element binding protein 1 (Screbp1) were down‐regulated in the Brg1LKO mice compared to the WT mice (Fig 2E). Notably, Brg1 deficiency did not influence alcohol intake or blood alcohol levels in mice (Appendix Fig S4). In addition, expression levels of key enzymes involved in metabolism of ethanol were not significantly altered by Brg1 manipulation in the liver (Appendix Fig S5), suggesting that regulation of ALD by Brg1 is unlikely attributable to alcohol metabolism. Similarly, in an acute gavage model of ALD it was found that the Brg1LKO mice exhibited a less severe phenotype than the WT mice (Appendix Fig S6).
**Figure 2:** *Manipulation of Brg1 expression influences alcoholic liver injury in mice
A–EAlcoholic liver injury was induced in WT and Brg1 LKO mice by NIAAA feeding as described in Methods. (A) Plasma ALT levels. (B) Plasma AST levels. (C) Hepatic triglyceride levels. (D) H&E staining and Oil Red O staining. (E) Gene expression levels were examined by qPCR. N = 4–8 mice for each group. Scale bar, 100 μm.F–JAlcoholic liver injury was induced in WT and Brg1 LKI mice by NIAAA feeding as described in Methods. (F) Plasma ALT levels. (G) Plasma AST levels. (H) Hepatic triglyceride levels. (I) H&E staining and Oil Red O staining. (J) Gene expression levels were examined by qPCR. N = 4–8 mice for each group. Scale bar, 100 μm.
Data information: Data are expressed as mean ± S.D. *P < 0.05, two‐tailed Student's test.
Source data are available online for this figure.*
Next, ALD was induced in the hepatocyte‐specific Brg1 over‐expression mice (Brg1LKI) (Kong et al, 2021a) by the NIAAA procedure. Plasma ALT (Fig 2F) and AST (Fig 2G) measurements suggested that Brg1 over‐expression enhanced alcoholic liver injury. In accordance, biochemical quantification (Fig 2H) and histological evaluation (Fig 2I) demonstrated an acceleration of lipid accumulation in the Brg1LKI livers than in the WT livers. Additionally, qPCR analysis showed a trend of up‐regulation in the expression levels of pro‐inflammatory/pro‐lipogenic genes (Fig 2J). Taken together, these data link Brg1 manipulation in hepatocytes to altered ALD phenotype in mice.
## Brg1 deficiency attenuates leukocyte trapping in the liver
There is a growing consensus that cells of different immune lineages play an important role in ALD pathogenesis (Zetterman & Sorrell, 1981; Vidali et al, 2008; Gao et al, 2011). Flow cytometric analysis was performed to evaluate the role of Brg1 on the composition of immune cells in the ALD livers. As shown in Fig 3A, Brg1 deficiency led to a significant decrease in Ly6G+ neutrophils in the livers but minimally affected the populations of F$\frac{4}{80}$+ macrophages, CD3+ T lymphocytes, B220+ B lymphocytes, and NK1.1+ NK cells. Immunohistochemical staining detected fewer Ly6G+ neutrophils in the Brg1LKO livers than in the WT livers (Fig 3B). In contrast, infiltration of F$\frac{4}{80}$+ macrophages and CD3+ lymphocytes was indistinguishable between the WT livers and the Brg1LKO livers (Appendix Fig S7). Based on these observations, we hypothesized that Brg1 might contribute to neutrophil trafficking by modulating hepatocyte‐derived chemoattractive cues. To test this hypothesis, Boyden chamber transwell assay was performed (Fig 3C). Co‐culture with WT hepatocytes exposed to ethanol treatment stimulated neutrophil migration stronger than with Brg1LKO hepatocytes (Fig 3D). Brg1 knockdown in HepaRG cells achieved similar effects by reducing the migration of neutrophils (Appendix Fig S8). On the contrary, more Ly6G+ neutrophils were detected in Brg1LKI livers than in the WT livers (Appendix Fig S9).
**Figure 3:** *Brg1 deficiency attenuates leukocyte trapping in the liver
AAlcoholic liver injury was induced in WT and LKO mice by NIAAA feeding as described in Methods. Flow cytometry was performed as described in Methods. N = 6 mice for each group. Data are expressed as mean ± S.D. *P < 0.05, two‐tailed Student's test.BImmunohistochemical staining of paraffin section with anti‐Ly6G. N = 6 mice for each group. Scale bar, 100 μm. Arrows, Ly6G+ neutrophils. Data are expressed as mean ± S.D. *P < 0.05, two‐tailed Student's test.CA scheme of Boyden chamber transwell assay.DPrimary hepatocytes were isolated from WT and LKO mice and treated with or without EtOH. Transwell assay was performed as described in Methods. N = 3 biological repeats. Data are expressed as mean ± S.D. *P < 0.05, two‐tailed Student's test.E–HAlcoholic liver injury was induced in WT and Brg1 LKO mice by NIAAA feeding as described in Methods. RNA‐seq was performed using liver tissues. Venn diagram (E). Volcano plot (F). GO and KEGG analyses (G). Heat map (H).
Source data are available online for this figure.*
To determine the nature of the chemoattractive cue that emanates from WT livers but diminishes in the Brg1LKO livers, we compared the liver transcriptomics of two WT mice subjected to the NIAAA procedure and two Brg1LKO mice subjected to the NIAAA procedure. RNA‐seq data revealed that over 100 genes were differentially expressed between the two groups (Fig 3E and F). Enrichment analysis suggested that cell–cell communication pathways were among those most influenced by Brg1 deficiency (Fig 3G). C‐X‐C motif ligand chemokine 14 (CXCL14) was detected to trend with Brg1 (Fig 3F and H). QPCR analysis indicated that NIAAA diet feeding led to robust induction of CXCL14 expression in liver parenchymal cells (PCs) but not in non‐parenchymal cells (NPCs), indicating that hepatocytes might be the major source from which CXCL14 is derived during ALD pathogenesis (Appendix Fig S10). Although several other genes appeared to be down‐regulated by Brg1 deletion (Fig 3H), none possess clear chemotactic activities. In addition, expression levels of CXCL1, a chemokine known to regulate neutrophil migration, were comparable between the WT group and the Brg1LKO group (Appendix Fig S11). Because of the well‐established role of CXCL chemoattractants in regulating immune cell trafficking (Griffith et al, 2014), we focused on the regulation of CXCL14 transcription by Brg1 for the remainder of the study.
## Brg1 regulates neutrophil migration by activating CXCL14 transcription
We next examined the transcriptional and functional relationship between Brg1 and CXCL14. QPCR (Fig 4A) and ELISA (Fig 4B) assays showed that CXCL14 levels were significantly lower in the NIAAA diet‐challenged Brg1LKO livers than in the WT livers. In contrast, increased CXCL14 levels were detected in the NIAAA diet‐challenged Brg1LKI livers compared to the WT livers (Appendix Fig S12). In response to ethanol treatment, primary hepatocytes isolated from the WT mice produced more CXCL14 molecules than those from the Brg1LKO mice (Fig 4C and D). To determine whether ethanol‐induced CXCL14 occurred at the transcriptional level, the CXCL14 promoter extending ~2 kb from TSS was fused to a reporter and introduced into HepG2 cells via transient transfection. As shown in Fig 4E, ethanol treatment significantly up‐regulated CXCL14 promoter‐reporter activity. In addition, the ethanol response element appeared to reside between −400 and − 100 relative to the TSS as revealed by truncation mutagenesis (Fig 4E). Indeed, ChIP assay demonstrated that Brg1 was recruited to the proximal CXCL14 promoter region, but not to the more distal regions, when hepatocytes were exposed to ethanol treatment (Fig 4F), suggesting that Brg1 could directly bind to the CXCL14 promoter and activate CXCL14 transcription. Binding of Brg1 to the proximal CXCL14 promoter was further validated in the murine livers (Appendix Fig S13).
**Figure 4:** *Brg1 regulates neutrophil migration by activating CXCL14 transcription
A, BAlcoholic liver injury was induced in WT and Brg1 LKO mice by NIAAA feeding as described in Methods. CXCL14 levels were examined by qPCR and ELISA. N = 3–6 mice for each group. Data are expressed as mean ± S.D. *P < 0.05, one‐way ANOVA with post‐hoc Scheff'e.CPrimary Mouse primary hepatocytes were exposed to ethanol (50 mM) and harvested at indicated time points. CXCL14 levels were examined by qPCR. N = 3 biological replicates. Data are expressed as mean ± S.D. *P < 0.05, one‐way ANOVA with post‐hoc Scheff'e.DPrimary Mouse primary hepatocytes were exposed to ethanol (50 mM) for 6 h. CXCL14 levels in the media were examined by ELISA. N = 3 biological replicates. Data are expressed as mean ± S.D. *P < 0.05, one‐way ANOVA with post‐hoc Scheff'e.ECXCL14 promoter constructs of different lengths was transfected into HepG2 cells with or without Brg1 followed by treatment with ethanol. Luciferase activities were normalized by protein concentration and GFP fluorescence. N = 3 biological replicates. Data are expressed as mean ± S.D. *P < 0.05, two‐tailed Student's test.FPrimary Mouse primary hepatocytes were exposed to ethanol (50 mM) and harvested at indicated time points. ChIP assays were performed with anti‐Brg1 or IgG. N = 3 biological replicates. Data are expressed as mean ± S.D. *P < 0.05, two‐tailed Student's test.GPrimary hepatocytes isolated from WT and Brg1 LKO mice were exposed to ethanol (50 mM) in the presence or absence of recombinant CXCL14. Transwell assay was performed as described in Methods. N = 3 biological repeats. Data are expressed as mean ± S.D. *P < 0.05, two‐tailed Student's test. Arrows, migrated neutrophils.H–KWT and Brg1 LKO mice were injected with AAV8‐CXCL14 or AAV8‐GFP followed by NIAAA feeding. Plasma ALT (H) and AST (I) levels. (J) Hepatic triglyceride levels. (K) Gene expression levels were examined by qPCR. N = 5 mice for each group. Data are expressed as mean ± S.D. *P < 0.05, one‐way ANOVA with post‐hoc Scheff'e.
Source data are available online for this figure.*
Transwell assay showed that the addition of recombinant CXCL14 rescued the deficiency in the ability of Brg1‐null hepatocytes to promote neutrophil migration indicating that CXCL14 might be downstream of Brg1 functionally (Fig 4G). To further authenticate the functional relationship between Brg1 and CXCL14 in ALD pathogenesis, AAV‐mediated delivery was exploited to re‐introduce CXCL14 into the Brg1LKO mice. Over‐expression of exogenous CXCL14 largely restored NIAAA diet‐induced liver injury (Fig 4H and I) and lipid accumulation (Fig 4J) in the Brg1LKO mice bringing the levels closer to those observed in the WT mice. Consistently, the expression levels of pro‐inflammatory and pro‐lipogenic genes were up‐regulated in AAV‐CXCL14 infected Brg1LKO mice compared to the AAV‐GFP infected Brg1LKO mice (Fig 4K).
## Manipulation of CXCL14 regulates alcoholic liver injury in mice
We then asked whether manipulation of CXCL14 by itself would be sufficient to influence ALD pathogenesis in mice. In the first set of experiments, ectopic CXCL14 was delivered through injection with AAV viral particles followed by ALD induction using the NIAAA procedure (Fig 5A). Levels of CXCL14 over‐expression were verified by qPCR and ELISA (Appendix Fig S14). CXCL14 over‐expression enhanced alcoholic injury as shown by measurements of plasma ALT (Fig 5B), plasma AST (Fig 5C), and hepatic triglyceride (Fig 5D) levels. Histological analyses revealed that AAV‐CXCL14 injection enhanced NIAAA‐induced steatosis, ROS production, and neutrophil infiltration (Fig 5E). In addition, higher levels of pro‐inflammatory/pro‐lipogenic genes were detected in the AAV‐CXCL14 injected livers than in the AAV‐GFP injected livers (Fig 5F).
**Figure 5:** *Manipulation of CXCL14 regulates alcoholic liver injury in mice
A–FC57/BL6 were injected via tailed AAV8‐CXCL14 or AAV8‐GFP followed by induction of alcoholic liver injury. (A) Scheme of animal protocol. Plasma ALT (B) and AST (C) levels. (D) Hepatic triglyceride levels. (E) Liver sections were stained with H&E, oil red O, DHE, and anti‐Lys6G (left panel). Steatosis score and quantifications of staining (right panel). (F) Gene expression levels were examined by qPCR. N = 3–5 mice for each group. Scale bar, 100 μm. Arrows, Ly6G+ neutrophils. Data are expressed as mean ± S.D. *P < 0.05, two‐tailed Student's test.G–KC57/BL6 were injected via tailed AAV8‐CXCL14 or AAV8‐GFP followed by induction of alcoholic liver injury. Plasma ALT (G) and AST (H) levels. (I) Hepatic triglyceride levels. (J) Liver sections were stained with H&E, oil red O, DHE, and anti‐Lys6G (left panel). Steatosis score and quantifications of staining (right panel). (K) Gene expression levels were examined by qPCR. N = 4–6 mice for each group. Scale bar, 100 μm. Arrows, Ly6G+ neutrophils. Data are expressed as mean ± S.D. *P < 0.05, two‐tailed Student's test.
Source data are available online for this figure.*
In the second set of experiments, endogenous CXCL14 was depleted by AAV delivery of targeting shRNA and the knockdown efficiency was confirmed by qPCR and ELISA (Appendix Fig S15). CXCL14 silencing led to an amelioration of alcoholic liver injury as indicated by plasma ALT (Fig 5G), plasma AST (Fig 5H), and hepatic triglyceride (Fig 5I) levels. Moreover, H&E staining, oil red O staining, DHE staining, and histochemical staining with an anti‐Ly6G antibody all pointed to a less severe ALD phenotype owing to CXCL14 knockdown (Fig 5J). QPCR measurements of pro‐inflammatory/pro‐lipogenic gene expression added further support to the notion that CXCL14 might be essential for ALD pathogenesis (Fig 5K). Of note, CXCL14 did not influence the infiltration of F$\frac{4}{80}$+ macrophages or CD3+ lymphocytes (Appendix Fig S16).
## Targeting the Brg1‐CXCL14 axis ameliorates alcoholic liver injury in mice
Based on the observation that manipulation of either Brg1 expression or CXCL14 expression was associated altered ALD phenotype, we next entertained the idea that small‐molecule compounds that specifically target Brg1 or CXCL14 might be effective in ALD intervention. To test this idea, the mice were induced to develop ALD by the NIAAA procedure followed by injection with a specific Brg1 inhibitor (PFI‐3) (Vangamudi et al, 2015; Wu et al, 2016; Sharma et al, 2021) or a specific antagonist to the CXCL14 receptor (AMD3100) (Salogni et al, 2009; Collins et al, 2017) (Fig 6A). Administration of PFI‐3 significantly alleviated alcoholic liver injury as evidenced by plasma ALT levels (Fig 6B), plasma AST levels (Fig 6C), and hepatic triglyceride levels (Fig 6D). Further evidence that Brg1 inhibition by PFI‐3 administration could potentially mitigate alcoholic liver injury was provided by histological stainings that showed reduced lipid droplets, ROS production, and neutrophil infiltration in the liver (Fig 6 E). QPCR examination of pro‐inflammatory/pro‐lipogenic gene expression levels further attested to the effectiveness of PFI‐3 administration (Fig 6F). Similarly, CXCL14 blockade by AMD3100 corroborated the finding that CXCL14 is essential for ALD pathogenesis (Fig 6G–K). In vitro trans‐well assays confirmed that treatment with either PFI‐3 (Appendix Fig S17) or AMD3100 (Appendix Fig S18) suppressed neutrophil migration. AMD3100 is known to target CXCR4, a receptor for CXCL12. To rule out the involvement of hepatocyte‐derived CXCL12 in neutrophil migration, endogenous CXCL12 was depleted with siRNAs. Notably, CXCL12 depletion did not influence the emission of ethanol‐induced, hepatocyte‐derived chemoattractive cue to promote neutrophil migration (Appendix Fig S19).
**Figure 6:** *Targeting the Brg1‐CXCL14 axis ameliorates alcoholic liver injury in mice
A–FChronic alcoholic liver injury was induced in mice as described in Methods and a Brg1 inhibitor PFI‐3 (5 mg/kg) was administered at day 10. Scheme of protocol (A). Plasma ALT (B) and AST (C) levels. Hepatic triglyceride levels (D). Liver sections were stained with H&E, oil red O, DHE, and anti‐Lys6G (E). Gene expression was measured by qPCR (F). Scale bar, 100 μm. Arrows, Ly6G+ neutrophils.G–KChronic alcoholic liver injury was induced in mice as described in Methods and a CXCR4 antagonist AMD3100 (5 mg/kg) was administered at day 10. Plasma ALT (G) and AST (H) levels. Hepatic triglyceride levels (I). Liver sections were stained with H&E, oil red O, DHE, and anti‐Lys6G (J). Gene expression was measured by qPCR (K). Scale bar, 100 μm. Arrows, Ly6G+ neutrophils.
Data information: N = 6 mice for each group. Data are expressed as mean ± S.D. *P < 0.05, two‐tailed Student's test.
Source data are available online for this figure.*
## The Brg1‐CXCL14 axis may play a role in alcoholic liver disease in humans
We finally assessed the relevance of the newly identified Brg1‐CXCL14 axis in ALD pathogenesis in humans. QPCR detected significantly higher levels of Brg1 and CXCL14 in the liver specimens from ALD patients compared to healthy individuals (Fig 7A). Positive correlation between Brg1 expression and CXCL14 expression was identified in the ALD specimens (Fig 7B). Using publicly deposited datasets, we were able to confirm the positive correlation between Brg1 expression and CXCL14 expression in the livers of ALD patients (Appendix Fig S20). Further, elevated levels of neutrophil infiltration were revealed by immunohistochemical staining (Fig 7C) and both Brg1 expression and CXCL14 expression were found to be positively correlated with neutrophil infiltration (Fig 7D).
**Figure 7:** *The Brg1‐CXCL14 axis may play a role in alcoholic liver disease in humans
BRG1 and CXCL14 expression levels in ALD patients and healthy individuals were examined by qPCR. N = 9 cases for each group. Data are expressed as mean ± S.D. *P < 0.05, two‐tailed Student's test.Linear regression was performed with Graphpad Prism.Representative images of anti‐Ly6G staining and anti‐CXCL14 staining. Scale bar, 100 μm.Linear regression was performed with Graphpad Prism.A schematic diagram.
Source data are available online for this figure.*
## Discussion
Alcoholic liver disease is the most prevalent form of alcohol use disorder. Herein we describe a novel regulatory axis where the chromatin remodeling protein Brg1 activates the transcription of chemokine CXCL14 to promote neutrophil infiltration and consequently ALD (Fig 7E). Previous studies have shown that Brg1 is able to stimulate the production of multiple hepatocyte‐derived chemoattractive substances to promote homing of different immune cells. In a model of concanavalin A induced fulminant hepatitis, Brg1 deficiency attenuates hepatic infiltration of T lymphocytes owing to reduced production of the chemokine nephronectin (Hong et al, 2020). Alternatively, Brg1 deficiency dampens macrophage infiltration by limiting the availability of the chemokine CCL7 (Kong et al, 2021a). Of note, Brg1 deficiency did not alter the recruitment of either macrophages or T lymphocytes in the ALD models (Fig 3A). The context‐dependent requirement for Brg1 in modulating the trafficking of specific immune cell sub‐populations remains unclear. Brg1 relies on sequence‐specific transcription factors to be recruited to target promoters and, by extension, participate in the regulation of pathophysiological processes. Therefore, it is possible that certain ethanol‐sensitive transcription factors (TFs) may become rate‐limiting for Brg1 recruitment, CXCL14 trans‐activation and, ultimately, neutrophil homing. Characterization of the proximal CXCL14 promoter reveals several TFs including C/EBPβ (Niu et al, 2020) and ETS1 (Komori et al, 2010). Both C/EBPβ (Fan et al, 2019) and ETS1 (Chen et al, 2020) have been indicated as potential binding partners for Brg1. Further, both C/EBPβ (Chen et al, 2009) and ETS1 (McMullen et al, 2005) have been implicated in ALD pathogenesis. It would be of interest to determine whether mice with hepatocyte‐specific deficiency in C/EBPβ or ETS1 would phenocopy the Brg1LKO mice and exhibit reduced neutrophil infiltration when challenged with alcohol.
Although there is compelling evidence to show that CXCL14 is directly downstream of Brg1 and mediates neutrophil infiltration during ALD pathogenesis, there might be alternative and otherwise indirect mechanism underlying Brg1‐dependent regulation of neutrophil trafficking. For instance, RNA‐seq data (Fig 3H) point to reduced TNFSF9 (also known as CD137L) expression in the Brg1LKO livers compared to the WT livers. Several reports have suggested that CD137L, along with its cognate receptor CD137, can be involved in reverse signaling to regulate chemotaxis. Of interest, Kim et al [2012] have shown that CD137L expression in tubular epithelial cells can induce the synthesis of CXCL1 and CXCL2, which in turn promote neutrophil chemotaxis in a murine model of renal ischemia–reperfusion injury (Kim et al, 2012). Another attention‐worthy Brg1‐dependent candidate gene revealed by RNA‐seq data is ADRA2A, which encodes a subunit of adrenoreceptor. Adrenoreceptor signaling is known to modulate the behaviors of neutrophils including migration and chemotaxis (de Coupade et al, 2004; Brunskole Hummel et al, 2013). Thus, the observation that there was subdued neutrophil infiltration in the Brg1LKO livers may be attributed to impaired adrenoreceptor signaling. Of note, neutrophil infiltration and consequent alcoholic hepatitis are associated with up‐regulation of the adhesion molecule E‐selectin (Bertola et al, 2013b), a well characterized transcriptional target of Brg1 (Fang et al, 2013). We have previously shown that Brg1 promotes neutrophil migration to the ischemic heart by up‐regulating the transcription of the adhesion molecule podocalyxin (PODXL) in vascular endothelial cells (Zhang et al, 2018). It is therefore likely that altered expression of adhesion molecules may contribute to suppression of neutrophil recruitment in the Brg1LKO livers. These lingering possibilities should be further investigated to clarify the mechanistic link between Brg1 and intrahepatic immune cell composition.
Although we establish a connection between Brg1 deletion/inhibition and blockade of neutrophil infiltration, it should be noted that non‐chemotactic role of Brg1 in ALD pathogenesis cannot be ruled out. One of the key pathological characteristics of ALD is the spillover of pro‐inflammatory mediators in the liver (Zhang et al, 2018). Tian et al [2013] have previously demonstrated that Brg1 mediates palmitate‐induced pro‐inflammatory mediators in cultured hepatocytes by interacting with NF‐κB (Tian et al, 2013). Because ethanol exposure can directly elevate the synthesis of pro‐inflammatory mediators in hepatocytes in vitro in an NF‐κB‐dependent manner (Szabo et al, 2001; Senthil Kumar et al, 2012; Chiu et al, 2014), we propose that Brg1 may contribute to ALD pathogenesis by, at least in part, by stimulating the production of pro‐inflammatory mediators from hepatocytes. Similarly, ALD patients typically display marked hepatic steatosis indicative of altered lipid metabolism. Mounting evidence suggests a direct role for Brg1 in metabolic reprograming in a range of settings. For instance, we have shown that Brg1, by interacting with SREBP, regulates the transcription of genes involved in fatty acid synthesis and cholesterol synthesis in hepatocytes (Li et al, 2018a; Fan et al, 2020; Kong et al, 2021b). These observations certainly caution the interpretation of the data presented by this report and allude to a more complicated scenario in which multiple Brg1‐dependent but otherwise independent threads collectively contribute to ALD pathogenesis.
There are several issues that deserve further attention. First, it appears counterintuitive that Brg1 deficiency selectively impacts neutrophils despite collective down‐regulation of cytokines/chemokines known to promote trafficking of macrophages (Fig 2E). It is possible that a compensatory mechanism in the absence of hepatocyte Brg1 is activated to promote macrophage infiltration despite low levels of chemokines. Alternatively, an early surge followed by a decline of F$\frac{4}{80}$+ macrophages has been well documented for a wide range of organ injuries. We did not profile the dynamic changes in macrophage population in the liver during the entire course of ALD pathogenesis. Therefore, the possibility that Brg1 may regulate macrophage infiltration at earlier points during ALD development cannot be conclusively excluded. Second, we observed that targeting CXCL14 appeared to attenuate both neutrophil infiltration and steatosis in mice. It remains unclear whether these two processes occur in tandem or parallel. On the one hand, increased neutrophil infiltration in the liver may promote steatosis by producing reactive oxygen species and pro‐inflammatory mediators to alter metabolism in hepatocytes. Neutrophil depletion by Ly6G blocking antibody has been shown to dampen steatosis in a model of non‐alcoholic fatty liver disease (Gonzalez‐Teran et al, 2016). A similar strategy has been exploited by Szabo et al [2001] in a model of binge alcohol intake to show that neutrophil depletion alleviates liver inflammation and injury (Bukong et al, 2018); it was not determined whether steatosis was also altered in this model. Therefore, it is reasonable to speculate that CXCL14 contributes to steatosis by virtue of promoting neutrophil infiltration. On the other hand, Clement et al [2008] have previously reported that adipose tissue‐derived MCP‐1, a classic chemokine, can directly induce lipid accumulation when added to and incubated with hepatocytes in culture (Clement et al, 2008), pointing to the possibility that chemokines can possess pro‐lipogenic activities in addition to chemoattractive activities. More studies are warranted to clearly and definitively resolve these lingering issues.
We focused on the role of hepatocyte‐specific Brg1 in ALD pathogenesis in the present study. However, the contribution of non‐parenchymal cell Brg1 in this process cannot be ruled out. For instance, it has been well documented that Brg1 in liver sinusoid endothelial cells (LSECs) is able to regulate liver injury and fibrosis (Li et al, 2019b; Dong et al, 2020; Shao et al, 2020). In addition, Zhou et al [2021] have shown that deletion of Brg1 in hepatic progenitor cells (HPCs) attenuates cholangiocarcinoma in mice (Zhou et al, 2021). More importantly, it is highly likely that Brg1 may directly regulate neutrophil‐autonomous behaviors to contribute to ALD pathogenesis given the extensive regulatory role of Brg1 in different immune cells (Chaiyachati et al, 2013; Bossen et al, 2015; Qi et al, 2021). Future studies using lineage‐specific Brg1 transgenic mice should help resolve the issue conclusively.
In summary, our data unveil a previously unrecognized role of the Brg1‐CXCL14 in ALD pathogenesis. Most significantly, small‐molecule Brg1 inhibitors and CXCL14 antagonists appear to possess therapeutic potentials in model animals. Since neither Brg1 deletion (Wang et al, 2019; Li et al, 2019a) or CXCL14 deletion (Nara et al, 2007) in adult animals leads to any discernable detrimental phenotype, targeting the Brg1‐CXCL14 axis would presumably yield safe and effective interventional strategies in treating ALD. It should be noted that ALD develops after years, if not decades, of heavy drinking in human patients, the pathophysiology of which is unlikely to be faithfully recapitulated by the current animal model in its entirety. Therefore, more work, ideally employing humanized animal model, is needed before the results of the present study can be translated into clinical applications.
## Animals
All animal protocols were reviewed and approved by the intramural Committee on Ethical Treatment of Experimental Animals. Hepatocyte conditional Brg1 knockout (Brg1LKO) mice were generated by cross‐breeding the Smarca4 f/f strain with the Alb‐Cre strain as previously described (Li et al, 2018b). Hepatocyte conditional Brg1 knock‐in (Brg1LKI) mice were generated by cross‐breeding the RosaBrg1/+ mice (Liu et al, 2019) with the Alb‐Cre mice. Alcoholic liver injury was induced in 8‐wk, male mice by oral gavage (Yin et al, 2007) or chronic‐plus‐single‐binge ethanol feeding (the National Institute on Alcohol Abuse and Alcoholism model, or the NIAAA model; hereafter referred to as the NIAAA feeding) (Bertola et al, 2013a) as previously described. To manipulate CXCL14 expression in mice, murine CXCL14 cDNA or short hairpin RNA (shRNA) targeting murine CXCL14 sequences (GCGAGGAGAAGAUGGUUAUTT) were cloned into the pAAV‐DJ‐CMV vector. Each mouse received a single injection of 100 μl viral particles (1X1011GV/ml) through tail vein.
## Cell culture, plasmids, and transient transfection
Human hepatoma cells (HepG2 and HepaRG) and human neutrophil‐like leukemia cells (HL‐60) were maintained in DMEM supplemented with $10\%$ fetal bovine serum (FBS, Hyclone). Primary hepatocytes were isolated and cultured as previously described (Fan et al, 2019). Human CXCL14 promoter‐luciferase construct was generated by amplifying genomic DNA spanning the proximal promoter and the first exon of CXCL14 gene (−2,000/+94) and ligating into a pGL3‐basic vector (Promega). The mouse Smarca4 promoter‐luciferase constructs were cloned using a similar strategy. Mutant constructs were generated by a QuikChange kit (Thermo Fisher, Waltham, cat# 200514) and verified by direct sequencing. Small interfering RNAs were purchased from Dharmacon. Transient transfections were performed with Lipofectamine 2000. Luciferase activities were assayed 24–48 h after transfection using a luciferase reporter assay system (Promega, cat# E1500).
## Protein extraction and Western blot
Whole cell lysates were obtained by re‐suspending cell pellets in RIPA buffer (50 mM Tris pH7.4, 150 mM NaCl, $1\%$ Triton X‐100) with freshly added protease inhibitor (Roche). Western blot analyses were performed with anti‐BRG1 (Santa Cruz, cat# sc‐10,768, 1:1000) and anti‐β‐actin (Sigma, cat# A2228, 1:5000) antibodies.
## RNA isolation and real‐time PCR
RNA was extracted with the RNeasy RNA isolation kit (Qiagen, cat# 74106). Reverse transcriptase reactions were performed using a SuperScript First‐strand Synthesis System (Invitrogen, cat# 12574026). Real‐time PCR reactions were performed on an ABI Prism 7500 system with the following primers: human CXCL14, 5′‐CGCTACAGCGACGTGAAGAA‐3′ and 5′‐GTTCCAGGCGTTGTACCAC‐3′; human BRG1, 5′‐TCATGTTGGCGAGCTATTTCC‐3′ and 5′‐GGTTCCGAAGTCTCAACGATG‐3′; mouse Cxcl14, 5′‐GAAGATGGTTATCGTCACCACC‐3′ and 5′‐CGTTCCAGGCATTGTACCACT‐3′; mouse Brg1, 5′‐CAAAGACAAGCATATCCTAGCCA‐3′ and 5′‐CACGTAGTGTGTGTTAAGGACC‐3′; mouse Il1b, 5′‐GCAACTGTTCCTGAACTCAACT‐3′ and 5′‐ATCTTTTGGGGTCCGTCAACT‐3′; mouse Il6, 5′‐TGGGGCTCTTCAAAAGCTCC‐3′ and 5′‐AGGAACTATCACCGGATCTTCAA‐3′; mouse Tnfa, 5′‐CTGGATGTCAATCAACAATGGGA‐3′ and 5′‐ACTAGGGTGTGAGTGTTTTCTGT‐3′; mouse Nos2, 5′‐GTTCTCAGCCCAACAATACAAGA‐3′ and 5′‐GTGGACGGGTCGATGTCAC‐3′; mouse Mcp1, 5′‐AAAACACGGGACGAGAAACCC‐3′ and 5′‐ACGGGAACCTTTATTAACCCCT‐3′; mouse Fasn, 5′‐GGAGGTGGTGATAGCCGGTAT‐3′ and 5′‐TGGGTAATCCATAGAGCCCAG‐3′; mouse Scd1, 5′‐ACTGTGGAGACGTGTTCTGGA‐3′ and 5′‐ACGGGTGTCTGGTAGACCTC‐3′; mouse Acc1, 5′‐GCGGCTACAGGGACTATACTG‐3′ and 5′‐CGGAAGTAAGAGCTACTAGCGG‐3′; mouse Srebp1, 5′‐TGACCCGGCTATTCCGTGA‐3′ and 5′‐CTGGGCTGAGCAATACAGTTC‐3′. Ct values of target genes were normalized to the Ct values of house keeping control gene (18 s, 5′‐CGCGGTTCTATTTTGTTGGT‐3′ and 5′‐TCGTCTTCGAAACTCCGACT‐3′ for both human and mouse genes) using the ΔΔCt method and expressed as relative mRNA expression levels compared to the control group which is arbitrarily set as 1.
## Chromatin immunoprecipitation (ChIP)
Chromatin immunoprecipitation (ChIP) assays were performed essentially as described before (Dong et al, 2022). In brief, chromatin in control and treated cells were cross‐linked with $1\%$ formaldehyde. Cells were incubated in lysis buffer (150 mM NaCl, 25 mM Tris pH 7.5, $1\%$ Triton X‐100, $0.1\%$ SDS, $0.5\%$ deoxycholate) supplemented with protease inhibitor tablet and PMSF. DNA was fragmented into ~200 bp pieces using a Branson 250 sonicator. Aliquots of lysates containing 200 μg of protein were used for each immunoprecipitation reaction with 5 μg of anti‐BRG1 (Santa Cruz, cat# sc‐10,768), anti‐E2F1 (Cell Signaling Tech, cat# 3472), or pre‐immune IgG.
## Flow cytometry
Hepatic tissue was perfused with warm HBSS (37°C) containing collagenase IV (500 mg/L), DNase I (50 μg/L), FCS ($2\%$) and BSA ($0.6\%$). After digestion, the tissue was gently teased apart with a sterile blade and incubated in warm collagenase/HBSS solution (37°C) for 15 min with frequent shaking. The cell suspension was collected, filtered, and washed with PBS followed by centrifugation at 50 × g for 2 min. The cell pellets (parenchymal cells) were discarded and supernatants (non‐parenchymal cells, NPCs) were re‐pelleted, washed, and re‐suspended in PBS for flow cytometric analysis using the following antibodies: anti‐Ly6G (Invitrogen, cat# 45–5,931‐30, 1:100), anti‐F$\frac{4}{80}$ (BD Biosciences, cat# 565853, 1:100), anti‐CD3 (BD Biosciences, cat# 557596, 1:100), anti‐NK1.1 (Invitrogen, Cat# 12–5,941‐83, 1:100), anti‐B220 (BD, cat# 563103, 1:100), anti‐CD45 (Biolegend, cat# 103108, 1:100) as previously described (Daemen et al, 2021).
## Neutrophil migration assay
Neutrophil migration was measured using the Boyden chamber inserts (5 μm, Corning cat# 3496). Prior to the assay, HL‐60 cells were differentiated with DMSO ($1.3\%$ v/v) for 6 days as previously described (Babatunde et al, 2021). The hepatocytes were seeded into the lower chamber and differentiated HL‐60 cells were seeded into the upper chamber. 24 h after seeding, the inserts were lifted using forceps and washed with PBS. The cells on the inside of the inserts were gently removed using moistened cotton swabs and the cells on the lower surface of the membrane were then stained with crystal violet. The inserts were then rinsed with PBS to remove unbound dye and air‐dried. The migrated cells were observed and imaged under a microscope. In certain experiments, recombinant human CXCL14 (20 ng/ml, R&D, cat# 866‐CX‐025) was directly added to the conditioned media. Migrated cells were counted in at least five different fields for each well. The data are expressed relative cell migration compared to the control group which is set arbitrarily as 1. All experiments were performed in triplicates and repeated three times.
## Enzyme‐linked immunosorbent assay
Secreted CXCL14 levels were examined by ELISA using commercially available kits according to vendor′s recommendations (Raybiotech, cat# ELM‐CXCL14‐1).
## RNA sequencing and data analysis
RNA‐seq was performed as previously described (Wu et al, 2021). Total RNA was extracted using the TRIzol reagent according to the manufacturer's protocol. RNA purity and quantification were evaluated using the NanoDrop 2000 spectrophotometer (Thermo Scientific, USA). RNA integrity was assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Then the libraries were constructed using TruSeq Stranded mRNA LT Sample Prep Kit (Illumina, San Diego, CA, USA) according to the manufacturer's instructions and sequenced on an Illumina HiSeq X Ten platform and 150 bp paired‐end reads were generated. Raw data (raw reads) of fastq format were firstly processed using Trimmomatic and the low quality reads were removed to obtain the clean reads. The clean reads were mapped to the mouse genome (Mus_musculus. GRCm38.99) using HISAT2. FPKM of each gene was calculated using Cufflinks, and the read counts of each gene were obtained by HTSeqcount. Differential expression analysis was performed using the DESeq [2012] R package. P value < 0.05 and fold change > 2 or fold change < 0.5 was set as the threshold for significantly differential expression. Hierarchical cluster analysis of differentially expressed genes (DEGs) was performed to demonstrate the expression pattern of genes in different groups and samples. GO enrichment and KEGG pathway enrichment analysis of DEGs were performed respectively using R based on the hypergeometric distribution. The raw RNA‐seq data have been deposited in the NCBI functional genomics data repository (GSE207090).
## Human specimen collection
Liver biopsies were collected from patients with ALD referring to Nanjing Drum Tower Hospital. Written informed consent was obtained from subjects or families of liver donors. All procedures that involved human samples were approved by the Ethics Committee of Nanjing Drum Tower Hospital and adhered to the World Medical Association (WMA) Declaration of Helsinki and to the Department of Health and Human Services Belmont Report. For inclusion in the study, the patients have to meet the following criteria: (i) daily alcohol consumption > 40 g for at least 30 years; (ii) significantly elevated (3X over normal threshold) levels of AST, ALT, and GGT with a AST/ALT ratio > 2; (iii) pathological score > 6 (based on steatosis/0–4, inflammation/0–4, and fibrosis/0–4). Those who have viral hepatitis, drug‐induced hepatitis, and/or autoimmune hepatitis were excluded. Patient information is summarized in the Appendix Table S1.
## Histology
Histological analyses were performed essentially as described before. Briefly, the paraffin embedded sections were blocked with $10\%$ normal goat serum for 1 h at room temperature and then incubated with an anti‐Ly6G antibody (Abcam, cat# ab238132, 1:100) or anti‐BRG1 antibody (Abcam, cat# ab110641, 1:100). Staining was visualized by incubation with anti‐rabbit secondary antibody and developed with a streptavidin‐horseradish peroxidase kit (Pierce, cat# 21140) for 20 min. Pictures were taken using an Olympus IX‐70 microscope. Slides were observed under a light microscope at high power (X40) by two pathologists independently in a double‐blind fashion. The scoring system was based on the following criterion: the staining intensity was divided into quintiles; the slides with the strongest staining were given a score of 5 (top quintile) whereas the slides with the dimmest staining were given a score of 1 (bottom quintile).
## Statistical analysis
One‐way ANOVA with post‐hoc Scheff'e analyses were performed by SPSS software (IBM SPSS v18.0, Chicago, IL, USA). Unless otherwise specified, values of $P \leq 0.05$ were considered statistically significant.
## Author contributions
Nan Li: Conceptualization; formal analysis; funding acquisition; investigation; methodology; writing – review and editing. Hong Liu: Investigation; methodology; writing – review and editing. Yujia Xue: Investigation; methodology; writing – review and editing. Zheng Xu: Investigation; methodology; writing – review and editing. Xiulian Miao: Investigation; methodology; writing – review and editing. Yan Guo: Investigation; methodology; writing – review and editing. Zilong Li: Conceptualization; funding acquisition; investigation; methodology; writing – review and editing. Zhiwen Fan: Conceptualization; funding acquisition; investigation; methodology; writing – review and editing. Yong Xu: Conceptualization; data curation; supervision; funding acquisition; writing – original draft; writing – review and editing.
## Disclosure and competing interests statement
The authors declare that they have no conflict of interest.
## Problem
Chronic alcoholic consumption leads to alcohol liver disease (ALD), a prelude to cirrhosis and hepatocellular carcinoma. Brahma‐related gene 1 (Brg1) has been associated with a range of liver pathologies, however, Brg1 function in ALD has not been investigated.
## Results
In this paper we describe a novel mechanism whereby the chromatin remodeling protein Brg1 contributes to ALD. There is both a correlative and a causal relationship between Brg1 and ALD pathogenesis. Brg1 orchestrates the transcription of CXCL14, a chemokine, in hepatocytes to direct trafficking of neutrophils to the liver leading to ALD. Importantly, Brg1 inhibition or CXCL14 antagonism by small‐molecule compounds attenuates ALD in mice.
## Impact
These data provide novel insights into ALD pathogenesis and suggest Brg1 and CXCL14 as potential targets for ALD treatment.
## Data availability
RNA‐seq data generated for this study have been deposited in the Gene Expression Omnibus repository with the accession number GSE207090 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE207090).
## References
1. Akinyemiju T, Abera S, Ahmed M, Alam N, Alemayohu MA, Allen C, Al‐Raddadi R, Alvis‐Guzman N, Amoako Y, Artaman A. **The burden of primary liver cancer and underlying etiologies from 1990 to 2015 at the global, regional, and National Level: results from the global burden of disease study 2015**. *JAMA Oncol* (2017) **3** 1683-1691. PMID: 28983565
2. Babatunde KA, Wang X, Hopke A, Lannes N, Mantel PY, Irimia D. **Chemotaxis and swarming in differentiated HL‐60 neutrophil‐like cells**. *Sci Rep* (2021) **11** 778. PMID: 33436661
3. Bertola A, Mathews S, Ki SH, Wang H, Gao B. **Mouse model of chronic and binge ethanol feeding (the NIAAA model)**. *Nat Protoc* (2013a) **8** 627-637. PMID: 23449255
4. Bertola A, Park O, Gao B. **Chronic plus binge ethanol feeding synergistically induces neutrophil infiltration and liver injury in mice: a critical role for E‐selectin**. *Hepatology* (2013b) **58** 1814-1823. PMID: 23532958
5. Bossen C, Murre CS, Chang AN, Mansson R, Rodewald HR, Murre C. **The chromatin remodeler Brg1 activates enhancer repertoires to establish B cell identity and modulate cell growth**. *Nat Immunol* (2015) **16** 775-784. PMID: 25985234
6. Brunskole Hummel I, Reinartz MT, Kalble S, Burhenne H, Schwede F, Buschauer A, Seifert R. **Dissociations in the effects of beta2‐adrenergic receptor agonists on cAMP formation and superoxide production in human neutrophils: support for the concept of functional selectivity**. *PLoS One* (2013) **8**. PMID: 23741338
7. Bukong TN, Cho Y, Iracheta‐Vellve A, Saha B, Lowe P, Adejumo A, Furi I, Ambade A, Gyongyosi B, Catalano D. **Abnormal neutrophil traps and impaired efferocytosis contribute to liver injury and sepsis severity after binge alcohol use**. *J Hepatol* (2018) **69** 1145-1154. PMID: 30030149
8. Bultman S, Gebuhr T, Yee D, La Mantia C, Nicholson J, Gilliam A, Randazzo F, Metzger D, Chambon P, Crabtree G. **A Brg1 null mutation in the mouse reveals functional differences among mammalian SWI/SNF complexes**. *Mol Cell* (2000) **6** 1287-1295. PMID: 11163203
9. Bultman SJ, Gebuhr TC, Magnuson T. **A Brg1 mutation that uncouples ATPase activity from chromatin remodeling reveals an essential role for SWI/SNF‐related complexes in beta‐globin expression and erythroid development**. *Genes Dev* (2005) **19** 2849-2861. PMID: 16287714
10. Celli R, Zhang X. **Pathology of alcoholic liver disease**. *J Clin Transl Hepatol* (2014) **2** 103-109. PMID: 26357621
11. Chaiyachati BH, Jani A, Wan Y, Huang H, Flavell R, Chi T. **BRG1‐mediated immune tolerance: facilitation of Treg activation and partial independence of chromatin remodelling**. *EMBO J* (2013) **32** 395-408. PMID: 23321680
12. Chen YH, Yang CM, Chang SP, Hu ML. **C/EBP beta and C/EBP delta expression is elevated in the early phase of ethanol‐induced hepatosteatosis in mice**. *Acta Pharmacol Sin* (2009) **30** 1138-1143. PMID: 19617893
13. Chen B, Zhao Q, Xu T, Yu L, Zhuo L, Yang Y, Xu Y. **BRG1 activates PR65A transcription to regulate NO bioavailability in vascular endothelial cell**. *Front Cell Dev Biol* (2020) **8** 774. PMID: 32903816
14. Chiu YH, Tsai JJ, Lin SL, Lin MY. **Lactobacillus casei MYL01 modulates the proinflammatory state induced by ethanol in an in vitro model**. *J Dairy Sci* (2014) **97** 2009-2016. PMID: 24485689
15. Cho Y, Szabo G. **Two faces of neutrophils in liver disease development and progression**. *Hepatology* (2021) **74** 503-512. PMID: 33314193
16. Clement S, Juge‐Aubry C, Sgroi A, Conzelmann S, Pazienza V, Pittet‐Cuenod B, Meier CA, Negro F. **Monocyte chemoattractant protein‐1 secreted by adipose tissue induces direct lipid accumulation in hepatocytes**. *Hepatology* (2008) **48** 799-807. PMID: 18570214
17. Collins PJ, McCully ML, Martinez‐Munoz L, Santiago C, Wheeldon J, Caucheteux S, Thelen S, Cecchinato V, Laufer JM, Purvanov V. **Epithelial chemokine CXCL14 synergizes with CXCL12 via allosteric modulation of CXCR4**. *FASEB J* (2017) **31** 3084-3097. PMID: 28360196
18. de Coupade C, Gear RW, Dazin PF, Sroussi HY, Green PG, Levine JD. **Beta 2‐adrenergic receptor regulation of human neutrophil function is sexually dimorphic**. *Br J Pharmacol* (2004) **143** 1033-1041. PMID: 15477226
19. Daemen S, Chan MM, Schilling JD. **Comprehensive analysis of liver macrophage composition by flow cytometry and immunofluorescence in murine NASH**. *STAR Protoc* (2021) **2**. PMID: 33997821
20. Das S, Maras JS, Hussain MS, Sharma S, David P, Sukriti S, Shasthry SM, Maiwall R, Trehanpati N, Singh TP. **Hyperoxidized albumin modulates neutrophils to induce oxidative stress and inflammation in severe alcoholic hepatitis**. *Hepatology* (2017) **65** 631-646. PMID: 27775820
21. Dong W, Kong M, Zhu Y, Shao Y, Wu D, Lu J, Guo J, Xu Y. **Activation of TWIST transcription by chromatin remodeling protein BRG1 contributes to liver fibrosis in mice**. *Front Cell Dev Biol* (2020) **8** 340. PMID: 32478075
22. Dong W, Zhu Y, Zhang Y, Fan Z, Zhang Z, Fan X, Xu Y. **BRG1 links TLR4 trans‐activation to LPS‐induced SREBP1a expression and liver injury**. *Front Cell Dev Biol* (2021) **9**. PMID: 33816466
23. Dong W, Kong M, Liu H, Xue Y, Li Z, Wang Y, Xu Y. **Myocardin‐related transcription factor a drives ROS‐fueled expansion of hepatic stellate cells by regulating p38‐MAPK signalling**. *Clin Transl Med* (2022) **12**. PMID: 35184409
24. Fan Z, Li N, Xu Z, Wu J, Fan X, Xu Y. **An interaction between MKL1, BRG1, and C/EBPbeta mediates palmitate induced CRP transcription in hepatocytes**. *Biochim Biophys Acta* (2019) **1862**
25. Fan Z, Kong M, Li M, Hong W, Fan X, Xu Y. **Brahma related gene 1 (Brg1) regulates cellular cholesterol synthesis by acting as a Co‐factor for SREBP2**. *Front Cell Dev Biol* (2020) **8** 259. PMID: 32500071
26. Fang F, Chen D, Yu L, Dai X, Yang Y, Tian W, Cheng X, Xu H, Weng X, Fang M. **Proinflammatory stimuli engage brahma related gene 1 and brahma in endothelial injury**. *Circ Res* (2013) **113** 986-996. PMID: 23963727
27. Friedmann PD. **Clinical practice. Alcohol use in adults**. *N Engl J Med* (2013) **368** 365-373. PMID: 23343065
28. Gao B, Seki E, Brenner DA, Friedman S, Cohen JI, Nagy L, Szabo G, Zakhari S. **Innate immunity in alcoholic liver disease**. *Am J Physiol Gastrointest Liver Physiol* (2011) **300** 516-525
29. Goldberg D, Ditah IC, Saeian K, Lalehzari M, Aronsohn A, Gorospe EC, Charlton M. **Changes in the prevalence of hepatitis C virus infection, nonalcoholic steatohepatitis, and alcoholic liver disease among patients with cirrhosis or liver failure on the waitlist for liver transplantation**. *Gastroenterology* (2017) **152** 1090-1099.e1. PMID: 28088461
30. Gonzalez‐Teran B, Matesanz N, Nikolic I, Verdugo MA, Sreeramkumar V, Hernandez‐Cosido L, Mora A, Crainiciuc G, Saiz ML, Bernardo E. **p38gamma and p38delta reprogram liver metabolism by modulating neutrophil infiltration**. *EMBO J* (2016) **35** 536-552. PMID: 26843485
31. Griffith JW, Sokol CL, Luster AD. **Chemokines and chemokine receptors: positioning cells for host defense and immunity**. *Annu Rev Immunol* (2014) **32** 659-702. PMID: 24655300
32. Hill DB, Marsano LS, McClain CJ. **Increased plasma interleukin‐8 concentrations in alcoholic hepatitis**. *Hepatology* (1993) **18** 576-580. PMID: 8359798
33. Hong W, Kong M, Qi M, Bai H, Fan Z, Zhang Z, Sun A, Fan X, Xu Y. **BRG1 mediates Nephronectin activation in hepatocytes to promote T lymphocyte infiltration in ConA‐induced hepatitis**. *Front Cell Dev Biol* (2020) **8**. PMID: 33553140
34. Joshi‐Barve S, Barve SS, Butt W, Klein J, McClain CJ. **Inhibition of proteasome function leads to NF‐kappaB‐independent IL‐8 expression in human hepatocytes**. *Hepatology* (2003) **38** 1178-1187. PMID: 14578856
35. Khavari PA, Peterson CL, Tamkun JW, Mendel DB, Crabtree GR. **BRG1 contains a conserved domain of the SWI2/SNF2 family necessary for normal mitotic growth and transcription**. *Nature* (1993) **366** 170-174. PMID: 8232556
36. Kim HJ, Lee JS, Kim JD, Cha HJ, Kim A, Lee SK, Lee SC, Kwon BS, Mittler RS, Cho HR. **Reverse signaling through the costimulatory ligand CD137L in epithelial cells is essential for natural killer cell‐mediated acute tissue inflammation**. *Proc Natl Acad Sci U S A* (2012) **109** 13-22
37. Komori R, Ozawa S, Kato Y, Shinji H, Kimoto S, Hata R. **Functional characterization of proximal promoter of gene for human BRAK/CXCL14, a tumor‐suppressing chemokine**. *Biomed Res* (2010) **31** 123-131. PMID: 20460740
38. Kong M, Dong W, Zhu Y, Fan Z, Miao X, Guo Y, Li C, Duan Y, Lu Y, Li Z. **Redox‐sensitive activation of CCL7 by BRG1 in hepatocytes during liver injury**. *Redox Biol* (2021a) **46**. PMID: 34454163
39. Kong M, Zhu Y, Shao J, Fan Z, Xu Y. **The chromatin remodeling protein BRG1 regulates SREBP maturation by activating SCAP transcription in hepatocytes**. *Front Cell Dev Biol* (2021b) **9**. PMID: 33718362
40. Lemmers A, Moreno C, Gustot T, Marechal R, Degre D, Demetter P, de Nadai P, Geerts A, Quertinmont E, Vercruysse V. **The interleukin‐17 pathway is involved in human alcoholic liver disease**. *Hepatology* (2009) **49** 646-657. PMID: 19177575
41. Li N, Li M, Hong W, Shao J, Xu H, Shimano H, Lu J, Xu Y. **Brg1 regulates pro‐lipogenic transcription by modulating SREBP activity in hepatocytes**. *Biochim Biophys Acta Mol Basis Dis* (2018a) **1864** 2881-2889. PMID: 29857051
42. Li N, Li M, Hong W, Shao J, Xu H, Shimano H, Lu J, Xu Y. **Brg1 regulates pro‐lipogenic transcription by modulating SREBP activity in hepatocytes**. *Biochim Biophys Acta* (2018b) **1864** 3487-3495
43. Li N, Kong M, Zeng S, Hao C, Li M, Li L, Xu Z, Zhu M, Xu Y. **Brahma related gene 1 (Brg1) contributes to liver regeneration by epigenetically activating the Wnt/beta‐catenin pathway in mice**. *FASEB J* (2019a) **33** 327-338. PMID: 30001167
44. Li Z, Chen B, Dong W, Kong M, Shao Y, Fan Z, Yu L, Wu D, Lu J, Guo J. **The chromatin remodeler Brg1 integrates ROS production and endothelial‐mesenchymal transition to promote liver fibrosis in mice**. *Front Dev Cell Biol* (2019b) **7** 245
45. Li Z, Zhao Q, Lu Y, Zhang Y, Li L, Li M, Chen X, Sun D, Duan Y, Xu Y. **DDIT4 S‐Nitrosylation aids p38‐MAPK signaling complex assembly to promote hepatic reactive oxygen species production**. *Adv Sci* (2021) **8** 2101957
46. Liu M, Sun T, Li N, Peng J, Fu D, Li W, Li L, Gao WQ. **BRG1 attenuates colonic inflammation and tumorigenesis through autophagy‐dependent oxidative stress sequestration**. *Nat Commun* (2019) **10** 4614. PMID: 31601814
47. Lucey MR, Mathurin P, Morgan TR. **Alcoholic hepatitis**. *N Engl J Med* (2009) **360** 2758-2769. PMID: 19553649
48. Mason BJ, Heyser CJ. **Alcohol use disorder: the role of medication in recovery**. *Alcohol Res* (2021) **41** 7
49. McMullen MR, Pritchard MT, Wang Q, Millward CA, Croniger CM, Nagy LE. **Early growth response‐1 transcription factor is essential for ethanol‐induced fatty liver injury in mice**. *Gastroenterology* (2005) **128** 2066-2076. PMID: 15940638
50. Mookerjee RP, Stadlbauer V, Lidder S, Wright GA, Hodges SJ, Davies NA, Jalan R. **Neutrophil dysfunction in alcoholic hepatitis superimposed on cirrhosis is reversible and predicts the outcome**. *Hepatology* (2007) **46** 831-840. PMID: 17680644
51. Nara N, Nakayama Y, Okamoto S, Tamura H, Kiyono M, Muraoka M, Tanaka K, Taya C, Shitara H, Ishii R. **Disruption of CXC motif chemokine ligand‐14 in mice ameliorates obesity‐induced insulin resistance**. *J Biol Chem* (2007) **282** 30794-30803. PMID: 17724031
52. Ng LG, Ostuni R, Hidalgo A. **Heterogeneity of neutrophils**. *Nat Rev Immunol* (2019) **19** 255-265. PMID: 30816340
53. Niu L, Zheng Z, Xue Q, Cheng H, Liu Y, Wang H, Hu X, Zhang A, Liu B, Xu X. **Two coupled mutations abolished the binding of CEBPB to the promoter of CXCL14 that displayed an antiviral effect on PRRSV by activating IFN signaling**. *FASEB J* (2020) **34** 11257-11271. PMID: 32648265
54. de Oliveira S, Rosowski EE, Huttenlocher A. **Neutrophil migration in infection and wound repair: going forward in reverse**. *Nat Rev Immunol* (2016) **16** 378-391. PMID: 27231052
55. Peacock A, Leung J, Larney S, Colledge S, Hickman M, Rehm J, Giovino GA, West R, Hall W, Griffiths P. **Global statistics on alcohol, tobacco and illicit drug use: 2017 status report**. *Addiction* (2018) **113** 1905-1926. PMID: 29749059
56. Poikolainen K. **Risk factors for alcohol dependence: a case‐control study**. *Alcohol Alcohol* (2000) **35** 190-196. PMID: 10787396
57. Qi X, Qiu J, Chang J, Ji Y, Yang Q, Cui G, Sun L, Chai Q, Qin J. **Brg1 restrains the pro‐inflammatory properties of ILC3s and modulates intestinal immunity**. *Mucosal Immunol* (2021) **14** 38-52. PMID: 32612160
58. Ramaiah SK, Jaeschke H. **Role of neutrophils in the pathogenesis of acute inflammatory liver injury**. *Toxicol Pathol* (2007) **35** 757-766. PMID: 17943649
59. Robbins RA, Zetterman RK, Kendall TJ, Gossman GL, Monsour HP, Rennard SI. **Elevation of chemotactic factor inactivator in alcoholic liver disease**. *Hepatology* (1987) **7** 872-877. PMID: 3653852
60. Robinson MW, Harmon C, O'Farrelly C. **Liver immunology and its role in inflammation and homeostasis**. *Cell Mol Immunol* (2016) **13** 267-276. PMID: 27063467
61. Salogni L, Musso T, Bosisio D, Mirolo M, Jala VR, Haribabu B, Locati M, Sozzani S. **Activin a induces dendritic cell migration through the polarized release of CXC chemokine ligands 12 and 14**. *Blood* (2009) **113** 5848-5856. PMID: 19339694
62. Seitz HK, Bataller R, Cortez‐Pinto H, Gao B, Gual A, Lackner C, Mathurin P, Mueller S, Szabo G, Tsukamoto H. **Alcoholic liver disease**. *Nat Rev Dis Primers* (2018) **4** 16. PMID: 30115921
63. Senthil Kumar KJ, Liao JW, Xiao JH, Gokila Vani M, Wang SY. **Hepatoprotective effect of lucidone against alcohol‐induced oxidative stress in human hepatic HepG2 cells through the up‐regulation of HO‐1/Nrf‐2 antioxidant genes**. *Toxicol In Vitro* (2012) **26** 700-708. PMID: 22484158
64. Shao J, Xu Y, Fang M. **BRG1 deficiency in endothelial cells alleviates thioacetamide induced liver fibrosis in mice**. *Biochem Biophys Res Commun* (2020) **521** 212-219. PMID: 31635808
65. Sharma T, Robinson DCL, Witwicka H, Dilworth FJ, Imbalzano AN. **The Bromodomains of the mammalian SWI/SNF (mSWI/SNF) ATPases Brahma (BRM) and Brahma related gene 1 (BRG1) promote chromatin interaction and are critical for skeletal muscle differentiation**. *Nucleic Acids Res* (2021) **49** 8060-8077. PMID: 34289068
66. Szabo G, Catalano D, Bellerose G, Mandrekar P. **Interferon alpha and alcohol augment nuclear regulatory factor‐kappaB activation in HepG2 cells, and interferon alpha increases pro‐inflammatory cytokine production**. *Alcohol Clin Exp Res* (2001) **25** 1188-1197. PMID: 11505050
67. Tian W, Xu H, Fang F, Chen Q, Xu Y, Shen A. **Brahma‐related gene 1 bridges epigenetic regulation of proinflammatory cytokine production to steatohepatitis in mice**. *Hepatology* (2013) **58** 576-588. PMID: 23281043
68. Vangamudi B, Paul TA, Shah PK, Kost‐Alimova M, Nottebaum L, Shi X, Zhan Y, Leo E, Mahadeshwar HS, Protopopov A. **The SMARCA2/4 ATPase domain surpasses the Bromodomain as a drug target in SWI/SNF‐mutant cancers: insights from cDNA rescue and PFI‐3 inhibitor studies**. *Cancer Res* (2015) **75** 3865-3878. PMID: 26139243
69. Vidali M, Stewart SF, Albano E. **Interplay between oxidative stress and immunity in the progression of alcohol‐mediated liver injury**. *Trends Mol Med* (2008) **14** 63-71. PMID: 18222109
70. Wang B, Kaufmann B, Engleitner T, Lu M, Mogler C, Olsavszky V, Ollinger R, Zhong S, Geraud C, Cheng Z. **Brg1 promotes liver regeneration after partial hepatectomy via regulation of cell cycle**. *Sci Rep* (2019) **9** 2320. PMID: 30787318
71. Wu Q, Sharma S, Cui H, LeBlanc SE, Zhang H, Muthuswami R, Nickerson JA, Imbalzano AN. **Targeting the chromatin remodeling enzyme BRG1 increases the efficacy of chemotherapy drugs in breast cancer cells**. *Oncotarget* (2016) **7** 27158-27175. PMID: 27029062
72. Wu X, Dong W, Kong M, Ren H, Wang J, Shang L, Zhu Z, Zhu W, Shi X. **Down‐regulation of CXXC5 de‐represses MYCL1 to promote hepatic stellate cell activation**. *Front Cell Dev Biol* (2021) **9**. PMID: 34621736
73. Xu R, Huang H, Zhang Z, Wang FS. **The role of neutrophils in the development of liver diseases**. *Cell Mol Immunol* (2014) **11** 224-231. PMID: 24633014
74. Yin HQ, Kim M, Kim JH, Kong G, Kang KS, Kim HL, Yoon BI, Lee MO, Lee BH. **Differential gene expression and lipid metabolism in fatty liver induced by acute ethanol treatment in mice**. *Toxicol Appl Pharmacol* (2007) **223** 225-233. PMID: 17655900
75. Zetterman RK, Sorrell MF. **Immunologic aspects of alcoholic liver disease**. *Gastroenterology* (1981) **81** 616-624. PMID: 6166511
76. Zhang X, Liu S, Weng X, Zeng S, Yu L, Guo J, Xu Y. **Brg1 deficiency in vascular endothelial cells blocks neutrophil recruitment and ameliorates cardiac ischemia‐reperfusion injury in mice**. *Int J Cardiol* (2018) **269** 250-258. PMID: 30049497
77. Zhang Y, Yuan Y, Li Z, Chen H, Fang M, Xiao P, Xu Y. **An interaction between BRG1 and histone modifying enzymes mediates lipopolysaccharide‐induced proinflammatory cytokines in vascular endothelial cells**. *J Cell Biochem* (2019) **120** 13216-13225. PMID: 30891798
78. Zhao J, Zhang S, Liu Y, He X, Qu M, Xu G, Wang H, Huang M, Pan J, Liu Z. **Single‐cell RNA sequencing reveals the heterogeneity of liver‐resident immune cells in human**. *Cell Discov* (2020) **6** 22. PMID: 32351704
79. Zhou Y, Chen Y, Zhang X, Xu Q, Wu Z, Cao X, Shao M, Shu Y, Lv T, Lu C. **Brahma‐related gene 1 inhibition prevents liver fibrosis and cholangiocarcinoma by attenuating progenitor expansion**. *Hepatology* (2021) **74** 797-815. PMID: 33650193
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---
title: Targeting gut dysbiosis against inflammation and impaired autophagy in Duchenne
muscular dystrophy
authors:
- Hilal Kalkan
- Ester Pagano
- Debora Paris
- Elisabetta Panza
- Mariarosaria Cuozzo
- Claudia Moriello
- Fabiana Piscitelli
- Armita Abolghasemi
- Elisabetta Gazzerro
- Cristoforo Silvestri
- Raffaele Capasso
- Andrea Motta
- Roberto Russo
- Vincenzo Di Marzo
- Fabio Arturo Iannotti
journal: EMBO Molecular Medicine
year: 2023
pmcid: PMC9994484
doi: 10.15252/emmm.202216225
license: CC BY 4.0
---
# Targeting gut dysbiosis against inflammation and impaired autophagy in Duchenne muscular dystrophy
## Abstract
Nothing is known about the potential implication of gut microbiota in skeletal muscle disorders. Here, we provide evidence that fecal microbiota composition along with circulating levels of short‐chain fatty acids (SCFAs) and related metabolites are altered in the mdx mouse model of Duchenne muscular dystrophy (DMD) compared with healthy controls. Supplementation with sodium butyrate (NaB) in mdx mice rescued muscle strength and autophagy, and prevented inflammation associated with excessive endocannabinoid signaling at CB1 receptors to the same extent as deflazacort (DFZ), the standard palliative care for DMD. In LPS‐stimulated C2C12 myoblasts, NaB reduces inflammation, promotes autophagy, and prevents dysregulation of microRNAs targeting the endocannabinoid CB1 receptor gene, in a manner depending on the activation of GPR109A and PPARγ receptors. In sum, we propose a novel disease‐modifying approach in DMD that may have benefits also in other muscular dystrophies.
Changes in gut microbiota composition are found to be associated with an excess of endocannabinoid system activity and increased expression of cannabinoid CB1 receptors, suggesting a novel pathological mechanism implicated in the severity and progression of Duchenne muscular dystrophy.
## Introduction
Myopathies is a general term that refers to a large group of rare skeletal muscle disorders many of which are associated with poor prognosis. Among them, Duchenne muscular dystrophy (DMD) is the most frequent and detrimental form, affecting approximately 1 in 3,500 male births worldwide (Crisafulli et al, 2020). In most cases, DMD is caused by deletions of one or more exons within the gene encoding for dystrophin, a rod‐shaped protein that physically interacts with other specialized proteins to form the dystrophin‐associated glycoprotein complex (DAPC), playing a crucial structural and signaling role both in cardiac and skeletal muscles (Cirak et al, 2012). Therefore, the alteration of dystrophin expression and function leads to the collapse of muscle structure and irreversible tissue degeneration, a condition that is further aggravated by ensuing persistent inflammation, impairment of autophagy, fibrosis, and tissue necrosis (Sandri et al, 2013; De Palma et al, 2014). Unfortunately, a cure for DMD is still not available, although experimental therapies have made important advances over the years (Sheikh & Yokota, 2020). For this reason, corticosteroids mainly including prednisolone (PRED) and deflazacort (DFZ) remain the mainstay of palliative care. Both agents were shown to produce beneficial effects on the preservation of functional abilities in several mouse models of DMD and randomized controlled trials (Bushby et al, 2010; Griggs et al, 2016). However, there is uncertainty regarding the long‐term benefits and safety of these treatments. Results from randomized controlled studies revealed that side effects including sudden weight gain, confusion, depression, growth‐related complication, and cataracts may be caused by both DFZ and PRED (Matthews et al, 2016; Biggar et al, 2022).
The central role of the gut microbiota in human health and disease is a fascinating and rapidly expanding field of research. In this regard, over the last decades, much has been learned about the association between the gut microbiota and skeletal muscle mass as well as metabolic and contractile properties (Bäckhed et al, 2007; Grosicki et al, 2018; Lahiri et al, 2019). However, the molecular mechanisms linking intestinal microorganisms to skeletal muscle remain largely unknown. In this context, recent studies have demonstrated the role of major classes of metabolites produced by, or related to the gut microbiota, such as short‐chain fatty acids (SCFAs), i.e., acetate, propionate and butyrate, and ketone bodies (KBs; acetoacetate acid, β‐hydroxybutyric acid, and acetone), in contributing to skeletal muscle mass, glucose and lipid metabolism, and physical performance (Frampton et al, 2020). One of the proposed mechanisms through which SCFAs and KBs provide various health benefits on host energy metabolism is through intracellular signaling dependent on the activation of GPR41, GPR43, GPR109A, and/or peroxisome proliferator‐activated receptor‐gamma (PPARγ) receptors (Schwab et al, 2006; Layden et al, 2013).
Additionally, more recent evidence points to the endocannabinoid system as a key regulator in the communication between the gut microbiota and the host (Cani et al, 2016; de Vos et al, 2022).
The endocannabinoid system (ECS) refers to a complex lipid cell‐signaling system playing an important role in human health and disease. Although the number of molecules linked to the ECS is constantly expanding, the major players remain the two lipid mediators anandamide (AEA) and 2‐arachidonoylglycerol (2‐AG), which primarily activate two G‐coupled receptors, differently distributed in the body, named cannabinoid receptor of type 1 (CB1) and type 2 (CB2) (Iannotti et al, 2016). Despite the promising use of endocannabinoids or plant cannabinoids as complementary and/or alternative medicines (Di Marzo, 2018), to date, their potential use in skeletal muscle disorders is still largely unexplored. Recently, we demonstrated that (i) in both murine and human skeletal muscle cell precursors (myoblasts), 2‐AG promotes proliferation and inhibits differentiation to mature muscle cells (myotubes); (ii) 2‐AG negatively controls skeletal muscle formation in vivo; (iii) the ECS is overactive in both murine and human skeletal muscles affected by DMD; and (iv) the pharmacological inhibition of the endocannabinoid CB1 receptor promotes differentiation of both satellite and myoblast cells into mature myotubes, prevents locomotor impairment in dystrophic mice, and reduces muscle inflammation (Iannotti et al, 2014, 2018). However, whether the ECS could represent an additional signaling mechanism through which the gut microbiota interacts with the skeletal muscle remains to date completely unexplored. Based on this background, in this study, by using a multidisciplinary approach, we aimed at exploring whether changes in the gut microbiota diversity and its consequent dysfunctional interplay with the ECS could represent a novel potential molecular mechanism to be targeted in DMD.
## Fecal microbiota composition is significantly altered in mdx mice
In this study, the microbiota composition was analyzed in fecal samples collected from control (wild‐type—wt) and dystrophic (mdx) mice of 16 weeks of age randomly separated into the following experimental groups: (i) wt mice receiving vehicle (DMSO); (ii) wt mice receiving DFZ; (iii) mdx mice receiving vehicle; and (iv) mdx mice receiving DFZ. DMSO or DFZ (1.2 mg/kg) was administered once daily by oral gavage for 3 weeks.
As shown in Fig 1A, principal coordinate analysis (PCoA) revealed a significant (PERMANOVA analysis; $P \leq 0.003$) clustering dissimilarity of fecal microbiota composition among the four groups of mice. Clustering analysis of bacterial families detected and changing between wt and mdx mice treated without or with DFZ revealed that mdx mice treated with DFZ clustered with wt mice (Fig 1B). Prevotellaceae, Saccharimonadaceae, Peptococcaceae, Helicobacteriaceae, and Clostridiales_vadinBB60 were the bacterial families showing the most prominent alteration between wt and mdx mice, with Prevotellaceae being increased in the latter, while the other families were decreased (Fig 1C–G). Alterations in Prevotellaceae, Saccharimonadaceae, Peptococcaceae, and Clostridiales_vadinBB60 levels were reversed by DFZ (Fig 1C and G). However, in mdx mice, the treatment with DFZ also significantly increased the relative abundance of Desulfovibrionaceae (Fig 1H) and reduced that of Erysipelotrichaceae and Burkholdariaceae (Fig 1I and J). Finally, we detected no significant differences in the microbiota composition between wt mice receiving DMSO or DFZ, with the only exception of Clostridiales_vadinBB60 levels (Fig 1G).
**Figure 1:** *Microbiota analysis in fecal samples of control and mdx mice
APrincipal coordinate analysis (PCoA) displays differences in gut microbiota composition diversity among the four animal groups. Bray–Curtis dissimilarity indexes were used to estimate B‐diversity. PERMANOVA P values (Adonis R function) are displayed above PCoA.BHeat map and hierarchical clustering of family composition using cumulative sum scaled (CSS) normalized bacterial counts.C–JBar Chart with data points showing the abundance of indicated bacterial families in the indicated group of mice. Data are expressed as CSS‐normalized bacterial counts.
Data Information: Each bar is the mean ± S.E.M. from 5 independent biological samples. ****P ≤ 0.0001; ***P ≤ 0.0003; **P ≤ 0.003; *P ≤ 0.05 vs. the indicated experimental group calculated using ANOVA.*
## Plasma levels of SCFAs and KBs are dysregulated in mdx mice
To understand whether the changes in fecal microbiota found in mdx mice resulted in changes in the levels of metabolites produced or associated with intestinal microbiota, we quantified the concentrations of SCFAs, KBs, and related molecules in blood plasma, skeletal muscles, and feces samples of mice subjected to our experimental conditions. Using GC/MS and NMR analysis, we found that plasma levels of propionate and acetate, but not butyrate (the only one not detectable by NMR), were significantly reduced in mdx vs. control mice (Fig 2A–C). The treatment with DFZ of mdx mice prevented changes in propionate and acetate levels (Fig 2A and B) and remarkably increased butyrate levels in mdx mice (Fig 2C). Additionally, using NMR, we found that plasma levels of pyruvate, succinate, and lactate, which are key precursors in the biosynthetic pathway of SCFAs from carbohydrates (Koh et al, 2016), were dysregulated in dystrophic mice (Fig 2D–F). The treatment with DFZ, also in this latter case, prevented these alterations while inducing a significant reduction of pyruvate, whereas lactate was increased in control mice (Fig 2D–F). The matrix shows the potential correlation between SCFAs and bacterial families changing in mdx mice (Fig 2G). Similar to SCFAs, plasma levels of KBs including acetone, acetoacetic acid (AA), and 3‐hydroxybutyric acid were reduced in mdx mice. Following the treatment with DFZ, the levels of 3‐hydroxybutyric and AA were restored to the control condition (Fig EV1). By contrast, no statistically significant differences in either SCFAs or KB levels were found in gastrocnemius and fecal samples between control and mdx mice (Fig EV2).
**Figure 2:** *Analysis of SCFAs and their precursors in the blood plasma of wt and mdx treated with DFZ or not
A–CBar chart with individual points showing the levels of propionate, acetate and butyrate in wt and mdx mice receiving DFZ or vehicle, measured by NMR or GC/MS. Data are expressed as μg/ml or bin intensity (arbitrary unit—a.u.)D–FBar chart with individual points showing the levels of pyruvate, succinate, and lactate in wt and mdx mice receiving vehicle or DFZ, measured by NMR.GCorrelation map based on Pearson correlation coefficients between butyrate, propionate, and acetate, and bacterial families changing in mdx mice. Rows and columns are rearranged according to the WARD‐based correlation matrix‐based hierarchical clustering (CMBHC).
Data Information: Each bar is the mean ± S.E.M. from 5 independent biological samples. ****P ≤ 0.0001; ***P ≤ 0.0003; **P ≤ 0.003; *P ≤ 0.05 vs. the indicated experimental group calculated using ANOVA.* **Figure EV1:** *Measurement of KBs in plasma samples of wt and mdx mice treated with or without DFZ
A–C Bar chart with individual points showing the levels of the indicated metabolites detected in the plasma of wt and mdx mice treated ± DFZ. Data are expressed as bin intensity (a. u., arbitrary unit).
Data Information: Each bar is the mean ± S.E.M. of 5 independent biological determinations. ****P ≤ 0.0001; ***P ≤ 0.0003; **P < 0.01; *P ≤ 0.05 vs. the indicated experimental group calculated using ANOVA.* **Figure EV2:** *Measurement of SCFAs and KBs in the gastrocnemius and fecal samples of wt and mdx mice treated with or without NaB or DFZ
A–FBar chart with individual points showing the levels of the indicated metabolites detected in the gastrocnemius and/or fecal samples of wt and mdx mice treated ± DFZ. Data are expressed as bin intensity (a. u., arbitrary unit).
Data Information: Each bar is the mean ± S.E.M. from 5 independent biological samples.*
These data, together with those described in the previous section, suggest that functional alterations of the gut microbiota are present in mdx mice as compared to wt mice and that many of such alterations are reversed following 3 weeks treatment with DFZ. Importantly, in dystrophic mice, DFZ elevated levels of butyrate, which was previously described to exert beneficial anti‐inflammatory and immunomodulatory activities (Prokopidis et al, 2021).
## Locomotor activity, muscle autophagy deficits, and inflammation in mdx mice are ameliorated after supplementation with sodium butyrate
In order to understand whether the perturbation of the gut microbiota (known as dysbiosis) is associated with muscle function impairment in mdx mice through the ensuing deficit of SCFAs production, we next measured muscle coordination and strength in wt and mdx mice receiving a daily oral supplementation of sodium butyrate (NaB; 100 mg/kg) or DFZ (1.2 mg/kg) for 3 weeks using a rotarod and weight test. In agreement with our previous findings (Iannotti et al, 2018), we found that 19‐week‐old mdx mice showed marked impairment of muscle coordination and strength compared with their age‐matched controls (Fig 3A and B). Remarkably, the treatment with NaB prevented the loss of locomotor function in mdx mice, to an extent comparable to that of DFZ, while both NaB and DFZ had no effect in control mice (Fig 3A and B). Transcriptomic analysis performed on RNA extracted from dissected gastrocnemius revealed that NaB, to the same extent as DFZ, was able to restore the defective expression of key genes regulating autophagy and/or mitophagy (Ulk1, Atg13, Pink1, Becn1, Fundc1, and Bnip) in mdx mice (Fig 4A–F) and concomitantly prevented the up‐regulation of pro‐inflammatory genes such as interleukin 6 (Il6), tumor necrosis factor‐α (Tnfα), and cyclooxygenase‐2 (Cox2) (Fig 4G–I). Additionally, using western blot analysis, we found that NaB, similar to DFZ, prevented the increased phosphorylation (hence activation) of AKT, a negative regulator of autophagy (De Palma et al, 2014; Fig 4J), and concomitantly reduced the expression of COX2 (Fig 4K), which is considered a key therapeutic target to counteract inflammation in DMD (Péladeau et al, 2018). These data suggest that the dysfunctional gut microbiota is implicated in the pathogenesis of DMD in mdx mice. In the same vein, NaB supplementation prevented some of the features of muscular dystrophy in these mice, i.e., impaired locomotion and muscle autophagy and enhanced muscle inflammation.
**Figure 3:** *Measurement of locomotor activity
A, BMuscle coordination and strength were measured in 19‐week‐old control and mdx mice treated with vehicle (DMSO), NaB (100 mg/kg/daily), or DFZ (1.2 mg/kg/daily) for 3 weeks using the rotarod and weight test. Bar charts show the latency to fall or drop the weight of wt and dystrophic mice.
Data Information: Each bar is the mean ± S.E.M. from 5 or more independent biological determinations. ****P ≤ 0.0001; ***P ≤ 0.0003; *P ≤ 0.05 vs. the indicated experimental group calculated using ANOVA.* **Figure 4:** *Expression of autophagy and inflammatory genes in wt and mdx mice treated with NaB or DFZ
A–IBar chart with individual points showing the mRNA expression levels of the indicated genes measured in the gastrocnemius of control and mdx mice treated with or without NaB and DFZ.J, KRepresentative blotting and bar chart with individual points showing the expression and/or phosphorylation of pAKT/AKT and COX2 in the gastrocnemius of the indicated six groups of mice.
Data Information: Each bar is the mean ± S.E.M. from 5 independent biological replicates. ****P ≤ 0.0001; ***P ≤ 0.0003; **P ≤ 0.005; *P ≤ 0.05 vs. the indicated experimental group calculated using ANOVA.*
## Endocannabinoid system overactivity in blood and skeletal muscle of mdx mice is reversed by NaB or DFZ
Next, we measured the levels of the two major endocannabinoids AEA and 2‐AG in the plasma of mice subjected to our experimental conditions. Liquid chromatography–mass spectrometry (LC–MS) analysis revealed that the levels of AEA, but not 2‐AG, were significantly increased in 19‐week‐old mdx mice compared with the control group (Fig 5A and B). Changes in AEA levels were associated with a significant up‐regulation of CB1 and CB2 mRNA and protein expression in the gastrocnemius of dystrophic mice (Fig 5C–F). Notably, in mdx mice, the dysregulated levels of AEA as well as CB1 and CB2 genes were prevented by NaB and DFZ (Fig 5A–F). In control mice, treatment with NaB or DFZ did not significantly change the levels of AEA or 2‐AG, nor the expression of CB1 and CB2 genes (Fig 5A–D).
**Figure 5:** *Measurement of endocannabinoid system activity in the plasma and skeletal muscle of wt and mdx mice treated with NaB and DFZ
A, BLevels of AEA and 2‐AG in plasma samples of wt and mdx mice expressed as pmol/mg of wet tissue weight.C, DBar charts with individual points showing the mRNA expression levels of CB1 and CB2 measured in the gastrocnemius of the indicated groups of mice.ERepresentative blots showing the expression levels of CB1 and CB2 proteins in the gastrocnemius of the indicated groups of mice.FQuantification of CB1 and CB2 proteins to the housekeeping protein GAPDH.
Data Information: Each bar is the mean ± S.E.M. of 4–5 independent biological samples. ***P ≤ 0.0003; **P ≤ 0.003; *P ≤ 0.05 vs. the indicated experimental group calculated using ANOVA.*
## Pharmacological blockade of CB1 rescues autophagy in skeletal muscles of mdx mouse
Our previous findings revealed that the ECS is overactive in mdx mice, predominantly via CB1 receptors, leading to a concurrent increase in the inflammatory burden and reduction in myotube formation from satellite and myoblast cells (Iannotti et al, 2018). Here, we evaluated the effect of CB1 on skeletal muscle autophagy. Intriguingly, the reduced expression of Ulk1, Pink, and *Becn1* genes observed in skeletal muscles (gastrocnemius) of dystrophic mice was prevented by treatment with rimonabant (0.5 mg/kg), a selective CB1 receptor antagonist (Fig 6A–C). In addition, using western blot, we found that in mdx mice, rimonabant restored the expression of LC3II, which is physiologically converted from LC3I to initiate the formation and lengthening of the autophagosome (Runwal et al, 2019) (Fig 6D–F). Conversely, in mdx mice treated with ACEA (2.5 mg/kg), a selective CB1 agonist (Hillard et al, 1999), the inactivation of autophagy genes tended to aggravate (Fig EV3). These findings, together with those described in the previous section, suggest that enhanced endocannabinoid signaling at CB1 receptors may act as an intermediate of the effects of the dysfunctional gut microbiota on inflammation and impaired locomotor activity and autophagy in mdx mice.
**Figure 6:** *Effect of rimonabant on autophagy in mdx mice
A–CBar charts with individual points showing the mRNA expression levels of Ulk, Pink, and Becn1 measured in control and mdx mice treated with rimonabant (0.5 mg/kg).DRepresentative blots showing the expression levels of LC3I and LC3II proteins in the gastrocnemius of the indicated groups of mice.E, FQuantification of LC3I and LC3II proteins to the housekeeping protein GAPDH.
Data Information: Each bar is the mean ± S.E.M. from 3 independent biological samples. **P ≤ 0.005 *P ≤ 0.05 vs. the indicated experimental group calculated using ANOVA.* **Figure EV3:** *Effect of ACEA on the expression of autophagy‐related genes in mdx mice
A–DBar charts with individual points showing the mRNA expression levels of Bnip, Becn1, Ulk1, and Pink measured in control and mdx mice treated with ACEA 2.5 mg/kg.
Data Information: Each bar is the mean ± S.E.M. from 6 independent biological samples. ***P ≤ 0.0003; *P ≤ 0.05 vs. the indicated experimental group calculated using ANOVA.*
## Butyrate protects skeletal muscle cells from LPS‐induced inflammation, promotes autophagy, and prevents endocannabinoid overactivity via multiple receptors
Next, we went on to elucidate the molecular mechanisms through which, upstream of endocannabinoid signaling at CB1 receptors, NaB exerts anti‐inflammatory and pro‐autophagic effects in skeletal muscle tissues. To this purpose, we employed murine C2C12 myoblasts and myotubes to measure the expression of potential molecular targets of SCFAs. There is evidence that some members of the large family of G protein‐coupled receptors (GPCRs) including GPR41 and GPR43 have an affinity for propionate, butyrate, and acetate, while GPR109A and peroxisome proliferator‐activated receptor‐gamma (PPARγ) show a selective affinity only for butyrate (Brown et al, 2003; Le Poul et al, 2003; Sun et al, 2017; Kumar et al, 2020). Therefore, using qPCR, we found that among GPCRs, GPR109A was the only gene expressed in both C2C12 myoblasts and myotubes (Table EV1). In agreement with others, we also found that PPARγ is expressed in myoblasts more than in myotubes (Table EV1) (Singh et al, 2007). Importantly, among SCFA targets, the expression of GPR109A and PPARγ was also predominant in the skeletal muscle (gastrocnemius) of control and dystrophic mice (Table EV1).
Subsequently, C2C12 myoblasts were stimulated with bacterial lipopolysaccharide (LPS) to mimic the inflammatory microenvironment, which prevails in DMD (Boursereau et al, 2018). Cytokines including IL‐6, IL‐1, and TNFα, as well as the enzyme COX2, are known to be increased during inflammation in DMD (Cruz‐Guzmán Odel et al, 2015). Therefore, following published procedures (Park et al, 2021), we observed that in C2C12 myoblasts stimulated with LPS (1 μg/ml for 3 h), the mRNA expression of Il6 and Cox2 was robustly increased (~ 15 fold). However, when C2C12 cells were pretreated with NaB (3 mM) or MK1903 (1 μM, a selective GPR109A agonist) for 30 min before the stimulation with LPS, the up‐regulation of Il6 and Cox2 was significantly prevented (Fig 7A and B). Additionally, in myoblasts silenced for GPR109A, the protective effect of NaB was partially abolished (Fig 7A and B). Rosiglitazone (1 μM), a selective PPARγ agonist, similar to NaB and MK1903 prevented the LPS‐induced up‐regulation of both Il6 and Cox2 (Fig 7A and B). Notably, the effect of NaB was fully abolished only when T007 1 μM (a selective PPARγ antagonist) was used in C2C12 cells silenced for GPR109A (Fig 7A and B), thus indicating that NaB exerts its anti‐inflammatory action in muscle cells through the concomitant activation of GPR109A and PPARγ. Moreover, in C2C12 cells not exposed to LPS, we found that NaB, also in this case in a manner depending on GPR109A and PPARγ activation, stimulates autophagy (Fig 7C).
**Figure 7:** *Effect of NaB on LPS‐stimulated C2C12 cells
A, BBar chart with individual points showing the mRNA expression levels of Il6 and Cox2 in control (vehicle, DMSO) and/or GPR109A‐silenced C2C12 myoblasts exposed to LPS (1 μg/ml) in the presence or absence of either MK1903 (1 μM), rosiglitazone (1 μM), or T007 (1 μM).CTime‐dependent effect of NaB (3 mM), MK1903 (1 μM), and rosiglitazone (1 μM) on autophagosome formation measured in C2C12 myoblasts. Data are expressed as fluorescence intensity normalized to controls (%).
Data Information: Each bar is the mean ± S.E.M. of at least 3 independent replicates. *P ≤ 0.05 vs. the veh group. ±
P ≤ 0.05 vs. the LPS group; #
P ≤ 0.05 vs. the other experimental groups (A) or the veh group (B) calculated using ANOVA.*
Next, we evaluated whether NaB could then regulate dysfunctional ECS activity also in myoblasts. In agreement with previous studies, we found that LPS significantly altered the expression of key genes regulating ECS activity (Turcotte et al, 2015). Among them, the mRNA expression of Cb1, Daglα, and Daglβ (the latter two are genes encoding for key enzymes producing 2‐AG), Magl (2‐AG degradation), and Napepld (AEA synthesis) was significantly increased. By contrast, the expression of Faah (AEA degradation) was reduced by LPS (Fig 8A–F). Pretreatment with NaB for 30 min before the stimulation with LPS prevented the dysregulated expression of all the aforementioned genes (Fig 8A–F). The effect of NaB was, with the only exception of Magl, abolished upon silencing of GPR109A and/or in presence of T007 (Fig 8A–F) and mimicked by rosiglitazone (1 μM) and MK1903 (1 μM) (Fig 8A–F). The drug alone (rosiglitazone, MK1903, and T007 for 3 h) did not significantly change ECS gene expression (Fig 8A–F). Additionally, following stimulation with LPS in C2C12 cells, we did not detect significant changes in AEA levels, although a tendency to reduce its levels was observed following NaB treatment (Fig 8G). By contrast, 2‐AG levels tended to be increased by LPS (Fig 8H). Notably, we also found that the promotion of autophagy in C2C12 cells induced by NaB was abolished in the presence of ACEA, but not rimonabant (Fig 8I).
**Figure 8:** *Effect of LPS on the endocannabinoid system activity in C2C12 cells
A–FBar chart with individual points showing the mRNA expression levels of Cb1, Daglα, Daglβ, Magl, Napepld, and Faah in control (vehicle, DMSO) and/or GPR109A‐silenced C2C12 myoblasts exposed to LPS (1 μg/ml) in the presence or absence of either MK1903 (1 μM), rosiglitazone (1 μM), or T007 (1 μM).G, HLevels of AEA and 2‐AG were measured in C2C12 cells exposed to LPS (1 μg/ml) or NaB (3 mM) for 24 h.IEffect of ACEA (1 μM) and rimonabant (1 μM) on autophagosome formation measured in C2C12 cells.
Data Information: Each bar is the mean ± S.E.M. from 3 independent biological replicates. **P ≤ 0.005; *P ≤ 0.05 vs. the veh group; ±
P ≤ 0.05 vs. the LPS group calculated using ANOVA.*
## Butyrate prevents LPS‐mediated down‐regulation of microRNA (miRNAs) targeting CB1
We next searched for the molecular mechanism through which the stimulation of GPR109A and PPARγ receptors by NaB prevents LPS‐induced dysregulation of CB1, the main effector of the ECS. Therefore, using bioinformatics analysis, we identified several microRNA (miRNAs) sequences targeting the three prime untranslated regions (3′‐UTR) of the Cb1 (Cnr1) murine gene. In particular, we focussed mostly on those conserved among mammals such as miR‐18, miR‐190, miR‐128, miR‐19, miR‐29, miR‐181, miR‐130, miR‐301, miR‐148, and miR‐152 (Fig 9A). However, our analysis also included miR‐429, miR‐489, and miR‐452, which target the murine 3′UTR Cb1 region, but not the human one. Subsequently, the expression of selected miRNAs was evaluated in C2C12 myoblasts exposed to LPS in the presence or absence of NaB, rosiglitazone, or MK1903. Using quantitative PCR, we found that in myoblasts only miR‐19, miR‐128, miR‐425, miR‐489, miR‐130, miR‐152, miR‐301, and miR‐29 were expressed. As shown in the heatmap (Fig 9B) and bar graphs (Fig 9C–J), the expression of all these miRNAs (except for miR‐489) was significantly reduced by LPS. In cells exposed to LPS in the presence of NaB, we observed that the LPS‐induced down‐regulation of miR‐19, miR‐128, miR‐425, miR‐130, miR‐152, miR‐301 and miR‐29 was prevented, with the expression of miR‐425 being significantly increased compared with the veh group (Fig 9C–J). The effect of NaB was, to a different extent, prevented in C2C12 silenced for Gpr109A or by T007 (1 μM). Accordingly, in cells exposed to LPS in the presence of rosiglitazone, we observed that miR‐19, miR‐130, miR‐152, and miR‐29 expression was significantly increased compared with both LPS alone and veh groups (Fig 9C, G, H, and J). Additionally, the expression of miR‐128, miR‐425, and miR‐301 was restored to levels comparable to those of the control (veh) group (Fig 9D, E, and I). Moreover, in cells exposed to LPS in the presence of MK1903, we found that the expression of miR‐128, miR‐425, and miR‐130 was significantly increased versus both the LPS and veh groups (Fig 9D, E, and G), whereas the expression of miR‐19, miR152, miR‐301, and miR‐29 was rescued to control levels (Fig 9C, H, I, and J). Instead, no significant effects were found in cells exposed to the drugs alone, with the only exception of miR‐19 and miR‐130 expression, which was reduced compared to that of the veh group by T007 and rosiglitazone, respectively (Fig 9C–J).
**Figure 9:** *Effect of LPS on the expression of miRNAs targeting the Cnr1 gene
ASchematic representation of miRNAs targeting the 3′‐UTR region of both murine and human CB1 gene.BHeatmap representation of the expression of selected miRNAs in the indicated biological replicates. Red—up‐regulated; green—down‐regulated.C–JBar chart with individual points showing the expression of selected miRNAs in control and Gpr109A‐silenced C2C12 myoblasts exposed to LPS (1 μg/ml) in the presence or absence of either NaB (3 mM), MK1903 (1 μM), or rosiglitazone (1 μM). NaB was also tested in the presence or absence of either rosiglitazone (1 μM) or T007 (1 μM).
Data Information: Each bar is the mean ± S.E.M. from 3 independent biological replicates. ±
P ≤ 0.05 vs. veh group; **P ≤ 0.03 vs. LPS group; #
P ≤ 0.05 vs. the other experimental groups calculated using ANOVA.*
In summary, these results show that NaB in skeletal muscle cells exposed to LPS exerts anti‐inflammatory and pro‐autophagy effects and concomitantly prevents the dysregulated expression of key genes regulating ECS activity through a mechanism depending sequentially on GPR109A and PPARγ activation and, for CB1 receptor expression, miRNA regulation.
## Butyrate protects primary myoblasts isolated from DMD donors from inflammation and impaired autophagy
Finally, we evaluated the effect of NaB, as well as of GPR109A and PPARγ activation by MK1903 and rosiglitazone, respectively, on the expression of inflammatory (IL6 and COX2) and autophagy (ULK1, ATG13, ATG4) genes in primary myoblasts isolated from muscle biopsies of young patients diagnosed with DMD (D1‐D5) caused by different mutations in the dystrophin gene (see Table EV2). Indeed, the expression of both inflammatory and autophagy genes observed in myoblasts from five DMD patients (D1‐D5) was dysregulated when compared to control cells obtained from healthy donors (HD). This dysregulation was largely, albeit not entirely, prevented following treatment with NaB (3 mM), MK1903 (1 μM), and rosiglitazone (1 μM) for 24 h (Fig 10A–E).
**Figure 10:** *Effect of NaB, MK1903, and rosiglitazone in primary myoblasts isolated from DMD donors
A–EBar chart showing the mRNA expression levels of IL6, COX2, ULK 1, ATG13, and ATG4 mRNA in primary human myoblasts isolated from one healthy donor (HD) and five DMD donors (D1–D5).
Data Information: The quantification of transcripts by quantitative real‐time PCR was measured twice for each sample.*
## Discussion
In recent decades, interest in the gut microbiota and its related metabolites has rapidly flourished owing to their prominent role in contributing to the overall health of the host. Gut microbial dysbiosis is, therefore, associated with the pathogenesis and/or progression of a broad spectrum of metabolic, neurological, and inflammatory disorders (Gentile et al, 2020; Morais et al, 2020). In line with this, increasing evidence shows that the therapeutic transplantation of fecal bacteria or the use of pro‐/prebiotic products can lead to future novel opportunities to treat numerous diseases through the correction of gut microbiota imbalance (i.e., dysbiosis) (Kho & Lal, 2018). Nutritional, metabolic and gastrointestinal problems frequently occur in patients with DMD (Pane et al, 2006; Brumbaugh et al, 2018). In this regard, it has been estimated that chronic persistent inflammation, forced sedentariness, and long‐term use of anti‐inflammatory steroid drugs cause overweight and obesity in more than half of the children with DMD. On the contrary, adolescents and adults with DMD frequently experience underweight due to swallowing dysfunction, lack of dietary fiber, and reduced intestinal motility (Kraus et al, 2016; Brumbaugh et al, 2018). Similar pathological features were found also in mdx mice, a widely used experimental model of DMD (Mulè et al, 2010; Radley‐Crabb et al, 2011). Thus, based on this background, in this study, we explored whether the gut microbiota is implicated in the development and progression of DMD. With this aim, we first performed a 16S rRNA Gene *Sequencing analysis* to characterize the type and relative abundance of bacterial taxa in fecal samples of wild‐type and mdx mice. Our analysis revealed that dystrophic mice are characterized by a higher abundance of the Prevotellaceae family, whereas, on the contrary, the relative abundance of the Saccharimonadaceae, Helicobacteriaceae, Peptococcaceae, and Clostridiales_vadinBB60 families was reduced. A plausible explanation of these changes might be provided by previous data showing that members of the Prevotellaceae family (which includes approximately 40 different species) rapidly grow and divide in inflammatory microenvironments like those caused, for instance, by colorectal cancer, arthritis, and inflammatory bowel diseases (Kleessen et al, 2002; Lucke et al, 2006; Hofer, 2014). On the contrary, Ortega‐Hernández et al [2020] recently documented that Saccharimonadaceae abundance is negatively correlated with the development of metabolic dysfunctions (Ortega‐Hernández et al, 2020). Changes in the abundance of Peptococcaceae, Helicobacteraceae, Desulfovibrionaceae, Erysipelotrichaceae, and Clostridiales observed here could be instead attributed to reduced physical activity (Liu et al, 2015). Importantly, DFZ, a standard of care for the treatment of DMD, improved several pathological features of the disease in mdx mice and concomitantly rescued Prevotellaceae, Saccharimonadaceae, and Clostridiales_vadinBB60 levels, suggesting that alterations of these gut bacterial families might be involved in these features. DFZ also altered the relative abundance of families that were not altered in mdx mice, i.e., Desulfovibrionaceae and Erysipelotrichaceae, which were, respectively, increased and reduced.
Irrespective of the underlying cause(s), we addressed next the question of the potential functional importance of the changes in gut microbiota composition in mdx mice, reported here for the first time. The physiological and pathological function of the gut microbiome is largely mediated by the specific metabolites that this multitude of microorganisms produce. SCFAs, and butyrate in particular, are among the most studied such metabolites, and we found here that their levels in the plasma were, or tended to be, reduced in mdx mice and elevated by DFZ, concomitantly with the therapeutic action of the latter drug. It is known that the main butyrate‐producing bacteria in the gut belong to the phylum Firmicutes (Parada Venegas et al, 2019). However, other species from the Bacteroidetes, Patescibacteria, and *Proteobacteria phyla* produce butyrate and the other SCFAs measured here,(Alkadhi et al, 2014; Cheng et al, 2018; Russell et al, 2019; Morya et al, 2020). Therefore, it is tempting to hypothesize that the reduced SCFA levels observed in the plasma of mdx mice might be due to the lower abundance of Peptococcaceae and Clostridioles_vadin BB60 (Firmicutes), Saccharimonadaceae (Patescibacteria), and Helicobacteriaceae (Proteobacteria), which were decreased, but not to the increased abundance of Prevotellaceae (Bacteroidetes). Conversely, the increased SCFA levels observed in DFZ‐treated mdx mice could be related to the increased abundance of Saccharimonadaceae and Clostridiales but also of a family that was not modified by the presence of dystrophy alone, i.e., Desulfovibrionaceae (Proteobacteria) (Alkadhi et al, 2014; Bosman et al, 2019; Morya et al, 2020), and not to Erysipelotrichaceae (Firmicutes) or Prevotellaceae, which were both decreased by the drug. Future studies employing shotgun metagenomics approaches and microbiota transfer experiments will be needed to fully understand what species are responsible for the observed changes in SCFAs in mdx mice, with or without treatment with DFZ. Additionally, it will also be interesting to investigate why SCFA levels were found here to be reduced only in the plasma, and not in the skeletal muscle or feces, of mdx mice. As reported in several other studies, SCFAs are produced at varying ratios, with acetate being the most abundant in the colon (~ $60\%$), followed by propionate (~ $25\%$), and butyrate (~ $15\%$) (Duncan et al, 2004), which may explain why, in plasma, we could not detect this latter metabolite when using a less sensitive analytical technique (NMR). Moreover, colonic SCFAs are largely utilized by colonocytes as an energy source. The remaining SCFAs reach the liver where they are metabolized, oxidized, or used as a substrate for gluconeogenesis and lipogenesis (Boets et al, 2017), which may explain why in plasma we could only detect hydroxyl‐butyrate with NMR. As a consequence, only SCFAs that are not processed by the liver end up in peripheral circulation and eventually in peripheral tissues such as the skeletal muscle (Richards et al, 2016). Thus, colonic absorption and hepatic metabolism of SCFAs might have masked differences in the concentrations of these metabolites in the muscle or feces.
As the next step in our study, we investigated the potential relationship between defective circulating SCFAs and autophagy and inflammation in the skeletal muscle of dystrophic mice and/or C2C12 cells. Increased inflammation and decreased autophagy are two hallmarks of DMD muscles, and both play a key role in the progression of this disorder (De Palma et al, 2014). Previous reports have shown that SCFAs (and KBs, which we also found here to be decreased in mdx mouse plasma) are intimately connected to both autophagy and inflammation, although there is only a limited number of studies investigating the signaling mechanisms underlying these actions (Tang et al, 2011; Rojas‐Morales et al, 2016; Feng et al, 2018). Here, we focussed our attention on butyrate as the SCFA whose mechanism of action has been perhaps most investigated in previous studies (Walsh et al, 2015; Gao et al, 2019) and found that this metabolite, administered for 3 weeks to mdx mice up to 19 weeks of age, is capable, like DFZ, of reducing locomotor impairment as well as skeletal muscle inflammation and autophagy deficits. Current evidence indicates that SCFAs exert their effects through three major mechanisms involving: (i) activation of G protein‐coupled receptors (GPCRs); (ii) activation of PPARγ receptors; and (iii) inhibition of histone deacetylases (HDACs) (Dalile et al, 2019). We have shown here, to our knowledge for the first time, that NaB, similarly to MK1903 (a selective GPR109A agonist) and rosiglitazone (a selective PPARγ agonist), counteracts LPS‐evoked inflammation and promotes autophagy in murine myoblasts. Noteworthy, this effect was partly abolished in GPR109A‐silenced cells, but fully abolished in these same cells in the presence also of T007 (a selective antagonist of PPARγ), pointing to the participation of both GPR109A and PPARγ in the mechanism of action of NaB, and arguing against inhibition of histone deacetylation as a potentially residual mechanism. Most importantly, the preservation of autophagy and the anti‐inflammatory effect of NaB, MK1093, and rosiglitazone were also observed in primary human myoblasts isolated from DMD donors.
SCFAs are also known to cross the blood–brain barrier (BBB) and modulate neuroendocrine stress reactivity (Dalile et al, 2020). However, to date, only a few studies investigated the role of SCFAs in the hypothalamic–pituitary–adrenal (HPA) axis reactivity. One study found that, in rats, high doses of NaB (1.2 g/kg) acted as a pharmacological stressor, increasing plasma levels of the stress markers corticosterone and adrenocorticotropic hormone (ACTH) (Gagliano et al, 2014). By contrast, others found that a low dose of NaB (200 mg/kg) only slightly increased ACTH (Dalile et al, 2020). In addition, Van de Wouw et al found that oral administration of a cocktail of SCFAs (67.5 mM acetate, 25 mM propionate, and 25 mM butyrate) for 1 week in mice ameliorated stress‐induced corticosterone potentiation after an acute stressor (van de Wouw et al, 2018). Therefore, we cannot exclude a no matter how minimal effect of NaB 100 mg/Kg, which is the dose used in this study, on the HPA axis in mdx mice. Further studies are needed to clarify this point.
Endocannabinoid signaling at CB1 is involved in DMD onset and progression (Iannotti et al, 2018) and has been suggested to cross‐talk with the gut microbiome (Cani et al, 2016; Manca et al, 2020). We previously demonstrated that the expression of CB1 receptors is increased in skeletal muscles of both 5‐week (disease onset) and 8‐week‐old mdx mice (Iannotti et al, 2018). Here, in skeletal muscles of 19‐week‐old mdx mice, characterized by strong inflammation and reduced autophagy, we found that, along with increased CB1 and CB2 mRNA and protein expression, also the plasma levels of the CB1 endogenous agonist, AEA, were remarkably higher. These findings, therefore, confirmed that the ECS is overactive also in mice with advanced muscular dystrophy. Since we previously demonstrated by RNA seq analysis performed in skeletal muscle tissues that CB1 is differentially expressed in satellite, myoblast, and myotube cells, while on the contrary, CB2 expression is mainly restricted to skeletal muscle‐resident macrophages (Iannotti et al, 2018), we focused our attention on CB1 receptors. Our results show that the dysregulated expression of autophagy‐related genes found in 19‐week‐old mdx mice is restored to physiological levels by counteracting ECS overactivity through antagonism of CB1 receptors with rimonabant, whereas CB1 activation by ACEA exacerbated the decreased expression of such genes. It is worth mentioning that previous studies demonstrated that CB1 receptor stimulation leads to the activation of the pAkt/mTOR pathway, a well‐known signaling pathway leading to the inhibition of autophagy (Gómez del Pulgar et al, 2000).
Notably, in these mice, the dysregulation of ECS activity was prevented by the administration of DFZ and NaB. This finding suggests that ECS overactivity, which plays a crucial role in the etiopathology of muscular dystrophy (Iannotti et al, 2018): (i) exerts this pathological action also by causing defective autophagy in the skeletal muscle, with ensuing exacerbation of the disorder, and (ii) is due, at least in part, to defective butyrate production by the gut microbiota, an effect reversed by DFZ.
Using again C2C12 cells, we next demonstrated that, in a manner dependent on GPR109A and PPARγ, NaB prevents the dysregulated expression of key genes regulating endocannabinoid activity and levels under basal conditions. Notably, the stimulatory effects of NaB on autophagy in these cells were obstructed by the CB1 agonist ACEA. This suggests that, while in healthy mice gut microbiota activity contributes to muscle functionality and regeneration through mechanisms depending on the production and release of SCFAs and consequent activation of GPR109A and PPARγ receptors, in dystrophic mice, the altered gut microbiota composition leads to inadequate production and circulation of butyrate, which causes excessive endocannabinoid system activity at CB1 receptors. This latter condition then participates in exacerbating inflammation and impairing muscle autophagy. It is worth mentioning that, in agreement with our proposed PPARγ‐mediated down‐regulation of ECS overactivity by butyrate, previous studies in preadipocytes and adipocytes have reported that PPARγ activation down‐regulates both endocannabinoid levels and CB1 receptor expression (Matias et al, 2006; Pagano et al, 2007). Conversely, no previous evidence exists suggesting that GPR109a down‐regulates ECS signaling. Here, by combining computational and experimental approaches, we found that microRNA sequences (miRNAs) targeting the Cnr1 mRNA 3′UTR region were down‐regulated following C2C12 exposure to LPS. The miRNAs are a class of noncoding RNAs playing a key role in regulating the expression of target genes. In the majority of cases, miRNAs interact with the 3′ UTR region of target mRNAs to induce their degradation and/or translational repression, thus affecting a multitude of biological processes including cell proliferation, differentiation, and survival (O'Brien et al, 2018). We found that in C2C12 myoblasts, LPS causes the down‐regulation of miR‐19, miR‐128, miR‐425, miR‐130, miR‐152, miR‐301, and miR‐29. Remarkably, the expression of most of these CB1‐down‐regulating miRNAs was variedly restored, or even further increased, in the presence of NaB, rosiglitazone, or MK1903. This finding suggests that butyrate exerts its protective down‐regulatory function on inflammation‐increased CB1 receptor expression (which is deleterious to skeletal muscle, as recently demonstrated also by others; Haddad, 2021) via the GPR109A‐ and PPARγ‐mediated up‐regulation of Cnr1‐targeting miRNAs.
In conclusion, we have reported here a novel mechanism by which gut dysbiosis associated with late‐stage muscular dystrophy in mdx mice may participate in some of the features of this disorder through the reduced release of SCFAs in the blood and impaired GPR109A and PPARγ activation in skeletal muscle, with subsequent disinhibition of endocannabinoid signaling at CB1 receptors and exacerbation of inflammation and autophagy deficit in this tissue. Importantly, we have also shown that butyrate, as well as GPR109A and PPARγ activation, counteract impaired autophagy and inflammation also in myotubes isolated from DMD patients. The finding of this new example of gut microbiome‐endocannabinoid system axis dysregulation [see (Cani et al, 2016) for review] may offer the opportunity to treat DMD using gut microbiota‐targeted strategies, on top of the current often poorly effective or unsafe treatments.
## Animal model and drug treatment
The Animal Study Protocol (IACUC; $\frac{536}{2018}$) was approved by the Italian Ministry of Health and Ethics Committee for the use of experimental animals being conformed to guidelines for the safe use and care of experimental animals following the Italian D.L. no. 116 of 27 January 1992 and associated guidelines in the European Communities Council ($\frac{86}{609}$/ECC and $\frac{2010}{63}$/UE). In this study, 5‐week‐old control (C57BL/10ScSnJ) and dystrophic (C57BL/10ScSn‐DMDmdx/J) mice weighing approximately 20–25 g were purchased from Charles River Laboratories (Milan IT). All mice were housed in an individually ventilated cage system with a 12‐h light–dark cycle and received standard mouse chow (Harlan Teklad) and water ab libitum. Animals belonging to each cage were randomly assigned to the different experimental groups. Each experimental group included at least five mice. The experimenter(s) performing the treatments and locomotor testing was blind to the genotype and treatment. Control or mdx mice were treated orally for 3 weeks with (i) vehicle (dimethyl sulfoxide – DMSO Cat# 276855 Sigma‐Aldrich), (ii) deflazacort (DFZ) 1.2 mg/kg/day (Cat# SML0123 Sigma‐Aldrich), (iii) sodium butyrate (NaB) 100 mg/kg/day (Cat# 303410, Sigma‐Aldrich); (iii) ACEA 2.5 mg/Kg (Cat# A9719 Sigma‐Aldrich), or (iv) rimonabant 0.5 mg/Kg (Cat# 9000484, Cayman) were intraperitoneally (IP) injected three times a week for 2 weeks (Iannotti et al, 2018).
## Rotarod test
The rotarod test was performed in control and dystrophic mice at the end of the pharmacological treatment. Briefly, the rotarod was settled with a start speed of 5 rpm, and the mice were placed on the rotating rod for 30 s. Then, the rotarod was accelerated to 40 rpm in 240 s. The time (s) when mice dropped from the rod was recorded. The results were expressed as an average of two different trials, and the interval time of each trial was 30 min (Iannotti et al, 2018).
## Muscle strength test
To test the forelimb strength of dystrophic mice treated or not with DFZ and NaB, four weights of 20, 33, 46, and 59 g were used. Mice were handled by the base of the tail and were allowed to grip the first weight (20 g) and to hold 3 s was the criterion. If the mouse dropped the weight in less than 3 s, we tried the same weight again for a maximum of three times. If the mouse held it for 3 s, then we tried it on the next heaviest weight. The mouse was assigned the maximum time/weight achieved. The final total score is calculated as the product of the number of links in the heaviest chain held for the full 3 s, multiplied by the time (s) it is held (Iannotti et al, 2018).
## DNA extraction and 16S rRNA gene sequencing
DNA was extracted from fecal samples using the QIAmp PowerFecal DNA kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. The DNA concentrations of the extracts were measured fluorometrically with the Quant‐iT PicoGreen dsDNA Kit (Thermo Fisher Scientific, MA, USA), and the DNAs were stored at −20°C until 16S rDNA library preparation. Briefly, 1 ng of DNA was used as a template, and the V3‐V4 region of the 16S rRNA gene was amplified by polymerase chain reaction (PCR) using the QIAseq 16S Region Panel protocol in conjunction with the QIAseq 16S/ITS 384‐Index I (Sets A, B, C, D) kit (Qiagen, Hilden, Germany). The 16S metagenomic libraries were eluted in 30 μl of nuclease‐free water, and 1 μl was qualified with a Bioanalyser DNA 1000 Chip (Agilent, CA, USA) to verify the amplicon size (expected size ~ 600 bp) and quantified with a Qubit (Thermo Fisher Scientific, MA, USA). Libraries were then normalized and pooled to 2 nM, denatured, and diluted to a final concentration of 6 pM. Sequencing (2 × 300 bp paired‐end) was performed using the MiSeq Reagent Kit V3 (600 cycles) on an Illumina MiSeq System. Sequencing reads were generated in less than 65 h. Image analysis and base calling were carried out directly on the MiSeq. Data were processed using the DADA2 pipeline (Callahan et al, 2016), and taxonomic assignation was performed against the SILVA 132 rRNA reference database (Quast et al, 2013). Relative microbiota abundances were obtained by Cumulative Sum Scaling (CSS, MetagenomeSeq R package) (Paulson et al, 2013), and microbiota composition was assessed by calculating α‐ and β‐diversity indexes and intra‐ and inter‐individual variations in microbial composition using PERMANOVA (vegan R package).
## Feces samples collection and SCFAs quantification
For fecal collection, mice were put in clean empty cages (without bedding and/or nestle) in the morning around 7:30–8 am (mice have better intestinal transit in the morning). When the last cage is changed, the first mice should have dropped their first feces. Feces were collected with sterile forceps. Samples were stored at −80°C until analysis. For SCFA extraction and measurement by gas chromatography, feces were dissolved in water and suspensions were homogenized for 2 min with a Bead Ruptor 12 (Omni International, Kennesaw, GA, USA) and then centrifuged at 18,000 g for 10 min at 4°C. The supernatant was collected and spiked with a solution containing an internal standard (4‐methyl valeric acid) and H3PO4 $10\%$ to obtain a pH of about 2. A volume of methyl tert‐butyl ether equivalent to the volume of the diluted sample was added and mixed by vortexing for 2 min. Samples were then centrifuged for 10 min at 18,000 g at 4°C, and the organic phases were transferred to glass vials. SCFA analysis was performed on a GC‐FID system (Shimadzu), consisting of a GC 2010 Plus gas chromatograph equipped with an AOC‐20s auto‐sampler, an AOC‐20i auto‐injector, and a flame ionization detector. The system was controlled by GC solution software. One microliter of the organic phase was injected in a split mode into a Nukol capillary GC column (30 m × 0.25 mm id, 0.25 μM film thickness, Supelco analytical), and hydrogen was used as the carrier gas. The injector and detector were set at 250°C. The oven temperature was initially programmed at 60°C, then increased to 200°C at 12°C/min, and held for 2 min. SCFAs were quantified using a 5‐point calibration curve prepared with a mix of standards (acetic acid, propionic acid, butyric acid, isobutyric acid, valeric acid, and isovaleric acid) extracted following the same procedure as samples.
## GC–MS analysis
Plasma was analyzed by gas chromatography–mass spectrometry (GC ‐ 7890A, Agilent Technologies; MS ‐ 5977A MSD, Agilent Technologies). In brief, 100 μl of plasma was diluted with 900 μl of saline. 500 μl of this solution was added to 20 μl of H3PO4 $85\%$ (w/v) and vortexed for 5 min. Then, to each sample, 500 μl of diethyl ether was added. The suspension was vortexed for 5 min and centrifuged at 14,000 rpm for 30 min at room temperature. After the supernatant was taken and sodium sulfate anhydrous was added. Finally, the organic phase was placed in a new glass tube for GC–MS analysis. The GC was programmed to achieve the following run parameters: initial temperature of 90°C, hold for 2 min, a ramp of 2°C/min up to a temperature of 100°C, hold of 10 min, and ramp of 5°C/min up to a final temperature of 110°C for a total run time of 21 min.
## Metabolites extraction from tissues
To extract metabolites, tissues were mechanically disrupted. We used the methanol/water/chloroform protocol as suggested (Lindon et al, 2005). After solvent removal with a rotary vacuum evaporator at room temperature, samples were stored at −80°C until analysis.
## NMR measurements of polar metabolites
Polar fractions of muscle samples were re‐suspended in 630 μl of phosphate buffer saline (PBS, pH 7.4 Cat# D8537) and 70 μl of 2H2O solution [containing 1 mM sodium 3‐trimethylsilyl [2,2,3,3‐2H4] propionate (TSP) was added as a chemical shift reference and assumed to resonate at δ = 0.00 ppm] to provide a field frequency lock. One‐dimensional (1D) spectra were acquired at 27°C on a Bruker Avance III‐600 spectrometer operating at 600.13 MHz and equipped with a TCI CryoProbe™, using the excitation‐sculpting sequence for solvent suppression (Hwang & Shaka, 1995). Subsequently, 330 μl of each serum sample was diluted with 300 μl of saline solution with $0.9\%$ sodium chloride (pH 7.4) and 70 μl of 2H2O. To strongly attenuate protein signals, T2‐edited 1D spectra were collected using short spin–spin relaxation times in the Carr‐Purcell‐Meiboom‐Gill (CPMG) pulse sequence with water presaturation (de Graaf & Behar, 2003) and using a fixed inter‐echo delay to eliminate diffusion and J‐modulation effects. Two‐dimensional (2D) clean total‐correlation spectroscopy (TOCSY) and heteronuclear single‐quantum coherence (HSQC) experiments were also acquired for metabolite identification. 2D spectra were referenced to the lactate doublet assumed to resonate at δ = 1.33 ppm for 1H and δ = 20.76 ppm for 13C. Metabolites were identified by comparison with an online database (Wishart et al, 2018).
## NMR data processing and multivariate statistical analysis
The 0.50–9.50 ppm region of the proton spectra of mice muscle (gastrocnemius) was automatically segmented into integrated regions (buckets) of 0.02 ppm each using the AMIX 3.6 package (Bruker Biospin, Germany). The 4.55–5.15 ppm region around the water resonance was excluded, and the binned regions were normalized to the total spectrum area. For sera, we used the selected 0.60–8.60 ppm spectral area, and spectra were analyzed as above, excluding the 4.46–5.16 ppm water resonance region. Multivariate statistical data analysis was applied to the muscle and serum dataset to differentiate treated/untreated mdx and healthy profiles according to their metabolic content. Each dataset was reshaped as a matrix and imported into SIMCA 14 package (Umetrics, Umea, Sweden), where unsupervised PCA followed by supervised OPLS‐DA discriminant analyses was performed (Eriksson et al, 2006). PCA was first applied to check outliers and uncover trends and clusters, while OPLS‐DA was used to improve group discrimination. Moreover, O2PLS analysis was performed to generate a bilinear and joint model for NMR and gut microbiota data. We also generated correlation maps with hierarchical clustering by combining microbiota families values and selected polar metabolite buckets considering Euclidean distance for the metrics and the WARD method for clustering criterion. The performance of each multivariate model was evaluated via R2 (the goodness of fit) and Q2 (the goodness of prediction) parameters. Each model was validated by a 7‐round internal iterative cross‐validation routine, permutation test response (800 repeats), and analysis of variance (ANOVA testing of cross‐validated predictive residuals). Selected and isolated signals with ¦P corr¦ ≥ 0.7, VIP (variable importance in the projection) > 1 were then considered for univariate statistical analysis and ANOVA test with Bonferroni correction.
## Cell culture and reagents
Murine C2C12 myoblasts were propagated in a growth medium (GM) composed of Dulbecco's modified Eagle's medium (Cat# 11995065; Life Technologies) supplemented with $10\%$ fetal bovine serum (FBS, Cat# 16000044; Life Technologies), 5,000 U/ml penicillin plus 5,000 μg/ml streptomycin (Cat# 15070063; Life Technologies), and $1\%$ l‐glutamine (Cat# A2916801; Life Technologies). Proliferating C2C12 cells were differentiated into myotubes following the exposure to differentiation medium (DM) composed of Dulbecco's modified Eagle's medium supplemented with $2\%$ horse serum heat‐inactivated (Cat# 26050070, Sigma‐Aldrich) for 3 days (McMahon et al, 1994). Primary myoblasts were established from muscle biopsies of DMD donors after they had signed informed consent forms and following the guidelines of the G. Gaslini Institute Ethical Committee and according to published procedures (Morosetti et al, 2010). The myoblasts were propagated in Full Aneural Medium composed by Dulbecco's modified Eagle's medium (DMEM; Cat# 11995065) supplemented with $15\%$ FBS, $20\%$ Medium 199 (Cat# 12350039), $1\%$ insulin (Cat# A11382II), $1\%$ l‐glutamine (Cat# A2916801), and 15,000 U/ml penicillin plus 5,000 μg/ml streptomycin (Cat# 15070063), FGF (Cat# PHG6015), EGF (Cat# PHG0311). Primary myoblasts were differentiated in myotubes using a commercially available skeletal muscle differentiation medium (Cat# C‐23061, PromoCell, USA) provided by VWR International PBI S.r.l. in the presence or not of NaB 3 mM (Cat# 303410 Life Technologies), MK1903 (Cat# 4622, Tocris UK), or rosiglitazone (Cat# 5325, Tocris UK).
## RNA extraction and quantitative PCR (qPCR)
Total RNA isolation, purification, and cDNA synthesis were performed as described (Iannotti et al, 2018). Total miRNA isolation was performed using RNeasy Mini Kit (cat# 217004, Qiagen). Reverse transcription of miRNA was performed using miScript II RT Kit (cat# 218161, Qiagen). Quantitative PCR (qPCR) was carried out in a real‐time PCR system CFX384 (Bio‐Rad) using the SYBR Green PCR Kit (Cat# 1725274, Bio‐Rad for mRNAs; Cat# 218073, Quiagen for miRNAs) detection technique and specific primer sequences reported in Table EV3. Primer sequences for miRNA were provided by Qiagen. Quantitative PCR was performed on independent biological samples ≥4–5 for each experimental group. Also, each sample was amplified simultaneously in quadruplicate in a one‐assay run with a nontemplate control blank for each primer pair to control for contamination or primer‐dimer formation, and the cycle threshold (Ct) value for each experimental group was determined. The housekeeping genes ribosomal protein S16, glyceraldehyde 3‐phosphate dehydrogenase (GAPDH), and U6 (RNU6‐1) were used to normalize the Ct values, using the 2^−ΔCt formula. Differences in mRNAs and miRNAs content between groups were expressed as 2^−ΔΔCt, as previously described (Iannotti et al, 2018).
## miRNA target prediction
Bioinformatic analysis to predict putative miRNA target sites within the 3′UTR region of both human and murine CB1 gene was performed using the free software TargetScan (http://www.targetscan.org/vert_80/).
## Western blot
Control and mdx mice were previously anesthetized with $75\%$ CO2/$25\%$ O2 and then sacrificed by cervical dislocation. Gastrocnemius was rapidly dissected on ice and kept on dry ice until the whole procedure was completed. Muscle tissues were homogenized in 1x TNE buffer plus $1\%$ (v/v) Triton X‐100 (Cat# T8787, Sigma‐Aldrich) protease Inhibitor (Cat# P8340, Sigma‐Aldrich) and phosphatase Inhibitor Cocktail 2 (Cat# P5726, Sigma‐Aldrich). Lysates were kept in an orbital shaker incubator at 220 rpm at 4°C for 30 min and then centrifuged for 15 min at 13,000 g at 4°C. The supernatants were transferred to tubes and quantified by DC Protein Assay (Cat# 5000116, Bio‐Rad, Milan, Italy). Subsequently, protein samples (60–80 μg of total protein) were heated at 70°C for 10 min in 1X LDS Sample Buffer (Cat# B0007, Life Technology) plus 1X sample reducing agent (Cat# B0009, Life Technology) and loaded on 4–$12\%$ Bis–Tris Protein Gels (Cat# NW04120, Life Technology) and then transferred the membrane using Trans‐Blot Turbo Mini 0.2 μm PVDF Transfer Packs (Cat# 1704156 Bio‐Rad). The primary antibodies used were (i) rabbit anti‐Akt Antibody (item n. 9272, Cell Signaling Technology USA); (ii) rabbit anti‐phospho Akt (Ser473) (D9E) XP® (Cat# 4060, Cell Signaling Technology USA); (iii) rabbit anti‐LC3 antibody (Cat# 2775, Cell Signaling Technology USA); (iv) rabbit anti‐CB1 (Cat# Y409605, ABM Canada); (v) mouse anti‐CB2 (Cat# WH0001269MI, Merck); and (vi) an anti‐rabbit Cox2 (D5H5) XP® (Cat# 12282, Cell Signaling ‐ USA). An anti‐GAPDH antibody (1D4) (Cat#. NB300‐221; Novus Biologicals) was used to check for equal protein loading. Reactive bands were detected by Clarity Western ECL Substrate (Cat# 1705061 Bio‐Rad). The intensity of bands was analyzed on a ChemiDoc station with Quantity‐one software (Bio‐Rad, Segrate, Italy).
## Cell transfection and LPS treatment
C2C12 myoblasts were plated in 6‐well culture dishes at a confluency of $60\%$. The next day GPR109A gene silencing was obtained by transfection of predesigned siRNA sequences (Cat# AM16708, Life Technologies) using Lipofectamine® 2000 reagent (Cat# 11668‐027, Life Technologies) according to the manufacturer's instructions. Control cells were transfected with a scrambled siRNA sequence (Cat# AM4642, Life Technologies) as a negative control. At 24 h following transfection, C2C12 were treated with 1 μg/ml lipopolysaccharides (LPS; O111:B4, Sigma‐Aldrich) for 3 h according to published procedures (Frost et al, 2003). NaB 3 mM (den Besten et al, 2015) (Cat# 303410 Life Technologies), T0070907 1 μM (Cat# 2301, Tocris UK), MK1903 (Cat# 4622, Tocris UK), and rosiglitazone (Cat# 5325, Tocris UK) were preincubated 1 h before the LPS stimulation.
## Autophagy assay
Control and GPR109A silenced C2C12 myoblasts were cultured in 96‐well flat‐bottom black plates with optimal density (2 × 104 cells/well). The day following plating, C2C12 cells were treated with NaB, MK1903, rosiglitazone, and T007 for 24 and 48 h. Autophagosome activity was detected with a specific dye using an autophagy assay kit (Cat# MAK138, Sigma‐Aldrich). The protocol was performed following the manufacturer's instructions. Fluorescence intensity was measured using the Promega GloMax® Plate Reader.
## Measurement of endocannabinoids
Lipids were extracted from plasma (5 ml) and AEA and 2‐AG prepurified and quantified by isotope dilution liquid chromatography–atmospheric pressure chemical ionization–mass spectrometry (LC‐APCI‐MS) as described previously (Annuzzi et al, 2010).
## Statistical analysis
All datasets were subjected to outlier identification and subsequent removal, using the ROUT method using GraphPad Prism version 9. D'Agostino‐Pearson or Shapiro–Wilk tests were used to consider the data normal distribution. Normal data were assessed via one‐way analysis of variance (ANOVA) followed by the Tukey's analysis to determine statistically significant differences between two or more independent biological groups. Data are expressed as mean ± SEM of values. Significance was determined as $P \leq 0.05.$
## Problem
Duchenne muscular dystrophy (DMD) is the most frequent form of genetic disorder characterized by an irreversible degeneration of skeletal muscles. Therefore, the identification of novel translational approaches aimed to halt or delay disease progression remains an important unmet need.
## Results
In this study, we found that in mdx mice, a validated preclinical model of DMD, the disease is associated with a significant alteration in the gut microbiota composition compared with healthy controls. Along with this alteration, the plasma of mdx mice showed a reduction in the levels of gut microbiota‐related metabolites, the short‐chain fatty acids (SCFAs), and an elevation of those of endocannabinoids. Supplementation with the SCFA, sodium butyrate (NaB), rescued muscle strength and autophagy, and prevented inflammation associated with excessive endocannabinoid signaling at CB1 receptors to the same extent as deflazacort (DFZ), the standard palliative care for DMD. In C2C12 myoblasts stimulated with lipopolysaccharide, a pro‐inflammatory molecule derived from a malfunctioning gut microbiota, NaB exerted anti‐inflammatory effects, promoted autophagy, and prevented dysregulation of microRNAs that keep under negative control the CB1 receptor gene and did so in a manner depending on the activation of GPR109A and PPARγ receptors.
## Impact
We highlight the translational value of the gut microbiota‐endocannabinoid system cross‐talk as a novel disease‐modifying approach in DMD, with potential benefits also in other muscular dystrophies.
## Author contributions
Hilal Kalkan: Formal analysis; investigation. Ester Pagano: Formal analysis; investigation. Debora Paris: Formal analysis; investigation. Elisabetta Panza: Formal analysis; investigation. Mariarosaria Cuozzo: Investigation. Claudia Moriello: Formal analysis; investigation. Fabiana Piscitelli: Formal analysis; investigation. Armita Abolghasemi: Investigation. Elisabetta Gazzerro: Methodology. Cristoforo Silvestri: Formal analysis. Raffaele Capasso: Formal analysis. Andrea Motta: Formal analysis. Roberto Russo: Formal analysis. Vincenzo Di Marzo: Conceptualization; funding acquisition; writing – original draft; writing – review and editing. Fabio Arturo Iannotti: Conceptualization; funding acquisition; writing – original draft; writing – review and editing.
## Disclosure statement and competing interests
The authors declare that they have no conflict of interest.
## Data availability
Raw sequencing data of 16S rRNA sequencing are deposited at the following link: https://www.ncbi.nlm.nih.gov/bioproject/913018
## References
1. Alkadhi S, Kunde D, Cheluvappa R, Randall‐Demllo S, Eri R. **The murine appendiceal microbiome is altered in spontaneous colitis and its pathological progression**. *Gut Pathog* (2014) **6** 25. PMID: 25002910
2. Annuzzi G, Piscitelli F, Di Marino L, Patti L, Giacco R, Costabile G, Bozzetto L, Riccardi G, Verde R, Petrosino S. **Differential alterations of the concentrations of endocannabinoids and related lipids in the subcutaneous adipose tissue of obese diabetic patients**. *Lipids Health Dis* (2010) **9** 43. PMID: 20426869
3. Bäckhed F, Manchester JK, Semenkovich CF, Gordon JI. **Mechanisms underlying the resistance to diet‐induced obesity in germ‐free mice**. *Proc Natl Acad Sci USA* (2007) **104** 979-984. PMID: 17210919
4. den Besten G, Bleeker A, Gerding A, van Eunen K, Havinga R, van Dijk TH, Oosterveer MH, Jonker JW, Groen AK, Reijngoud D‐J. **Short‐chain fatty acids protect against high‐fat diet‐induced obesity via a PPARγ‐dependent switch from lipogenesis to fat oxidation**. *Diabetes* (2015) **64** 2398-2408. PMID: 25695945
5. Biggar WD, Skalsky A, McDonald CM. **Comparing deflazacort and prednisone in Duchenne muscular dystrophy**. *J Neuromuscul Dis* (2022) **9** 463-476. PMID: 35723111
6. Boets E, Gomand SV, Deroover L, Preston T, Vermeulen K, De Preter V, Hamer HM, Van den Mooter G, De Vuyst L, Courtin CM. **Systemic availability and metabolism of colonic‐derived short‐chain fatty acids in healthy subjects: a stable isotope study**. *J Physiol* (2017) **595** 541-555. PMID: 27510655
7. Bosman ES, Albert AY, Lui H, Dutz JP, Vallance BA. **Skin exposure to narrow band ultraviolet (UVB) light modulates the human intestinal microbiome**. *Front Microbiol* (2019) **10**. PMID: 31708890
8. Boursereau R, Abou‐Samra M, Lecompte S, Noel L, Brichard SM. **Downregulation of the NLRP3 inflammasome by adiponectin rescues Duchenne muscular dystrophy**. *BMC Biol* (2018) **16** 33. PMID: 29558930
9. Brown AJ, Goldsworthy SM, Barnes AA, Eilert MM, Tcheang L, Daniels D, Muir AI, Wigglesworth MJ, Kinghorn I, Fraser NJ. **The orphan G protein‐coupled receptors GPR41 and GPR43 are activated by propionate and other short chain carboxylic acids**. *J Biol Chem* (2003) **278** 11312-11319. PMID: 12496283
10. Brumbaugh D, Watne L, Gottrand F, Gulyas A, Kaul A, Larson J, Tomezsko J. **Nutritional and gastrointestinal management of the patient with duchenne muscular dystrophy**. *Pediatrics* (2018) **142** S53-S61. PMID: 30275249
11. Bushby K, Finkel R, Birnkrant DJ, Case LE, Clemens PR, Cripe L, Kaul A, Kinnett K, McDonald C, Pandya S. **Diagnosis and management of Duchenne muscular dystrophy, part 1: diagnosis, and pharmacological and psychosocial management**. *Lancet Neurol* (2010) **9** 77-93. PMID: 19945913
12. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. **DADA2: high‐resolution sample inference from Illumina amplicon data**. *Nat Methods* (2016) **13** 581-583. PMID: 27214047
13. Cani PD, Plovier H, Van Hul M, Geurts L, Delzenne NM, Druart C, Everard A. **Endocannabinoids — at the crossroads between the gut microbiota and host metabolism**. *Nat Rev Endocrinol* (2016) **12** 133-143. PMID: 26678807
14. Cheng D, Ngo HH, Guo W, Liu Y, Chang SW, Nguyen DD, Nghiem LD, Zhou J, Ni B. **Anaerobic membrane bioreactors for antibiotic wastewater treatment: performance and membrane fouling issues**. *Bioresour Technol* (2018) **267** 714-724. PMID: 30082132
15. Cirak S, Feng L, Anthony K, Arechavala‐Gomeza V, Torelli S, Sewry C, Morgan JE, Muntoni F. **Restoration of the dystrophin‐associated glycoprotein complex after exon skipping therapy in Duchenne muscular dystrophy**. *Mol Ther* (2012) **20** 462-467. PMID: 22086232
16. Crisafulli S, Sultana J, Fontana A, Salvo F, Messina S, Trifirò G. **Global epidemiology of Duchenne muscular dystrophy: an updated systematic review and meta‐analysis**. *Orphanet J Rare Dis* (2020) **15** 141. PMID: 32503598
17. Cruz‐Guzmán Odel R, Rodríguez‐Cruz M, Escobar Cedillo RE. **Systemic inflammation in Duchenne muscular dystrophy: association with muscle function and nutritional status**. *Biomed Res Int* (2015) **2015**
18. Dalile B, Van Oudenhove L, Vervliet B, Verbeke K. **The role of short‐chain fatty acids in microbiota–gut–brain communication**. *Nat Rev Gastroenterol Hepatol* (2019) **16** 461-478. PMID: 31123355
19. Dalile B, Vervliet B, Bergonzelli G, Verbeke K, Van Oudenhove L. **Colon‐delivered short‐chain fatty acids attenuate the cortisol response to psychosocial stress in healthy men: a randomized, placebo‐controlled trial**. *Neuropsychopharmacology* (2020) **45** 2257-2266. PMID: 32521538
20. De Palma C, Perrotta C, Pellegrino P, Clementi E, Cervia D. **Skeletal muscle homeostasis in Duchenne muscular dystrophy: modulating autophagy as a promising therapeutic strategy**. *Front Aging Neurosci* (2014) **6**. PMID: 25104934
21. Di Marzo V. **New approaches and challenges to targeting the endocannabinoid system**. *Nat Rev Drug Discov* (2018) **17** 623-639. PMID: 30116049
22. Duncan SH, Holtrop G, Lobley GE, Calder AG, Stewart CS, Flint HJ. **Contribution of acetate to butyrate formation by human faecal bacteria**. *Br J Nutr* (2004) **91** 915-923. PMID: 15182395
23. Eriksson L, Johansson E, Kettaneh‐Wold N, Trygg J, Wikström C, Wold S. *Multi‐ and megavariate data analysis: Part I: Basic principles and applications* (2006)
24. Feng Y, Wang Y, Wang P, Huang Y, Wang F. **Short‐chain fatty acids manifest Stimulative and protective effects on intestinal barrier function through the inhibition of NLRP3 inflammasome and autophagy**. *Cell Physiol Biochem* (2018) **49** 190-205. PMID: 30138914
25. Frampton J, Murphy KG, Frost G, Chambers ES. **Short‐chain fatty acids as potential regulators of skeletal muscle metabolism and function**. *Nat Metab* (2020) **2** 840-848. PMID: 32694821
26. Frost RA, Nystrom GJ, Lang CH. **Lipopolysaccharide and proinflammatory cytokines stimulate interleukin‐6 expression in C2C12 myoblasts: role of the Jun NH2‐terminal kinase**. *Am J Physiol Regul Integr Comp Physiol* (2003) **285** R1153-R1164. PMID: 12842862
27. Gagliano H, Delgado‐Morales R, Sanz‐Garcia A, Armario A. **High doses of the histone deacetylase inhibitor sodium butyrate trigger a stress‐like response**. *Neuropharmacology* (2014) **79** 75-82. PMID: 24212060
28. Gao F, Lv Y‐W, Long J, Chen J‐M, He J, Ruan X‐Z, Zhu H. **Butyrate improves the metabolic disorder and gut microbiome dysbiosis in mice induced by a high‐fat diet**. *Front Pharmacol* (2019) **10**. PMID: 31607907
29. Gentile F, Doneddu PE, Riva N, Nobile‐Orazio E, Quattrini A. **Diet, microbiota and brain health: unraveling the network intersecting metabolism and neurodegeneration**. *Int J Mol Sci* (2020) **21**. PMID: 33050475
30. Gómez del Pulgar T, Velasco G, Guzmán M. **The CB1 cannabinoid receptor is coupled to the activation of protein kinase B/Akt**. *Biochem J* (2000) **347** 369-373. PMID: 10749665
31. de Graaf RA, Behar KL. **Quantitative 1H NMR spectroscopy of blood plasma metabolites**. *Anal Chem* (2003) **75** 2100-2104. PMID: 12720347
32. Griggs RC, Miller JP, Greenberg CR, Fehlings DL, Pestronk A, Mendell JR, Moxley RT, King W, Kissel JT, Cwik V. **Efficacy and safety of deflazacort vs prednisone and placebo for Duchenne muscular dystrophy**. *Neurology* (2016) **87** 2123-2131. PMID: 27566742
33. Grosicki GJ, Fielding RA, Lustgarten MS. **Gut microbiota contribute to age‐related changes in skeletal muscle size, composition, and function: biological basis for a gut‐muscle Axis**. *Calcif Tissue Int* (2018) **102** 433-442. PMID: 29058056
34. Haddad M. **The impact of CB1 receptor on inflammation in skeletal muscle cells**. *J Inflamm Res* (2021) **14** 3959-3967. PMID: 34421307
35. Hillard CJ, Manna S, Greenberg MJ, DiCamelli R, Ross RA, Stevenson LA, Murphy V, Pertwee RG, Campbell WB. **Synthesis and characterization of potent and selective agonists of the neuronal cannabinoid receptor (CB1)**. *J Pharmacol Exp Ther* (1999) **289** 1427-1433. PMID: 10336536
36. Hofer U. **Pro‐inflammatory prevotella?**. *Nat Rev Microbiol* (2014) **12** 5. PMID: 24270843
37. Hwang TL, Shaka AJ. **Water suppression that works. Excitation sculpting using arbitrary wave‐forms and pulsed‐field gradients**. *J Magn Reson A* (1995) **112** 275-279
38. Iannotti FA, Silvestri C, Mazzarella E, Martella A, Calvigioni D, Piscitelli F, Ambrosino P, Petrosino S, Czifra G, Bíró T. **The endocannabinoid 2‐AG controls skeletal muscle cell differentiation via CB1 receptor‐dependent inhibition of Kv7 channels**. *Proc Natl Acad Sci USA* (2014) **111** E2472-E2481. PMID: 24927567
39. Iannotti FA, Di Marzo V, Petrosino S. **Endocannabinoids and endocannabinoid‐related mediators: targets, metabolism and role in neurological disorders**. *Prog Lipid Res* (2016) **62** 107-128. PMID: 26965148
40. Iannotti FA, Pagano E, Guardiola O, Adinolfi S, Saccone V, Consalvi S, Piscitelli F, Gazzerro E, Busetto G, Carrella D. **Genetic and pharmacological regulation of the endocannabinoid CB1 receptor in Duchenne muscular dystrophy**. *Nat Commun* (2018) **9** 3950. PMID: 30262909
41. Kho ZY, Lal SK. **The human gut microbiome – a potential controller of wellness and disease**. *Front Microbiol* (2018) **9**. PMID: 30154767
42. Kleessen B, Kroesen AJ, Buhr HJ, Blaut M. **Mucosal and invading bacteria in patients with inflammatory bowel disease compared with controls**. *Scand J Gastroenterol* (2002) **37** 1034-1041. PMID: 12374228
43. Koh A, De Vadder F, Kovatcheva‐Datchary P, Bäckhed F. **From dietary fiber to host physiology: short‐chain fatty acids as key bacterial metabolites**. *Cell* (2016) **165** 1332-1345. PMID: 27259147
44. Kraus D, Wong BL, Horn PS, Kaul A. **Constipation in Duchenne muscular dystrophy: prevalence, diagnosis, and treatment**. *J Pediatr* (2016) **171** 183-188. PMID: 26831528
45. Kumar J, Rani K, Datt C. **Molecular link between dietary fibre, gut microbiota and health**. *Mol Biol Rep* (2020) **47** 6229-6237. PMID: 32623619
46. Lahiri S, Kim H, Garcia‐Perez I, Reza MM, Martin KA, Kundu P, Cox LM, Selkrig J, Posma JM, Zhang H. **The gut microbiota influences skeletal muscle mass and function in mice**. *Sci Transl Med* (2019) **11**. PMID: 31341063
47. Layden BT, Angueira AR, Brodsky M, Durai V, Lowe WL. **Short chain fatty acids and their receptors: new metabolic targets**. *Transl Res* (2013) **161** 131-140. PMID: 23146568
48. Le Poul E, Loison C, Struyf S, Springael J‐Y, Lannoy V, Decobecq M‐E, Brezillon S, Dupriez V, Vassart G, Van Damme J. **Functional characterization of human receptors for short chain fatty acids and their role in polymorphonuclear cell activation**. *J Biol Chem* (2003) **278** 25481-25489. PMID: 12711604
49. Lindon JC, Nicholson JK, Holmes E, Keun HC, Craig A, Pearce JTM, Bruce SJ, Hardy N, Sansone S‐A, Antti H. **Summary recommendations for standardization and reporting of metabolic analyses**. *Nat Biotechnol* (2005) **23** 833-838. PMID: 16003371
50. Liu T‐W, Park Y‐M, Holscher HD, Padilla J, Scroggins RJ, Welly R, Britton SL, Koch LG, Vieira‐Potter VJ, Swanson KS. **Physical activity differentially affects the Cecal microbiota of ovariectomized female rats selectively bred for high and low aerobic capacity**. *PLoS One* (2015) **10**. PMID: 26301712
51. Lucke K, Miehlke S, Jacobs E, Schuppler M. **Prevalence of**. *J Med Microbiol* (2006) **55** 617-624. PMID: 16585651
52. Manca C, Shen M, Boubertakh B, Martin C, Flamand N, Silvestri C, Di Marzo V. **Alterations of brain endocannabinoidome signaling in germ‐free mice**. *Biochim Biophys Acta Mol Cell Biol Lipids* (2020) **1865**. PMID: 32795503
53. Matias I, Gonthier M‐P, Orlando P, Martiadis V, De Petrocellis L, Cervino C, Petrosino S, Hoareau L, Festy F, Pasquali R. **Regulation, function, and dysregulation of endocannabinoids in models of adipose and beta‐pancreatic cells and in obesity and hyperglycemia**. *J Clin Endocrinol Metab* (2006) **91** 3171-3180. PMID: 16684820
54. Matthews E, Brassington R, Kuntzer T, Jichi F, Manzur AY. **Corticosteroids for the treatment of Duchenne muscular dystrophy**. *Cochrane Database Syst Rev* (2016) **2016**. PMID: 27149418
55. McMahon DK, Anderson PA, Nassar R, Bunting JB, Saba Z, Oakeley AE, Malouf NN. **C2C12 cells: biophysical, biochemical, and immunocytochemical properties**. *Am J Physiol* (1994) **266** C1795-C1802. PMID: 8023908
56. Morais LH, Schreiber HL, Mazmanian SK. **The gut microbiota‐brain axis in behaviour and brain disorders**. *Nat Rev Microbiol* (2020) **19** 241-255. PMID: 33093662
57. Morosetti R, Broccolini A, Sancricca C, Gliubizzi C, Gidaro T, Tonali PA, Ricci E, Mirabella M. **Increased aging in primary muscle cultures of sporadic inclusion‐body myositis**. *Neurobiol Aging* (2010) **31** 1205-1214. PMID: 18823681
58. Morya R, Salvachúa D, Thakur IS. **Burkholderia: an untapped but promising bacterial genus for the conversion of aromatic compounds**. *Trends Biotechnol* (2020) **38** 963-975. PMID: 32818444
59. Mulè F, Amato A, Serio R. **Gastric emptying, small intestinal transit and fecal output in dystrophic (mdx) mice**. *J Physiol Sci* (2010) **60** 75-79. PMID: 19784719
60. O'Brien J, Hayder H, Zayed Y, Peng C. **Overview of microRNA biogenesis, mechanisms of actions, and circulation**. *Front Endocrinol* (2018) **9**
61. Ortega‐Hernández A, Martínez‐Martínez E, Gómez‐Gordo R, López‐Andrés N, Fernández‐Celis A, Gutiérrrez‐Miranda B, Nieto ML, Alarcón T, Alba C, Gómez‐Garre D. **The interaction between mitochondrial oxidative stress and gut microbiota in the cardiometabolic consequences in diet‐induced obese rats**. *Antioxidants* (2020) **9**. PMID: 32708095
62. Pagano C, Pilon C, Calcagno A, Urbanet R, Rossato M, Milan G, Bianchi K, Rizzuto R, Bernante P, Federspil G. **The endogenous cannabinoid system stimulates glucose uptake in human fat cells via phosphatidylinositol 3‐kinase and calcium‐dependent mechanisms**. *J Clin Endocrinol Metab* (2007) **92** 4810-4819. PMID: 17785353
63. Pane M, Vasta I, Messina S, Sorleti D, Aloysius A, Sciarra F, Mangiola F, Kinali M, Ricci E, Mercuri E. **Feeding problems and weight gain in Duchenne muscular dystrophy**. *Eur J Paediatr Neurol* (2006) **10** 231-236. PMID: 17045498
64. Parada Venegas D, De la Fuente MK, Landskron G, González MJ, Quera R, Dijkstra G, Harmsen HJM, Faber KN, Hermoso MA. **Short chain fatty acids (SCFAs)‐mediated gut epithelial and immune regulation and its relevance for inflammatory bowel diseases**. *Front Immunol* (2019) **10**. PMID: 30915065
65. Park C, Lee H, Hong S, Molagoda IMN, Jeong J‐W, Jin C‐Y, Kim G‐Y, Choi SH, Hong SH, Choi YH. **Inhibition of lipopolysaccharide‐induced inflammatory and oxidative responses by trans‐cinnamaldehyde in C2C12 myoblasts**. *Int J Med Sci* (2021) **18** 2480-2492. PMID: 34104079
66. Paulson JN, Stine OC, Bravo HC, Pop M. **Differential abundance analysis for microbial marker‐gene surveys**. *Nat Methods* (2013) **10** 1200-1202. PMID: 24076764
67. Péladeau C, Adam NJ, Jasmin BJ. **Celecoxib treatment improves muscle function in mdx mice and increases Utrophin A expression**. *FASEB J* (2018) **32** 5090-5103. PMID: 29723037
68. Prokopidis K, Chambers E, Ni Lochlainn M, Witard OC. **Mechanisms linking the gut‐muscle Axis with muscle protein metabolism and anabolic resistance: implications for older adults at risk of sarcopenia**. *Front Physiol* (2021) **12**. PMID: 34764887
69. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO. **The SILVA ribosomal RNA gene database project: improved data processing and web‐based tools**. *Nucleic Acids Res* (2013) **41** D590-D596. PMID: 23193283
70. Radley‐Crabb HG, Fiorotto ML, Grounds MD. **The different impact of a high fat diet on dystrophic mdx and control C57Bl/10 mice**. *PLoS Curr* (2011) **3**. PMID: 22094293
71. Richards JL, Yap YA, McLeod KH, Mackay CR, Mariño E. **Dietary metabolites and the gut microbiota: an alternative approach to control inflammatory and autoimmune diseases**. *Clin Transl Immunol* (2016) **5**
72. Rojas‐Morales P, Tapia E, Pedraza‐Chaverri J. **β‐Hydroxybutyrate: a signaling metabolite in starvation response?**. *Cell Signal* (2016) **28** 917-923. PMID: 27083590
73. Runwal G, Stamatakou E, Siddiqi FH, Puri C, Zhu Y, Rubinsztein DC. **LC3‐positive structures are prominent in autophagy‐deficient cells**. *Sci Rep* (2019) **9**. PMID: 31300716
74. Russell JT, Roesch LFW, Ördberg M, Ilonen J, Atkinson MA, Schatz DA, Triplett EW, Ludvigsson J. **Genetic risk for autoimmunity is associated with distinct changes in the human gut microbiome**. *Nat Commun* (2019) **10**. PMID: 31399563
75. Sandri M, Coletto L, Grumati P, Bonaldo P. **Misregulation of autophagy and protein degradation systems in myopathies and muscular dystrophies**. *J Cell Sci* (2013) **126** 5325-5333. PMID: 24293330
76. Schwab M, Reynders V, Ulrich S, Zahn N, Stein J, Schröder O. **PPARgamma is a key target of butyrate‐induced caspase‐3 activation in the colorectal cancer cell line Caco‐2**. *Apoptosis* (2006) **11** 1801-1811. PMID: 16927016
77. Sheikh O, Yokota T. **Advances in genetic characterization and genotype‐phenotype correlation of Duchenne and Becker muscular dystrophy in the personalized medicine era**. *J Pers Med* (2020) **10**. PMID: 32899151
78. Singh J, Verma NK, Kansagra SM, Kate BN, Dey CS. **Altered PPARγ expression inhibits myogenic differentiation in C2C12 skeletal muscle cells**. *Mol Cell Biochem* (2007) **294** 163-171. PMID: 16838108
79. Sun M, Wu W, Liu Z, Cong Y. **Microbiota metabolite short chain fatty acids, GPCR, and inflammatory bowel diseases**. *J Gastroenterol* (2017) **52** 1-8. PMID: 27448578
80. Tang Y, Chen Y, Jiang H, Nie D. **Short‐chain fatty acids induced autophagy serves as an adaptive strategy for retarding mitochondria‐mediated apoptotic cell death**. *Cell Death Differ* (2011) **18** 602-618. PMID: 20930850
81. Turcotte C, Chouinard F, Lefebvre JS, Flamand N. **Regulation of inflammation by cannabinoids, the endocannabinoids 2‐arachidonoyl‐glycerol and arachidonoyl‐ethanolamide, and their metabolites**. *J Leukoc Biol* (2015) **97** 1049-1070. PMID: 25877930
82. de Vos WM, Tilg H, Van Hul M, Cani PD. **Gut microbiome and health: mechanistic insights**. *Gut* (2022) **71** 1020-1032. PMID: 35105664
83. Walsh ME, Bhattacharya A, Sataranatarajan K, Qaisar R, Sloane L, Rahman MM, Kinter M, Van Remmen H. **The histone deacetylase inhibitor butyrate improves metabolism and reduces muscle atrophy during aging**. *Aging Cell* (2015) **14** 957-970. PMID: 26290460
84. Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vázquez‐Fresno R, Sajed T, Johnson D, Li C, Karu N. **HMDB 4.0: the human metabolome database for 2018**. *Nucleic Acids Res* (2018) **46** D608-D617. PMID: 29140435
85. van de Wouw M, Boehme M, Lyte JM, Wiley N, Strain C, O'Sullivan O, Clarke G, Stanton C, Dinan TG, Cryan JF. **Short‐chain fatty acids: microbial metabolites that alleviate stress‐induced brain–gut axis alterations**. *J Physiol* (2018) **596** 4923-4944. PMID: 30066368
|
---
title: Microbiota dysbiosis influences immune system and muscle pathophysiology of
dystrophin‐deficient mice
authors:
- Andrea Farini
- Luana Tripodi
- Chiara Villa
- Francesco Strati
- Amanda Facoetti
- Guido Baselli
- Jacopo Troisi
- Annamaria Landolfi
- Caterina Lonati
- Davide Molinaro
- Michelle Wintzinger
- Stefano Gatti
- Barbara Cassani
- Flavio Caprioli
- Federica Facciotti
- Mattia Quattrocelli
- Yvan Torrente
journal: EMBO Molecular Medicine
year: 2022
pmcid: PMC9994487
doi: 10.15252/emmm.202216244
license: CC BY 4.0
---
# Microbiota dysbiosis influences immune system and muscle pathophysiology of dystrophin‐deficient mice
## Abstract
Duchenne muscular dystrophy (DMD) is a progressive severe muscle‐wasting disease caused by mutations in DMD, encoding dystrophin, that leads to loss of muscle function with cardiac/respiratory failure and premature death. Since dystrophic muscles are sensed by infiltrating inflammatory cells and gut microbial communities can cause immune dysregulation and metabolic syndrome, we sought to investigate whether intestinal bacteria support the muscle immune response in mdx dystrophic murine model. We highlighted a strong correlation between DMD disease features and the relative abundance of Prevotella. Furthermore, the absence of gut microbes through the generation of mdx germ‐free animal model, as well as modulation of the microbial community structure by antibiotic treatment, influenced muscle immunity and fibrosis. Intestinal colonization of mdx mice with eubiotic microbiota was sufficient to reduce inflammation and improve muscle pathology and function. This work identifies a potential role for the gut microbiota in the pathogenesis of DMD.
The susceptibility of DMD patients to inflammatory events cannot solely be explained by skeletal muscle genetic defects but rather favors a new paradigm linking the development of chronic inflammation with a strict regulation between epigenetics factors and degenerative environment.
## Problem
In the mucosa of the gastrointestinal tract, immune cells drive innate and adaptive responses, and thus inflammatory events that are common features of Duchenne muscular dystrophy (DMD). Although the gut microbiota likely influences muscle metabolism and physiology, molecular players involved in the gut‐muscle axis remain to be identified. In patients with muscular dystrophies, the lack of dystrophin determines a severe impairment of the intestinal homeostasis. Gastrointestinal disturbances may therefore belong to the clinical symptoms and can appear long before the typical DMD musculoskeletal features occur. Although microbiota modulation could influence DMD inflammatory environment, gastrointestinal dysfunctions in this disease has not been rigorously studied.
## Results
We revealed a significant reduction of microbial richness in 3‐month‐old DMD animal model (mdx mice) compared with age‐matched C57Bl mice, with an enrichment of genera Prevotella and different metabolic profiles. Using murine models of DMD mimicking different degrees of disease severity, we highlighted the correlation between gut inflammation and muscular damage. To directly assess the potential contribution of dysbiosis in muscular wasting, we generated germ‐free dystrophic mice (GFmdx). In 3‐month‐old mdx mice, gut microbiota depletion reduced innate immune response while altering muscle metabolism and function. In line with these results, dysbiotic microbiota of mdx negatively affected intestinal, spleen, and muscle inflammation following injection in WT mice, and inversely correlated with muscle function.
## Impact
Nutraceutical supplementation and dietary metabolites have been successfully proposed as strategies to co‐adjuvate the treatment of DMD, as well as immune‐modulators. Therefore, the identification of bacteria involved in triggering the inflammation‐driven muscular degeneration could pave the way to manipulate these taxa for therapeutic purposes. Corticosteroids are the current therapeutic standards for treating DMD, prolonging ambulation and muscle function. However, many patients experience serious adverse side effects. The use of nutraceutical with immunomodulating properties would provide DMD patients with an improved quality of life, as well as a reduction of costs associated with recurrent hospitalization that is usually needed to treat corticosteroid side effects.
## Introduction
Duchenne muscular dystrophy (DMD) is an X‐linked disease caused by mutations in the DMD gene and loss of the dystrophin protein, leading to myofiber membrane fragility and necrosis with weakness and contractures. Affected DMD boys typically die in their second or third decade of life due to either respiratory failure or cardiomyopathy (Emery, 2002). Although the primary defects rely on skeletal muscle structure, a multitude of secondary defects exist involving deregulated metabolic and inflammatory pathways. Immune cell infiltration into skeletal muscle is, indeed, a typical feature of DMD pathophysiology and is strongly associated with disease severity (Farini et al, 2009). In the dystrophic dystrophin‐deficient mdx murine model, we recently found the presence of activated T lymphocytes and the overexpression of immunoproteasome (IP), an enzymatic complex that cleaves peptides to produce epitopes for antigen presentation to T lymphocytes. We have demonstrated that IP inhibition improved dystrophic muscle functions by reducing the number of both circulating and infiltrating activated T cells, confirming a pathogenic role of immune cells (Farini et al, 2016). Dystrophic muscle features were also improved by the depletion of B‐ and T‐cells in immunodeficient dystrophinopathic (SCID/mdx) and dysferlinopathic (SCID/BlAJ) murine models (Farini et al, 2012). So far, immunosuppressive drugs, such as glucocorticoids, are the only effective therapies to delay the onset and control symptoms (Shan et al, 2017a), ameliorating ambulation and muscle function, but their use in patients is still limited by serious side effects. In this scenario, the individual susceptibility to inflammatory events cannot be simply explained by the genetic defects of skeletal muscles; rather, there is a new emerging paradigm explaining the development of chronic inflammation that comprehend a strict regulation of epigenetics factors, genetic components, and the environment. In particular, environmental intrinsic (as innate and adaptive immunity) and extrinsic (as nutrition) mechanisms are connected to each other in a well‐defined temporal and spatial way, whose dysfunctions are the main causes of chronic inflammatory conditions (Shan et al, 2017b). Tissue‐environment interface is a preferential site critically involved in triggering mechanisms of chronic inflammation, especially in the mucosa of the gastrointestinal tract, where commensal microorganisms forming the so‐called microbiota provide nutrients by digestion of dietary components, modulate the development of the mucosal immune system and protect from pathogens (Yan et al, 2016). Thus, alterations of gut microbial communities (dysbiosis) can cause immune dysregulation and metabolic syndrome, contributing to a multitude of diseases of different aetiologies (Sperduto et al, 2019). The commensal population that constitutes the microbiota is extremely variable among individuals and its composition is dependent on the immune responses that are mediated in the gut and on host genotypes/phenotypes (Spor et al, 2011). Accordingly, the intestinal homeostasis is maintained through the mutualistic interactions between the microbiota and intestinal immune cells: dysfunctions cause serious problem including chronic inflammatory state (Kabat et al, 2016). In some way, gut microbiota modulation can also alter the regulatory molecules secreted by skeletal muscles and adipose tissues—myokines and adipokines—whose function is strictly dependent on the production of short‐chain fatty acids (SCFAs) and branched‐chain amino acids (Suzumura et al, 1986). In muscle tissue, dysbiosis interferes with the proper development of muscle progenitor cells, likely through reactive oxygen species generation and antioxidant genes (Tidball, 2017), and with endothelial cell function (van Bladel et al, 2014). The latter has been already confirmed by the occurrence of vascular development dysfunctions in pathogen‐free mice, possibly dependent on defects in nitric oxide synthase activity and in the expression of vessel inflammatory genes that were reversed by the restoration of normal gut flora (Tidball et al, 2018).
Duchenne muscular dystrophy patients present alterations of gastrointestinal motility and suffer from constipation, pseudo‐obstruction, and acute dilatation. Although no attention was paid to investigate these processes, smooth muscle fibrosis was observed throughout the gastrointestinal tract (Mule et al, 2010). Mdx mice shared impairments in intestinal contractility, linked to important abnormalities of the mucosal epithelial morphology (wider villi, reduced muscular and submucosa layer) normally associated to inflammatory state (Durbeej et al, 2000), and especially to nitric oxide (NO) production (Tomasi et al, 2017a). In addition, it was demonstrated that L‐arginine dietary supplementation improved colonic motility and increased NO signaling, ameliorating the pathological phenotype of mdx mice (Tomasi et al, 2017b). These evidences confirmed the involvement of a muscle‐gut axis‐mediated pathway that contributes to jeopardize the pathophysiology of DMD (Alves et al, 2014). Modulation of microbiota is also responsible for modifications of immunological and inflammatory features in organs distant from the gut. For instance, experiments of gnotobiology have shown that inhibition of microbiota function diminished the development of arthritis and autoimmune encephalomyelitis in murine models, whereas colonization of germ‐free mice with specific bacterial strains modified the expression of circulating monocytes, Th17 T lymphocytes and B lymphocytes (Tonegawa, 1983; Turner & Hilton‐Jones, 2010). Likely, inflammatory molecules derived from microbiota leak from the disrupted gut barrier of the mdx mice, activate inflammatory cells and circulate through blood toward muscles, where they modulate the immune system worsening the dystrophic phenotype.
Thus, it is possible to envision a connection between gut microbiota, immunity, and muscle homeostasis but the molecular details of this cascade of events in muscular dystrophy are still elusive. Here, we demonstrate dysbiosis in mdx mice that is associated with alterations of peripheral and local mdx immune landscape and muscle integrity. Treatment with broad‐spectrum antibiotics depleting the gut microbiota in 3‐month‐old (3m) mdx mice determined reduced muscle inflammation and enhanced fatty acid oxidation with consequent shift in fiber type toward an oxidative phenotype associated with muscle wasting. We also show that germ‐free mdx (GFmdx) mice were impaired in muscle function except for reduced fibrosis and absence of chronic muscle inflammation. Furthermore, intestinal colonization of mdx mice with eubiotic microbiota was sufficient to reduce inflammation improving muscle pathology and function. Our work provides new insights in DMD pathogenesis by highlighting the role of intestinal microbiota in shaping the muscular inflammatory response and conferring a distinct susceptibility to dystrophic muscle disease. From a therapeutic perspective, our results allow the identification of intestinal microorganisms, microbial products, and metabolites as potential targets to tailor innovative therapeutic strategies for the treatment of DMD patients.
## Altered gut microbiota composition in mdx mice
Full‐length dystrophin is predominantly expressed by skeletal muscles but dystrophic patients present impairment of gastrointestinal functions, altered motility and histological evidences for smooth muscle fibrosis throughout the gastrointestinal tract (Mule et al, 2010). Mdx mice shared impairments in the intestinal contractility, mainly due to NO dysfunctions (Baccari et al, 2000), increased calcium influx and deregulated tachinergic NK2 receptors (Mule & Serio, 2001). Slow fecal output and transit time of fecal material revealed motor disturbance in mdx mice, highlighting a delay in the propulsion (Mule et al, 2010). We found alterations of histological structures in the colon of 3m mdx mice mainly consisting in epithelial atrophy, shorter villi and thinner muscular and submucosal layers (Fig 1A–C). Total SCFA content in the stool isolated from colon, which contributes to immune regulation and anti‐inflammatory effects (Gul et al, 2014), was found to be similar between 3m C57Bl wild‐type (WT) and 3m mdx mice (Fig 1D). However, the imaging mass spectrometry of lipids in mdx small intestine tissues demonstrated an enrichment of phosphatidylcholines (PC: 34:2; PC 36:2) and PC cleavage by products as lyso‐phosphatidylcholines (LysoPC: 16:0) (Fig 1E). PC and LysoPC activate multiple signaling pathways that are involved in oxidative stress and inflammatory responses triggered through Toll‐like receptors (TLRs) (Vidarsson et al, 2014) leading to increased release of cytokines—i.e. interleukin (IL)‐1β, IL‐6, and tumor necrosis factor‐α (TNFα) (Wang et al, 2019)—and activation of lymphocytes (Wenninger et al, 2018) and pro‐inflammatory M1 macrophages (Yang et al, 2005). Compared with WT, the mdx intestinal tissues showed a trend for increased pro‐inflammatory IL‐6 and TNFα cytokines and TNF receptor‐associated factor 6 (TRAF6) inflammatory mediator (Fig 1F). Accordingly, significant upregulation of mediators of innate immunity, as proteasome subunit beta type‐8 (PMSB8), pentraxin‐3 (PTX‐3) and v‐rel Reticuloendotheliosis viral oncogene homolog B (RelB), was observed in mdx versus WT intestine tissues (Fig 1F). Modifications of neither transforming growth factor (TGF)‐β1 nor TLR4 were found (Fig 1F). Interestingly, we determined that TLR2—which is involved in maintenance of tight junction integrity and regulation of gut chronic inflammation (Wu et al, 2019; Xepapadaki et al, 2019)—was significantly downregulated in mdx intestine (Fig 1F).
**Figure 1:** *Colon characterization of 3m mdx mice
Representative images of H&E staining of colon from 3m C57Bl (n = 4) and mdx (n = 4) mice. High magnification (scale bar: 20 μm) and low magnification (scale bar: 200 μm).Mucus layer, area between yellow dash lines; crypt length, yellow‐headed arrow. Scale bar: 100 μm.Mucus layer thickness and crypt length were quantified for n = 4 mice per group (with pooled samples of n = 60 for mucus layer thickness and n = 80 for crypt length).Short‐chain fatty acid fecal quantification of 3m C57Bl (n = 3) and mdx (n = 4–5) mice.Colon images captured with the iMScope TRIO described altered pattern of expression of different phosphatidylcholines (PC) and lysophosphatidylcholines (LysoPC) (as indicated by m/z values) in 3m mdx mice (n = 3). Scale bar: 50 μm. For each lipid, the mean intensities measured at 12 positions throughout colon images are shown on the right side where bars are mean ± SEM (n = 3).Cropped images of representative WB showing the expression of proteins involved in inflammation and fibrosis in colon tissues of 3m C57Bl (n = 3) and 3m mdx (n = 5). Densitometric analyses of protein expression was shown as ratio to actin.
Data information: Data are presented as mean ± SD (*P < 0.05; **P < 0.01, ***P < 0.001, ****P < 0.0001; Student's t‐test).
Source data are available online for this figure.*
To verify the intestinal microbial community structure, we performed a metataxonomic analysis. The analysis of gut microbiota alpha‐diversity showed a significant reduction of microbial richness in 3m mdx compared with age matched C57Bl animals (Fig 2A; FDR corrected $P \leq 0.05$, pairwise comparisons using Wilcoxon rank‐sum test) suggesting that mdx mice are characterized by a dysbiotic microbiota. The microbial community structures among groups were significantly different as measured by beta‐diversity of Unweighted UniFrac distances and Bray–Curtis dissimilarity (Fig 2B; PERMANOVA $P \leq 0.05$). An in‐depth analysis of the gut microbiota (Fig 2C and D) showed the enrichment of different amplicon sequence variants (ASVs) belonging to the genera Alistipes and Prevotella, among others, in 3m mdx compared with C57Bl mice. Notably, LysoPC have been associated with the abundance of the genus Alistipes (Jolles et al, 2014; Heydemann, 2017). By using a Random Forest classifier, we further observed that the gut microbiota was able to classify samples according to status (OOB = $18.2\%$; $P \leq 0.0001$; Accuracy = 0.81; Kappa = 0.725) and that the genus Prevotella was the most important, fully classified, feature to categorize samples according to status (Fig 2E). According to these data, Prevotella was among the taxa whose abundance was significantly higher in 3m mdx (Fig 2F).
**Figure 2:** *Microbiome analysis of 3m mdx mice
Observed number of enriched ASVs in 3m C57Bl (n = 6) (maximum: 666; median: 583.67; minimum: 475) and 3m mdx (n = 8) (maximum: 522; median: 465.625; minimum: 404). Data are presented as the exact number of ASVs (*P < 0.05; Student's t‐test).PCA of beta‐diversity of 3m C57Bl (n = 6) and 3m mdx (n = 7) as measured by Unweighted UniFrac distance and Bray–Curtis dissimilarity.Mean relative abundance at genus level among groups. All genera with relative abundance < 0.1% are reported together and labeled as “others.”Volcano plots of 3m C57Bl (n = 6) and 3m mdx (n = 7) showing the significantly enriched bacterial amplicon sequence variants (ASVs) (with P < 0.05) by the DEseq2 analysis. The names of the significantly enriched bacterial ASVs classified to the genus level and P < 0.005 are reported. All P‐values were false discovery rate–corrected.Random forest analysis. The top 20 bacterial genera with the highest discriminatory power sorted by mean decrease GINI value are showed.Relative abundance of different genus in 3m C57Bl (n = 6) and 3m mdx (n = 7). Prevotella: 3m C57Bl: maximum: 0.28438734; median: 0.162312199; minimum: 0. 3m mdx: maximum: 4.650449086; median: 1.928565583; minimum: 0.308938765. Alistipes: 3m C57Bl: maximum: 2.381488226; median: 1.622780995; minimum: 0.52990159. 3m mdx: maximum: 24.79693926; median: 11.74146327; minimum: 5.71434417. Parasutterella: 3m C57Bl: maximum: 1.491499069; median: 0.923747366; minimum: 0.390776848. 3m mdx: maximum: 0.114573317; median: 0.036571394; minimum: 0.002045952. Rikenella: 3m C57Bl: maximum: 0.202549256; median: 0.124121093; minimum: 0.067516419. 3m mdx: maximum: 0; median: 0; minimum: 0. *P < 0.05; Student's t‐test.Multidimensional scaling analysis of small intestinal metabolomic profiles from of 3m C57Bl (n = 4) and 3m mdx (n = 3) mice calculated by samples' distance similarities (Bray‐Curtis) with the most discriminatory metabolites (top variable importance in projection score) identified.Concentration of the significantly different metabolites isolated from the small intestinal content of 3m C57Bl (n = 4) and 3m mdx (n = 3) mice. *P < 0.05, **P < 0.01 and ***P < 0.001; Wilcoxon rank‐sum test.
Source data are available online for this figure.*
Multidimensional scaling (MDS) analysis evidenced significantly different metabolic profiles among groups (Fig 2G). Accordingly, the predicted functional potential of the mdx‐associated microbiota showed alterations of metabolic pathways related to carbohydrate and amino acid metabolism (Metabolic Maps C57Bl and 3m mdx; Dataset EV1 and Fig 3A). Indeed, 3m mdx mice showed a significant reduction in the predicted gene content of the key SCFA biosynthetic enzymes propionyl‐CoA:succinate CoA transferase (scpC), propionate CoA‐transferase (pct), butyryl‐CoA:acetate CoA‐transferase (but) and butyrate kinase (buk) while no differences where observed for propionate (tdcD) and acetate (ack) kinases (Fig 3B). We observed a significant reduction in the concentration of different amino acids, namely alanine, aspartic acid, methionine, and phenylalanine, in 3m mdx mice compared with C57Bl animals (Fig 2H). Of note, tartarate may act as a muscle toxin by inhibiting the production of malic acid (Junghans et al, 2001).
**Figure 3:** *Metabolic maps of 3m mdx mice
The iPath3.0 representation of KEGG metabolic pathways inferred from Piphillin analysis significantly upregulated (in red) or downregulated (in blue) in 3m C57Bl (n = 6) versus 3m mdx (n = 8) mice. Nodes in the map colored in green, yellow, and orange correspond to acetate, propionate, and butyrate, respectively. Line thickness represents the level of statistical significance for the inferred pathways; thick lines with FDR‐corrected P‐value < 0.05, thin lines with nominal P‐value < 0.05.Predicted metagenomic gene content of the key enzymes catalyzing the final steps for the production of microbiota‐derived SCFAs in GI of 3m C57Bl (n = 5/6) and 3m mdx (n = 7/8) mice. Data are presented as mean ± SD (**P < 0.01, ***P < 0.001; Kruskal–Wallis test).
Source data are available online for this figure.*
To evaluate the extent of gut microbiota effects on systemic and muscle immunity, as well as inflammation, we correlated metataxonomic and immunophenotyping data (Dataset EV2 and Fig EV1). Specifically, we investigated the correlations among FACS analysis of different subsets of T cells (naïve, central memory, effector T cells and Tregs) and CD11b+ myeloid subset of spleen and muscle tissues in 3m mdx and C57Bl mice and the most representative microbiota genera (Dataset EV2 and Fig EV1). We observed that Prevotella significantly correlated with the frequency of splenic CD44+CD4+/CD8+ T cells and Tregs as well as with muscle effector/memory CD44+CD8+ T cells and central memory CD4+ T cells (Fig EV1).
**Figure 4:** *Characterization of gut tissue metabolome in 3m mdx mice and following antibiotics treatment
Partial least square discriminant analysis (PLS‐DA) models score plot used to evaluate the differences among 3m C57B1 (in gray), 3m mdx mice (in green) and 3m mdx+ABX (dark purple), with n = 4 each.Relevant metabolites (top variable importance in projection score) in the corresponding PLS‐DA separation, in blue metabolites with a negative fold change and in red metabolites with a positive fold change.Heatmap showing all the relevant metabolites concentration change among the groups. Both metabolites and classes were clusterized according to the Wald method. In blue metabolites' concentration with a negative fold change and in red metabolites' concentration with a positive fold change.Metabolic pathways involving the relevant metabolites obtained using the MetPa algorithm. The color and size of each circle are based on the P‐value and pathway impact value, respectively. The x‐axis represents the pathway impact, and the y‐axis represents the −log of P values from the pathway enrichment analysis for the key differential metabolites of 3m mdx and 3m mdx+ABX mice.Fecal content quantification of SCFAs in 3m mdx and 3m mdx+ABX mice (n = 5 per group). Data equal to 0. Data are presented as mean ± SD (**P < 0.01, ***P < 0.001; Student's t‐test).
Source data are available online for this figure.* **Figure EV1:** *Correlation between bacterial genera and immunityHeatmap of Spearman's rho correlations between the relative abundance of the most represented bacterial genera (with relative abundance > 0.1%) in the gut microbiota of 3m mdx animals (n = 3–8) with the indicated metabolites and immunological parameters. The significant correlations with FDR‐corrected P‐value < 0.1 are indicated with bubbles. Spearman correlation plots for the significant correlations between Prevotella and the indicated immunological parameters are also shown. Abbreviations used in the Figure: sp, spleen‐derived; msc, muscle‐derived; CEMEM, central memory cells; EFFECT, effector cells.Source data are available online for this figure.*
## Gut microbiota depletion reduces innate immune response but alters muscle metabolism and function in 3‐month‐old mdx mice
Since mdx mice harbor alterations in intestinal microbiome, we investigate whether early secondary effects of muscular dystrophy could be affected by the absence of gut microbiota in GFmdx dystrophic model or by long‐term depletion of intestinal bacteria of mdx by oral treatment with a cocktail of ampicillin, metronidazole, and vancomycin (ABX) (Krebs et al, 2016; Xie et al, 2017). ABX can affect luminal secondary metabolites and gut signaling (Zschuntzsch et al, 2016). Metabolomic analysis of the small intestine in 3m mdx+ABX demonstrated significant alterations of the metabolic profiles and inferred pathways (FRD‐corrected $P \leq 0.05$, ANOVA) suggesting an important role of the gut microbiota in affecting host metabolome (Fig 4A and B). Given that combined output of host‐microbes interactions influencing host energy metabolism, development, and function of the immune system (Juliao et al, 2017) is determined by the metabolome, we performed a metabolomic analysis of the small intestinal content from mdx and mdx+ABX mice. Metabolic pathways reconstruction based on the metabolites with the highest discriminatory power (Dataset EV1 and Fig 4C and D) revealed that mdx over‐expressed glycine, coprostanol, threonine, phosphate and galacticol related to age‐matched C57Bl while leucine, methionine, aspartic acid and alanine were downregulated. Interestingly, 3m mdx+ABX mice showed metabolites' expression similar to WT mice, except for maltose and mandelic acid, whose amount was lower in dystrophic mice related to 3m C57Bl (Fig 4C and D). Hence, ABX changed the SCFA pool of mdx, most notably by decreasing butyric, propionic, isopropionic, and valeric acids to undetectable levels, while acetic acid was significantly decreased compared with untreated mdx mice (Fig 4E). We further highlighted the influence of gut microbiota depletion on immune response of 3m mdx. The amount of splenic CD45+CD11b+CD4−CD8− myeloid cells was not modified by ABX treatment (Fig 5A) neither the effector CD4+ T cells which remained upregulated in both mdx and mdx+ABX compared with C57Bl (Fig 5B). In muscle, we did not find differences in the CD45+ myeloid cells (Fig 5C) since ABX only slightly modified the effector CD4+ T cells in mdx compared with untreated age‐matched C57Bl (Fig 5D). According to these evidences and to literature describing modulation of immune system and microbiota in GF mice (Kapur et al, 2015), we investigated the consequences of gut microbiota depletion on the amounts of lymphocytes in 3m GFmdx. We observed similar amount of CD4+ and CD8+ T cells between 3m mdx and 3m GFmdx (Fig 5E), whereas a significant reduction of splenic Treg cells and effector/memory CD44+ T cells (Fig 5F) was shown in 3m GFmdx compared with age‐matched mdx mice. The amount of inflammatory CD45+ cells was also diminished in skeletal muscle of 3m GFmdx related to 3m mdx mice as demonstrated by FACS analysis (Fig 5G) and specific‐muscle staining (Fig 5H). Similarly, the number of CD3+ inflammatory cells was higher in 3m mdx muscles as assessed by quantification of immunofluorescence staining (Fig 5I). Compared with 3m mdx, ABX‐treated mdx and GFmdx muscles showed significant decrease in IL‐6 but not in TNFα cytokines, as well as a significant reduction of Nuclear Factor kappa‐light‐chain‐enhancer of activated B cells (NF‐kB) and RelB inflammatory mediators toward the levels of WT (Fig EV2A), suggesting a reduced muscle innate immune response (Ticinesi et al, 2017). There was no change in the expression of TLR4 and osteopontin (OPN), but mdx, mdx+ABX and GFmdx muscles showed an increase in matrix metallopeptidase (MMP) 9 relative to WT (Fig EV2A). To evaluate the influence of the gut microbiota on the dystrophic skeletal muscle architecture and function, we performed RNAseq of tibialis anterior (TA) muscles from mdx, ABX‐treated mdx and GFmdx mice. Principal component analysis (PCA) of RNAseq datasets showed treatment‐dependent clustering of samples, separating mdx+ABX and GFmdx from mdx (Dataset EV3 and Fig 6A). As additional quality control, mdx RNA datasets clustered separately from the ones of age‐matched, background‐matched C57Bl muscles (Fig 6A). Comparing ABX‐treated and GFmdx with age‐matched mdx muscle, we found 1,381 genes convergently upregulated and 2,722 genes convergently downregulated (Fig 6B). Gene abundance cutoff was set at 10CPM for these initial analyses to focus our comparisons on genes of mid‐to‐high expression in muscle. We performed gene ontology (GO) analysis on both groups of convergent genes. GO analysis of the convergent upregulated genes showed enrichment for pathways of oxidative metabolism (Lpin1, Ppard, Ppargc1a, Ppara) and nutrient uptake/processing (Pfkm, Pck1, Pfkfb3, Pcx, Slc2a3, Slc2a5, Tkt, Pygl, Plin1, Lipe, Acer2) (Dataset EV4 and Fig 6B and C). Conversely, ABX and GF treatments converged on downregulating genes involved in inflammation and fibrosis (Timp1, Mmp15, several members of the Adamts extracellular proteases and *Collagen* gene families) (Dataset EV4 and Fig 6B and C). Furthermore, gene set enrichment analysis (GSEA) revealed alterations in inflammatory response, epithelial‐to‐mesenchymal transition, complement activity, angiogenesis and interferon‐γ response in 3m mdx versus age‐matched C57Bl muscles (Fig EV3). Moreover, genes involved in G2M checkpoint transition, interferon‐α and ‐γ response, E2F transcriptional activity and myogenesis were reduced in 3m mdx+ABX versus age‐matched mdx muscles (Fig EV3). *Muscle* genes involved in adipogenesis, fatty acid metabolism and cholesterol homeostasis were upregulated in 3m GFmdx versus age‐matched mdx mice; conversely, muscle genes involved in interferon‐αresponse, E2F transcriptional activity and inflammatory response were downregulated in the 3m GFmdx versus age‐matched mdx mice (Fig EV3).
**Figure 5:** *Microbiota depletion induces modulation of immune cells
A–DFACS analysis of spleen and muscle homogenates from 3m C57Bl (n = 4), mdx (n = 5) and mdx+ABX (n = 7) mice demonstrates no significant alteration of CD45+CD11b+CD4−CD8− myeloid cells (A and C) and few differences in CD4+ or CD8+ naïve (CD62L+ CD44−), central memory (CD62L+ CD44+) and effector (CD62L− CD44+) T cells (B and D).EFACS analysis of spleen of 3m C57Bl (n = 7), mdx (n = 9) and GFmdx (n = 5) mice revealed similar proportions of CD4+ and CD8+ T cells but reduced activated CD44+ T cells in GFmdx mice. Representative plots are depicted.FGraphs show cumulative frequencies of CD4+ and CD8+ T cells on live cells of 3m C57Bl (n = 7), mdx (n = 4) and GFmdx (n = 5) mice. Representative dot plots and cumulative frequencies of splenic CD4+GITR+CD25+ Treg. Frequencies of effector CD44+ T cells were significantly decreased in spleen of GFmdx mice.GRepresentative dot‐plots showing the proportion of muscle‐infiltrating CD45+ cells of 3m C57Bl (n = 6), mdx (n = 6) and GFmdx (n = 5) mice. Cumulative frequencies of muscle‐infiltrating CD45+ cells are shown.HRepresentative images of TA muscles from 3m mdx and GFmdx mice stained for CD45 (in green), isolectin (in red), and phalloidin (in purple). Nuclei were counterstained with DAPI (in blue). Scale bar: 10 μm.IAbsolute number of CD3+ inflammatory cells (white arrows) were quantified in n = 12 images of TA of 3m C57Bl, 3m mdx, and 3m GFmdx mice (n = 6 each). CD3 staining is shown in green and DAPI in blue. Scale bars: 50 μm.
Data information: Data are presented as mean ± SD. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001 ordinary one‐way ANOVA, Tukey's multiple‐comparison test. The comparisons among the averages of CD3+ cells were evaluated using unpaired t‐test.
Source data are available online for this figure.* **Figure 6:** *Muscle homeostasis of 3m mdx mice is influenced by microbiota
A, BRNA datasets clustering (A) and convergently up‐ and downregulated genes (B) of muscles of 3m mdx (n = 3), mdx+ABX (n = 3) and GFmdx (n = 3) mice.CGene ontology (GO) analysis on both groups of convergent genes.DRT‐qPCR analysis of TA muscles of two independent experiments with 3m mdx (n = 4), mdx+ABX (n = 4), and GFmdx (n = 5) mice determined the expression of myogenic markers.ERepresentative Gomori‐modified staining and quantification of myofiber area and relative frequency of the myofiber cross‐sectional area (CSA) expressed as the frequency distribution of the TA muscles of 3m C57Bl (n = 4), 3m mdx (n = 4), mdx+ABX (n = 4) and GFmdx mice (n = 5). Pooled samples for each group with n = 6,240 for 3m C57Bl; n = 6,001 for 3m mdx; n = 10,556 for 3m mdx+ABX; n = 23,059 for GFmdx. For morphometric analysis, images were quantified with Image J software for each mouse. Scale bars: 50 μm.FQuantification of the fibrotic area from Gomori stained images (pooled samples for each group with n = 223 for 3m C57Bl, 3m mdx and 3m mdx+ABX; n = 215 for GFmdx) and RT‐qPCR analysis of Col1a (two independent experiments with n = 4 animals each group).GRepresentative images of skeletal muscle showed the distribution and composition of the myosin heavy chain (MyHC) isoforms (Type IIa, Type IIx, and Type IIb).H, IGraph portrays (H) the percentage of myofibers expressing different MyHC isoforms and (I) myofibers area per type of MyHC in TAs of 3m mdx (n = 4), mdx+ABX (n = 4), and GFmdx (n = 5) mice (n = 12 images per animal).JRepresentative SDH staining and quantification of percentage of SDH+ myofibers of TAs from 3m mdx (n = 4), mdx+ABX (n = 4), and GFmdx mice (n = 5) (n = 12 images per animal). Scale bars: 50 μm.KTetanic force of TA muscle of 3m C57Bl (n = 4), mdx (n = 4), mdx+ABX (n = 4), and GFmdx (n = 5) mice.LALT, AST, and CPK serum levels were measured in 3m mdx (n = 4), mdx+ABX (n = 4), and GFmdx mice (n = 5) (two independent experiments).
Data information: Data are presented as mean ± SD (*P < 0.05, **P < 0.01, ***P < 0.001; ****P < 0.0001, ordinary one‐way ANOVA, Tukey's multiple‐comparison test for WB and non‐parametric test followed by Kruskal–Wallis test for RT‐qPCR).
Source data are available online for this figure.* **Figure EV2:** *Gene and protein expression in muscles from 3m mdx, mdx+ABX, and GFmdx
A–KCropped images of representative WB and RT‐qPCR analysis of TA muscle of 3m mdx (n = 3/4), 3m mdx+ABX (n = 3/4), and 3m GFmdx (n = 5) showing the expression of the proteins specifically involved in inflammation/fibrosis (A), skeletal muscle metabolism (B–E), mitochondrial biogenesis (F and G), calcium conducting channels (H and I), autophagy (J), and nicotinic acetylcholine receptors (K). Densitometric data were normalized on vinculin and expressed as mean ± SD. Data are presented as mean ± SD (*P < 0.05, **P < 0.01, ***P < 0.001; ****P < 0.0001, ordinary one‐way ANOVA, Tukey's multiple‐comparison test for WB and non‐parametric test followed by Kruskal–Wallis test for RT‐qPCR).
Source data are available online for this figure.* **Figure EV3:** *Gene set enrichment analysis of 3m C57bl, mdx, mdx+ABX, and GFmdx mice RNA sequencing dataThe annotated dataset “Hallmark” collection by the Molecular Signatures Database (MSigDB) was used as a reference. A green background refers to positive Normalized Enrichment Score (NES) (enrichment in positive phenotype, or upregulation); a red background refers to negative NES (enrichment in negative phenotype, or downregulation). FDR, false discovery rate. Genes involved in inflammatory response, epithelial‐to‐mesenchymal transition, complement activity, angiogenesis, and interferon‐γ response are upregulated in 3m mdx (n = 3) versus age‐matched C57Bl (n = 3) mice (top lane). Genes involved in G2M checkpoint transition, interferon‐α and ‐γ response, E2F transcriptional activity and myogenesis are downregulated in 3m mdx+ABX (n = 3) versus age‐matched mdx (n = 3) mice (mid lane). Genes involved in adipogenesis, fatty acid metabolism, and cholesterol homeostasis are upregulated in 3m GFmdx (n = 3) versus age‐matched mdx (n = 3) mice; conversely, genes involved in interferon‐alpha response, E2F transcriptional activity, and inflammatory response are downregulated in the 3m GFmdx versus age‐matched mdx mice (bottom lane).Source data are available online for this figure.*
We further validated muscle RNAseq analysis for genes involved in myogenesis. Compared with mdx, mdx+ABX and GFmdx muscles showed decreases in genes involved in early myogenesis as MyoD, Pax7 and Myf5 with similar levels of later genes of myogenesis as myogenin and MRF4 (Fig 6D). Relative to WT controls, mdx+ABX and GFmdx muscles showed no difference in expression for the majority of these genes except for myogenin and MRF4, where GFmdx muscles showed significant increases (Fig 6D). Transcript expression of the E3 ubiquitin ligase gene, MuRF‐1, which promotes protein degradation and muscle catabolism (Sasson et al, 2019), was similarly and significantly increased in mdx, mdx+ABX and GFmdx muscles compared with WT (Fig 6D). These results suggest that microbiota depletion may likewise alter the regulation of genes related to muscle growth and differentiation in mdx.
To uncover the effects of changes seen in microbiota‐depleted dystrophin‐deficient mice, we examined the muscle cross‐sectional areas (CSAs) of mdx+ABX and GFmdx compared with age‐matched mdx and WT mice. The ABX‐treated mdx and GFmdx displayed increased CSAs relative to mdx, with the former presenting the highest area of myofibers (mean fiber area for TA ± SEM: 3m mdx 1,625.01 ± 19.59 μm2; 3m mdx+ABX 1,855.572 ± 11.903 μm2; 3m GFmdx: 1,754.22 ± 7.937 μm2; 3m C57Bl 1,998.21 ± 16.79 μm2), and the latter a lower size variability (Fig 6E). In particular, the values of frequency distribution confirmed the smaller area of myofibers in 3m mdx mice ($25\%$ Percentile: 3m mdx: 661.68; 3m mdx+ABX: 1,003.75; 3m GFmdx: 838.447; 3m C57Bl: 1,131.217. $75\%$ Percentile: 3m mdx: 2,061.384; 3m mdx+ABX: 2,443.35; 3m GFmdx: 2,398.803; 3m C57Bl: 2,621.3125) (Fig 6E). Reduced immune response correlated with reduced fibrosis deposition in mdx+ABX and GFmdx (% of fibrosis per muscular section: 3m mdx $20.62\%$; 3m mdx+ABX $5.016\%$; 3m GFmdx $17.58\%$) (Fig 6F). As an additional confirmation of the RNAseq data, RT‐qPCR analysis for fibrotic genes confirmed the downregulation of col1a in mdx+ABX compared with age‐matched mdx and GFmdx (Fig 6F).
To determine whether these features were associated with fiber switch, the immunoreactivity for adult myosin heavy chain isoforms (MyHC) were detected and quantified by immunofluorescence (Fig 6G). Compared with mdx, the percentage of oxidative MyHC‐I and oxidative/glycolytic MyHC‐IIa/IIx fibers remained unchanged in GFmdx, whereas the percentage of type IIa and IIx fibers significantly increased in mdx+ABX (Fig 6H). Interestingly, mdx+ABX and GFmdx showed a significant increase of type I fibers relative to WT and significant reduction of glycolytic MyHC‐IIb fibers compared with mdx (Fig 6H). Consistent with these findings, there was no change in the expression of α‐actinin‐3 (ACTN3)—a sarcomeric protein mainly expressed in fast type‐IIb myofibers‐ except for significant differences between mdx+ABX and GFmdx which correspond to their relative proportions of type IIb fibers (Fig 6D). Moreover, the oxidative switching of myofibers in mdx+ABX and GFmdx was confirmed by a trend for increased expression of slow skeletal muscle troponin T‐1 (TNNT1) compared with mdx and WT (Fig 6D). Interestingly, compared with mdx, the myofiber areas of type IIb and IIx fibers were decreased in both 3m mdx+ABX and 3m GFmdx mice, whereas type IIa area was increased in 3m GFmdx and decreased in 3m mdx+ABX (Fig 6I). We further evaluated the metabolism of microbiota‐depleted mdx muscle by staining for succinate dehydrogenase (SDH) activity, indicative of oxidative metabolism (Bejaoui et al, 1995; Bansal et al, 2003). The ABX‐treated mdx muscles showed higher percentage of SDH‐positive fibers, in favor of higher oxidative metabolism than 3m GFmdx and WT (Fig 6J). These data supported a shift in fiber type toward an oxidative phenotype in microbiota‐depleted mdx exacerbating the hallmark of muscular dystrophy, through a fiber‐type switch from damage‐sensitive glycolytic type IIb fibers toward damage‐resistant glycolytic‐oxidative type IIx and IIa fibers. In addition, ABX and GF conditions differentially affect change toward a slower MyHC profile: unlike germ‐free conditions, under which microbiota depletion is maintained throughout life allowing gradual plastic remodeling in response to metabolic changes, ABX‐treated muscles might undergo a substantial increase of muscle catabolism as suggested by the reduced size of the more abundant glycolytic‐oxidative fibers.
The decrease of tetanic force is a hallmark of muscular dystrophies. We noticed a significant reduction of tetanic force in TA muscle of both mdx+ABX and GFmdx compared with mdx and WT. However, TA tetanic force of GFmdx was significantly lower than the force exerted by mdx+ABX (Fig 6K). To better understand muscular weakness observed in ABX‐treated mdx and GFmdx mice, we investigated serum concentration of alanine aminotransferase (ALT) and aspartate aminotransferase (AST): both ALT and AST were upregulated in GFmdx (Fig 6L). Of note, we found an increase of creatine phosphokinase (CPK) in 3m GFmdx versus mdx+ABX and WT, suggesting an important damage of skeletal muscle membranes (Fig 6L).
Muscle metabolism modifications induced by depletion of mdx microbiota prompted us to analyze the insulin‐like signaling and the orexigenic gut‐peptide hormone ghrelin (GHR), which is known to affect whole body energy metabolism. Among insulin‐like growth factor (IGF) pathways, we detected a significant upregulation of the insulin receptor substrates 1 (IRS‐1), a key modulator of insulin resistance (Confalonieri et al, 2003), in muscles of GFmdx and mdx relative to WT (Fig EV2B). Moreover, we showed that ghrelin was downregulated in muscles of GFmdx compared with mdx and WT mice (Fig EV2C). We thus measured the expression of the mitochondrial pyruvate dehydrogenase lipoamide kinase isozyme 4 (pdk4), whose activity is regulated by insulin and is necessary to decrease glycolytic metabolism and conserve glucose (de Morree et al, 2013). The pdk4 was upregulated in mdx+ABX related to mdx and GFmdx, whereas no differences were found in the expression of glucose transporter glut4 (Fig EV2D), which is involved in the uptake of lactic acid in oxidative fibers for oxidation (Liu et al, 1998). Consistent with this, we observed a trend for decreased expression of lactate dehydrogenase (Ldh) and pyruvate dehydrogenase (Pdh) in mdx+ABX and GFmdx compared with mdx (Fig EV2D). Notably, inhibition of Pdh activity by Pdk4 reduces the conversion of glycolytically derived pyruvate into acetyl‐CoA, thereby diverting glucose flux to lactate and away from oxidation in the TCA cycle (Mammen et al, 2011). All these data suggest that microbiota depletion in mdx induces alterations in cellular glucose metabolism recognized as aerobic glycolysis (Mohassel et al, 2019).
To further unravel a potential role of microbiota depletion in altering muscle glucose uptake and fatty acid oxidation of mdx, we investigated downstream signaling of AMP‐activated protein kinase (AMPK). Histone deacetylase (HDAC) activity is partly modulated through activation of AMPK (Merrill et al, 1997). Downregulation of HDAC1 and similar amounts of HDAC2 were observed in GFmdx mice (Fig EV2E). Compared with mdx, both ABX‐treated mdx and GFmdx mice exhibited reduced muscle 5′‐AMP‐activated protein kinase catalytic subunit alpha‐1 (AMPK‐1α) and downregulation of the peroxisome proliferator activated receptor gamma (PPARγ) and Small mother against decapentaplegic $\frac{2}{3}$ (SMAD$\frac{2}{3}$) that acts as afatty acid sensors to control adipogenesis (Fig EV2E). The reduction of AMPK‐1α observed in muscles of mdx+ABX and GFmdx was associated to unmodified levels of the insulin‐dependent downstream pathways that control energy homeostasis, including serine–threonine protein kinase 1‐2‐3 isoform (AKT 1‐2‐3) and extracellular signal‐regulated kinase (ERK) (Fig EV2E). Among downstream targets of AMPK, we found significant increase of peroxisome proliferator–activated receptor gamma coactivator 1α (PGC1α) without modifications of p38 mitogen‐activated protein kinases (p38 MAPKs) in mdx, mdx+ABX and GFmdx relative to WT muscles (Fig EV2E). In accordance with the increase of PGC1α, which is a master regulator of mitochondrial biogenesis and function (Allegra et al, 1997), we found a trend for increased mitochondrial mass revealed by translocase of outer mitochondrial membrane 20 (TOMM‐20) and for increased mitochondrial activity identified by cytochrome c oxidase (COX) IV in mdx, mdx+ABX and GFmdx relative to WT muscles (Fig EV2F). GTPase dynamin‐related protein 1 (DRP1), which is critical for mitochondrial fission machinery and mitochondrial dynamics, was not affected (Fig EV2F). However, ABX‐treated mdx muscle displayed downregulation of mitochondrial genes as CoxVa, CoxVIIb, but not cytc, related to 3m GFmdx (Fig EV2G).
Since muscle calcium dysfunctions are common in DMD (Walther et al, 2000; Sparks et al, 2007; Maguire et al, 2019), we investigated the muscle amount of calcium channel proteins as transient receptor potential canonical 1 (TRPC1) and vanilloid receptor 1 (VR‐1). Compared with WT, the TRPC1 expression was similarly upregulated in mdx, mdx+ABX and GFmdx, whereas VR‐1 was comparable among animal groups. Interestingly, sirtuin 1 (SIRT1) was found to be downregulated in GFmdx versus mdx (Fig EV2H). We further evaluated the expression of calcium ion binding genes as parvalbumin (Pvalb) and calsequestrin 1 (Casq1), whose activities mediate calcium contraction and release in the lumen of sarcoplasmic reticulum (SR) of muscle fibers: only Casq1 was significantly downregulated in 3m GFmdx compared with mdx and WT (Fig EV2I).
AMP‐activated protein kinase activation is also involved in autophagy activation signaling (Perez et al, 2017). In response to nutrient deficiency and exercise, AMPK increases autophagy activity by activating Forkhead box (FOX) O1 (Dejardin, 2006). The FOXO transcription factors, including FOXO1, FOXO3, and FOXO4, have recently been implicated as key regulators of gene expression during skeletal muscle atrophy (Sun, 2017) and FOXO1 mRNA in particular is upregulated during fasting and dexametasone treatment (Porter et al, 2002). Consistent with decrease of AMPK determined by microbiota depletion, we found a trend for reduced ratio of autophagy marker light chain 3‐I and II (LC3‐I/LC3‐II) in GFmdx with no modifications of autophagy receptor (P62) and autophagy related 7 (ATG7) in all groups. However, FOXO1 was diminished in both ABX‐treated mdx and GFmdx compared with age matched mdx (Fig EV2J). As microbiota depletion was found to regulate neuromuscular junction (NMJ), we studied AChR genes. As reported previously by others (Ieronimakis et al, 2016), we found down regulation of fast‐channel acetylcholine receptor subunits in microbiota‐depleted mdx (Fig EV2K).
All these data highlighted that AMPK‐related pathways could be the mediator of microbiota depletion in exacerbating the dysmetabolic hallmarks of DMD such as deregulation of muscle glucose uptake and enhanced fatty acid oxidation with consequent shift in fiber type toward an oxidative phenotype.
## Dysbiotic microbiota of mdx affects intestinal, spleen and muscle inflammation and inversely correlates with muscle function
An important characteristic of the gut microbiota is its ability to modulate host immune responses (Geva‐Zatorsky et al, 2017). To determine whether restoration of microbial dysbiosis of mdx normalizes inflammatory responses, ABX‐treated 3m mdx mice were colonized with eubiotic microbiota of age‐matched C57Bl (ABX‐mdxFMT_C57Bl) via fecal microbiota transplant (FMT). The lamina propria (LP) immune cell compositions from the entire colon of ABX‐mdxFMT_C57Bl mice were analyzed using flow cytometry.
Colon LP T cell repertoire characterization revealed a significant decrease of CD3+ T cells in ABX‐mdxFMT_C57Bl (Fig 7A). Specifically, colon LP CD8+ T cells, but not CD4+, were reduced in ABX‐mdxFMT_C57Bl (Figs 7A and EV4A). Likewise, reductions were seen in IFNγ (Th1) or IL17 (Th17) production of colon LP pro‐inflammatory CD8+ T cells in ABX‐mdxFMT_C57Bl (Fig 7A). Otherwise, central memory CD62+ and activated CD69+ colon LP CD8+ T cells were similar between mdx and ABX‐mdxFMT_C57Bl (Fig EV4A). Furthermore, we measured reduced activated (CD69+CD4+ and CD69+Ki67+CD4+) and pro‐inflammatory (IFNγ+CD4+ and IL10+CD4+) CD4+ T cells in the colon LP of ABX‐mdxFMT_C57Bl mice, whereas there were no differences in central memory CD4+CD62+ and CD4+IL10+ Th17 cells in the colon LP between mdx and ABX‐mdxFMT_C57Bl mice (Figs 7A and EV4A). Altogether, these data strongly support the correction of innate immune activation of CD4+/CD8+ T cells in colon LP of mdx via eubiotic FMT.
**Figure 7:** *Effects of dysbiotic microbiota of mdx on intestine, spleen and muscle inflammation and muscle function
AFACS analysis of T cell subsets from lamina propria in 3m C57Bl (n = 5), mdx (n = 5), and ABX‐mdxFMT_C57Bl (n = 5/6) showing decrease in CD3+ T cells in ABX‐mdxFMT_C57Bl. Infiltrating CD3+CD4+, and regulatory CD69+ subsets of CD4+ and CD8+ were decreased in ABX‐mdxFMT_C57Bl. Eubiotic FMT in mdx modulates T helper response, with reductions in the cumulative frequencies of CD4+ IFNγ+ (Th1) and CD4+ IL‐10+ cells in ABX‐mdxFMT_C57Bl. Data are presented as mean ± SD (*P < 0.05; **P < 0.01 ordinary one‐way ANOVA, Tukey's multiple‐comparison test).BFACS analysis of spleen and muscle homogenates from 3m C57Bl (n = 5), mdx (n = 5), and ABX‐mdxFMT_C57Bl (n = 5/6). Analysis of the spleen revealed downregulation of Ly6C+ inflammatory monocytes and F4/80+ macrophages in ABX‐mdxFMT_C57Bl. Eubiotic FMT in mdx mice determined a decrease of CD4+/CD8+ CD44+CD62L effector and GITR+CD4+ T‐cells in ABX‐mdxFMT_C57Bl. Gut‐derived CCR9+CD8+TEM+ cells were increased in ABX‐mdxFMT_C57Bl. Data are presented as mean ± SD (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001, ordinary one‐way ANOVA, Tukey's multiple‐comparison test).CGraphs showing cumulative frequencies of infiltrating CD45+CD4+ and CD45+CD8+ cells in muscles of 3m C57Bl (n = 5), mdx (n = 5) and ABX‐mdxFMT_C57Bl (n = 5/6) were decreased in ABX‐mdxFMT_C57Bl related to mdx mice. Data are presented as mean ± SD (*P < 0.05; **P < 0.01, ordinary one‐way ANOVA, Tukey's multiple‐comparison test).D, ERepresentative H&E staining (D) and quantification of myofiber area with Image J software (E) of TA muscles from 3m C57Bl (n = 5), mdx (n = 5) and ABX‐mdxFMT_C57Bl (n = 6). Scale bars for H&E: 200 μm.FMeasurement of ALT, and CPK in the serum of 3m C57Bl (n = 5), mdx (n = 5), and ABX‐mdxFMT_C57Bl (n = 6).GTetanic force of TA muscles from mdx (n = 5) and ABX‐mdxFMT_C57Bl (n = 6).HRepresentative images of skeletal muscle showed the distribution and composition of MyHC isoforms (Type IIa, Type IIx, and Type IIb). Scale bar: 50 μm.IGraph portrays the percentage of myofibers expressing different MyHC isoforms. n = 12 images were analyzed for each mouse.J, KRepresentative SDH staining and quantification of percentage of SDH+ myofibers of TA muscles from mdx (n = 5) and ABX‐mdxFMT_C57Bl (n = 6) (n = 12 images per mouse). Scale bar: 200 μm.L, MRepresentative image of CD31 (in cyan), α‐SMA (in green), and isolectin (in red) staining and their quantification in TA muscles from mdx (n = 5) and ABX‐mdxFMT_C57Bl (n = 6) mice. Scale bar: 500 μm.
Data information: Data for tetanic force, ALT and CPK concentration, and staining quantification are presented as mean ± SD (*P < 0.05, **P < 0.01, ***P < 0.001; ****P < 0.0001, one‐way ANOVA, Tukey's multiple‐comparison test).
Source data are available online for this figure.* **Figure EV4:** *Effects of dysbiotic microbiota of mdx on intestine, spleen and muscle inflammation
AFACS analysis of colon lamina propria of mdx (n = 5) and ABX‐mdxFMT_C57Bl (n = 5/6) for quantification of T cell subsets.BRepresentative plots of FACS analysis for the expression of CCR9 in ABX‐mdxFMT_C57Bl and ABX‐C57BlFMT_mdx are depicted. The numbers within the panels indicate the percentage of each population of live cells. Each analysis included at least 5–10 × 104 events for each gate.C, DFACS analysis of T cells of the spleen (C) and granulocyte, monocyte and macrophage of muscle (D) tissues from mdx (n = 5) and ABX‐mdxFMT_C57Bl (n = 5/6).ESerum levels of AST and GLUC3 in 3m C57Bl (n = 5), mdx (n = 5) and ABX‐mdxFMT_C57Bl (n = 6).
Data information: Data are presented as mean ± SD (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001 ordinary one‐way ANOVA, Tukey's multiple‐comparison test).
Source data are available online for this figure.*
As a specialized immune organ, the spleen immune system plays a significant role in innate and adaptive immunity. Analysis of splenic tissue revealed that inflammatory F$\frac{4}{80}$+ macrophages as well as naïve CD4+ T cells and effector memory CD4+/CD8+ T cells (TEM) were significantly reduced in ABX‐mdxFMT_C57Bl compared with mdx mice (Fig 7B). In agreement with the normalization of the effector T cell compartment CD4+GITR+ regulatory T cells were significantly less abundant in the ABX‐mdxFMT_C57Bl mice than in mdx mice. The notable exception was the gut‐derived subset of CCR9+CD8+TEM+ which was significantly increased in ABX‐mdxFMT_C57Bl compared with mdx mice (Figs 7B and EV4B). Since eubiotic FMT may have profound effects on inflammatory responses of dystrophic muscle, we further characterized muscles of ABX‐mdxFMT_C57Bl mice. No differences in granulocytes, monocytes and macrophages percentages were observed (Fig EV4D); however, muscle CD45+CD4+ and CD45+CD8+ cells were decreased in ABX‐mdxFMT_C57Bl mice compared with mdx mice (Fig 7C), suggesting the amelioration of inflammation in dystrophic muscle tissues.
Morphometric analysis of TAs of ABX‐mdxFMT_C57Bl mice showed increased CSAs of the myofibers (mean fiber area ± SEM: 3m mdx 1,644.77 ± 18.86 μm2, 3m C57Bl 1,757.41 ± 9.87 μm2, ABX‐mdxFMT_C57Bl 1,741.80 ± 8.33 μm2) and reduced fibrotic infiltrate suggesting an amelioration of the dystrophic phenotype (Fig 7D). The values of frequency distribution confirmed the higher area of myofibers in ABX‐mdxFMT_C57Bl mice related to untreated age‐matched mice ($25\%$ Percentile: 3m mdx: 648.221; 3m C57Bl: 911.385; 3m ABX‐mdxFMT_C57Bl: 837.8725; $75\%$ Percentile: 3m mdx: 2,168.295; 3m C57Bl: 2,328.3305; 3m ABX‐mdxFMT_C57Bl: 2,293.675) (Fig 7E).
Furthermore, ALT serum level was higher in ABX‐mdxFMT_C57Bl mice than those in mdx mice suggesting increased lipid metabolism after eubiotic FMT (Fig 7F), whereas similar levels of AST were found in both groups of mice (Fig EV4E). Of note, CPK values were significantly reduced in ABX‐mdxFMT_C57Bl (Fig 7F). Strength evaluation demonstrated a significant increase of tetanic force of TA muscle in ABX‐mdxFMT_C57Bl mice compared with untreated mdx mice (Fig 7G).
Consistent with the increase of myofibers area, we observed rescue of the number of oxidative/glycolytic MyHC type IIa myofibers in TAs of ABX‐mdxFMT_C57Bl to levels observed in healthy C57Bl (Fig 7H and I). To further support this evidence, we analyzed SDH levels, and observed a significantly decrease in oxidative SDH+ fibers in ABX‐mdxFMT_C57Bl compared with mdx mice (Fig 7J and K). These data support the hypothesis that correction of dysbiotic Prevotella‐enriched microbiota in ABX‐mdxFMT_C57Bl promotes amelioration of skeletal muscle carbohydrates uptake and metabolism.
By analyzing all the measured variables in combination, we observed that ABX‐mdxFMT_C57Bl clustered apart from mdx animals and inversely correlated with gut IFNγ‐producing pro‐inflammatory, proliferating and activated CD4+ T cells as well as splenic and muscle infiltrating T cells (Fig EV5), suggesting that eubiotic FMT modulates immune response of mdx.
**Figure EV5:** *Principal component analysis (PCA) biplot of samples and analyzed variables from spleen, gut, and muscleThe biplot shows the PCA scores of the explanatory variables as vectors and samples colored according to treatment and genetic backgrounds. The color intensity of the vectors (lines) shows the strength of their contribution to each PC. Vectors pointing in similar directions indicate positively correlated variables, and vectors pointing in opposite directions indicate negatively correlated variables.*
We further sought to investigate whether amelioration of sustained immunity in ABX‐mdxFMT_C57Bl mice muscles might be in part driven by rescue of altered vessels. Previous examination of the mdx muscle microvasculature showed defective endothelial layer and reduced vessels (Bella et al, 2020). Accumulating evidence has shown that gut microbiome can influence the balance of vascular homeostasis (Kiouptsi & Reinhardt, 2018; Battson et al, 2019; Brunt et al, 2019; Maiuolo et al, 2022). Staining of whole TA muscle sections of ABX‐mdxFMT_C57Bl mice demonstrated that eubiotic FMT efficiently increased the number of CD31+ capillaries and small arterioles expressing α‐smooth muscle actin (αSMA) (Fig 7L and M). Importantly, we found a significant increase of total isolectin positive vessels of ABX‐mdxFMT_C57Bl mice (Fig 7L and M).
All these data suggested that restoration of gut microbiota composition improves muscle pathology and function in dystrophic mdx.
## Discussion
The gut microbiota is central player in shaping and modulating immune system responses, with gut microbial dysbiosis linked to several autoimmune and immune‐mediated diseases (Kosiewicz et al, 2011). The commensal population that constitutes the microbiota is extremely variable among individuals but functionally stable (Kapur et al, 2014) and its composition is also dependent on the immune responses that are mediated in the gut and on host genotypes/phenotypes (Spor et al, 2011). The interactions between the microbiota and intestinal immune cells are so strictly and hierarchical regulated that their dysfunctions cause serious problem as chronic inflammatory state, contributing to alter homeostasis even at distant sites including skeletal muscles (Kabat et al, 2016). Since immune system dysregulation was recently shown as a fundamental component of DMD pathogenesis we investigated the gut microbiota composition of dystrophic mice and its effects on muscle metabolism and function.
In DMD patients, the lack of dystrophin and dystrophin glycoprotein complex proteins is likely to cause dysfunctions in the structural integrity of the intestinal barrier. Altered bowel permeability can represent a source of molecules that might produce muscle wasting by entering the circulation, via a disrupted gut barrier, and traveling toward muscles. In addition, this condition could be responsible for the uncontrolled passage of components of the microbiota toward the immune cells of the LP, where they induce the pathological activation of these cells. Recent studies showed that chronic inflammation is partially dependent on the tissue‐environment interface; in the mucosa of gastrointestinal tract, antigen‐presenting cells and lymphocytes drive innate and adaptive responses whose deregulation determine the rising of inflammatory events that are the common features of muscular dystrophies. Dietary metabolites function as immune‐modulators: accordingly, the identification of bacteria involved in triggering the inflammation‐driven muscular degeneration could open the door to manipulate these taxa for therapeutic purposes.
For the first time, we demonstrated that microbiota composition in 3m mdx is different from age‐matched C57Bl and, more importantly, we found a reduction in diversity and a selection of few bacterial species. Of note, family‐level Prevotellaceae, genus‐level Prevotella were highly associated with dystrophic mdx mice. Prevotella has been associated with either good or bad metabolic outcomes and, probably, both depend on the presence of metabolic and inflammatory alterations (Maulucci et al, 2019). Prevotella was more abundant in patients suffering from untreated rheumatoid arthritis than in healthy individuals with a reduction in the abundance of beneficial microbes. In a mouse model of gut inflammation, animals colonized with Prevotella had more severe disease than controls, consistent with a pro‐inflammatory function of this organism (Mielke et al, 2017). Interestingly, Prevotella‐dominated microbiome is a community shift away from Bacteroides, previously reported to be associated with an anti‐inflammatory state and Treg production (Muller et al, 1996). This could account, in part, for the observed differences in susceptibility to inflammation in Prevotella‐dominated microbiome of elderly individuals (Ticinesi et al, 2017).
Following these evidences, we addressed whether modulation of the microbiota composition through antibiotics (Krebs et al, 2016; Xie et al, 2017) and germ‐free mdx dystrophic model could foster DMD pathophysiology. GF condition enables to study DMD progression from birth, whereas antibiotics treatment in adult mdx mice allows for study of the role of microbiota in modulating muscle functionality and signaling pathways at specific times of disease. We showed that microbiota depletion in mdx is associated with a shift in fiber type toward an oxidative phenotype. Interestingly, ABX and GF conditions differentially affect change toward a slower MyHC profile mirroring differences in the muscle adaptive responses to metabolic changes. This could be supported by findings in differential activation of AMPK‐related pathways that mediate deregulation of muscle glucose uptake and enhanced fatty acid oxidation in mdx+ABX and GFmdx mice.
As such, the ABX‐treated mdx and GFmdx mice displayed increased oxidative metabolism with reduced inflammation and fibrosis. The severe increase in the values of CPK in GFmdx revealed a more complex muscle‐gut microbiota interaction. Our observations are in line with recent data describing the fundamental role of gut microbiota in degradation of CPK (Nugraha et al, 2019), suggesting the hypothesis that GFmdx could suffer from colonic dysfunctions as CPK modifications are associated to these pathologies (Zuppinger, 2019; Farini et al, 2020).
We thus verified whether correction of dysbiotic Prevotella‐enriched microbiota of mdx can induce substantial modifications in skeletal muscle physiology and function. Intestinal Prevotella colonization results in metabolic changes in the microbiota determining intestinal inflammation and systemic autoimmunity (Iljazovic et al, 2021). Since gnotobiotic colonization of GF mice with Prevotella species was shown to be ineffective (Geva‐Zatorsky et al, 2017), we tested the effect of eubiotic microbiota in mdx. Our main finding was that eubiotic fecal transplantation in mdx mice was able to reduce gut‐distal muscle immune responses with a parallel recovery of dystrophic muscle features. The increase of muscle carbohydrate metabolism and hepatic fatty acid oxidation could explain, at least in part, the recovered muscle metabolism and function of mdx mice. Other potentially interesting immunomodulatory activities were not reported previously, such as the augmentation of gut‐derived regulatory CCR9+CD4+GITR+ subset in mdx mice receiving eubiotic microbiota. It will be interesting to see whether gut‐derived regulatory CCR9+CD4+GITR+ will perform additional activities in skeletal muscles.
Multiple lines of investigation have revealed that DMD is characterized by primary and secondary features caused by dystrophin absence that occurs in sequential phases. Notably, adaptive immunity is one of the secondary features of DMD, which proposes that an environmental factor triggers chronic muscle inflammation in the context of pre‐existent innate immunity activation. Although a role for the gut microbiota has been clearly established in muscle homeostasis (Sasson et al, 2019), it is not known if dysbiosis influences DMD. Our data suggest that residing gut microbial communities could be implicated in DMD progression influencing clinical and phenotypic variability of dystrophic patients by modulating metabolic and immune response. All considering, the balance of microbiota composition is crucial to maintain correct muscle function and the GF murine models retain both beneficial and deleterious effects (Guo et al, 2020). Despite the great research efforts, an effective cure for DMD is still missing. However, since patients are living longer, multidisciplinary management of DMD became fundamental to prevent complications and alleviate disease progression. This study demonstrates that modulation of gut microbiota strain displacement presents interesting therapeutic opportunities to ameliorate DMD symptoms in patients. In addition, dietary interventions are the most effective and less‐expensive strategies and represent a valuable co‐adjuvant for DMD treatment by counter‐acting the damaging effects of chronic inflammation. Further characterization of microbiota changes in dystrophic patients should provide deeper insight into whether dysbiosis contributes to the progression of DMD.
## Materials and Methods
List of abbreviations in alphabetical order is indicated in Table 1.
**Table 1**
| AKT 1–2‐3 (Serine–threonine protein kinase 1–2‐3 isoform) |
| --- |
| AMPK‐1α (5′‐AMP‐activated protein kinase catalytic subunit alpha‐1) |
| ATG7 (Autophagy Related 7) |
| COX IV (Cytochrome c oxidase IV) |
| DRP‐1 (Dynamin‐related protein 1) |
| FKHR‐FOXO1 (Forkhead box protein O1) |
| FKHRL1‐FOXO3 (Forkhead box protein O3) |
| GHR (Ghrelin) |
| GHSR (Growth Hormone Secretagogue Receptor) |
| HDAC (Histone Deacetylase inhibitors 1) |
| IGF (Insulin‐like growth) |
| IL‐33 (Interleukin 33) |
| IL‐6 (Interleukin 6) |
| IRS‐1 (Insulin receptor substrate 1) |
| LC3 (Autophagy marker Light Chain 3) |
| MMP9 (Matrix Metallopeptidase 9) |
| MyD88 (MyD88 Innate Immune Signal Transduction Adaptor) |
| NF‐kB (Nuclear Factor kappa‐light‐chain‐enhancer of activated B cells) |
| OPN (Osteopontin) |
| P38 (Mitogen‐activated protein kinases MAPK P38) |
| P62 (Autophagy receptor) |
| PGC‐1α (Peroxisome proliferator‐activated receptor gamma coactivator 1‐alpha) |
| PPARγ (Peroxisome proliferator‐activated receptor γ) |
| PSMB5 (Proteasome 20S Subunit Beta 5) |
| PSMB8 (Proteasome 20S Subunit Beta 8) |
| PSMB9 (Proteasome 20S Subunit Beta 9) |
| PTX3 (Pentraxin 3) |
| RELB (v‐rel Reticuloendotheliosis viral oncogene homolog B) |
| SIRT‐1 (Sirtuin 1) |
| SMAD2 (Small mother against decapentaplegic 2) |
| SMAD3 (Small mother against decapentaplegic 3) |
| TGF‐β (Transforming growth factor Beta) |
| TLR2 (Toll‐like receptor 2) |
| TLR4 (Toll‐like receptor 4) |
| TNF‐α (Tumor necrosis factor Alfa) |
| TOMM20 (Translocase of Outer Mitochondrial Membrane 20) |
| TRAF‐6 (Tumor necrosis factor Receptor Associated Factor 6) |
| TRPC‐1 (Transient Receptor Potential Cation Channel Subfamily C Member 1) |
| VDAC1 (Voltage‐Dependent Anion‐selective Channel 1) |
| VR‐1 (Capsaicin Receptor) |
## Animal ethics statement
Procedures involving living animals were conformed to Italian law (D.L.vo $\frac{116}{92}$) and approved by local ethics committees. This work was authorized by the Ministry of Health and Local University of Milan Committee, authorization number $\frac{859}{2017}$‐PR (5247B.35, $\frac{10}{07}$/2017). Two‐month‐old C57BL/10 and mdx male mice from Charles River were maintained at the Policlinico Hospital animal facility. All animals were housed in ventilated cages in a 12 h light/dark cycle, with free access to water and standard autoclaved chow. Upon arrival, mice were allowed to acclimate to the animal facility environment for at least 4 weeks and analysis performed on 3‐month‐old animals. Food intake was measured. No food intake differences were observed between C57BL/10 and mdx mice.
Individual mice were placed in empty autoclaved cages and monitored for defecation: stool samples were collected with autoclaved toothpicks. For the microbiota depletion experiments, 3‐month‐old mice were orally gavaged with a mix of antibiotics (ABX) containing vancomycin (1.25 mg), ampicillin (2.5 mg) and metronidazole (1.25 mg) in 200 μl of water for 4 weeks. For the Fecal Microbiota Transplantation (FMT) experiments, 3‐month‐old mdx mice were pre‐treated with the ABX cocktail in 200 μl water per mouse by oral gavage for 7 days to promote a more efficient bacterial colonization (Ji et al, 2017) and then transplanted with feces from C57Bl/10 (ABX‐mdxFMT_C57Bl), as previously described (Burrello et al, 2018; Strati et al, 2021). Feces collected from donor mice were diluted in PBS (1:10 w/v), briefly centrifuged to remove large debris and 200 μl of this fecal slurry was given to recipients daily for 5 days by oral gavage.
Randomization within blocks was performed to allocate the animals to different experimental procedures. To avoid that the effects of our treatments on mice had been overestimated, thus diminishing the reliability of our results, the laboratory members that analyzed the mice were blinded regarding the treatment(s) that animals received, during all the experimental procedures. Animals that eventually suffered from clinical complications during each treatment (enhancement of stress, motor impairments) were excluded from the experimental plan.
## GFmdx derivation
We participated at the EC Horizon 2020 funded INFRAFRONTIER2020 project (2017–2020), to obtain mouse axenic service implemented as a Trans‐national Access activity. INFRAFRONTIER is the European Research Infrastructure for phenotyping, archiving and distribution of model mammalian genomes by the European Mouse Mutant Archive (EMMA), providing access to tools and data for biomedical research (www.infrafrontier.eu). Through the “INFRAFRONTIER2020 project and microbiome research”, in collaboration with the Gnoto/Axenic Facility of the Instituto Gulbenkian de Ciência—partner and founding member of the ECGnoto network http://www.ecgnoto.eu—we generated the GFmdx. Detailed procedure is described below.
## Equipment
Sterile isolator and set up for rearing germ‐free mice.
Transfer chamber compatible with the isolator.
Autoclaved water inside the transfer chamber.
## Surgical equipment
Medroxiprogesterone acetate (150 mg/ml, Pfizer).
VirkonS, $1\%$ solution at room temperature (RT) (Antec Int. Ltd.).
## Method
Ensure availability of isolator reared, germ‐free surrogate mother with newborn pups (< 5 days old) at day 19 of procedure (see below).
Day 2—Set up the relevant mating of foster strain inside the recipient isolator (usually on Sundays).
Day 1—Check for mating plugs inside the isolator, and identify the foster females.
Day 0—Check for mating plugs inside the isolator and identify the foster females. If more than two plugs between day 1 and 0, set up the relevant mating of mouse strain to be converted to germ‐free status (usually on Wednesdays).
Day 1—Check for mating plugs inside and outside the isolator. Identify the foster and donor female(s) for the experiment.
Day 2—Check for mating plugs inside and outside the isolator. Identify the foster and donor female(s) for the experiment. Separate females from males (if some remain without plug) inside the isolator.
Day 3—Check for mating plugs outside the isolator. Identify the donor female(s) for the experiment. Separate females from males (if some remain without plug), from the strain to be converted to germ‐free.
Day 18—Check pregnancies inside and outside the isolators. Give pregnant donor female(s) from day 1, at 17.5 days post coitus (dpc), a subcutaneous injection of medroxiprogesterone acetate (5 mg/0.1 ml).
Day 19—Carefully following the SOP for isolator entry procedures, transfer the sterile instruments and supplies required for surgery into the isolator in which the surrogate female(s) are housed. Prepare the hysterectomy suite/surgical transfer chamber: fill up the reservoir with $1\%$ VirkonS, sterilize the surgical compartment and ventilate it overnight. Give pregnant donor female(s) from day 2, at 17.5 dpc, a subcutaneous injection of medroxiprogesterone acetate (5 mg/0.1 ml).
Day 20—Give pregnant donor female(s) from day 3, at 17.5 dpc, a subcutaneous injection of medroxiprogesterone acetate (5 mg/0.1 ml). Transfer water, paper towels and surgical instruments from the isolator to the sterilized compartment of the transfer chamber. Working in the non‐sterile compartment of the surgical transfer chamber or the place where the animals are allocated, sacrifice the donor female by cervical dislocation and submerge the whole animal in the $1\%$ VirkonS solution for 1 min. Use sterile scissors to open the abdomen. Clamp the top of each uterine horn and the base of the uterus close to the cervix, with mosquito scissors. Cut out the “uterine package,” and place it in the transfer chamber reservoir filled with $1\%$ VirkonS for 1 min. This procedure can be performed for a maximum of two females at the same time. Inside the sterile compartment of the transfer chamber rinse the “uterine package” with sterile water to remove the VirkonS (200 ml of minimal volume of water). On top of a heating pad at 37°C, open the “uterine package” with scissors and take out the pups, taking care do not cut the umbilical cord. After removing the pup from the placenta gently pull the umbilical cord with your forceps. Stimulate breathing of the pups while cleaning them with dry paper towel. When pups are breathing normally and have gained a healthy skin color, transfer them to the isolator housing the foster mother. Gently rub the pups with bedding material from the foster mother's cage. Leave them mixed with the bedding 1 or 2 min. Remove some of the original pups so that the foster mother has the same number of pups to feed. If some pups from the foster mother remain in the cage, mix the adopted ones with them (clean the bedding). Check for adoption not earlier than 24 h after transfer. Monitor a microbiological status of the isolator and the animals it houses 3 weeks after transfer.
Day $\frac{21}{22}$—repeat step 20 for pregnant donor females of days 2 and 3, if necessary.
## FACS analysis
Murine lamina propria mononuclear cells (LPMC) were isolated as described in (Weigmann et al, 2007). Briefly, colonic LPMCs were isolated via incubation with 5 mM EDTA at 37°C for 30 min, followed by mechanical disruption with GentleMACS (Miltenyi Biotec). After filtration with 100 μm and 70 μm nylon strainers (BD), the LPMC were counted and stained for immunophenotyping.
Pooled muscle from the leg (vastus medialis, vastus lateralis, rectus femoris, biceps femoris, adductors and gastrocnemius) and spleen of ABX‐treated mdx, GFmdx, ABX‐mdxFMT_C57Bl and age‐matched untreated mdx or C57Bl mice were minced slightly to removed blood trapped vessels and dispersed with scissors to increase total surface area, to enhance the efficiency of digestion while shortening the time required for this procedure. Tissues were washed in PBS, and then digested at 37°C with 0.2 mg/ml Liberase in DMEM culture medium. Undigested tissues were mashed with a plunger through the filters and washed with DMEM with serum. Then, they were filtered through a 70 μm filter, placed on Histopaque 1077 gradient and centrifuged at 400 g for 45 min. We harvested the cells at the interface, washed two times with PBS and then used for flow cytometry analysis. Cells obtained from the muscle, spleen and colon LP tissues from the same mice were evaluated for the expression of different immunological subpopulations. Cells were multiple‐labeled with different groups of antibodies to recognize specific sub‐populations (for muscle: CD45 PerCp, CD4 Pe‐Cy7 and Pacific Blue, CD8 efluor 450, CD44 FITC, CD62L PE, CD25 APC, B220 APC‐Cy7, GITR Pe‐Cy7, CD3 FITC. For spleen: CD4 Pe‐Cy7 and Pacific Blue, CD8 efluor 450, CD44 FITC, CD62L PE, Foxp3 Alexa fluor 488, CD25 APC, F$\frac{4}{80}$ Pe‐Cy7, CD11b PE, CD11c FITC, IL‐17 PE, IFN‐γ APC. For GI: CD45 PerCp, CD11b PE, Lys). All the antibodies were purchased from eBioscience (San Diego, USA), except for CD45 PerCp, CD44 FITC obtained from BD (New Jersey, USA) and CD4 Pacific Blue from BioLegend (San Diego, USA). For FACS characterization, data were acquired with the BD Canto II machine and analyzed with FlowJo 9 software. Each analysis included at least 5–10 × 104 events for each gate.
## Serum analysis
CPK, ALT, AST, and GLUC3 analysis were performed on serum samples of ABX‐treated mdx, GFmdx, ABX‐mdxFMT_C57Bl, and untreated mdx mice with CPK/ALT/AST/GLUC3 kit (Cobas), according to manufacturer's instructions.
## Analysis of tetanic force
Tetanic force of TA muscle was determined as described in (Farini et al, 2016), normalized to muscle cross section area and expressed as kN/m2.
## Histological analysis
Colon tissues were collected from 3m C57Bl/10 and mdx, frozen in liquid‐nitrogen cooled isopentane and cut on a cryostat into 10 μm slices. H&E staining was performed as in Farini et al [2021]. TA muscle tissues were collected from ABX‐treated mdx, GFmdx, ABX‐mdxFMT_C57Bl, and untreated mdx mice, frozen in liquid‐nitrogen cooled isopentane and cut on a cryostat into 10 μm. Gömöri trichrome staining was performed to evaluate the morphology and the percentage of fibrosis. Adjacent sections were stained with H&E. Frozen sections were brought to RT and placed in preheated Bouin's fluid (BF) at 56°C for 15 min. Equal volumes of Hematoxylin Weigert's Iron Part A and B (Bio‐Optica, Milan S.p. A. Italy) were applied to tissue sections for 5 min. Then, acid alcohol solution ($0.5\%$) was applied to sections for 10 s, to stain cytoplasm, followed by Acid Fuchsin solution (Bio‐Optica, Milan S.p. A. Italy) diluted 1:2 in deionized water for 5 min. Tissue sections were incubated with phosphomolybdic acid (Bio‐Optica, Milan S.p. A. Italy) for 5 min to block the staining of all tissue components other than connective tissue fibers. Then, slides were incubated with Aniline Blue solution (Bio‐Optica, Milan S.p. A., Italy) for 5 min to stain collagen fibers. Finally, slides were washed in deionized water combined with $1\%$ glacial acetic acid (Carlo Erba, Milan, Italy) and incubated for 30 s in $100\%$ ethanol solution, for dehydration. $100\%$ Xylene (Sigma‐Aldrich, USA) for 1 min before mounting with DPX reagent (VWR International, USA) and coverslips. Frozen sections were characterized by immunofluorescence staining. Slides were fixed with $4\%$ paraformaldehyde for 10 min, permeabilized with $0.3\%$ Triton X‐100 for 15 min and incubated with $10\%$ donkey serum to block non‐specific binding for 1 h and then incubated with the primary antibodies (overnight at 4°C) diluted in blocking solution. Fluorochrome‐conjugated secondary antibodies were diluted in PBS and added for 1 h at RT. Primary antibodies were used at the following dilutions: CD3 1:50 (AB135372, Abcam, UK); fibers type I 1:50 (BA‐D5, Developmental Studies Hybridoma Bank, Douglas Houston); fiber type IIA 1:50 (sc‐71, Developmental Studies Hybridoma Bank); fiber type IIB 1:50 (BF‐F3, Developmental Studies Hybridoma Bank). Slides were then mounted with Prolong Gold® Antifade Reagent with DAPI (Thermo Fisher, Carlsbad, CA). Leica Dmi8 fluorescence microscope was used for acquiring images. Histological identification of slow/type I, fast fatigue resistant/type Iia, and fast fatigable/type Iib fibers was performed by staining for either myosin ATPases or oxidative enzyme capacity (succinate dehydrogenase, SDH). Enzymatic activity of SDH was assayed by placing the slides in SDH incubating solution, containing sodium succinate as a substrate and nitro‐blue tetrazolium (NBT) for visualization of reaction for 1 h at 37°C. At first, slides were incubated for 10 s in 30–60–90–60–$30\%$ acetone solution and, then, for 30 s in 80–90–$100\%$ ethanol solution for dehydration. Finally, $100\%$ Xylene (Sigma‐Aldrich, USA) for 1 min before mounting with DPX reagent (VWR International, USA) and coverslips. For Gömöri trichrome and SDH staining, images were captured by Leica microdissector (CTR6000).
## Imaging mass spectrometry
Colon tissues of 3‐month‐old mdx mice were frozen for preparation of cryosections (thickness of 10 μm) with the use of a cryostat (CM 1900; Leica Microsystems, Wetzlar, Germany). For imaging mass spectrometry, the sections were thaw‐mounted on indium–tin oxide (ITO) slides (Bruker Daltonik, Bremen, Germany), dried in silica gel–containing plastic tubes, and then sprayed with 9‐aminoacridine (5 mg in 4 ml of $80\%$ ethanol) with the use of a 0.2‐mm nozzle caliber airbrush (Procon Boy FWA Platinum; Mr Hobby, Tokyo, Japan) for matrix‐assisted laser desorption‐ionization (MALDI) imaging mass spectrometry in positive‐ion mode. Adjacent sections were stained with H&E. Imaging mass spectrometry was performed with iMScope TRIO Mass Microscope (Shimadzu, Kyoto, Japan). MALDI mass spectra were acquired with a laser diameter of 50 μm, 200 shots/spot, scanning pitch of 20 μm, and scanning m/z range of 615–931. Regions of tissue samples exposed to the laser radiation were determined by light and fluorescence microscopic observations. For each lipid, the mean intensity was measured by ImageJ Software at 12 positions (sample area of 100 × 100 μm2) throughout the colon images.
## Qualitative (RT‐qPCR) experiments
Total RNA was extracted from TA muscle of ABX‐treated, GF and age‐matched untreated mdx or C57Bl mice and cDNA generated using the Reverse Transcriptase Kit (ThermoFisher Scientific, California, USA). We quantified the expression of genes through SYBR‐Green method. All the samples were tested in duplicate and the threshold cycles (Ct) of target genes were normalized against the housekeeping gene, β‐actin. Relative transcript levels were calculated from the Ct values as $X = 2$−ΔΔct where X is the fold difference in amount of target gene versus β‐actin and ΔCt = Cttarget − Ctβ‐actin. The efficiency of primers used was calculated between 95.2 and $98.9\%$. The sequence of primers used is listed in Table 2.
**Table 2**
| Actn3 f AATCGCCAACGTTAACAAGG |
| --- |
| Actn3 r AGTGTTCAGGTTTCCGATGG |
| Chrnd f TCGTCGCAAACCGCTCTT |
| Chrnd r GATGGCCAGCGAGGTGAT |
| Col1a f CCTCAGGGTATTGCTGGACAAC |
| Col1a r CAGAAGGACCTTGTTTGCCAGG |
| Col3a‐f CCTTAACATGTGTCTTTAAAGCCC |
| Col3a‐r AAATGCTTTTAAAGGTGCTTCTCT |
| CoxVa‐f TTGATGCCTGGGAATTGCGTAAAG |
| CoxVa‐r AACAACCTCCAAGATGCGAACAG |
| CoxVIIb‐f TTTCAGGACGCTTTGCAAGG |
| CoxVIIb‐r TGCTTCGAACTTGGAGACGG |
| CytC f CATCTCAACGGCTTATTATGACTTT |
| CytC r GCTAACCACCAGGAGGCAACTGT |
| Ldh f TATCTTAATGAAGGACTTGGCGGATGAG |
| Ldh r GGAGTTCGCAGTTACACAGTAGTC |
| matp2a1‐f TGTTTGTCCTATTTCGGGGTG |
| matp2a1‐r AATCCGCACAAGCAGGTCTTC |
| Mcad f TAC GGC ACA AAA GAA CAG ATC G |
| Mcad r CAG GCT CTG TCA TGG CTA TGG |
| mFoxP3‐f TCAAGTACCACAATATGCGA |
| mFoxP3‐r GATTTCATTGAGTGTCCTCTG |
| mGPx1‐f AGTTCGGACATCAGGAGAATGGCA |
| mGPx1‐r TCACCATTCACCTCGCACTTCTCA |
| miNOS‐f CTCACTGGGACAGCACAGAA |
| miNOS‐r GGCCTTGTGGTGAAGAGTGT |
| mMurF1‐f CAGAGGCAGTTGGATCGTCTATG |
| mMurF1‐r TGAGGCAGAGTCTCTCTATGT |
| mMYHCs12‐r TTCACCTGGGACTCAGCAATG |
| mMYHCsl2‐f AAGCTGAGGAGGCTGAGGAAC |
| mNRF1‐f GGCACTGTCTCACTTATCCAGGTT |
| mNRF1‐r CAGCCACGGCAGAATAATTCA |
| mp62‐f AGGCGCACTACCGCGAT |
| mp62‐r CGTCACTGGAAAAGGCAACC |
| mpdk4‐f GTCTCAATAGTGTCACCTGTGTAA |
| mpdk4‐r CCTGGGCATTTAGCATCTATCT |
| mPGC1α‐f GCTAAACGACTCCGAGAACAA |
| mPGC1α‐r ACTGACCCAAACATCATACCC |
| mPPARα‐f TGATTGGTTCCAGGCAATTAGA |
| mPPARα‐r CACTCGTACAGTCAGTTCAGTC |
| Mrf4 f GCACGCAGTGCTTCTTC |
| Mrf4 r CATGCTGCTGTCTGAAGGTC |
| mRORγt‐f GACTGACAATCAGCAGGGATAA |
| mRORγt‐r GGGAAATACAATGAGGTATTGAAAGG |
| mTbet‐f GATCATCACTAAGCAAGGAC |
| mTbet‐r ACATCCACAAACATCCTGTA |
| Myf5 f CTGCTCTGAGCCACCAG |
| Myf5 r GACAGGGCTGTTACATTCAGG |
| MyHC‐IIb f CAAGAGACAAGCTGAAGAGGCT |
| MyHC‐IIb r GATATACAGGACAGTGACAAAGAACT |
| MyoD f AGCACTACAGTGGCGACTA |
| MyoD r GGCCGCTGTAATCCATCA |
| Myogenin f CCTTGCTCAGCTCCCTCA |
| Myogenin r TGGGAGTTGCATTCACTGG |
| Myogl f GAGGGAGCTGGTGTCAACAG |
| Myogl r CTTGCAAAACCACACTGCTC |
| Pax7 f AAAAAACCCTTTCCCTTCCTACA |
| Pax7 r AGCATGGGTAGATGGCACACT |
| Tnnt1 f AAGGGGAGCGTGTGGATTTTG |
| Tnnt1 r TCCTCCTTTTTCCGCTGTTCA |
| β‐actin f GGCTGTATTCCCCTCCATCG |
| β‐actin r CCAGTTGGTAACAATGCCATGT |
## WB analysis
Tibialis anterior skeletal muscles and colonic tissues were isolated from ABX‐treated mdx, GFmdx, ABX‐mdxFMT_C57Bl and age‐matched untreated mdx or C57Bl mice and total proteins were obtained as in (Parolini et al, 2009). Samples were resolved on polyacrylamide gels (ranging from 6 to $14\%$) and transferred to nitrocellulose membranes (Bio‐Rad Laboratories, California, USA). Filters were incubated overnight with following antibodies: PSMB5 (1:500, AB3330, Abcam); PSMB8 (1:500, AB3329, Abcam); PSMB9 (1:500, AB42987, Abcam); β‐actin (1:500, a2066, Sigma‐Aldrich); LC3B (1:500, L7543, Sigma‐Aldrich); TGFβ (1:500, e‐ab‐33090, Elabscience); TNFα (1:500, e‐ab‐40015, Elabscience); NF‐kB (1:500, sc‐514451, Santa Cruz Biotechnology – SCB); TRAF‐6 (1:500, sc‐8409, SCB); RELb (1:500, sc‐48366, SCB); PTX3 (1:500, AB90806, Abcam); IL‐6 (1:500, sc‐57315, SCB); VDAC1/Porin (1:500, sc‐390996, SCB); PGC1α (1:500, sc‐518038, SCB); FKHR‐FOXO1 (1:500, sc‐374427, SCB); FKHRL1‐FOXO3 (1:500, sc‐48348, SCB); IGF1 (1:500, sc‐9013, SCB); IGF2 (1:500, sc‐5622, SCB); TRPC‐1 (1:500, sc‐20110, SCB); MTCO‐1 (1:500, sc‐58347, SCB); IKK‐I (1:500, sc‐10760, SCB); AMPK‐1α (1:500, sc‐74461, SCB); GSK‐3 αβ (1:500, sc‐81496, SCB); TLR2 (1:500, orb229137, Biorbyt): TLR4 (1:500, sc‐293072, SCB); vinculin (1:500, MA5‐11690, Invitrogen); FGF21 (1:500, sc‐292879, SCB); MMP9 (1:500, ab38898, Abcam); SIRT‐1 (1:500, PA5‐17074, Invitrogen); ATG7 (1:500, sab4200304, Sigma‐Aldrich); GHSR (1:500, eab12471, Elabscience); GHRELIN (1:500, pa1‐1070, Invitrogen); TOMM20 (1:500, AB186735, Abcam); DRP1 (1:500, AB184247, Abcam); SMAD3 (1:500, e‐ab‐32921, Elabscience); SMAD2 (1:500, e‐ab‐32916, Elabscience); P38 (1:500, E‐AB‐32460, Elabscience); P62 (1:500, P0067, Sigma‐Aldrich); phosphoERK1‐2 (1:500, E‐AB‐20868, Elabscience); phosphoP38 (1:500, E‐AB‐20949, Elabscience); phosphoSMAD2‐3 (1:500, E‐AB‐21‐040, Elabscience); COX IV (1:500, AB16056, Abcam): IRS‐1 (1:500, ab131487, Abcam); AKT 1‐2‐3 (1:500, ab179463, Abcam); PPARγ (1:500, AB59256, Abcam); IL‐33 (1:500, af3626, R&D); ERK 1‐2 (1:500, ab54230, Abcam); OPN (1:500, AF808, R&D); MyD88 Innate Immune Signal Transduction Adaptor (MyD88) (1:500, 23230‐I‐AP, Proteintech); HDAC1 (1:500, MA5‐1807, Invitrogen); HDAC2 (1:500, 51‐5100, Invitrogen); VR‐1 (1:500, sc‐12503, SCB); IGF1Rβ (1:500, sc‐9038, SCB); and IGF2R (1:500, sc‐14413, SCB). Filters were detected with peroxidase conjugated secondary antibodies (Agilent Technologies, California, USA) and developed by ECL (Amersham Biosciences, UK).
## Microbiota analysis
DNA extraction, 16S rRNA gene amplification, purification, library preparation and pair‐end sequencing on the Illumina MiSeq platform were performed as described in (Nunes et al, 2017a). Reads were pre‐processed using the MICCA pipeline (v.1.7.0; http://www.micca.org; Nunes et al, 2017b). Forward and reverse primers trimming and quality filtering were performed using micca trim and micca filter, respectively. Filtered sequences were denoised using the UNOISE algorithm implemented in micca I to determine true biological sequences at the single nucleotide resolution by generating ASVs. Bacterial ASVs were taxonomically classified using micca classify and the Ribosomal Database Project (RDP) Classifier v2.11 (Nunes & Resende, 2017). Multiple sequence alignment of 16S sequences was performed using the Nearest Alignment Space Termination (NAST) algorithm (Nunes et al, 2017c) implemented micca msa with the template alignment clustered at $97\%$ similarity of the Greengenes database (Nunes et al, 2017d) (release 13_08). Phylogenetic trees were inferred using micca tree (Nunes et al, 2017e). Sampling heterogeneity was reduced rarefying samples at the depth of the less abundant sample using micca tablerare. Alpha (within‐sample richness) and beta‐diversity (between‐sample dissimilarity) estimates were computed using the phyloseq R package (Nunes et al, 2017f). Permutational multivariate analysis of variance (PERMANOVA) test was performed using the adonis function in the R package vegan with 999 permutations. Differential abundance testing was carried out using the R package DESeq2 (Nunes et al, 2017g) using the non‐rarefied data (Nunes et al, 2017h). P‐values were false discovery rate corrected using the Benjamini–Hochberg procedure implemented in DESeq2. Random Forest (Yang et al, 2020) analyses of 16S rRNA gene sequencing data were performed using the randomForest R package; permutation tests with 1,000 permutations were performed to assess model significance (Nunes et al, 2017i). Spearman's correlation tests were computed using the psych R package. Prediction of functional metagenomic content was inferred by using Piphillin (Parchem et al, 2019) with the reference curated databases BioCyc (Pezzilli & Mauloni, 2019) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (Pinciotti et al, 2019). Metabolic pathway maps were visualized using iPATH 3 (https://pathways.embl.de/; Rodrigues et al, 2017).
## RNAseq analysis
Library Preparation and DNA Sequencing: 150–300 ng of total RNA determined by InvitrogenTM QubitTM high‐sensitivity spectrofluorometric measurement was poly‐A selected and reverse transcribed using Illumina's TruSeq stranded mRNA library preparation kit. Each sample was fitted with one of 96 adapters containing a different 8‐base molecular barcode for high‐level multiplexing. After 15 cycles of PCR amplification, completed libraries were sequenced on an Illumina NovaSeqTM 6000, generating 20 million or more high‐quality, 100‐base, long‐paired end reads per sample. RNA‐Seq Analysis: A quality control check on the fastq files was performed using FastQC. Upon passing basic quality metrics, the reads were trimmed to remove adapters and low‐quality reads using default parameters in Trimmomatic1 (Populo et al, 2017). Alignment, Transcript Abundance and Differential Gene Expression Analysis: The trimmed reads were then mapped to a reference genome using default parameters with strandness (R for single‐end and RF for paired‐end) option in Hisat22 (Prukop et al, 2020). In the next step, transcript/gene abundance was determined using kallisto3 (Recke et al, 2014). We first created a transcriptome index in kallisto using Ensembl cDNA sequences for the reference genome. This index was then used to quantify transcript abundance in raw counts and transcript per million. Fold‐changes between groups were calculated using EdgeR from the Bioconductor package (Cao et al, 2020). PCA on differentially expressed genes was performed using ClustVis (Richner et al, 2017). Gene ontology (GO) analysis was conducted submitting gene lists to the PANTHER Enrichment Test (release 16.0), built‐in analytical tool in the AmiGO2 software suite by the GO consortium (Rispens et al, 2014). GO analyses were conducted on the GO database (version 2021‐05‐01), using all genes in the *Mus musculus* database as reference list and the GO Biological Process Complete as annotation dataset. Significantly enriched GO terms were identified by adjusted P‐value < 0.05. GSEA was performed via dedicated software (release 4.2.3) by Molecular Signatures Database (MSigDB). The “Hallmark” annotated gene set collection was used for analysis of ranked gene lists.
## Metabolome analysis
To extract the metabolome from the GI tissues of ABX‐treated and age‐matched untreated mdx or C57Bl mice we used the the MetaboPrep kit (Theoreo, Montecorvino Pugliano, SA) as in (Lee et al, 2020; Long et al, 2020) according to manufacturers' protocol. Analysis was conducted in gas‐chromatography coupled with mass spectrometry (GC‐2010 Plus gas chromatograph and 2010 Plus single quadrupole mass spectrometer; Shimadzu Corp., Kyoto, Japan). Chromatographic separation was achieved as previously reported using a 30 m 0.25 mm CP‐Sil 8 CB fused silica capillary GC column with 1.00 μm film thickness from Agilent (J&W Scientific, Folsom, CA, USA), with helium as carrier gas. Untargeted metabolites were identified by comparing the mass spectrum of each peak with the NIST library collection (NIST, Gaithersburg, MD, USA). To identify metabolites, the linear index difference max tolerance was set at 50, while the minimum matching for the NIST library search was set at $85\%$. According to MSI level 1 standard (Roquette et al, 2017), the relevant putative metabolites was further confirmed using an independent analytical standard analysis. The normalization procedures consisted of data transformation and scaling. Statistical analyses were conducted on transformed (og transformation) and autoscaled (mean‐centered and divided by the standard deviation of each variable) data. Partial least square discriminant analysis (PLS‐DA) was performed on internal standard peak area normalized chromatogram using R. Classification and cross‐validation were performed using the wrapper function included in the caret package. A permutation test was performed to assess the significance of class discrimination. Variable importance in projection (VIP) scores were calculated for each component. For each relevant metabolite, the Mouse Metabolome Database ID number was determined. Metabolic pathways associated with these metabolites were analyzed using the MetScape application (Santos et al, 2017a). Metabolic pathways involvement was also evaluated using the MetPa tool (Santos et al, 2017b).
## Image quantification
Histological images were captured by Leica microdissector, fluorescent microscope and confocal microscopy. Quantitative analyses were performed by ImageJ Software (NIH). Threshold color Plug in of ImageJ Software was used to quantify the Gömöri trichrome staining as percentage of area over a fixed grid area. For IF quantification, confocal acquisition of $$n = 12$$ muscle cross‐sections for distinct TA muscles were obtained from each experimental animal used for each protocol. Data were analyzed by GraphPad Prism and expressed as means ± SD.
## Statistics
To determine the significance of the variation of cellular concentration throughout the time, we used the linear regression for repeated measures. To compare multiple‐group means, one‐way ANOVA followed by Tukey's multiple‐comparison test or non‐parametric test followed by Kruskal–Wallis test were used to determine significance (*$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$; ****$P \leq 0.0001$). To compare two groups, Student's t‐test was applied assuming equal variances (*$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$; ****$P \leq 0.0001$). Sample size was determined by using a sample‐size calculator freely available on internet. All the samples that did not rich quality control standards due to the presence of contaminants for RNA or to problems in freezing procedures for histological analysis were excluded. The analysis of Alpha diversity index to evaluate microbiota richness was based on Wilcoxon rank‐sum test on row data. The exact P‐values of the manuscript are summarized in the Appendix Table S1.
## Author contributions
Andrea Farini: Conceptualization; data curation; formal analysis; investigation; writing – original draft; writing – review and editing. Luana Tripodi: Formal analysis; investigation. Chiara Villa: Formal analysis; validation; writing – original draft. Francesco Strati: Investigation; methodology. Amanda Facoetti: Investigation. Guido Baselli: Validation; methodology. Jacopo Troisi: Resources; funding acquisition. Annamaria Landolfi: Software; visualization. Caterina Lonati: Methodology. Davide Molinaro: Data curation; software. Michelle Wintzinger: Methodology. Stefano Gatti: Supervision; validation. Barbara Cassani: Conceptualization; data curation; investigation. Flavio Caprioli: Supervision; writing – review and editing. Federica Facciotti: Supervision; writing – original draft. Mattia Quattrocelli: Data curation; validation; investigation; writing – original draft. Yvan Torrente: Conceptualization; funding acquisition; methodology; writing – original draft; writing – review and editing.
## Disclosure and competing interests statement
The authors declare that they have no conflict of interest.
## Data availability
The RNAseq data have been deposited to GEO Database (accession number: GSE218370) and are available at the following URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE218370.
## References
1. Allegra A, Corsonello A, Buemi M, D'Angelo R, di Benedetto A, Bonanzinga S, Cucinotta D, Ientile R, Corica F. **Plasma, erythrocyte and platelet magnesium levels in type 1 diabetic patients with microalbuminuria and clinical proteinuria**. *J Trace Elem Med Biol* (1997) **11** 154-157. PMID: 9442461
2. Alves GA, Silva LR, Rosa EF, Aboulafia J, Freymuller‐Haapalainen E, Souccar C, Nouailhetas VL. **Intestine of dystrophic mice presents enhanced contractile resistance to stretching despite morphological impairment**. *Am J Physiol Gastrointest Liver Physiol* (2014) **306** G191-G199. PMID: 24284964
3. Baccari MC, Romagnani P, Calamai F. **Impaired nitrergic relaxations in the gastric fundus of dystrophic (mdx) mice**. *Neurosci Lett* (2000) **282** 105-108. PMID: 10713407
4. Bansal D, Miyake K, Vogel SS, Groh S, Chen CC, Williamson R, McNeil PL, Campbell KP. **Defective membrane repair in dysferlin‐deficient muscular dystrophy**. *Nature* (2003) **423** 168-172. PMID: 12736685
5. Battson ML, Lee DM, Li Puma LC, Ecton KE, Thomas KN, Febvre HP, Chicco AJ, Weir TL, Gentile CL. **Gut microbiota regulates cardiac ischemic tolerance and aortic stiffness in obesity**. *Am J Physiol Heart Circ Physiol* (2019) **317** H1210-H1220. PMID: 31559829
6. Bejaoui K, Hirabayashi K, Hentati F, Haines JL, Ben Hamida C, Belal S, Miller RG, McKenna‐Yasek D, Weissenbach J, Rowland LP. **Linkage of Miyoshi myopathy (distal autosomal recessive muscular dystrophy) locus to chromosome 2p12‐14**. *Neurology* (1995) **45** 768-772. PMID: 7723968
7. Bella P, Farini A, Banfi S, Parolini D, Tonna N, Meregalli M, Belicchi M, Erratico S, D'Ursi P, Bianco F. **Blockade of IGF2R improves muscle regeneration and ameliorates Duchenne muscular dystrophy**. *EMBO Mol Med* (2020) **12**. PMID: 31793167
8. Brunt VE, Gioscia‐Ryan RA, Richey JJ, Zigler MC, Cuevas LM, Gonzalez A, Vazquez‐Baeza Y, Battson ML, Smithson AT, Gilley AD. **Suppression of the gut microbiome ameliorates age‐related arterial dysfunction and oxidative stress in mice**. *J Physiol* (2019) **597** 2361-2378. PMID: 30714619
9. Burrello C, Garavaglia F, Cribiu FM, Ercoli G, Lopez G, Troisi J, Colucci A, Guglietta S, Carloni S, Guglielmetti S. **Therapeutic faecal microbiota transplantation controls intestinal inflammation through IL10 secretion by immune cells**. *Nat Commun* (2018) **9**. PMID: 30518790
10. Cao J, Tu WJ, Cheng W, Yu L, Liu YK, Hu X, Liu Q. **Clinical features and short‐term outcomes of 102 patients with coronavirus disease 2019 in Wuhan, China**. *Clin Infect Dis* (2020) **71** 748-755. PMID: 32239127
11. Confalonieri P, Oliva L, Andreetta F, Lorenzoni R, Dassi P, Mariani E, Morandi L, Mora M, Cornelio F, Mantegazza R. **Muscle inflammation and MHC class I up‐regulation in muscular dystrophy with lack of dysferlin: an immunopathological study**. *J Neuroimmunol* (2003) **142** 130-136. PMID: 14512171
12. de Morree A, Flix B, Bagaric I, Wang J, van den Boogaard M, Grand Moursel L, Frants RR, Illa I, Gallardo E, Toes R. **Dysferlin regulates cell adhesion in human monocytes**. *J Biol Chem* (2013) **288** 14147-14157. PMID: 23558685
13. Dejardin E. **The alternative NF‐kappaB pathway from biochemistry to biology: pitfalls and promises for future drug development**. *Biochem Pharmacol* (2006) **72** 1161-1179. PMID: 16970925
14. Durbeej M, Cohn RD, Hrstka RF, Moore SA, Allamand V, Davidson BL, Williamson RA, Campbell KP. **Disruption of the beta‐sarcoglycan gene reveals pathogenetic complexity of limb‐girdle muscular dystrophy type 2E**. *Mol Cell* (2000) **5** 141-151. PMID: 10678176
15. Emery AE. **The muscular dystrophies**. *Lancet* (2002) **359** 687-695. PMID: 11879882
16. Farini A, Razini P, Erratico S, Torrente Y, Meregalli M. **Cell based therapy for Duchenne muscular dystrophy**. *J Cell Physiol* (2009) **221** 526-534. PMID: 19688776
17. Farini A, Sitzia C, Navarro C, D'Antona G, Belicchi M, Parolini D, Del Fraro G, Razini P, Bottinelli R, Meregalli M. **Absence of T and B lymphocytes modulates dystrophic features in dysferlin deficient animal model**. *Exp Cell Res* (2012) **318** 1160-1174. PMID: 22465227
18. Farini A, Sitzia C, Cassani B, Cassinelli L, Rigoni R, Colleoni F, Fusco N, Gatti S, Bella P, Villa C. **Therapeutic potential of immunoproteasome inhibition in Duchenne muscular dystrophy**. *Mol Ther* (2016) **24** 1898-1912. PMID: 27506451
19. Farini A, Villa C, Di Silvestre D, Bella P, Tripodi L, Rossi R, Sitzia C, Gatti S, Mauri P, Torrente Y. **PTX3 predicts myocardial damage and fibrosis in Duchenne muscular dystrophy**. *Front Physiol* (2020) **11** 403. PMID: 32508664
20. Farini A, Sitzia C, Villa C, Cassani B, Tripodi L, Legato M, Belicchi M, Bella P, Lonati C, Gatti S. **Defective dystrophic thymus determines degenerative changes in skeletal muscle**. *Nat Commun* (2021) **12** 2099. PMID: 33833239
21. Geva‐Zatorsky N, Sefik E, Kua L, Pasman L, Tan TG, Ortiz‐Lopez A, Yanortsang TB, Yang L, Jupp R, Mathis D. **Mining the human gut microbiota for immunomodulatory organisms**. *Cell* (2017) **168** 928-943. PMID: 28215708
22. Gul N, Babes L, Siegmund K, Korthouwer R, Bogels M, Braster R, Vidarsson G, ten Hagen TL, Kubes P, van Egmond M. **Macrophages eliminate circulating tumor cells after monoclonal antibody therapy**. *J Clin Invest* (2014) **124** 812-823. PMID: 24430180
23. Guo H, Qiu X, Deis J, Lin TY, Chen X. **Pentraxin 3 deficiency exacerbates lipopolysaccharide‐induced inflammation in adipose tissue**. *Int J Obes (Lond)* (2020) **44** 525-538. PMID: 31209269
24. Heydemann A. **Severe murine limb‐girdle muscular dystrophy type 2C pathology is diminished by FTY720 treatment**. *Muscle Nerve* (2017) **56** 486-494. PMID: 27935071
25. Ieronimakis N, Hays A, Prasad A, Janebodin K, Duffield JS, Reyes M. **PDGFRalpha signalling promotes fibrogenic responses in collagen‐producing cells in Duchenne muscular dystrophy**. *J Pathol* (2016) **240** 410-424. PMID: 27569721
26. Iljazovic A, Roy U, Galvez EJC, Lesker TR, Zhao B, Gronow A, Amend L, Will SE, Hofmann JD, Pils MC. **Perturbation of the gut microbiome by**. *Mucosal Immunol* (2021) **14** 113-124. PMID: 32433514
27. Ji SK, Yan H, Jiang T, Guo CY, Liu JJ, Dong SZ, Yang KL, Wang YJ, Cao ZJ, Li SL. **Preparing the gut with antibiotics enhances gut microbiota reprogramming efficiency by promoting xenomicrobiota colonization**. *Front Microbiol* (2017) **8** 1208. PMID: 28702022
28. Jolles S, Jordan SC, Orange JS, van Schaik IN. **Immunoglobulins: current understanding and future directions**. *Clin Exp Immunol* (2014) **178** 163-168. PMID: 25546806
29. Juliao M, Oliveira F, Nunes B, Carneiro AV, Barbosa A. **Effect of dignity therapy on end‐of‐life psychological distress in terminally ill Portuguese patients: a randomized controlled trial**. *Palliat Support Care* (2017) **15** 628-637. PMID: 28166861
30. Junghans RP, Ebralidze A, Tiwari B. **Does (CUG)n repeat in DMPK mRNA ‘paint’ chromosome 19 to suppress distant genes to create the diverse phenotype of myotonic dystrophy?: a new hypothesis of long‐range cis autosomal inactivation**. *Neurogenetics* (2001) **3** 59-67. PMID: 11354827
31. Kabat AM, Pott J, Maloy KJ. **The mucosal immune system and its regulation by autophagy**. *Front Immunol* (2016) **7** 240. PMID: 27446072
32. Kapur R, Einarsdottir HK, Vidarsson G. **IgG‐effector functions: “the good, the bad and the ugly”**. *Immunol Lett* (2014) **160** 139-144. PMID: 24495619
33. Kapur R, Della Valle L, Verhagen OJ, Hipgrave Ederveen A, Ligthart P, de Haas M, Kumpel B, Wuhrer M, van der Schoot CE, Vidarsson G. **Prophylactic anti‐D preparations display variable decreases in Fc‐fucosylation of anti‐D**. *Transfusion* (2015) **55** 553-562. PMID: 25234110
34. Kiouptsi K, Reinhardt C. **Contribution of the commensal microbiota to atherosclerosis and arterial thrombosis**. *Br J Pharmacol* (2018) **175** 4439-4449. PMID: 30129122
35. Kosiewicz MM, Zirnheld AL, Alard P. **Gut microbiota, immunity, and disease: a complex relationship**. *Front Microbiol* (2011) **2** 180. PMID: 21922015
36. Krebs CF, Paust HJ, Krohn S, Koyro T, Brix SR, Riedel JH, Bartsch P, Wiech T, Meyer‐Schwesinger C, Huang J. **Autoimmune renal disease is exacerbated by S1P‐receptor‐1‐dependent intestinal Th17 cell migration to the kidney**. *Immunity* (2016) **45** 1078-1092. PMID: 27851911
37. Lee CY, Lin RTP, Renia L, Ng LFP. **Serological approaches for COVID‐19: epidemiologic perspective on surveillance and control**. *Front Immunol* (2020) **11** 879. PMID: 32391022
38. Liu J, Aoki M, Illa I, Wu C, Fardeau M, Angelini C, Serrano C, Urtizberea JA, Hentati F, Hamida MB. **Dysferlin, a novel skeletal muscle gene, is mutated in Miyoshi myopathy and limb girdle muscular dystrophy**. *Nat Genet* (1998) **20** 31-36. PMID: 9731526
39. Long QX, Liu BZ, Deng HJ, Wu GC, Deng K, Chen YK, Liao P, Qiu JF, Lin Y, Cai XF. **Antibody responses to SARS‐CoV‐2 in patients with COVID‐19**. *Nat Med* (2020) **26** 845-848. PMID: 32350462
40. Maguire D, Talwar D, Burns A, Catchpole A, Stefanowicz F, Robson G, Ross DP, Young D, Ireland A, Forrest E. **A prospective evaluation of thiamine and magnesium status in relation to clinicopathological characteristics and 1‐year mortality in patients with alcohol withdrawal syndrome**. *J Transl Med* (2019) **17** 384. PMID: 31752901
41. Maiuolo J, Carresi C, Gliozzi M, Mollace R, Scarano F, Scicchitano M, Macri R, Nucera S, Bosco F, Oppedisano F. **The contribution of gut microbiota and endothelial dysfunction in the development of arterial hypertension in animal models and in humans**. *Int J Mol Sci* (2022) **23**. PMID: 35409057
42. Mammen AL, Chung T, Christopher‐Stine L, Rosen P, Rosen A, Doering KR, Casciola‐Rosen LA. **Autoantibodies against 3‐hydroxy‐3‐methylglutaryl‐coenzyme A reductase in patients with statin‐associated autoimmune myopathy**. *Arthritis Rheum* (2011) **63** 713-721. PMID: 21360500
43. Maulucci G, Cohen O, Daniel B, Ferreri C, Sasson S. **The combination of whole cell lipidomics analysis and single cell confocal imaging of fluidity and micropolarity provides insight into stress‐induced lipid turnover in subcellular organelles of pancreatic beta cells**. *Molecules* (2019) **24**. PMID: 31627330
44. Merrill GF, Kurth EJ, Hardie DG, Winder WW. **AICA riboside increases AMP‐activated protein kinase, fatty acid oxidation, and glucose uptake in rat muscle**. *Am J Physiol* (1997) **273** E1107-E1112. PMID: 9435525
45. Mielke GI, Brown WJ, Nunes BP, Silva ICM, Hallal PC. **Socioeconomic correlates of sedentary behavior in adolescents: systematic review and meta‐analysis**. *Sports Med* (2017) **47** 61-75. PMID: 27260683
46. Mohassel P, Landon‐Cardinal O, Foley AR, Donkervoort S, Pak KS, Wahl C, Shebert RT, Harper A, Fequiere P, Meriggioli M. **Anti‐HMGCR myopathy may resemble limb‐girdle muscular dystrophy**. *Neurol Neuroimmunol Neuroinflamm* (2019) **6**. PMID: 30588482
47. Mule F, Serio R. **Increased calcium influx is responsible for the sustained mechanical tone in colon from dystrophic (mdx) mice**. *Gastroenterology* (2001) **120** 1430-1437. PMID: 11313313
48. Mule F, Amato A, Serio R. **Gastric emptying, small intestinal transit and fecal output in dystrophic (mdx) mice**. *J Physiol Sci* (2010) **60** 75-79. PMID: 19784719
49. Muller HE, Brenner DJ, Fanning GR, Grimont PA, Kampfer P. **Emended description of**. *Int J Syst Bacteriol* (1996) **46** 50-63. PMID: 11534554
50. Nugraha B, Buono MF, von Boehmer L, Hoerstrup SP, Emmert MY. **Human cardiac organoids for disease modeling**. *Clin Pharmacol Ther* (2019) **105** 79-85. PMID: 30415499
51. Nunes B, Resende ST. **Cholinesterase characterization of two autochthonous species of Ria de Aveiro (**. *Environ Sci Pollut Res Int* (2017) **24** 12155-12167. PMID: 28353098
52. Nunes B, Nunes J, Soares A, Figueira E, Freitas R. **Toxicological effects of paracetamol on the clam**. *Aquat Toxicol* (2017a) **192** 198-206. PMID: 28982071
53. Nunes B, Pinho C, Sousa C, Melo AR, Bandarra N, Silva MC. **Relevance of omega‐3 and omega‐6/omega‐3 ratio in preventing cognitive impairment**. *Acta Med Port* (2017b) **30** 213-223. PMID: 28550831
54. Nunes B, Serdoura F, Vital L, Seara M, Pinto R. **Posterior interosseous nerve syndrome after pneumatic hammer use: an uncommon condition**. *Am J Ind Med* (2017c) **60** 670-672. PMID: 28485505
55. Nunes B, Silva V, Campos I, Pereira JL, Pereira P, Keizer JJ, Goncalves F, Abrantes N. **Off‐site impacts of wildfires on aquatic systems—biomarker responses of the mosquitofish**. *Sci Total Environ* (2017d) **581–582** 305-313
56. Nunes BG, Loures FV, Bueno HMS, Cangussu EB, Goulart E, Coatti GC, Caldini EG, Condino‐Neto A, Zatz M. **Immunoglobulin therapy ameliorates the phenotype and increases lifespan in the severely affected dystrophin‐utrophin double knockout mice**. *Eur J Hum Genet* (2017e) **25** 1388-1396. PMID: 29255177
57. Nunes BM, Xavier TC, Martins RR. **Antimicrobial drug‐related problems in a neonatal intensive care unit**. *Rev Bras Ter Intensiva* (2017f) **29** 331-336. PMID: 28876403
58. Nunes BP, Chiavegatto Filho ADP, Pati S, Cruz Teixeira DS, Flores TR, Camargo‐Figuera FA, Munhoz TN, Thume E, Facchini LA, Rodrigues Batista SR. **Contextual and individual inequalities of multimorbidity in Brazilian adults: a cross‐sectional national‐based study**. *BMJ Open* (2017g) **7**
59. Nunes BP, Soares MU, Wachs LS, Volz PM, Saes MO, Duro SMS, Thume E, Facchini LA. **Hospitalization in older adults: association with multimorbidity, primary health care and private health plan**. *Rev Saude Publica* (2017h) **51** 43. PMID: 28492761
60. Nunes JD, Saes MO, Nunes BP, Siqueira FCV, Soares DC, Fassa MEG, Thume E, Facchini LA. **Functional disability indicators and associated factors in the elderly: a population‐based study in Bage, Rio Grande do Sul, Brazil**. *Epidemiol Serv Saude* (2017i) **26** 295-304. PMID: 28492771
61. Parchem K, Sasson S, Ferreri C, Bartoszek A. **Qualitative analysis of phospholipids and their oxidised derivatives—used techniques and examples of their applications related to lipidomic research and food analysis**. *Free Radic Res* (2019) **53** 1068-1100. PMID: 31419920
62. Parolini D, Meregalli M, Belicchi M, Razini P, Lopa R, Del Carlo B, Farini A, Maciotta S, Bresolin N, Porretti L. **CD20‐related signaling pathway is differently activated in normal and dystrophic circulating CD133**. *Cell Mol Life Sci* (2009) **66** 697-710. PMID: 19153665
63. Perez EE, Orange JS, Bonilla F, Chinen J, Chinn IK, Dorsey M, El‐Gamal Y, Harville TO, Hossny E, Mazer B. **Update on the use of immunoglobulin in human disease: a review of evidence**. *J Allergy Clin Immunol* (2017) **139** S1-S46. PMID: 28041678
64. Pezzilli R, Mauloni PA. **Acute pancreatitis associated with myotonic dystrophy type I**. *Pancreas* (2019) **48** e63-e64. PMID: 31425487
65. Pinciotti CM, Allen CE, Milliken JM, Orcutt HK, Sasson S. **Reliability and findings from an instrument examining sexual assault disclosure content and context: the sexual assault inventory of disclosure**. *Violence Vict* (2019) **34** 260-295. PMID: 31019012
66. Populo H, Nunes B, Sampaio C, Batista R, Pinto MT, Gaspar TB, Miranda‐Alves L, Cai RZ, Zhang XY, Schally AV. **Inhibitory effects of antagonists of growth hormone‐releasing hormone (GHRH) in thyroid cancer**. *Horm Cancer* (2017) **8** 314-324. PMID: 28924876
67. Porter JD, Khanna S, Kaminski HJ, Rao JS, Merriam AP, Richmonds CR, Leahy P, Li J, Guo W, Andrade FH. **A chronic inflammatory response dominates the skeletal muscle molecular signature in dystrophin‐deficient mdx mice**. *Hum Mol Genet* (2002) **11** 263-272. PMID: 11823445
68. Prukop T, Wernick S, Boussicault L, Ewers D, Jager K, Adam J, Winter L, Quintes S, Linhoff L, Barrantes‐Freer A. **Synergistic PXT3003 therapy uncouples neuromuscular function from dysmyelination in male Charcot‐Marie‐Tooth disease type 1A (CMT1A) rats**. *J Neurosci Res* (2020) **98** 1933-1952. PMID: 32588471
69. Recke A, Trog LM, Pas HH, Vorobyev A, Abadpour A, Jonkman MF, van Zandbergen G, Kauderer C, Zillikens D, Vidarsson G. **Recombinant human IgA1 and IgA2 autoantibodies to type VII collagen induce subepidermal blistering**. *J Immunol* (2014) **193** 1600-1608. PMID: 25024393
70. Richner JM, Jagger BW, Shan C, Fontes CR, Dowd KA, Cao B, Himansu S, Caine EA, Nunes BTD, Medeiros DBA. **Vaccine mediated protection against Zika virus‐induced congenital disease**. *Cell* (2017) **170** 273-283. PMID: 28708997
71. Rispens T, Davies AM, Ooijevaar‐de Heer P, Absalah S, Bende O, Sutton BJ, Vidarsson G, Aalberse RC. **Dynamics of inter‐heavy chain interactions in human immunoglobulin G (IgG) subclasses studied by kinetic fab arm exchange**. *J Biol Chem* (2014) **289** 6098-6109. PMID: 24425871
72. Rodrigues S, Antunes SC, Nunes B, Correia AT. **Histological alterations in gills and liver of rainbow trout (**. *Environ Toxicol Pharmacol* (2017) **53** 164-176. PMID: 28599186
73. Roquette R, Painho M, Nunes B. **Spatial epidemiology of cancer: a review of data sources, methods and risk factors**. *Geospat Health* (2017) **12** 504. PMID: 28555468
74. Santos AJ, Kislaya I, Machado A, Nunes B. **Beliefs and attitudes towards the influenza vaccine in high‐risk individuals**. *Epidemiol Infect* (2017a) **145** 1786-1796. PMID: 28434418
75. Santos AJ, Nunes B, Kislaya I, Gil AP, Ribeiro O. **Exploring the correlates to depression in elder abuse victims: abusive experience or individual characteristics?**. *J Interpers Violence* (2017b) **36** NP115-NP134. PMID: 29294926
76. Sasson SC, Corbett A, McLachlan AJ, Chen R, Adelstein SA, Riminton S, Limaye S. **Enhanced serum immunoglobulin G clearance in myotonic dystrophy‐associated hypogammaglobulinemia: a case series and review of the literature**. *J Med Case Reports* (2019) **13** 338
77. Shan C, Muruato AE, Jagger BW, Richner J, Nunes BTD, Medeiros DBA, Xie X, Nunes JGC, Morabito KM, Kong WP. **A single‐dose live‐attenuated vaccine prevents Zika virus pregnancy transmission and testis damage**. *Nat Commun* (2017a) **8** 676. PMID: 28939807
78. Shan C, Muruato AE, Nunes BTD, Luo H, Xie X, Medeiros DBA, Wakamiya M, Tesh RB, Barrett AD, Wang T. **A live‐attenuated Zika virus vaccine candidate induces sterilizing immunity in mouse models**. *Nat Med* (2017b) **23** 763-767. PMID: 28394328
79. Sparks S, Rakocevic G, Joe G, Manoli I, Shrader J, Harris‐Love M, Sonies B, Ciccone C, Dorward H, Krasnewich D. **Intravenous immune globulin in hereditary inclusion body myopathy: a pilot study**. *BMC Neurol* (2007) **7** 3. PMID: 17261181
80. Sperduto PW, Fang P, Li J, Breen W, Brown PD, Cagney D, Aizer A, Yu J, Chiang V, Jain S. **Survival and prognostic factors in patients with gastrointestinal cancers and brain metastases: have we made progress?**. *Transl Res* (2019) **208** 63-72. PMID: 30885538
81. Spor A, Koren O, Ley R. **Unravelling the effects of the environment and host genotype on the gut microbiome**. *Nat Rev Microbiol* (2011) **9** 279-290. PMID: 21407244
82. Strati F, Pujolassos M, Burrello C, Giuffre MR, Lattanzi G, Caprioli F, Troisi J, Facciotti F. **Antibiotic‐associated dysbiosis affects the ability of the gut microbiota to control intestinal inflammation upon fecal microbiota transplantation in experimental colitis models**. *Microbiome* (2021) **9** 39. PMID: 33549144
83. Sun SC. **The non‐canonical NF‐kappaB pathway in immunity and inflammation**. *Nat Rev Immunol* (2017) **17** 545-558. PMID: 28580957
84. Suzumura A, Yamada H, Matsuoka Y, Sobue I. **Immunoglobulin abnormalities in patients with myotonic dystrophy**. *Acta Neurol Scand* (1986) **74** 132-139. PMID: 3490729
85. Ticinesi A, Milani C, Lauretani F, Nouvenne A, Mancabelli L, Lugli GA, Turroni F, Duranti S, Mangifesta M, Viappiani A. **Gut microbiota composition is associated with polypharmacy in elderly hospitalized patients**. *Sci Rep* (2017) **7**. PMID: 28894183
86. Tidball JG. **Regulation of muscle growth and regeneration by the immune system**. *Nat Rev Immunol* (2017) **17** 165-178. PMID: 28163303
87. Tidball JG, Welc SS, Wehling‐Henricks M. **Immunobiology of inherited muscular dystrophies**. *Compr Physiol* (2018) **8** 1313-1356. PMID: 30215857
88. Tomasi E, Cesar MA, Neves RG, Schmidt PR, Thume E, da Silveira DS, Siqueira FC, Nunes BP, Fassa AG, Saes MO. **Diabetes care in Brazil: program to improve primary care access and quality‐PMAQ**. *J Ambul Care Manage* (2017a) **40** S12-S23. PMID: 28252499
89. Tomasi E, Fernandes PA, Fischer T, Siqueira FC, Silveira DS, Thume E, Duro SM, Saes MO, Nunes BP, Fassa AG. **Quality of prenatal services in primary healthcare in Brazil: indicators and social inequalities**. *Cad Saude Publica* (2017b) **33**. PMID: 28380149
90. Tonegawa S. **Somatic generation of antibody diversity**. *Nature* (1983) **302** 575-581. PMID: 6300689
91. Turner C, Hilton‐Jones D. **The myotonic dystrophies: diagnosis and management**. *J Neurol Neurosurg Psychiatry* (2010) **81** 358-367. PMID: 20176601
92. van Bladel ER, Laarhoven AG, van der Heijden LB, Heitink‐Polle KM, Porcelijn L, van der Schoot CE, de Haas M, Roest M, Vidarsson G, de Groot PG. **Functional platelet defects in children with severe chronic ITP as tested with 2 novel assays applicable for low platelet counts**. *Blood* (2014) **123** 1556-1563. PMID: 24385537
93. Vidarsson G, Dekkers G, Rispens T. **IgG subclasses and allotypes: from structure to effector functions**. *Front Immunol* (2014) **5** 520. PMID: 25368619
94. Walther LE, Winnefeld K, Solch O. **Determination of iron, copper, zinc, magnesium and selenium in plasma and erythrocytes in neurosurgical patients**. *J Trace Elem Med Biol* (2000) **14** 92-95. PMID: 10941720
95. Wang T, Zhu H, Hou Y, Gu W, Wu H, Luan Y, Xiao C, Zhou C. **Galantamine reversed early postoperative cognitive deficit via alleviating inflammation and enhancing synaptic transmission in mouse hippocampus**. *Eur J Pharmacol* (2019) **846** 63-72. PMID: 30586550
96. Weigmann B, Tubbe I, Seidel D, Nicolaev A, Becker C, Neurath MF. **Isolation and subsequent analysis of murine lamina propria mononuclear cells from colonic tissue**. *Nat Protoc* (2007) **2** 2307-2311. PMID: 17947970
97. Wenninger S, Montagnese F, Schoser B. **Core clinical phenotypes in myotonic dystrophies**. *Front Neurol* (2018) **9** 303. PMID: 29770119
98. Wu YF, Huang WH, Gu MH, Lin SR, Chu SC, Wang TF, Li CC. **Higher CD56**. *Thromb Res* (2019) **183** 63-68. PMID: 31669825
99. Xepapadaki E, Maulucci G, Constantinou C, Karavia EA, Zvintzou E, Daniel B, Sasson S, Kypreos KE. **Impact of apolipoprotein A1‐ or lecithin:cholesterol acyltransferase‐deficiency on white adipose tissue metabolic activity and glucose homeostasis in mice**. *Biochim Biophys Acta Mol Basis Dis* (2019) **1865** 1351-1360. PMID: 30742993
100. Xie X, Yang Y, Muruato AE, Zou J, Shan C, Nunes BT, Medeiros DB, Vasconcelos PF, Weaver SC, Rossi SL. **Understanding Zika virus stability and developing a chimeric vaccine through functional analysis**. *MBio* (2017) **8**. PMID: 28174309
101. Yan H, Diao H, Xiao Y, Li W, Yu B, He J, Yu J, Zheng P, Mao X, Luo Y. **Gut microbiota can transfer fiber characteristics and lipid metabolic profiles of skeletal muscle from pigs to germ‐free mice**. *Sci Rep* (2016) **6**. PMID: 27545196
102. Yang LV, Radu CG, Wang L, Riedinger M, Witte ON. **Gi‐independent macrophage chemotaxis to lysophosphatidylcholine via the immunoregulatory GPCR G2A**. *Blood* (2005) **105** 1127-1134. PMID: 15383458
103. Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, Wu Y, Zhang L, Yu Z, Fang M. **Clinical course and outcomes of critically ill patients with SARS‐CoV‐2 pneumonia in Wuhan, China: a single‐centered, retrospective, observational study**. *Lancet Respir Med* (2020) **8** 475-481. PMID: 32105632
104. Zschuntzsch J, Zhang Y, Klinker F, Makosch G, Klinge L, Malzahn D, Brinkmeier H, Liebetanz D, Schmidt J. **Treatment with human immunoglobulin G improves the early disease course in a mouse model of Duchenne muscular dystrophy**. *J Neurochem* (2016) **136** 351-362. PMID: 26230042
105. Zuppinger C. **3D cardiac cell culture: a critical review of current technologies and applications**. *Front Cardiovasc Med* (2019) **6** 87. PMID: 31294032
|
---
title: Correlation between plasma glutathione peroxidase 4 and N-acetylneuraminic
acid levels with clinical risk stratification and prognosis of patients with acute
coronary syndrome
authors:
- Miao-Nan Li
- Bing-Wei Bao
- Ding Si-Yu
- Ji Chun-Fei
- Shi Xiao-Jun
- Gao Da-Sheng
- Gao Qin
- Wang Hong-Ju
journal: Saudi Medical Journal
year: 2022
pmcid: PMC9994492
doi: 10.15537/smj.2022.43.10.20220444
license: CC BY 4.0
---
# Correlation between plasma glutathione peroxidase 4 and N-acetylneuraminic acid levels with clinical risk stratification and prognosis of patients with acute coronary syndrome
## Body
In the global population, the acute coronary syndrome (ACS) is a leading cause of death and morbidity. Clinically, ACS can be divided into 3 types: I) unstable angina pectoris (UAP), ST-segment elevation myocardial infarction (STEMI), and non-STEMI. 1 Because ACS has a heavy economic burden on families and society, it is imperative to identify factors that might help with diagnosis, prediction, or both of the risk and prognosis of ACS patients. To achieve this, it is important to understand the etiology of ACS. Although a significant amount of research has been carried out to decipher the molecular basis that underlies the pathogenesis of ACS, the exact mechanisms remain unknown. However, it is accepted that the mechanisms that lead to the pathogenesis of ACS are multifactorial, including metabolic abnormality. 2,3 Additionally, new research indicates that iron death (ferroptosis), a new form of cell death, plays an important role in cardiovascular disease. For instance, targeted intervention of iron death could effectively prevent and treat heart diseases. 4-6 Selenium-dependent glutathione peroxidase 4 (GPX4) is a member of the GPX family and protects cells from membrane lipid oxidation-linked damage. 7 Glutathione peroxidase 4 is antioxidative, which makes it effective at preventing and treating a variety of tissue injuries and diseases. 8-10 Several studies have linked ferroptosis to the deactivation of GPX4, which then causes the accumulation of reactive oxygen radicals on membrane lipids. 3,11 In addition, GPX4 can alleviate inflammation. 12 Researchers have found that decreased levels of GPX1 are associated with higher cardiovascular risk. 13 However, another study showed that the circulating levels of GPX factors, including GPX4, were significantly enhanced in ACS patients compared to controls, and attributed this increase to the bodies response to oxidative stress during ACS. 14 Therefore, how the plasma GPX4 levels change exactly in ACS patients and what roles GPX4 has in the initiation and progression of ACS need to be determined.
Numerous glycoproteins, glycopeptides, and glycolipids contain N-acetyl neuraminic acid (Neu5Ac) as their basic component, and the Neu5Ac protein serves a wide range of biological functions and plays an important role in cancer, for example. 15 In addition, Neu5Ac is involved in heart diseases. Serum Neu5Ac levels are linked to atrial fibrillation and play a key role in human acute myocardial infarction. 16 These observations support the theory that high circulating levels of Neu5Ac might contribute to the development of heart diseases. Our group recently reported that increased serum Neu5Ac levels were related to injury of cardiomyocytes in patients with ACS. 17 However, whether the circulating levels of Neu5Ac and GPX4 were related to the clinical outcomes of ACS remains unknown. Therefore, this study was carried out to evaluate the correlation between plasma GPX4 and Neu5Ac levels with thrombolysis in myocardial infarction (TIMI) risk score and the prognosis of ACS patients.
## Abstract
### Objectives:
To investigate the correlation between plasma glutathione peroxidase 4 (GPX4) and N-acetyl-neuraminic acid (Neu5Ac) with clinical risk stratification and outcomes of acute coronary syndrome (ACS) patients.
### Methods:
Between October 2018 and July 2019, 413 patients that were scheduled for coronary angiography were enrolled in this prospective study at the First Affiliated Hospital of Bengbu Medical College, Bengbu, China. Patients were divided into control and ACS groups. Patients with ACS were divided into 3 risk levels based on their thrombolysis in myocardial infarction risk score. After discharge, ACS patients were followed for the incidence of major adverse cardiac events (MACEs). For the analysis of cumulative endpoint event occurrences, the Kaplan-Meier method was applied.
### Results:
The ACS group had lower plasma GPX4 but higher Neu5Ac levels than the control group. There was a greater increase in plasma Neu5Ac in the high-risk group when compared with the medium-risk and low-risk groups, while GPX4 levels were higher in the low-risk group. The MACEs group had higher plasma Neu5Ac but lower GPX4 levels than the non-MACEs group. The plasma Neu5Ac was an independent risk factor but GPX4 was a protective factor for MACEs.
### Conclusion:
Glutathione peroxidase 4 and Neu5Ac levels in plasma can be used to diagnose, stratify risks, and predict long-term outcomes in patients with ACS.
## Methods
This single-center prospective observational study enrolled 413 (240 males and 173 females) ACS patients (aged 62.2±10.9 years) who were scheduled to undergo coronary angiography at the First Affiliated Hospital of Bengbu Medical College, Bengbu, China, between October 2018 and July 2019. Patients that had one of the following ACS, which included: I) UAP; II) STEMI, and III) non-STEMI, were included in this study. 18,19 The results of coronary angiography were interpreted according to the criteria recommended by the 2001 American College of Cardiology (ACC)/American Heart Association (AHA). 20 The control group included patients that underwent coronary angiography in the hospital over the same period without ACS. This study excluded patients with any of the following conditions: I) severe liver and kidney dysfunction; II) hematopoietic diseases; III) infectious diseases; IV) tumors; or V) other wasting diseases. Based on the inclusion and exclusion criteria, the control group had 108 patients and the ACS group had 305 patients.
An informed consent form was completed by all participants before being enrolled in the study, and approval from the First Affiliated Hospital of Bengbu Medical College, Bengbu, China, was obtained (approval number: BYYFY-2018KY23).
Smoking in this study was defined as a person who has smoked continuously ≥1 cigarette per day for >1 year. 21 The Diagnostic Criteria in Diabetes-2021 were used to diagnose diabetes. 22 The definition of hypertension was a systolic blood pressure over 140 mm Hg or a diastolic blood pressure over 90 mm Hg after repeated measurements over time in accordance with the 2020 International Society of Hypertension Global Hypertension Practice Guidelines. 23 Atrial fibrillation was diagnosed by the criteria proposed by the task force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology. 24 Major adverse cardiac events (MACEs): recurrent angina, heart failure, recurrent myocardial infarction, stroke, hemorrhage, revascularization, stent thrombosis, stent restenosis, cardiogenic death, and all-cause death. Re-hospitalization due to one or more of the previously mentioned reasons was counted as a MACE.
Patients were admitted to hospital early morning with an empty stomach, and 5 mL of cubital venous blood drawn into heparin- and ethylenediamine tetraacetic acid (EDTA) treated tubes. The blood samples in the heparin-treated tubes were sent to the testing center for the analysis of biochemical tests, including fast blood glucose, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), D-dimer, and C-reactive protein (CRP).
To measure plasma GPX4 and Neu5Ac, blood samples in the EDTA-treated tubes were sent to the heart and lung laboratory and serum was separated within 30 minutes, stored at -80°, and used to measure plasma GPX4 and Neu5Ac levels using an enzyme-linked immunosorbent method and liquid chromatography tandem mass spectrometry. 25,26 Coronary angiography was carried out via the Judkins method, and the results were evaluated using the 2001 ACC/AHA report for the management of cardiovascular diseases. 20,27 The angiography showed that there was coronary artery stenosis ≥$70\%$ for coronary stent implantation and that a drug-eluting stent was implanted in the lesion in patients. Individual patient’s coronary angiographic results and stent implantation process were recorded. The success criteria for stent implantation were based on international practice, for example, residual stenosis ≤$20\%$, and a TIMI3 blood flow. The Gensini score was used to quantitatively calculate the degree of stenosis for each diseased vessel, which was independently evaluated by 2 cardiologists, and the average data were calculated. 28 The clinical risk score for TIMI risk score was used to stratify patients in the UAP and acute myocardial infarction groups, and patients with UAP, non-STEMI, and STEMI were stratified according to different scoring standards. 29,30 All patients were monitored during hospitalization. An outpatient clinic or telephone follow-up was carried out monthly for 15 months following discharge for ACS patients with MACEs. 31 Four patients were lost to follow-up. Major adverse cardiac events in this study were defined as ≥1 of the following: I) recurring chest pain; II) heart failure; III) stroke; IV) recurring myocardial infarction; V) hemorrhage; VI) revascularization; VII) stent thrombosis; VIII) restenosis in the stent; and IX) death. 31 Based on the presence or absence of MACEs, ACS patients were divided into the MACEs group ($$n = 37$$) and the non-MACEs group ($$n = 268$$).
Research and outcomes were not developed with the involvement of patients or the public. We aim to publish the study results as open access, which will be readily available to the public.
## Statistical analysis
All analyses were carried out using the Statistical Package for the Social Sciences, version 21.0 (IBM Corp., Armonk, NY, USA). Measured data are presented as mean ± standard deviation (SD). Data that are normally distributed were analyzed with variance analysis, whereas data that are not normally distributed were analyzed using non-parametric tests. Comparing data between the 2 groups was accomplished by using the Student’s T-test; describing the classification data was accomplished by using the composition ratio and comparing it by using the Chi-squared test. The correlation analysis was carried out using a bivariate correlation analysis. The risk factors for MACEs were determined using Cox’s risk proportional regression model, and Kaplan-Meier was used to illustrate the curve of endpoints, which included recurrent angina pectoris, heart failure, recurrent myocardial infarction, stroke, hemorrhage, revascularization, stent thrombosis, stent restenosis, cardiogenic death, and all-cause death. P-values of <0.05 indicate a significant difference.
## Results
This study included 413 patients, who were divided into a control group ($$n = 108$$; age: 57.01±9.84 years; 43 males and 65 females) and an ACS group ($$n = 305$$; age: 63.88±10.76 years; 197 males and 108 females). Patients in these 2 groups were compared in terms of demographics and clinical characteristics. As listed in Table 1, no significant differences were observed for TC, TG, AF, HDL-C, LDL-C, D-dimer, and CRP ($p \leq 0.05$). However, the ACS group had older patients and more males and smokers than the control group ($p \leq 0.01$). In addition, the ACS group had a higher diabetic rate, higher levels of blood uric acid, blood sugar, blood creatinine, and high-density lipoprotein ($p \leq 0.01$). In addition, the ACS group had lower levels of plasma GPX4 but higher levels of plasma Neu5Ac than the control group ($p \leq 0.05$).
Receiver operating characteristic (ROC) curves were used to determine whether the levels of plasma GPX4 and Neu5Ac held a value in the auxiliary diagnosis of ACS. The plasma GPX4 levels were valuable in the auxiliary diagnosis of ACS (area under the curve [AUC]: 0.723 [0.657-0.790]), sensitivity of $97.7\%$, specificity $50.0\%$, and a cut-off value of 131.06 ng/mL (Figure 1A). Similarly, the ROC curve showed that the plasma Neu5Ac levels were valuable in the auxiliary diagnosis of ACS (AUC: 0.667 [0.610-0.724]), sensitivity $39.3\%$, specificity $88.0\%$, and a cut-off value of 286.50 ng/mL (Figure 1B). The cut-off values of these 2 indicators were combined as a positive group and a joint calculation of the ROC curve was carried out. This demonstrated that there was still a value for the auxiliary diagnosis of ACS (AUC: 0.661 [0.607-0.715]), sensitivity of $38.7\%$, specificity of $93.5\%$ (Figure 1C). Therefore, the circulating levels of GPX4 and Neu5Ac (alone or combined) could have value in the auxiliary diagnosis of ACS.
**Figure 1:** *- Determination of cut-off levels for plasma glutathione peroxidase 4 (GPX4) and N-acetyl-neuraminic acid (Neu5Ac) in auxiliary diagnosis of acute coronary syndrome. A) GPX4 receiver operating characteristic (ROC) curve; B) Neu5Ac ROC curve; and C) combined GPX4 and Neu5Ac curve.*
The ACS patients were further divided into 3 groups on the basis of TIMI scores: I) high-risk group ($$n = 42$$); II) medium-risk group ($$n = 221$$); and III) low-risk group ($$n = 42$$), and the association between plasma GPX and Neu5Ac levels with TIMI risk stratification was examined. There was a significantly higher plasma level of GPX4 in the low-risk group than in the medium- and high-risk groups. Figure 2A indicated an inverse correlation between the plasma GPX4 levels with the TIMI risk stratification ($p \leq 0.05$). Additionally, the Neu5Ac levels in high-risk patients were significantly higher than in medium- and low-risk patients, which indicated a positive correlation between Neu5Ac and TIMI risk (Figure 2B; $p \leq 0.05$).
**Figure 2:** *- Correlation between plasma glutathione peroxidase 4 (GPX4) and N-acetyl-neuraminic acid (Neu5Ac) levels with thrombolysis in myocardial infarction (TIMI) risk stratification: A) GPX4 is negatively correlated with TIMI risk score; B) Neu5Ac is positively correlated with TIMI risk score.* TABLE_PLACEHOLDER:Table 1 Telephone or outpatient visits were used to follow up with patients in the ACS group for an average of 15 months after discharge, and 4 of them were lost. The MACEs were developed in 37 patients. The correlation between plasma GPX4 and Neu5Ac levels with the occurrence of MACEs in ACS was examined. The MACEs group had significantly lower plasma GPX4 levels than the non-MACEs group (92.66 [82.78-105.11] versus 106.29 [92.15-121.63] ng/mL; $p \leq 0.05$), but had significantly higher plasma Neu5Ac levels than the non-MACEs group (270.00 [134.93-340.69] versus 247.50 [123.54-313.50] ng/mL; $p \leq 0.05$). In addition, Cox’s regression analysis was used to retrieve the risk factors for MACEs. As listed in Table 2, hypertension, HDL-C, DM, and atrial fibrillation were not risk factors for MACEs. However, plasma Neu5Ac and TC levels were independent risk factors for MACEs and plasma GPX4 levels were a protective factor for MACEs (Table 2).
**Table 2**
| Variables | B | OR (95% CI) | P-values |
| --- | --- | --- | --- |
| GPX4 (ng/mL) | -0.24 | 0.976 (0.955-0.997) | 0.026 |
| Neu5Ac (ng/mL) | 0.001 | 1.001 (1.000-1.002) | 0.003 |
| TC (mmol/L) | 0.383 | 1.466 (1.084-1.982) | 0.013 |
| HDL-C (mmol/L) | -0.966 | 0.381 (0.073-1.977) | 0.25 |
| Hypertension | 0.03 | 1.031 (0.452-2.349) | 0.943 |
| DM | 0.138 | 1.148 (0.467-2.821) | 0.763 |
| AF | 0.169 | 1.184 (0.154-9.122) | 0.871 |
The ability of plasma GPX4 and Neu5Ac to predict the long-term endpoint events in ACS patients was investigated. Medians of GPX4 (104.39 ng/mL) and Neu5Ac (250 ng/mL) were used as the cut-offs. In total, 9 out of 149 patients in the GPX4 ≥104.39 ng/mL group had an endpoint event and 28 out of 156 patients in the GPX4 <104.39 ng/mL group had an endpoint event during follow. The average time for the occurrence of endpoint event between these 2 groups was 408.59 days and 373.36 days, and the log-rank test yielded χ 2 =11.091, ($p \leq 0.05$; Figure 3). In the Neu5Ac groups, there was no significant difference in the average days for an endpoint event to occur and the number of patients who experienced an endpoint event ($p \leq 0.05$).
**Figure 3:** *- Predictive value of glutathione peroxidase 4 and N-acetyl-neuraminic acid for long-term endpoint events in acute coronary syndrome patients. GPX4: glutathione peroxidase 4, Neu5Ac: N-acetyl-neuraminic acid*
## Discussion
In this study, the correlations between plasma GPX4 and Neu5Ac with the clinical risk stratification and prognosis of ACS patients were investigated. The major findings from this study were: I) ACS patients had significantly lower plasma GPX4 levels but higher plasma Neu5Ac levels than the control subjects; II) GPX4 had a negative correlation but Neu5Ac had a positive correlation with the TIMI risk stratification; III) plasma Neu5Ac may be independent risk factor for the incidence of MACEs; however, plasma GPX4 was a protective factor for MACEs; IV) plasma GPX4, or Neu5Ac, or a combination of them, had a value in the auxiliary diagnosis of ACS; and V) plasma GPX4 had a value to predict the prognosis of ACS.
Discovering the biomarkers for the diagnosis and prognosis of ACS has received extensive research. Currently, a number of biomarkers, including cardiac troponin I, creatine kinase MB isoform (CK-MB), and creatine kinase, have been used in clinics. 32 *In this* study, the potential of plasma GPX4 and Neu5Ac to serve as potential biomarkers for the risk stratification and prognosis of ACS were assessed. The findings revealed that ACS patients had lower plasma GPX4 levels than control subjects, which indicated that plasma GPX4 had a negative correlation with ACS. A previous study showed that GPX4 was significantly upregulated in ACS patients compared with control patients, and another study suggested that lower activity of GPX was associated with increased risk of cardiovascular diseases. 13,14 This study suggested that lower circulating levels of GPX4 were observed in ACS patients. The reason for these findings was not clear; however, patient selection and different diagnostic criteria could have had an impact. Because GPX4 exhibits antioxidative activity, and oxidative stress has an important role in the pathogenesis of ACS, the decreased activity of GPX4 could have detrimental effects on the cardiovascular system and increased activity of GPX4 improves it. 33 In addition, the ROC analysis suggested that the circulating levels of GPX4 might be valuable in the auxiliary diagnosis of ACS with sensitivity of $97.7\%$, specificity of $50.0\%$, and a cut-off value of 131.06 ng/mL. Mechanistically, GPX4 might provide beneficial effects for ACS patients through a number of mechanisms, including antioxidative activity, and rebalancing iron metabolism to reduce ferroptosis. 34-36 *In this* study, the circulating levels of Neu5Ac, a family of monosaccharides with a 9-carbon backbone, were found higher in ACS patients than controls, which suggested that plasma Neu5Ac was positively correlated with ACS. 37 Metabolomics in cardiovascular diseases has received significant attention and might provide early diagnosis, intervention, and prognosis for ACS. 38,39 Current research suggests that plasma Neu5Ac might promote atherosclerosis, which agrees with the results of this study. 37 Mechanistically, Neu5Ac negatively affects the cardiovascular system through inflammation, interference with iron metabolism, and the promotion of platelet thrombosis and has been suggested as a target for prevention of atherosclerosis. 40 *In this* study, the ROC analysis suggested that the circulating levels of Neu5Ac were valuable in the auxiliary diagnosis of ACS (AUC: 0.667 [0.610-0.724]), with sensitivity of $39.3\%$, specificity of $88.0\%$, and a cut-off value of 286.50 ng/mL.
In this study, GPX4 or Neu5Ac could serve as a potential biomarker for ACS diagnosis. A combination of them could have value in the auxiliary diagnosis of ACS. However, which one was superior was not determined in this study and this could be the subject of future research.
Based on the previous observations, the correlation between plasma GPX4 and Neu5Ac levels and TIMI risk score, which is a widely used scoring system to stratify the risk of ACS patients, was examined. 29 *The plasma* Neu5Ac levels in the high-risk group were significantly higher than those in the medium- and low-risk groups, which suggested that the plasma Neu5Ac levels were positively associated with high-risk ACS patients. However, the plasma GPX4 levels in the low-risk group were significantly higher than the medium- and high-risk groups, which suggested that the plasma GPX4 levels were negatively associated with the high-risk ACS patients. In combination, these observations indicate that the levels of plasma GPX4 and Neu5Ac might be used as indicators for the TIMI risk stratification of ACS patients.
The patients in the ACS group were followed-up for an average of 15 months. Based on the previous observations, the MACEs group had significantly higher plasma Neu5Ac levels but lower plasma GPX4 levels than the non-MACEs group. Cox regression analysis showed that plasma Neu5Ac and TC levels were independent risk factors for MACEs; however, GPX4 was a protective factor for MACEs. In addition, the average time of the endpoint event in the GPX4 <104.39 ng/mL group was earlier than that in the GPX4 ≥104.39 ng/mL group, which indicated that GPX4 could be used as a predictor for the prognosis of ACS patients. Of interest, Neu5Ac did not demonstrate this function in this study.
## Study limitations
First, this study was a single-center observational study with relatively small sample size. Second, ACS patients with severe liver and kidney insufficiency and cardiopulmonary insufficiency were excluded from this study, which was probably linked to the low incidence of MACEs that were observed in this study. Third, the patients in this study were heterogenous. In addition, the potential differences in dietary sources of Neu5Ac among the included patients might have affected the blood circulating levels of Neu5Ac, which might be a confounding factor of this study. Fourth, the follow-up period was short; therefore, the long-term association between plasma GPX4 and Neu5Ac levels for the prognosis of ACS patients requires further investigation. Finally, the efficacy of both biomarkers with the currently used clinical used biomarkers for ACS diagnosis in this study was not compared.
In conclusion, plasma GPX4 and Neu5Ac levels are associated with the clinical risk stratification of ACS patients and could have value for the auxiliary diagnosis and prognostic prediction of ACS patients. Therefore, plasma GPX4 and Neu5Ac levels could offer valuable guidance for the clinical application and targeted prevention and treatment of ACS.
## References
1. Eisen A, Giugliano RP, Braunwald E.. **Updates on acute coronary syndrome: a review**. *JAMA Cardiol* (2016) **1** 718-730. PMID: 27438381
2. Sinha SK, Goel A, Madaan A, Thakur R, Krishna V, Singh K. **Prevalence of metabolic syndrome and its clinical and angiographic profile in patients with naive acute coronary syndrome in North Indian population**. *J Clin Med Res* (2016) **8** 667-673. PMID: 27540441
3. Zaliūnas R, Slapikas R, Luksiene D, Slapikiene B, Statkeviciene A, Milvidaite I. **Prevalence of metabolic syndrome components in patients with acute coronary syndromes**. *Medicina (Kaunas)* (2008) **44** 182-188. PMID: 18413984
4. Zhang Y, Ren X, Wang Y, Chen D, Jiang L, Li X. **Targeting ferroptosis by polydopamine nanoparticles protects heart against ischemia/reperfusion injury**. *ACS Appl Mater Interfaces* (2021) **13** 53671-53682. PMID: 34730938
5. Menon AV, Liu J, Tsai HP, Zeng L, Yang S, Asnani A. **Excess heme upregulates heme oxygenase 1 and promotes cardiac ferroptosis in mice with sickle cell disease**. *Blood* (2022) **139** 936-941. PMID: 34388243
6. Park TJ, Park JH, Lee GS, Lee JY, Shin JH, Kim MW. **Quantitative proteomic analyses reveal that GPX4 downregulation during myocardial infarction contributes to ferroptosis in cardiomyocytes**. *Cell Death Dis* (2019) **10** 835. PMID: 31685805
7. Savaskan NE, Ufer C, Kühn H, Borchert A.. **Molecular biology of glutathione peroxidase 4: from genomic structure to developmental expression and neural function**. *Biol Chem* (2007) **388** 1007-1017. PMID: 17937614
8. Yoo MH, Gu X, Xu XM, Kim JY, Carlson BA, Patterson AD. **Delineating the role of glutathione peroxidase 4 in protecting cells against lipid hydroperoxide damage and in Alzheimer’s disease**. *Antioxid Redox Signal* (2010) **12** 819-827. PMID: 19769463
9. Bellinger FP, Bellinger MT, Seale LA, Takemoto AS, Raman AV, Miki T. **Glutathione peroxidase 4 is associated with neuromelanin in substantia nigra and dystrophic axons in putamen of Parkinson’s brain**. *Mol Neurodegener* (2011) **6** 8. PMID: 21255396
10. Necheles TF, Boles TA, Allen DM.. **Erythrocyte glutathione-peroxidase deficiency and hemolytic disease of the newborn infant**. *Journal of Pediatrics* (1968) **72** 319-324
11. Fang X, Wang H, Han D, Xie E, Yang X, Wei J. **Ferroptosis as a target for protection against cardiomyopathy**. *Proc Natl Acad Sci U S A* (2019) **116** 2672-2680. PMID: 30692261
12. Li C, Deng X, Xie X, Liu Y, Friedmann Angeli JP, Lai L.. **Activation of glutathione peroxidase 4 as a novel anti-inflammatory strategy**. *Front Pharmacol* (2018) **9** 1120. PMID: 30337875
13. Blankenberg S, Rupprecht HJ, Bickel C, Torzewski M, Hafner G, Tiret L. **Glutathione peroxidase 1 activity and cardiovascular events in patients with coronary artery disease**. *N Engl J Med* (2003) **349** 1605-1613. PMID: 14573732
14. Holley A, Pitman J, Miller J, Harding S, Larsen P.. **Glutathione peroxidase activity and expression levels are significantly increased in acute coronary syndromes**. *J Investig Med* (2017) **65** 919-925
15. Zhou X, Yang G, Guan F.. **Biological functions and analytical strategies of sialic acids in tumor**. *Cells* (2020) **9** 273. PMID: 31979120
16. Hu W, Xie J, Zhu T, Meng G, Wang M, Zhou Z. **Serum N-acetylneuraminic acid is associated with atrial fibrillation and left atrial enlargement**. *Cardiol Res Pract* (2020) **2020** 1358098. PMID: 32351730
17. Li MN, Qian SH, Yao ZY, Ming SP, Shi XJ, Kang PF. **Correlation of serum N-acetylneuraminic acid with the risk and prognosis of acute coronary syndrome: a prospective cohort study**. *BMC Cardiovasc Disord* (2020) **20** 404. PMID: 32912159
18. **Guideline and consensus for the management of patients with non-ST-elevation acute coronary syndrome(2016)**. *Zhonghua Xin Xue Guan Bing Za Zhi* (2017) **45** 359-376. PMID: 28511320
19. **2019 Chinese Society of Cardiology (CSC) guidelines for the diagnosis and management of patients with ST-segment elevation myocardial infarction**. *Zhonghua Xin Xue Guan Bing Za Zhi* (2019) **47** 766-783. PMID: 31648459
20. Smith SC, Dove JT, Jacobs AK, Kennedy JW, Kereiakes D, Kern MJ. **ACC/AHA guidelines for percutaneous coronary intervention (revision of the 1993 PTCA guidelines)-executive summary: a report of the American College of Cardiology/American Heart Association task force on practice guidelines (Committee to revise the 1993 guidelines for percutaneous transluminal coronary angioplasty) endorsed by the Society for Cardiac Angiography and Interventions**. *Circulation* (2001) **103** 3019-3041. PMID: 11413094
21. Chen MJ, Chiou YY, Wu DC, Wu SL.. **Lifestyle habits and gastric cancer in a hospital-based case-control study in Taiwan**. *Am J Gastroenterol* (2000) **95** 3242-3249. PMID: 11095349
22. **2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2021**. *Diabetes Care* (2021) **44** S15-S33. PMID: 33298413
23. Unger T, Borghi C, Charchar F, Khan NA, Poulter NR, Prabhakaran D. **2020 International Society of Hypertension Global Hypertension Practice Guidelines**. *Hypertension* (2020) **75** 1334-1357. PMID: 32370572
24. Hindricks G, Potpara T, Dagres N, Arbelo E, Bax JJ, Blomström-Lundqvist C. **2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): the task force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC**. *Eur Heart J* (2021) **42** 373-498. PMID: 32860505
25. Fang M, Xu X, Zhang M, Shi Y, Gu G, Liu W. **Quantitation of cytidine-5’-monophospho-N-acetylneuraminic acid in human leukocytes using LC-MS/MS: method development and validation**. *Biomed Chromatogr* (2020) **34** e4735. PMID: 31691999
26. Rehm S, Rentsch KM.. **A 2D HPLC-MS/MS method for several antibiotics in blood plasma, plasma water, and diverse tissue samples**. *Anal Bioanal Chem* (2020) **412** 715-725. PMID: 31900530
27. Page HL. **The Judkin’s technique**. *Cathet Cardiovasc Diagn* (1979) **5** 187-189. PMID: 487422
28. Gensini GG.. **A more meaningful scoring system for determining the severity of coronary heart disease**. *Am J Cardiol* (1983) **51** 606. PMID: 6823874
29. Antman EM, Cohen M, Bernink PJ, McCabe CH, Horacek T, Papuchis G. **The TIMI risk score for unstable angina/non-ST elevation MI: a method for prognostication and therapeutic decision making**. *JAMA* (2000) **284** 835-842. PMID: 10938172
30. Morrow DA, Antman EM, Charlesworth A, Cairns R, Murphy SA, de Lemos JA. **TIMI risk score for ST-elevation myocardial infarction: a convenient, bedside, clinical score for risk assessment at presentation: an intravenous nPA for treatment of infarcting myocardium early II trial substudy**. *Circulation* (2000) **102** 2031-2037. PMID: 11044416
31. Baluja A, Rodríguez-Mañero M, Cordero A, Kreidieh B, Iglesias-Alvarez D, García-Acuña JM. **Prediction of major adverse cardiac, cerebrovascular events in patients with diabetes after acute coronary syndrome**. *Diab Vasc Dis Res* (2020) **17** 1479164119892137. PMID: 31841030
32. Mueller C.. **Biomarkers and acute coronary syndromes: an update**. *Eur Heart J* (2014) **35** 552-556. PMID: 24357507
33. Vichova T, Motovska Z.. **Oxidative stress: predictive marker for coronary artery disease**. *Exp Clin Cardiol* (2013) **18** e88-e91. PMID: 23940453
34. Friedmann Angeli JP, Conrad M.. **Selenium and GPX4, a vital symbiosis**. *Free Radic Biol Med* (2018) **127** 153-159. PMID: 29522794
35. Bebber CM, Müller F, Prieto Clemente L, Weber J, von Karstedt S.. **Ferroptosis in cancer cell biology**. *Cancers (Basel)* (2020) **12** 164. PMID: 31936571
36. Han D, Jiang L, Gu X, Huang S, Pang J, Wu Y. **SIRT3 deficiency is resistant to autophagy-dependent ferroptosis by inhibiting the AMPK/mTOR pathway and promoting GPX4 levels**. *J Cell Physiol* (2020) **235** 8839-8851. PMID: 32329068
37. Park SS.. **Post-glycosylation modification of sialic acid and its role in virus pathogenesis**. *Vaccines (Basel)* (2019) **7** 171. PMID: 31683930
38. Fu H, Zhu K, Zhou D, Guan Y, Li W, Xu S.. **Identification and validation of plasma metabolomics reveal potential biomarkers for coronary heart disease**. *Int Heart J* (2019) **60** 1387-1397. PMID: 31666452
39. Orlenko A, Kofink D, Lyytikäinen LP, Nikus K, Mishra P, Kuukasjärvi P. **Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning**. *Bioinformatics* (2020) **36** 1772-1778. PMID: 31702773
40. Zhang C, Chen J, Liu Y, Xu D.. **Sialic acid metabolism as a potential therapeutic target of atherosclerosis**. *Lipids Health Dis* (2019) **18** 173. PMID: 31521172
|
---
title: Self-esteem mediates the relationship perceived stigma with self-efficacy for
diabetes management in individuals with type 2 diabetes mellitus
authors:
- Ayfer Ozturk
- Semih Akin
- Necla Kundakci
journal: Saudi Medical Journal
year: 2022
pmcid: PMC9994505
doi: 10.15537/smj.2022.43.10.20220344
license: CC BY 4.0
---
# Self-esteem mediates the relationship perceived stigma with self-efficacy for diabetes management in individuals with type 2 diabetes mellitus
## Body
Diabetes mellitus (DM) is a global health problem that threatens the entire world whose prevalence is increasing fast. According to the data of the International Diabetes Federation (IDF), there were 537 million people with diabetes in the world in 2021. It is estimated that this number will reach approximately 643 million in 2030 and 783 million in 2045. 1 Moreover, according to IDF, approximately $90\%$ of diabetes cases consist of type-2 diabetes mellitus (T2DM), and one in every 11 people in the world have diabetes. 1 *Diabetes is* not only a disease that progresses with physical symptoms but also a disease that has psychiatric and psychosocial aspects. 2 Such individuals may also experience psychological problems due to pressure and stigmatization by people around them. 3 *Stigma is* founded on cognitive, emotional, and behavioral reactions to individuals with some diseases due to the cognitive schemas and prejudices of society against some patient groups. Stigma may be at least as dangerous as the disease itself. The stigmatized person is attributed a characteristic that is not based on facts and is infamizing. 4 This social stigma, in time, leads to the person’s development of stigmatizing attitudes towards self. 5 Self-stigma affects the mental health of the individual and their feelings on healthy living negatively. 5 If the fact that genetic and environmental factors playing a role in the development of T2DM is neglected, perceptions that this disease is only related to the person’s lifestyle emerge. In this case, diabetes is perceived as a situation that is under the control of the diagnosed person, and thus, patients may think that they have caused their diabetes diagnosis by themselves. 6 Studies carried out with people with diabetes have revealed that patients experience serious levels of stigma regarding the disease. 3,7-10 In their study carried out with 12,000 Americans with type 1-2 diabetes, Liu et al 7 reported high levels of perceived stigma in the people with diabetes. In another qualitative study carried out by Browne et al, 9 people with T2DM shared their stigmatization experiences and described DM as a disease involving shame and blame. Such prejudiced and stigmatizing attitudes regarding the lifestyle that causes the disease affect disease-related self-management and the disease process negatively. 9
Diabetes management requires treatment compliance and the person to make some behavioral changes in their daily life. In addition, studies have shown that stigma perceived by people with diabetes affects their DM management negatively. 5,11-14 Kato et al 5 reported that internalized stigma had a negative effect on the self-management of people with T2DM. In another study by Kato et al 13 a negative relationship was found between the self-stigma levels and self-care behaviors of people with T2DM. In the study carried out by Lin et al 14 with 115 people with T2DM, the authors reported that the self-stigma perceptions of the patients affected their DM-related self-care behaviors negatively.
Self-esteem, which is one of the main elements of the concept of self, was defined as the person’s value, embracement, trust, and respect for themselves. 15 It was reported that individuals with high levels of self-esteem had better compliance with self-care activities. 16 A study showed that the stigma perceived by people with T2DM led to a reduction in their self-esteem and their self-efficacy in DM management. 17 *It is* known that individuals with high self-esteem show better compliance with DM-related self-care activities. In addition to this, the perception of stigma in people with diabetes affects their self-management of DM negatively. It has been reported that there is a negative relationship between stigma and self-esteem, where self-esteem decreases as stigma increases. From this perspective, it is considered that the variable of self-esteem plays a role as a mediator variable in the relationship between the perceived stigma and DM-related self-efficacy people with diabetes. In other words, the effect of the stigma perceived by people with diabetes on their self-efficacy may change when self-esteem is included in this relationship. There are limited number of studies on this subject in the literature.
From this perspective, in this study, primarily it was aimed to investigate the predictive effects of self-esteem and stigma in individuals with T2DM on their perceived self-efficacy in the management of DM. The second purpose of this study was to examine the mediating effects of self-esteem in the relationship between the stigma perceived by people with T2DM and their perceived self-efficacy related to DM management.
## Abstract
### Objectives:
To determine the mediating effect of self-esteem in the relationship between the perceived stigmatization of individuals with type 2 diabetes mellitus (T2DM) and their self-efficacy regarding diabetes management.
### Methods:
The study was carried out with 162 patients with T2DM who visited the Internal Medicine outpatient clinic, Bartin Public Hospital, Bartin, Turkey, between December 2020 and May 2021. A descriptive information form, diabetes management self-efficacy scale, Rosenberg self-esteem scale, and type-2 diabetes stigma assessment scale were used in data collection.
### Results:
As a result of regression analyses, it was determined that the variables of stigmatization (ß= -0.294) and self-esteem (ß=0.875) had a significant predictive effect on self-efficacy of patients with T2DM, and that as self-esteem was added to the model, the effect of stigmatization on self-efficacy (ß= -0.294) decreased (ß= -0.230, $p \leq 0.05$). According to these findings and the results of the Sobel test, it was determined that self-esteem had a partial mediator role (z= -3.347; $p \leq 0.05$).
### Conclusion:
Minimizing the perceived stigmatization can improve patients’ diabetes management self-efficacy. With patient training programs and individualized nursing care plans prepared by psychiatric nurses to provide psychological support patients and through their interventions that increase self-esteem, self-stigmatization can be reduced.
## Methods
This study was carried out with an analytic cross-sectional design. The study was carried out with 162 patients with T2DM who visited the Internal Medicine outpatient clinic, Bartin Public Hospital, Bartin, Turkey, between December 2020 and May 2021. The STROBE guidelines for cross-sectional studies were followed. The population of the study consisted of all patients diagnosed with T2DM who attended examinations within a year in the Internal Medicine outpatient clinics, Bartin Public Hospital, Bartin, Turkey. As the total number of cases within a year was not exactly known, a power analysis was carried out using the G*Power, version 3.1.7 software. The effect size was obtained as a moderate effect size (f2=0.15) according to the multiple regression analysis reported by Cohen. 18 It was calculated that the study should include at least 107 individuals to obtain a power of $95\%$ with an effect size of 0.15 and a significance level of $5\%$. The study was completed with 162 patients. At the end of the study, the effect size was 0.5 ($$p \leq 0.05$$).
Patients were selected with the convenience sampling method based on whether they met the following inclusion criteria: I) being diagnosed with T2DM; II) taking medication (such as oral antidiabetic and insulin therapy); and III) voluntarily agreeing to take part in the study. We excluded patients with a diagnosis of type 1 diabetes, gestational diabetes mellitus, younger than 18, having psychiatric illness, and not taking medication.
The data were collected through face-to-face interviews with diagnosed T2DM patients after they had been informed on the research process. Before the surveys were distributed by the researcher, the interviewee was informed regarding the purpose of the study, the inclusion/exclusion criteria for sampling, the research process, and the content of the surveys. All patients provided written informed consent for their participation before study entry.
Ethical approval to carry out the study was obtained from the Bartin University Ethics Committee (date: 12.07.2019, approval number: $\frac{2019}{156}$), and permissions were obtained from the Provincial Health Directorate (date: 03.09.2019, No.: 78239813-799). Before carrying out this study, permission to use the scales was obtained from their original developers by e-mail. The patients were informed regarding the purpose of the study, its content and that the data would only be used for scientific purposes. Identifying information was not requested from the patients. The study was carried out according the principles of Helsinki Declaration.
## Measures
Descriptive Information Form included questions to collect information on the sociodemographic characteristics (such as age, gender, marital status, and educational level) and diabetes management-related characteristics (duration of diabetes, history of diabetes in first- and second-degree relatives, status of having received education on diabetes, regular health follow-up status, exercise status, and diabetic diet status) of the patients.
Rosenberg Self-Esteem Scale (RSES) which was developed by Rosenberg, 19 consists of a total of 63 questions under 12 categories. The first 10 items of the Turkish form of the scale adapted by Çuhadaroğlu, 20 measured the self-esteem dimension. In this study, to determine the self-esteem levels of the patients, these 10 items were used. This part of the scale containing 5 positive and 5 negative statements has a 4-point Likert-type scoring system. The scores of the form vary between 10-40 after the inversely scored items are converted, and higher scores represent higher levels of self-esteem. As self-esteem is assumed to be a one-dimensional concept the total score was used in this study. In the reliability study carried out by Rosenberg, 19 the test-retest reliability coefficients of the dimensions of RSES were found to be in the range of 0.82-0.88, while the Cronbach’s alpha coefficients of the dimensions were in the range of 0.77-0.88. In this study, the Cronbach’s alpha internal consistency coefficient for the self-esteem dimension that was used was identified as 0.78.
Type 2 Stigma Assessment Scale which was developed by Browne et al, 21 is a self-report scale that assesses perceived and experienced stigma in adults with T2DM. The scale that was tested for validity and reliability by Can Gür et al 6 consists of 19 items and 3 dimensions. These dimensions are: I) different behaviors (1st-6th items); II) blame and judgment (7th-13th items); and III) self-stigmatization (14th-19th items). Each item is scored as a 5-point Likert-type scale in the form of: 1=“absolutely disagree”; 2=“disagree”; 3=“undecided”; 4=“agree”; and 5=“absolutely agree”. The range of the possible total scores of the scale is 19-95, and higher scores in each dimension indicate more severe stigmatization of the person. The Cronbach’s alpha coefficient of the original version of the scale was reported as 0.95 while it was calculated as 0.91 in this study.
Diabetes Management Self-Efficacy Scale was developed by Bijl et al 22 to determine the perception of people with diabetes regarding their capacity to carry out self-care activities in their management of T2DM, and it was tested for validity and reliability by Usta Yeşilbakan. 23 It consists of 20 items and is a 5-point Likert-type scale (1=strongly disagree; 2=disagree; 3=somewhat agree; 4=agree; and 5=strongly agree). The minimum and maximum scores of the scale are 20 and 100. Based on the general average score obtained from the item average scores of all subscales, those who have scores under the general average score were considered to have low self-efficacy, while those with scores over the general average score were considered to have high self-efficacy. The Cronbach’s alpha coefficient of the scale was reported as 0.89. 23 *In this* study, the Cronbach’s alpha coefficient was calculated as 0.96 for the scale.
## Statistical analysis
The data collected in this study were analyzed using the Statistical Package for the Social Sciences, version 22.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were presented as frequencies and percentages. Skewness and kurtosis values were examined to determine whether the data were normally distributed. In the relevant literature, data were accepted to be normally distributed if the skewness and kurtosis values are in the range of -1.5 - +1.5 or -2.0 - +2.0. In this study, the skewness and kurtosis values were found to be within the specified reference ranges. Additionally normality was tested using the Komogorov-Smirnov (KS=0.007). As the data found to be normally distributed, parametric tests were used in the analyses. The continuous variables of the study were subjected to Pearson’s correlation, linear regression, and hierarchical regression analyses. To test the significance of the mediating effect, we used the Baron and Kenny method 24 and the Sobel test. The level of statistical significance was set at $p \leq 0.05$ for all analyses. In the Sobel, the full or partial mediation status of a variable was determined by measuring the reduction in the rate of the total variance explained by the independent variable. 25 The relative contributions of self-esteem and stigma in predicting diabetes management-related self-efficacy were tested using multiple regression analysis. The variance inflation factor (VIF) values that show the degree of multicollinearity must be smaller than 10, and the tolerance value must be greater than 0.1. The Durbin-Watson value, referring to auto-correlation, must be in the range of 1.5-2.5. No auto-correlation was identified between the independent variables (1.5<DW>2.5). Moreover, the tolerance and VIF values showed the absence of a multicollinearity problem (T>0.1; VIF<10). Data were analysed using a 3-step hierarchical regression analysis and the Sobel test.
## Results
It was determined that $36.4\%$ of the patients were at the ages of 41-50, and $57.4\%$ were male. While $83.3\%$ were married, $49.4\%$ were primary school graduates. Almost half ($46.9\%$) had been with diabetes for 1-5 years, and $40.7\%$ had a history of DM in their first-degree relatives.
The vast majority ($82.1\%$) stated that they had previously received diabetes education. More than half ($54.9\%$) of those who had received diabetes education reported that they did not find the education they had received adequate.
In the sample, $83.3\%$ were using oral antidiabetic medication for treating their DM. While $86.4\%$ said they used their medication regularly, $52.5\%$ stated that they did not follow a diabetic diet, and $52.5\%$ reported that they did not regularly exercise (Table 1).
**Table 1**
| Variables | n (%) |
| --- | --- |
| Age, mean±SD (minimum-maximum) | 49.59±9.30 (28-65) years |
| 40 years old or younger | 32 (19.8) |
| 41-50 years old | 59 (36.4) |
| 51-60 years old | 50 (30.9) |
| Older than 60 years old | 21 (13.0) |
| Gender | Gender |
| Female | 69 (42.6) |
| Male | 93 (57.4) |
| Marital status | Marital status |
| Married | 135 (83.3) |
| Single | 27 (16.7) |
| Education level | Education level |
| Primary school | 80 (49.4) |
| Secondary school | 20 (12.3) |
| High school | 41 (25.3) |
| University or higher | 21 (13.0) |
| Duration of diabetes | Duration of diabetes |
| Shorter than one year | 62 (38.3) |
| 1-5 years | 76 (46.9) |
| Longer than 5 years | 24 (14.8) |
| Family history of diabetes | Family history of diabetes |
| Yes, in first-degree relatives | 66 (40.7) |
| Yes, in second-degree relatives | 62 (38.3) |
| No | 34 (21.0) |
| Has received diabetes education? | Has received diabetes education? |
| Yes | 133 (82.1) |
| No | 29 (17.9) |
| Has received adequate education about diabetes? (n=133) | Has received adequate education about diabetes? (n=133) |
| Yes | 60 (45.1) |
| No | 73 (54.9) |
| Attends regular health follow-ups? | Attends regular health follow-ups? |
| Yes | 136 (84.0) |
| No | 26 (16.0) |
| Treatment method | Treatment method |
| Only oral antidiabetic medication | 135 (83.3) |
| Only insulin | 18 (11.1) |
| Oral antidiabetics and insulin | 9 (5.6) |
| Uses medication regularly? | Uses medication regularly? |
| Yes | 140 (86.4) |
| No | 22 (13.6) |
| Regular exercise habit? | Regular exercise habit? |
| Yes | 27 (16.7) |
| Sometimes | 50 (30.9) |
| No | 85 (52.5) |
| Follows a diabetic diet? | Follows a diabetic diet? |
| Yes | 77 (47.5) |
| No | 85 (52.5) |
Correlations between the perceived stigma, self-esteem, and diabetes management self-efficacy levels of the patients were analyzed. According to the analysis results, there was a negative correlation between self-esteem and perceived stigma (r= -0.29, $p \leq 0.05$). There was also a negative correlation between self-efficacy and perceived stigma (r= -0.25, $p \leq 0.05$). Additionally, a positive correlation was identified between self-efficacy and self-esteem ($r = 0.25$, $p \leq 0.05$; Table 2).
**Table 2**
| Scales | Stigma total scores | Self-esteem total scores | Diabetes management self-efficacy total scores |
| --- | --- | --- | --- |
| Stigma total scores | r=1.000 | | |
| Stigma total scores | p=0.000 | | |
| Self-esteem scores | r= -0.29 | r=1.000 | |
| Self-esteem scores | p=0.000 | p=0.000 | |
| Diabetes management self-efficacy total scores | r= -0.25 | r=0.25** | r=1.000 |
| Diabetes management self-efficacy total scores | p=0.001 | p=0.001 | p=0.000 |
In Model 1, regression analysis was carried out to determine the effect of self-esteem on self-efficacy in diabetes management, and the results are given in Table 3. According to the ANOVA results that tested the validity and significance in the regression analysis, the F value for self-esteem was calculated as 10.464 and the significance value was calculated as $$p \leq 0.001$$ at the $5\%$ significance level. The R 2 value of the independent variable, which is the level of explanation of the dependent variable, was calculated as 0.056. According to this result, $5.6\%$ of the change in self-efficacy is explained by self-esteem. It was found that self esteem had a positive predictive effect on diabetes management self-efficacy (ß=0.875). A model study to determine the effect of self-esteem on self-efficacy in diabetes is significant as a whole (R 2 =0.056; $F = 10.464$).
**Table 3**
| Dependent variables | Independent variables | ß | t | P-values | F | Model (p-values) | Adjusted R 2 | R 2 change | ß (95% CI) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Diabetes management self-efficacy (Model 1) | Constant | 35.084 | 4.752 | 0.0 | 10.464 | 0.001 | 0.056 | | 0.875 (0.342-1.408) |
| Diabetes management self-efficacy (Model 1) | Self-esteem | 0.875 | 3.235 | 0.001 | | | | | |
| Self-esteem (Model 2) | Constant | 32.792 | 19.963 | 0.0 | 14.237 | 0.0 | 0.057 | | -0.095 (-0.145 - -0.045) |
| Self-esteem (Model 2) | Stigma | -0.095 | -3.773 | 0.0 | | | | | |
| Diabetes management self-efficacy (Model 3) | Constant | 77.047 | 13.152 | 0.0 | 10.659 | 0.001 | 0.057 | 0.062 | -0.294 (-0.472 - -0.116) |
| Diabetes management self-efficacy (Model 3) | Stigma | -0.294 | -3.265 | 0.001 | | | | | |
| Diabetes management self-efficacy (Model 4) | Constant | 54.822 | 5.086 | 0.0 | 8.473 | 0.0 | 0.085 | | |
| Diabetes management self-efficacy (Model 4) | Stigma | -0.23 | -2.479 | 0.014 | | | | 0.062* | -0.230 (-0.413 - -0.047) |
| Diabetes management self-efficacy (Model 4) | Self-Esteem | 0.678 | 2.441 | 0.016 | | | | 0.034** | 0.678 (0.129-1.226) |
In Model 2, regression analysis was carried out to determine the effect of stigma on self-esteem, and the results are given in Table 3. According to the ANOVA results that tested the validity and significance in the regression analysis, the F value for stigmatization was calculated as 14.237 and the significance value was calculated as $$p \leq 0.000$$ at the $5\%$ significance level. The R 2 value of the independent variable was calculated as 0.057. According to this result, $5.7\%$ of the change in self-esteem is explained by stigma. It was found that perceived stigma had a negative predictive effect on self-esteem (ß= -0.095). A model study to determine the effect of perceived stigma on self-esteem is significant as a whole (R 2 =0.057; $F = 14.237$).
In Model 3, regression analysis was carried out to determine the effect of percived stigma on self-efficacy in diabetes, and the results are given in Table 3. According to the ANOVA results that tested the validity and significance in the regression analysis, the F value for stigmatization was calculated as 10.659 and the significance value was calculated as $$p \leq 0.001$$ at the $5\%$ significance level. The R 2 value of the independent variable was calculated as 0.057. According to this result, $5.7\%$ of the change in self-efficacy is explained by perceived stigma. It was found that perceived stigma had a negative predictive effect on self-efficacy in diabetes (ß= -0.294). A model study to determine the effect of perceived stigma on self-efficacy in diabetes is significant as a whole (R 2 =0.057; $F = 10.659$).
The R 2 value was calculated as 0.085 for Model 4, in which the stigma variable and the self-esteem variable were included in the model. According to this result, $8.5\%$ of the change in self-efficacy in diabetes is explained by stigma and self-esteem (R 2 =0.085; $F = 8.473$; Table 3).
In the process of adding variables to the regression model, the change caused by the self-esteem variable in R 2 was calculated using the Stepwise method. While it was calculated as R 2 change =0.62; $F = 10.659$ when there was only stigma variable in the model, it was calculated as R 2 change=0.034; $F = 5.957$ when self-esteem was added to the model. This change shows that when the self-esteem variable is included in the model, the explanatory power of the model changes by $3.4\%$ (Table 3). Additionally, the negative predictive effect of stigma on self-efficacy decreases when self-esteem intervenes (ß= -0.230) According to these findings and the results of the Sobel test, it was determined that self-esteem was a partial mediator (partial moderator) (Z= -3.347; $p \leq 0.05$). In other words, stigma affects self-efficacy in diabetes management both directly and through self-esteem.
## Discussion
This study was carried out to investigate the mediating role of self-esteem in the relationship between the perceived stigma levels of people with T2DM and their self-efficacy regarding diabetes management. In this study, it was determined that as the self-esteem levels of the people with T2DM increased, their diabetes management self-efficacy levels also increased, thus showing that self-esteem predicted self-efficacy in a positive direction (ß=0.875; $p \leq 0.01$). This result was consistent with similar study findings. 26,27 Mikaeili et al 26 determined that people with diabetes with high self-esteem levels had higher levels of self-efficacy related to diabetes self-management. Kenowitz et al 27 also found that people with diabetes with high self-esteem had better compliance with their insulin treatments and exercise schedules. Based on these results, it may be argued that self-esteem makes the adaptation of people with diabetes to self-care activities easier and increases their self-efficacy levels. To increase the self-esteem of patients, healthcare professionals and particularly health and diabetic educators should help the recognition and expression of emotions by having effective communication with them based on empathy, respect, confidence, and care. It may be helpful to focus on past achievements of the person with diabetes and to use the support of family members.
Another result of this study was that perceived stigma had a negative predictive effect on diabetes management self-efficacy (ß= -0.294). Accordingly, the stigma perceived by people with diabetes affects their self-efficacy in the management of DM negatively, and as the level of stigma perceived by patients increases, this leads their self-efficacy regarding their disease to decrease. This result supported the results of similar studies in the literature. 5,11-14,22,28,29 Kato et al 13 reported a strong negative predictive effect of self-stigma on the self-care behaviors of people with T2DM. Brazeau et al 11 stated that in diabetic young people, stigmatization was associated with lower self-efficacy levels, higher A1c levels, severe hypoglycemia, and reduced feelings of wellbeing. A study carried out on T2DM patients revealed that stigma was a significant predictor of the negative perception of insulin treatment. 30 In individuals with chronic diseases like DM, self-stigma may affect their diabetes self-management negatively by leading these individuals to evade treatment or reducing their treatment adherence. Additionally, it may prevent these individuals from acting in favor of their care by themselves. Increasing the self-care behaviors of a person with diabetes by itself is not sufficient. These individuals need to develop positive attitudes towards the disease and get the help that will reduce their self-stigmatization. Recently published guidelines for the treatment of diabetes emphasize the importance of accounting for the psychological statuses of patients while managing their diabetes, especially their potential to stigmatize themselves. 31 While they are providing education for people with diabetes, healthcare professionals and particularly health and diabetic educators should keep stigma in their minds and plan the appropriate precautions. Healthcare professionals for education for people with diabetes should focus on not only the physical but also the psychological wellbeing of patients and encourage the diabetic individual, their friends and family members to ask questions on stigma and share their feelings. In clinical practice, there is a need for routine assessment and interventions regarding self-stigma in person with diabetes. In both clinical and social settings, regular health education for increasing psychological wellbeing through reducing self-stigma is recommended.
Perceived stigma in people with diabetes leads their self-efficacy on their disease to decrease and their self-esteem to decline. In a qualitative study carried out by in-depth interviews with diabetes people, Seo et al 32 reported that people with diabetes had lower self-esteem than the healthy individuals and negative attitudes regarding themselves. Another study showed the negative predictive effect of stigma on self-esteem in people with diabetes. 17 In the current study, a negative significant relationship was identified between self-esteem and perceived stigma. Moreover, as a result a regression analysis was carried out to determine the effect of percived stigma on self-efficacy, it was found that perceived stigma had a negative predictive effect on self-esteem.
One of the issues of curiosity in this study was whether or not self-esteem played a mediating role in the relationship between the perceived stigma and diabetes management self-efficacy levels of people with T2DM. While research on this topic is limited, existing studies have reported that stigma reduces the diabetes management self-efficacy levels of people with diabetes not only directly but also by lowering their self-esteem. 17,33,34 A previous study used a model to determine how self-stigma affected the self-care behaviors of people with diabetes regarding diabetes management, where both the direct effect of the variable and its effect mediated by self-esteem were investigated. The authors demonstrated that self-stigma affected the patient’s activation both directly and under the mediation of self-esteem. 17 *In a* study carried out with 501 people with T2DM, Pedrero et al 34 investigated the mediating role of psychosocial variables in the relationship between perceived stigma and self-management behaviors in people with diabetes, and they reported that self-esteem had a mediating role in this relationship. In similarity to the results of other studies, in this study, it was determined that the effect of perceived stigma in the patients on their diabetes management self-efficacy decreased when the variable of self-esteem was added to the model. In other words, when self-esteem was added as the mediator variable, the effect of stigma on self-efficacy was reduced. According to these results, interventions that increase self-esteem and self-efficacy may reduce self-stigmatization in people with T2DM, and thus, increase patient’s activation for self-care. It was emphasized that interventions that aim to improve self-care behaviors among people with T2DM should continue to directly target stigma, in addition to targeting self-esteem and self-efficacy at the same time. 17 In order to manage the self-care behaviors of patients for optimizing their treatment outcomes, healthcare professionals should firstly assess the self-stigma levels of people with T2DM. These professionals should also try to encourage patients who stigmatize themselves to develop a positive self-image and a positive sense of sensitivity regarding their disease. Previous studies have provided evidence that interventions towards reducing self-stigma in psychiatric patients are effective in developing their skills for coping with self-stigma, improving their readiness to change their problematic behaviors, raising their self-esteem, and in turn, making their treatment compliance easier. 35 Intervention programs designed for reducing self-stigmatization may also provide similar favorable effects among individuals diagnosed with T2DM, and they may improve treatment compliance by reducing self-stigma levels through patient’s education programs. It is seen that there is a need for more studies focusing on interventional efforts towards eliminating stigma for both patients and healthcare professionals in relation to people with T2DM. Specific and interventional studies that examine the effectiveness of methods focused on reducing self-stigma among people with T2DM and increase self-esteem and self-efficacy should be carried out.
## Study limitations
This study adopted a cross-sectional research design. In the future, a longitudinal study could be used to investigate the long-term influences of self-esteem and perceived stigma on self-efficacy for diabetes management of individuals with T2DM. For all that, the current study provides preliminary evidence for these results worthy of further investigation in prospective and interventional studies. In addition, the mediating role of self-esteem in larger sample groups can be analyzed using the structural equation model and path analysis.
In conclusion, the results of this study supported the evidence in the literature on the interactive relationships among perceived stigma, self-esteem, and diabetes management self-efficacy in individuals diagnosed with T2DM. It was shown that stigma affected the self-efficacy of the people with T2DM who were included in this study not only directly but also by the mediating effect of self-esteem. The study in which these 3 variables considered together is limited. This study also provides new preliminary evidence for the moderator effect of self-esteem on the negative impact to diabetes management self-efficacy of diabetes stigma. Therefore, while efforts need to be made to reduce the occurrence of diabetes stigma in the future, the evidence presented here suggests that interventions to mitigate the effects of existing diabetes stigma may be warranted.
Minimizing the stigma perceived by people with diabetes may improve their diabetes management self-efficacy and motivate them more to participate in diabetes-related self-care behaviors. For the effective management of DM in people with T2DM, it is important to lower their self-stigma perceptions by designing more effective and innovative education programs.
The strategies to be developed by healthcare professionals in general and psychiatric nurses in particular to reduce the self-stigmatization levels of people with diabetes should include encouraging these patients to speak regarding their negative emotions, promoting their positive thinking, strengthening their capacity to cope with diabetes, increasing their self-esteem through empowerment, and referring them to psychological counseling if needed. The provision of psychological support for people with T2DM by psychiatric nurses who will prepare psychoeducation programs and care plans and their interventions that increase the self-esteem of these patients may lower the self-stigma levels of the patients.
## References
1. 1.
International Diabetes Federation. Diabetes atlas 2021
(10th edition). [Updated 2021; accessed 2022 Jan]. Available from: https://diabetesatlas.org/. *Diabetes atlas 2021* (2021)
2. Bahar A, Tanrıverdi D.. **Diabetes’ psychiatric and psychosocial aspects: a review**. *Yeni Symposium* (2017) **55** 13-18
3. Abdoli S, Doosti Irani M, Hardy LR, Funnell M.. **A discussion paper on stigmatizing features of diabetes**. *Nurs Open* (2018) **5** 113-119. PMID: 29599986
4. Gredig D, Bartelsen-Raemy A.. **Diabetes-related stigma affects the quality of life of people living with diabetes mellitus in Switzerland: implications for healthcare providers**. *Health Soc Care Community* (2017) **25** 1620-1633. PMID: 27489251
5. Kato A, Fujimaki Y, Fujimori S, Izumida Y, Suzuki R, Ueki K. **A qualitative study on the impact of internalized stigma on T2DM self-management**. *Patient Education and Counseling* (2016) **99** 1233-1239. PMID: 27873575
6. Can Gür G, Yılmaz E.. **Validity and reliability study of the Turkish version of the stigma assessment scale in type 2 diabetes patients**. *Turkiye Klinikleri J Nurs Sci* (2021) **13** 667-675
7. Liu NF, Brown AS, Folias AE, Younge MF, Guzman SJ, Close KL. **Stigma in people with type 1 or type 2 diabetes**. *Clin Diabetes* (2017) **35** 27-34. PMID: 28144043
8. Abdoli S, Abazari P, Mardanian L.. **Exploring diabetes type 1-related stigma**. *Iran J Nurs Midwifery Res* (2013) **18** 65-70. PMID: 23983731
9. Browne JL, Ventura A, Mosely K, Speight J.. **‘I call it the blame and shame disease’: a qualitative study on perceptions of social stigma surrounding type 2 diabetes**. *BMJ Open* (2013) **3** e003384
10. Browne JL, Ventura A, Mosely K, Speight J.. **‘I’m not a druggie, I’m just a diabetic’: a qualitative study of stigma from the perspective of adults with type 1 diabetes**. *BMJ Open* (2014) **4** e005625
11. Brazeau AS, Nakhla M, Wright M, Panagiotopoulos C, Pacaud D, Henderson M. **Stigma and is impact on glucose control among youth with diabetes: protocol for a Canada-wide study**. *JMIR Res Protoc* (2016) **5** e242. PMID: 27979791
12. Brazeau AS, Nakhla M, Wright M, Henderson M, Panagiotopoulos C, Pacaud D. **Stigma and its association with glycemic control and hypoglycemia in adolescents and young adults with type 1 diabetes: cross-sectional study**. *J Med Internet Res* (2018) **20** e151. PMID: 29678801
13. Kato A, Fujimaki Y, Fujimori S, Isogawa A, Onishi Y, Suzuki R. **Association between self-stigma and self-care behaviors in patients with type 2 diabetes: a cross-sectional study**. *BMJ Open Diabetes Res Care* (2016) **4** e000156
14. Lin MH, Ou HY, Wang RH, Lin CH, Liao HY, Chen HM.. **Glycaemic control mediates the relationships of employment status and self-stigma with self-care behaviours in young adults with type 2 diabetes**. *J Clin Nurs* (2022) **31** 582-591. PMID: 34131958
15. Thelan LA, Urden LD, Lough ME.. *Critical Care Nursing: Diagnosis and management* (1998)
16. Kneckt MC, Keinänen-Kiukaanniemi SM, Knuuttila ML, Syrjälä AM.. **Self-esteem as a characteristic of adherence to diabetes and dental self-care regimens**. *J Clin Periodontol* (2001) **28** 175-180. PMID: 11168743
17. Kato A, Fujimaki Y, Fujimori S, Isogawa A, Onishi Y, Suzuki R. **How self-stigma affects patient activation in people with type 2 diabetes: a cross-sectional study**. *BMJ Open* (2020) **10** e034757
18. Cohen J.. *Statistical power analysis for the behavioral sciences* (1988)
19. Rosenberg M.. *Society and the adolescent self-image* (1965)
20. Çuhadaroğlu F.. *Adölesanlarda Benlik Saygisi* (1986)
21. Browne JL, Ventura AD, Mosely K, Speight J.. **Measuring the stigma surrounding type 2 diabetes: development and validation of the type 2 diabetes stigma assessment scale (DSAS-2)**. *Diabetes Care* (2016) **39** 2141-2148. PMID: 27515964
22. Bijl JV, Poelgeest-Eeltink AV, Shortridge-Baggett L.. **The psychometric properties of the diabetes management self-efficacy scale for patients with type 2 diabetes mellitus**. *J Adv Nurs* (1999) **30** 352-359. PMID: 10457237
23. Usta Yeşilbalkan Ö.. **Investigating factors affecting self-efficacy and self-adequacy in self-care of patients with type II diabetes mellitus**. *Ege Üniversitesi Hemşirelik Yüksekokulu Dergisi* (2004) **20** 11-29
24. Baron RM, Rodin J., Baum A, Singer J, Valins S. **Personal control as a mediator of crowding**. *Advances in Environmental Psychology* (1978) 144-190
25. Sobel ME.. **Asymptotic confidence intervals for indirect effects in structural equation models**. *Sociological Methodology* (1982) **13** 290-312
26. Mikaeili N, Samadifard H.. **The relationship between self-efficacy and self-esteem with spiritual health in patients with diabetes mellitus**. *Chron Dis J* (2016) **4** 34-38
27. Kenowitz JR, Hoogendoorn CJ, Commissariat PV, Gonzalez JS.. **Diabetes-specific self-esteem, self-care and glycaemic control among adolescents with type 1 diabetes**. *Diabet Med* (2020) **37** 760-767. PMID: 31215059
28. Kato A, Fujimaki Y, Fujimori S, Isogawa A, Onishi Y, Suzuki R. **Psychological and behavioural patterns of stigma among patients with type 2 diabetes: a cross-sectional study**. *BMJ Open* (2017) **7** e013425
29. Holmes-Truscott E, Browne JL, Ventura AD, Pouwer F, Speight J.. **Diabetes stigma is associated with negative treatment appraisals among adults with insulin-treated type 2 diabetes: results from the second diabetes MILES - Australia (MILES-2) survey**. *Diabet Med* (2018) **35** 658-662. PMID: 29417612
30. Arda Sürücü H, Baran Durmaz G, Turan E.. **Does type 1 diabetic adolescents’ fear of stigmatization predict a negative perception insulin treatment?**. *Clin Nurs Res* (2020) **29** 235-242. PMID: 30472886
31. **Standards of medical care in diabetes-2018**. *Diabetes Care* (2018) **41** S3. PMID: 29222370
32. Seo K, Song Y.. **Self-stigma among Korean patients with diabetes: a concept analysis**. *J Clin Nurs* (2019) **28** 1794-1807. PMID: 30667129
33. Holmes-Truscott E, Ventura AD, Thuraisingam S, Pouwer F, Speight J.. **Psychosocial moderators of the impact of diabetes stigma: results from the second diabetes MILES - Australia (MILES-2) study**. *Diabetes Care* (2020) **43** 2651-2659. PMID: 32887706
34. Pedrero V, Manzi J, Alonso LM.. **A cross-sectional analysis of the stigma surrounding type 2 diabetes in Colombia**. *Int J Environ Res Public Health* (2021) **18** 12657. PMID: 34886383
35. Shimotsu S, Horikawa N, Emura R, Ishikawa S, Nagao A, Ogata A. **Effectiveness of group cognitive-behavioral therapy in reducing self-stigma in Japanese psychiatric patients**. *Asian J Psychiatr* (2014) **10** 39-44. PMID: 25042950
|
---
title: Clinical outcomes of drug-coated balloon for treatment of de novo coronary
artery disease with and without diabetes
authors:
- Zhaoqian Zhang
- Qiang Tan
- Jiarui Zhang
- Xinhui Wang
- Qian Wang
journal: Saudi Medical Journal
year: 2022
pmcid: PMC9994511
doi: 10.15537/smj.2022.43.12.20220534
license: CC BY 4.0
---
# Clinical outcomes of drug-coated balloon for treatment of de novo coronary artery disease with and without diabetes
## Body
Diabetes mellitus (DM) is a prevalent disease all over the world. It is estimated that the number of patients with DM will be at least 592 million cases in 2035. 1 Coronary artery disease is one of the most important complications of DM. Although the use of drug-eluting stents (DES) decreased the rates of restenosis in patients with DM, diabetic patients still have an increased risk of adverse events after percutaneous coronary intervention (PCI) than patients without diabetes. 2 Previous studies demonstrated that the risk of stent thrombosis was $25\%$-$80\%$ higher in diabetic patients than in non-diabetic patients. 3,4 In-stent restenosis (ISR) was more common in patients with DM than patients without DM. 5 Pre-diabetes is an early condition of diabetes with impaired glucose tolerance or impaired fasting glucose. 6 Because pre-diabetes is a reversible condition, the influence of pre-diabetes on clinical outcomes of patients performing PCI is unclarity.
It has been testified that drug-coated balloons (DCB) are effective in treatment for de novo coronary artery disease. 7,8 Compared to drug-eluting stents, DCB has some special advantages, such as the absence of metal stent and rapid delivery of Paclitaxel. 9 Theoretically, due to these benefits, DCB therapy in diabetic patients would have comparable results to non-diabetic patients. However, few studies comparing clinical outcomes in patients with DM, pre-DM and normoglycemia who performed PCI with DCB.
The aim of this study was to compare the effect of DCB on major adverse cardiac events (MACE) in patients of de novo coronary artery disease with DM, pre-DM or without DM.
## Abstract
### Objectives:
To retrospectively evaluate the efficacy of drug-coated balloon (DCB) in patients with de novo coronary artery disease with and without diabetes.
### Methods:
Patients with de novo coronary artery and undergoing percutaneous coronary intervention (PCI) with DCB were enrolled from March 2018 and March 2020, including 312 patients being divided into the diabetes group ($$n = 110$$), pre-diabetes group ($$n = 48$$) and non-diabetes group ($$n = 154$$). The primary endpoint was major adverse cardiac events (MACE) (MACE; cardiovascular death, non-fatal myocardial infarction, target lesion revascularization, and target vessel revascularization) at 24 months.
### Results:
In diabetes group, the incidence of MACE at 24 months ($19.1\%$) was higher than in the non-diabetes group ($12.5\%$) and pre-diabetes group ($10.4\%$) ($p \leq 0.05$). Cox regression revealed that diabetes (HR [hazard ratios] 2.049, $95\%$ CI 1.056-4.284), bifurcation lesion (HR 5.255, $95\%$ CI 2.765-9.986), Syntax score (HR 1.098, $95\%$ CI 1.040-1.559) and hemoglobin A1c (HR 1.084, $95\%$ CI 1.160-1.741) were independent predictors of MACE in patients performing PCI with DCB (all $p \leq 0.05$). However, pre-diabetes did not increase the risk of MACE (HR 1.560, $95\%$ CI 0.542-4.490, $p \leq 0.05$).
### Conclusion:
Diabetes increased the risk of MACE in patients performing PCI with DCB.
## Methods
A retrospective cohort study was carried out on enrolled patients who received PCI with DCB between March 2018 and March 2020. The inclusion criteria: i) patients aged at least 18 years with stable angina pectoris, unstable angina pectoris, and non-ST segment elevation myocardial infarction (NSTEMI); ii) Patients received DCB treatment in de novo coronary artery. While the exclusion criteria: i) Patients with ISR; ii) Unprotected left main lesion; iii) Prior coronary artery bypass grafting (CABG); iv) Heavily calcified in culprit vessel; v) *Residual stenosis* no less than $30\%$ after balloon pre-dilation or C-type dissection after balloon dilation.
Diagnosis of diabetes and pre-diabetes were based on American Diabetes Association definition and diagnosis of diabetes mellitus. 10 Diabetes, fasting plasma glucose test (FPG) ≥7.0 mmol/L or glycated hemoglobin A1C (HbA1C) ≥$6.5\%$; pre-diabetes, 5.5 mmo/L ≤FPG <7.0 mmol/L or $5.7\%$ ≤HbA1C) <$6.5\%$.
This study was approved by the ethics committee and informed consent had been obtained from the study participants prior to study commencement.
Similar to the methods employed in our previous study (Tan et al 11), interventional procedures were performed. Patients underwent coronary angiography and PCI using transradial or transfemoral approach. The decision to perform PCI with DCB or DES depended on the recommendation of the interventional cardiologist. A quantitative coronary angiography (QCA) system (GE QCA, Centricity AI 1000) was conducted to analyze reference diameter, lesion length, pre-procedure minimal lumen diameter (MLD) and post-procedure MLD.
Pre-dilated balloon was used to perform pre-dilation of the target vessel before DCB treatment. The inflation time for DCB was 30-40 seconds with an overlap of ≥2 mm on each edge of the pre-dilatation balloon-treated segment.
The DCB (Bingo; Yinyi company, China) was covered with a surface area of 3 µg paclitaxel/mm 2 and ranged from 15 mm to 30 mm in length and 2.0 mm to 3.5 mm in diameter.
All patients were administered 100 mg aspirin daily and received 75 mg Clopidogrel daily for at least 3 months. The average duration of diabetes was 27±22 months, the types of medication were insulin ($54.6\%$), metformin ($61.3\%$), SGLT-2 inhibitor ($37.1\%$), GLP-1 receptor agonist ($27.3\%$), DPP-4 inhibitor ($25.3\%$), Alpha Glucosidase Inhibitor ($31.3\%$). Patients underwent clinical observation at clinic for 24 months. Clinical follow-up was carried out at 1 month, 6 months, 12 months, and 24 months. Blood examination and electrocardiogram were performed during follow-up. Angiography follow-up was performed 9-12 months after DCB procedure (angiography follow-up at 9-12 months after the procedure was routinely advised by physicians, which was not triggered by angina or other symptoms).
The primary endpoint of this study was incidence of combined MACE of 24 months, defined as cardiovascular death, non-fatal myocardial infarction (MI), target lesion revascularization (TLR), and target vessel revascularization (TVR). Elevation of serum troponin I to 3 times the upper limit of normal with chest pain lasting more than 30 minutes was defined as MI. 11 Target lesion revascularization was defined as any repeat revascularization due to restenosis of the DCB-treated lesion (both proximal and distal to the treated segment beyond 5 mm). Target vessel revascularization was defined as any repeat revascularization of the DCB treated vessel. 11
## Statistics analysis
The SPSS Statistics for Windows, (Version 17.0. Chicago: SPSS Inc.) was used to do statistical analyses. Continuous variables were expressed as mean ± standard deviation of the mean, and compared using one-way Anova; χ 2 statistics or Fisher exact test was used in Categorical variables. Kaplan-Meier method was conducted to estimate incidence of MACE of 24 months. Cox proportional hazards regression analysis was used to estimate the hazard ratios (HR) and its $95\%$ confidence intervals (CI) of MACE. A probability value <0.05 was considered statistically significant.
## Results
The enrolled patients were divided into 3 groups, DM group ($$n = 110$$), pre-DM group ($$n = 48$$) and non-DM group ($$n = 154$$), with baseline clinical characteristics shown in Table 1. There were no difference in age, gender, family history, hypertension, smoking, previous myocardial infarction, and clinical presentation among the 3 groups. Glucose levels, triglycerides and HbA1C were higher in diabetic patients than in pre-diabetes group and non-diabetes group. Pre-diabetic patients also had higher levels of glucose and HbA1C than non-diabetic patients. Other laboratory characteristics such as left ventricular ejection fraction (LVEF), cholesterol, low density lipoprotein-cholesterol, high density lipoprotein-cholesterol, and homocysteine had no significantly difference among the 3 groups.
**Table 1**
| Characteristics | diabetes (n=110) | Pre-diabetes (n=48) | Non-diabetes (n=154) | P-value |
| --- | --- | --- | --- | --- |
| Age | 62.41±9.84 | 60.51±9.58 | 60.80±9.79 | 0.105 |
| Gender (M/F) | 66/44 | 33/15 | 113/41 | 0.071 |
| Current smoker | 47 (42.7) | 18(37.5) | 64(41.6) | 0.826 |
| Family history | 23 (20.9) | 13(27.1) | 30 (19.5) | 0.529 |
| Hypertension | 51 (46.4) | 21(43.8) | 74 (48.1) | 0.867 |
| Prior stroke | 7 (6.4) | 4 (8.3) | 9 (6.0) | 0.828 |
| Prior myocardial infarction | 6 (5.5) | 3 (6.3) | 7 (4.5) | 0.88 |
| LVEF (%) | 66.09±6.67 | 61.21±9.45 | 65.69±6.98 | 0.122 |
| LVD (mm) | 47.60±3.92 | 49.46±4.05 | 48.64±4.73 | 0.209 |
| LA (mm) | 38.26±5.24 | 39.08±7.81 | 36.951±5.85 | 0.005 |
| Clinical presentation | | | | 0.629 |
| Stable CHD | 24 | 7 | 29 | |
| Unstable angina | 65 | 30 | 101 | |
| NSTEMI | 21 | 11 | 24 | |
| TC (mmol/l) | 4.38±1.13 | 4.52±1.13 | 4.37±1.02 | 0.786 |
| TG(mmol/l) | 2.54±1.61 | 1.95±0.91 | 1.68±0.99 | 0.009 |
| LDL-C(mmol/l) | 2.45±0.86 | 2.52±0.85 | 2.44±0.77 | 0.894 |
| HDL-C(mmol/l) | 1.04±0.21 | 1.07±0.23 | 1.08±0.23 | 0.55 |
| Creatinine(µmol/l) | 62.53±15.42 | 64.76±18.86 | 65.72±15.43 | 0.37 |
| Glucose (mmol/l) | 6.98±2.06 | 6.66±0.54 | 5.15±0.45 | 0.0 |
| HbA1c (%) | 6.75±0.92 | 6.14±0.19 | 5.24±0.35 | 0.0 |
| Urine acid (mmol/l) | 334.36±89.80 | 332.10±97.24 | 332.16±94.21 | 0.987 |
| HCY (mmol/l) | 15.79±9.92 | 15.32±7.17 | 18.06±14.72 | 0.559 |
| BMI | 26.01±3.38 | 25.90±3.05 | 24.96±2.92 | 0.065 |
As shown in Table 2, the target artery, Syntax score, the rate of bifurcation and multi-vessel disease had no significant difference among the 3 groups. However, diabetes group had more numbers of diseased vessels than pre-diabetes group and non-diabetes group. Quantitative coronary angiography analysis showed that reference vessel diameter, pre-procedure MLD, and post-procedure MLD were bigger in non-diabetes group than in diabetes group and pre-diabetes group. Diabetic patients had longer lesion length and DCB length than non-diabetic patients and pre-diabetic patients.
**Table 2**
| Characteristics | Diabetes (n=110) | Pre-diabetes (n=48) | Non-diabetes (n=154) | P-value |
| --- | --- | --- | --- | --- |
| Target artery | | | | |
| Left anterior descending | 47/110 (42.7) | 19/48 (39.6) | 62/154 (40.3) | 0.9 |
| Diagonal | 17/110 (15.5) | 8/48 (16.7) | 13/154 (8.4) | 0.134 |
| Left circumflex | 19/110 (17.3) | 9/48 (18.8) | 34/154 (22.1) | 0.614 |
| Right coronary artery | 27 /11(27.5) | 12/48 (25.0) | 45/154 (29.2) | 0.664 |
| Bifurcation | 31/110 (28.2) | 13/48 (27.1) | 51/154 (33.1) | 0.729 |
| Multivessel disease | 61/110 (55.5) | 25/48 (52.1) | 79/154 (51.3) | 0.795 |
| Number of diseased vessels | 2.48±0.77 | 2.14±0.79 | 2.08±0.84 | 0.001 |
| Syntax score | 12.81±5.30 | 12.95±4.71 | 12.50±4.72 | 0.851 |
| Target lesion | | | | |
| Reference diameter (mm) | 2.57±0.21 | 2.57±0.24 | 2.64±0.19 | 0.047 |
| Lesion length (mm) | 20.46±9.60 | 17.37±6.83 | 18.49±8.15 | 0.042 |
| Diameter stenosis (%) | 91.07±7.01 | 90.87±7.33 | 89.68±8.23 | 0.271 |
| Pre-procedure MLD (mm) | 0.58±0.41 | 0.59±0.34 | 0.61±0.42 | 0.036 |
| Post-procedure MLD (mm) | 2.54±0.19 | 2.55±0.36 | 2.61±0.13 | 0.024 |
| The characteristics of DCB | | | | |
| Diameter (mm) | 2.61±0.14 | 2.62±0.11 | 2.63±0.13 | 0.067 |
| Length (mm) | 22.53±11.76 | 20.75±10.15 | 20.62±12.41 | 0.012 |
Survival analyses by Kaplan-Meier method showed a poor prognosis in diabetes group with a higher incidence of MACE at 24 months compared to non-diabetes group and pre-diabetes group (Figure 1). But there were no difference between pre-diabetes group and non-diabetes group. As shown in Table 3, the incidence of the primary endpoint in diabetic patients was significantly higher than that in non-diabetic patients and pre-diabetic patients ($p \leq 0.05$), which was mainly driven by the increase in TVR and TLR. However, the incidence of cardiovascular death, non-fatal MI had no significant difference among the 3 groups during follow-up.
**Figure 1:** *- Major adverse cardiac events (MACE)-tree survival in patient performing drug coasted balloon* TABLE_PLACEHOLDER:Table 3 The baseline clinical and angiographic characteristic of patients, stratified by MACE, are summarized in Table 4. Patients with MACE had higher levels of glucose and HbA1C than patients without MACE. Syntax score, numbers of diseased vessels and the rate of bifurcation were higher in MACE group. Patients with MACE also had longer lesion length and DCB length than patients without MACE.
**Table 4**
| Characteristics | MACE group (n=43) | Non-MACE (n=269) | P-value |
| --- | --- | --- | --- |
| Age | 61.54 ± 10.87 | 61.21 ±9.66 | 0.841 |
| Gender (M/F) | 31/12 | 182/87 | 0.562 |
| Current smoker | 21(48.8) | 113 (42.0) | 0.401 |
| Family history | 14 (32.6) | 52 (19.3) | 0.821 |
| Hypertension | 24 (55.8) | 132 (49.1) | 0.412 |
| Prior myocardial infarction | 5 (11.6) | 11 (4.1) | 0.03 |
| Prior stroke | 6 (14.0) | 14 (5.2) | 0.037 |
| Diabetes | 21 (48.8) | 79 (29.4) | 0.011 |
| Pre-diabetes | 9 (20.9) | 39 (14.5) | 0.278 |
| Clinical presentation | | | 0.927 |
| Stable angina | 7 (16.3) | 49 (18.2) | |
| Unstable angina | 27 (62.8) | 169 (62.8) | |
| NSTEMI | 9 (20.9) | 51 (19.0) | |
| TC (mmol/L) | 4.33±1.31 | 4.43±1.61 | 0.681 |
| TG (mmol/L) | 2.51±1.61 | 1.93±1.59 | 0.104 |
| LDL-C (mmol/L) | 2.50±1.06 | 4.41±0.02 | 0.768 |
| HDL-C (mmol/L) | 2.37±0.73 | 1.93±1.19 | 0.942 |
| Glucose (mmol/L) | 6.51±1.59 | 2.45±0.75 | 0.025 |
| Hemoglobin A1c (%) | 6.35±0.99 | 1.06±0.23 | 0.0 |
| Creatinine (µmol/L) | 64.92±21.43 | 5.94±1.53 | 0.872 |
| Urine acid | 341.82±133.56 | 5.83±0.88 | 0.585 |
| HCY (mmol/L) | 16.56±7.28 | 55.18±15.27 | 0.833 |
| Body mass index | 26.07± 3.49 | 331.39±84.38 | 0.198 |
| LVD (mm) | 48.68 ± 4.67 | 48.42±4.41 | 0.759 |
| LAD (mm) | 39.30±8.01 | 37.70±5.79 | 0.185 |
| LVEF (%) | 64.65±9.78 | 65.25±6.70 | 0.625 |
| Target artery | | | |
| Left anterior descending | 11(25.6) | 117 (43.5) | 0.027 |
| Diagnal | 7(16.3) | 31 (11.5) | 0.295 |
| Left circumflex artery | 6(14.0) | 56 (20.8) | 0.376 |
| Right coronary artery | 19(44.2) | 65 (24.2) | 0.006 |
| Bifurcation | 25 (58.1) | 70 (26.0) | 0.0 |
| Multivessel disease | 31 (72.1) | 134 (49.8) | 0.007 |
| Number of diseased vessel | 2.67±0.71 | 2.16±0.82 | 0.0 |
| Syntax score | 16.18±4.45 | 12.07±4.82 | 0.0 |
| Target lesion | | | |
| Reference diameter (mm) | 2.68±0.15 | 2.71±0.21 | 0.475 |
| Lesion length (mm) | 21.46±8.80 | 18.64±8.46 | 0.049 |
| Diameter stenosis (%) | 92.93±6.14 | 90.01±7.78 | 0.019 |
| Pre-procedure MLD (mm) | 0.58±0.15 | 0.59±0.16 | 0.738 |
| Post-procedure MLD (mm) | 2.49±0.19 | 2.48±0.24 | 0.692 |
| The characteristics of DES | | | |
| Diameter (mm) | 2.62±0.21 | 2.61±0.21 | 0.729 |
| Length (mm) | 26.51±12.89 | 21.81±11.57 | 0.016 |
As Table 5 showed, the results of Cox regression revealed that diabetes (HR 2.049, $95\%$ CI 1.056-4.284), bifurcation lesion (HR 5.255, $95\%$ CI 2.765-9.986), Syntax score (HR 1.098, $95\%$ CI 1.040-1.559) and HbA1C (HR 1.084, $95\%$ CI 1.160-1.741) were independent predictors of MACE in patients performing PCI with DCB (all $p \leq 0.05$).
**Table 5**
| Characteristics | Univariate analysis Coefficient (95% CI) | P-value | Multiple analysis Coefficient (95% CI) | P-value.1 |
| --- | --- | --- | --- | --- |
| Diabetes | 2.348 (1.142-4.829) | 0.02 | 2.049 (1.056-4.284) | 0.047 |
| Pre-diabetes | 1.560 (0.542-4.490) | 0.56 | --- | --- |
| Bifurcation | 5.265 (3.077-9.066) | 0.0 | 5.255 (2.765-9.986) | 0.000 |
| Syntax score | 1.138 (1.089-1.189) | 0.0 | 1.098 (1.040-1.559) | 0.001 |
| DCB length | 1.033 (1.015-1.050) | 0.011 | 1.028 (1.006-1.051) | 0.072 |
| DCB diameter | 1.475 (0.277-7.860) | 0.646 | --- | --- |
| Post MLD | 1.138 (0.350-3.703) | 0.829 | --- | --- |
| Reference diameter | 0.606 (0.143-2.577) | 0.059 | 1.730 (0.4545-4.393) | 0.422 |
| Lesion length | 1.041(1.014-1.069) | 0.002 | 1.870(1.202-2.909) | 0.076 |
| BMI | 1.064 (0.960-1.179) | 0.238 | --- | --- |
| Glucose | 1.153 (1.002-1.327) | 0.046 | 1.103 (1.001-1.377) | 0.151 |
| Hemoglobin A1c | 1.522 (1.190-1.948) | 0.001 | 1.084 (1.160-1.741) | 0.014 |
| Number of vessel diseased | 2.793 (1.937-4.033) | 0.0 | 1.321 (0.864-2.081) | 0.198 |
| Age | 0.993 (0.959-1.129) | 0.711 | --- | --- |
| Hypertension | 1.135 (0.571-2.038) | 0.139 | --- | --- |
| Smoke | 1.131 (0.570-2.245) | 0.724 | --- | ---- |
| Prior MI | 0.950 (0.670-1.348) | 0.775 | --- | --- |
| LDL-C | 1.018 (0.656-1.580) | 0.936 | ---- | --- |
## Discussion
The main finding of this study was that clinical outcomes were poorer in diabetic patients treated with DCB, compared to non-diabetic patients. This finding indicated that diabetes increased the incidence of MACE in patients performing PCI with DCB.
The number of patients with diabetes and coronary artery disease is increasing rapidly worldwide. It is reported that patients with DM compose $25\%$ to $30\%$ of all patients undergoing coronary artery revascularization. 12 Several studies had testified that diabetic patients treated with new-generation DES remained at higher risk of adverse events following PCI. BIO-RESORT study reported that diabetes increased one year risks of mortality and repeat revascularization after treatment with DES. 13 Korea Acute Myocardial Infarction Registry (KAMIR) study found a higher 2-year incidence of stent thromboses in patients with diabetes compared to patients without diabetes. 14 However, the association between pre-diabetes and adverse outcomes after PCI has not been clearly established. A combined analysis of BIO-RESORT and BIO-NYX showed that pre-diabetes increased 3 years MACE rate in patients performing DES. 15 Kim et al 14 compared MACE rate of patients with diabetes or pre-diabetes after successful performing PCI with second generation DES. The results showed that incidence of MI in the pre-diabetes group was significantly lower than that of the diabetes group. 16 One of the beneficial feature of DCB is the local delivery of paclitaxel to coronary artery without leaving metal sten. 17 *As a* result, DCB decreased the incidence of vessel thrombosis after PCI compared with DES implantation. Lots of studies have shown that DCB is effective in the treatment of in stent restenosis (ISR) and de novo lesion, especially in small vessel disease. 17,18 However adverse events risk in patients with diabetes treated with DCB had not been fully assessed. Until now, only one research has discussed outcomes of DCB in diabetic patients. Pan et al 17 reported that diabetic patients had higher TLF and TLR rates following DCB angioplasty without a substantial increase in the risk of MACE, cardiac death, myocardial infarction, and revascularization.
In this observational study, we evaluated the outcomes of PCI with DCB in diabetic patients versus non-diabetic patients and pre-diabetic patients, suggesting diabetic patients treated with PCI with DCB exhibited a higher incidence of MACE, TLR and TVR than non-diabetic patients. However, the incidence rates of cardiac death and MI were comparable in the 3 groups. These findings enhance our understanding of the high risk of diabetes in patients with PCI. We did not find different MACE rate between pre-diabetes patients and non-diabetes patients, pre-diabetes was not an independent predictor of MACE in this current study.
Previous studies found that patients with DM have more diffuse and complex CAD than non-diabetic patients, which is also consistent with the current study that patients with diabetes had a smaller diameter of the coronary artery, longer lesions and more serious lesions. 17-19 These researches demonstrated that CAD in the presence of DM has unique characteristics. The high risk of MACE in patients with diabetes may be secondary to the complex pathophysiological mechanisms, including endothelial dysfunction, chronic inflammation, and activation of platelet. 18 *Our previous* study found that diabetes impaired the functions of endothelial progenitor cells (EPC) which play a key role in maintaining endothelial function. 20,21 EPC dysfunction leads to defects of endothelium repairment and vascular complications in diabetic patients. 22 *Inflammation is* another mechanism of diabetes-induced vascular remodeling and progression of adverse myocardial diseases. 23 Platelet activation and atherosclerotic thrombosis are increased in diabetic patients compared to non-diabetic patients. 10
## Study limitations
The current study has several limitations. First, it was a single-center study, the sample size was relatively small. Second, it is a retrospective and observational but not a randomized controlled study. More research is required to determine how DCB affects diabetic people.
In conclusion, our findings suggested that diabetic patients experience higher MACE, TVR and TLR rates upon DCB angioplasty with compared to non-diabetic patients. The risk of cardiac mortality and MI, however, was not significantly increased by DM. To demonstrate the effectiveness of DCB in diabetic patients, additional research and effort are still required.
## References
1. Khan R, Chua ZJY, Tan JC, Yang Y, Liao Z, Zhao Y.. **From pre-diabetes to diabetes: diagnosis, treatments and translational research**. *Medicina (Kaunas)* (2019.0) **55** 546. PMID: 31470636
2. Wilson S, Mone P, Kansakar U, Jankauskas SS, Donkor K, Adebayo A. **Diabetes and restenosis**. *Cardiovasc Diabetol* (2022.0) **21** 23. PMID: 35164744
3. Konigstein M, Ben-Yehuda O, Smits PC, Love MP, Banai S, Perlman GY. **Outcomes among diabetic patients undergoing percutaneous coronary intervention with contem porary drug-eluting stents: analysis from the BIONICS randomized trial**. *J Am Coll Cardiol Intv* (2018.0) **11** 2467-2476
4. Ploumen EH, Pinxterhuis TH, Zocca P, Roguin A, Anthonio RL, Schotborgh CE. **Impact of prediabetes and diabetes on 3-year outcome of patients treated with new-generation drug-eluting stents in two large-scale randomized clinical trials**. *Cardiovasc Diabetol* (2021.0) **20** 217. PMID: 34717627
5. Baan J, Claessen B E, Dijk K B, Vendrik J, van der Schaaf RJ, Meuwissen M. **A randomized comparison of paclitaxel-eluting balloon versus everolimus-eluting stent for the treatment of any in-stent restenosis. The DARE trial**. *J Am Coll Cardiol Intv* (2018.0) **11** 275-283
6. Beulens J, Rutters F, Rydén L, Schnell O, Mellbin L, Hart HE. **Risk and management of pre-diabetes**. *Eur J Prev Cardiol* (2019.0) **26** 47-54. PMID: 31766914
7. Giannini F, Latib A, Jabbour RJ, Costopoulos C, Chieffo A, Carlino M. **Comparison of paclitaxel drug-eluting balloon and paclitaxel-eluting stent in small coronary vessels in diabetic and nondiabetic patients—results from the BELLO (balloon elution and late loss optimization) trial**. *Cardiovasc Revascul Med* (2017.0) **18** 4-9
8. Sinaga DA, Ho HH, Watson TJ, Sim A, Nyein TT, Jafary FH. **Drug-coated balloons: a safe and effective alternative to drug-eluting stents in small vessel coronary artery disease**. *J Interv Cardiol* (2016.0) **29** 454-460. PMID: 27578540
9. Mohiaddin H, Wong TD, Burke-Gaffney A, Bogle RG.. **Drug-coated balloon-only percutaneous coronary intervention for the treatment of de novo coronary artery disease: a systematic review**. *Cardiol Ther* (2018.0) **7** 127-149. PMID: 30368735
10. **2) Classification and diagnosis of diabetes**. *Diabetes Care* (2015.0) **38** S8-S16
11. Tan Q, Chen M, Hao J, Wei K.. **Impact of hyperinsulinemia on long-term clinical outcomes of percutaneous coronary intervention in patients without diabetes who have acute myocardial syndrome**. *Diabetes Metab Syndr Obes* (2021.0) **7** 3949-3957
12. Kok MM, von Birgelen C, Sattar N, Zocca P, Lowik MM, Danse PW. **Prediabetes and its impact on clinical outcome after coronary intervention in a broad patient population**. *EuroIntervention* (2018.0) **14** e1049-e1056. PMID: 29313817
13. Kim YH, Her AY, Jeong MH, Kim BK, Hong SJ, Kim S. **Efects of prediabetes on long-term clinical outcomes of patients with acute myocardial infarction who underwent PCI using new-generation drug-eluting stents**. *Diabetes Res Clin Pract* (2020.0) **160** 107994. PMID: 31881240
14. Kim YH, Her AY, Jeong MH, Kim BK, Hong SJ, Kim S. **Effect of renin-angiotensin system inhibitors on major clinical outcomes in patients with acute myocardial infarction and prediabetes or diabetes after successful implantation of newer-generation drug-eluting stents**. *J Diabetes Complications* (2020.0) **34** 107574. PMID: 32147394
15. Kufner S, Cassese S, Valeskini M, Neumann F, Schulz-Schüpke S, Hoppmann P. **Long-term efficacy and safety of paclitaxel-eluting balloon for the treatment of drug-eluting stent restenosis 3-year results of a randomized controlled trial**. *J Am Coll Cardiol Intv* (2015.0) **8** 877-8415
16. Picard F, Doucet S, Asgar AW.. **Contemporary use of drug-coated balloons in coronary artery disease: where are we now?**. *Arch Cardiovasc Dis* (2017.0) **110** 259-272. PMID: 28274589
17. Pan L, Lu W, Han Z, Pan S, Wang X, Shan Y. **Clinical outcomes of drug-coated balloon in coronary patients with and without diabetes mellitus: a multicenter, propensity score study**. *J Diabetes Res 2021* (2021.0) **29** 5495219
18. Jarrah MI, Al-Khatib S, Khader Y, AlKharabsheh HN, Hammoudeh A, Alzoubi KH. **The impact of coexistence of smoking and diabetes on the coronary artery severity and outcomes following percutaneous coronary intervention: Results from the 1ST Jordanian PCI Registry**. *Int J Vasc Med 2020* (2020.0) **2** 762415818
19. Zhao X, Lan J, Yu X, Zhou J, Tan Y, Sheng Z. **Primary percutaneous coronary intervention in patients with type 2 diabetes with late/very late stent thrombosis and de novo lesions: A single-center observational cohort study of clinical outcomes and influencing factors**. *Front Cardiovasc Med* (2021.0) **8** 653467. PMID: 34239902
20. Tan Q, Qiu L, Li G, Li C, Zheng C, Meng H. **Transplantation of healthy but not diabetic outgrowth endothelial cells could rescue ischemic myocardium in diabetic rabbits**. *Scand J Clin Lab Invest 2010* **70** 313-321
21. Tan Q, Li Y, Li X, Zhang S.. **Hyperinsulinemia impairs functions of circulating endothelial progenitor cells**. *Acta Diabetol* (2019.0) **56** 785-795. PMID: 30859314
22. Choi J H, Gimble J M, Vunjak-Novakovic G, Kaplan D L.. **Effects of hyperinsulinemia on lipolytic function of three-dimensional adipocyte endothelial co-cultures**. *Tissue Eng Part C Methods* (2010.0) **16** 1157-1165. PMID: 20144013
23. Halim M, Halim A.. **The effects of inflammation, aging and oxidative stress on the pathogenesis of diabetes mellitus (type 2 diabetes)**. *Diabetes Metab Syndr* (2019.0) **13** 1165-1172. PMID: 31336460
|
---
title: Role and mechanism of alpa1-antitrypsin in polycystic ovary syndrome
authors:
- Shouxi Pan
- Yilihaer Aizezi
- Honglei Wang
- Yingli Lu
- Baigong Xue
journal: Saudi Medical Journal
year: 2022
pmcid: PMC9994513
doi: 10.15537/smj.2022.43.12.20210398
license: CC BY 4.0
---
# Role and mechanism of alpa1-antitrypsin in polycystic ovary syndrome
## Body
Polycystic ovary syndrome (PCOS) is a female endocrine disorder associated with metabolic and reproductive dysfunction. 1,2 In many patients with PCOS, obesity or being overweight is the major factor leading to anovulatory infertility. 3 Polycystic ovary syndrome starts to develop at the onset of increased gonadal and adrenal activity around puberty, and both gonadarche and PCOS reflect functional changes in the hypothalamic-pituitary-ovarian axis. 4 Its exact etiology and pathogenic mechanism are not known, and it continues to present a complex challenge in the field of gynecological endocrinology. Many studies on the condition have shown that pro-inflammatory cytokines have critical effects on the pathogenic mechanism of PCOS, while autoimmune inflammation might also underly it. 5,6 Inflammation theory, and its interaction with PCOS, is a highly important aspect of research, shaping the current perspective that PCOS is a chronic inflammatory disorder.
Alpha1-antitrypsin (A1AT) is an important serine protease inhibitor in humans. It is mainly synthesized in hepatic cells, and its level in circulation increases considerably in inflammatory disorders. 7,8 Alpha1-antitrypsin is not only a glycoprotein found in the acute phase; but also a critical neutrophil protease inhibitor, protecting tissues against protein hydrolysis injury under various inflammatory conditions. 9,10 As PCOS is often accompanied by obesity, hyperinsulinemia, and insulin resistance, inflammation might be a mechanism related to its development. 11 *Obesity is* a common comorbidity complicating PCOS, and studies have found that overweight PCOS female patients exhibit a high risk of anovulation and abnormal menstruation compared to women of normal weight: human serum A1AT levels decrease as BMI increases. 12 The anti-inflammatory properties of A1AT have been widely reported in clinical application, but few have been reported in female reproductive disorders.
In this study, we determined the relationship between A1AT and pro-inflammatory cytokines, solely among PCOS patients also developing comorbid obesity, by analyzing variations in the neutrophil elastase (NE), A1AT, interleukin (IL)-1β, and IL-8 levels. We also investigated the A1AT-related mechanism in PCOS to identify effective targets for the treatment of PCOS.
We randomly assigned 20 rats (8 normal rats and 12 PCOS rats) into 2 groups. The rats in the PCOS group were given letrozole ($1\%$ carboxymethyl cellulose [CMC] solution) CMC by a high-fat diet, combined with gavage at l mg/kg body weight for 23 days. 13 All experiments were performed following the National Institutes of Health guidelines for the care and use of animals.
All serum samples were collected from 40 cases (average age: 32.8±3.1 years) with normal blood glucose levels, who visited The Second Hospital of Jilin University, Changchun, China from October 2021 to November 2021 for obesity concurrent with PCOS. The diagnostic criteria of PCOS have not been standardized due to the heterogeneous nature of PCOS. Polycystic ovary syndrome was diagnosed from the following aspects: i) The presence of symptoms from puberty, ii) menstrual irregularities, iii) chronic anovulation, iv) hirsutism, v) high blood LH (luteinizing hormone) or the LH/follicle-stimulating hormone ratio, combined with excess testosterone levels, vi) ultrasound examination of polycystic ovarian symptoms to exclude similar diseases Obesity was diagnosed in PCOS cases whose body mass index (BMI) was ≥30 kg/m 2. Each subject excluded pulmonary, hepatic, renal, and cardiovascular diseases, family or genetic history of tumors, and cardiopulmonary and renal dysfunction.
Additionally, 50 healthy individuals participated in the study as normal controls. This study was approved by the Ethics Committee of The Second Hospital of Jilin University and was carried out following the guidelines of the Declaration of Helsinki. Each participant provided informed consent before participation.
## Abstract
### Objectives:
To determine the relationships among alpha1-antitrypsin (A1AT), pro-inflammatory cytokines, neutrophil elastase (NE), interleukin (IL)- 1β, and IL-8 in cases with polycystic ovary syndrome (PCOS).
### Methods:
Female rats in the control group were fed and watered normally. Female rats in the PCOS group were given high-fat diets and letrozole ($1\%$ carboxymethyl cellulose’ [CMC] solution) CMC by gavage at a dose of l mg/kg body weight daily for 23 days. The mRNA levels of A1AT and NE in rat ovaries were detected by performing real-time polymerase chain reaction (PCR) in the laboratory of Norman Bethune College of Medicine, Jilin University, China in 2017. All serum samples were collected from the Second Hospital of Jilin University from October 2021 to November 2021 for obesity concurrent with PCOS. Molecular docking of A1AT with NE, IL-8, and IL-1β was investigated using the Insight II software ZDOCK tool. This study was carried out at the Reproductive Center, The Second Hospital of Jilin University, Changchun, China from June 2021 to July 2022.
### Results:
The expression of the A1AT mRNA decreased in the ovary tissues of PCOS rats relative to that of healthy controls, while the expression of NE mRNA increased compared to that of normal controls. The serum A1AT expression in PCOS cases decreased considerably relative to their expression in normal controls. However, the expression of NE, IL-1β, and IL-8 increased significantly relative to their expression in the control ($p \leq 0.05$ for all). The Insight II ZDOCK molecular docking simulations showed that A1AT has direct interaction sites for NE, IL-1β, and IL-8.
### Conclusion:
Alpha1-antitrypsin is closely associated with NE, IL-1β, and IL-8. Therefore, we speculate that A1AT might ameliorate PCOS symptoms by inhibiting pro-inflammatory factors: NE, IL-1β, and IL-8.
## Methods
In this study, 20 female rats were randomly divided into 2 groups, 10 each in the control group and the PCOS group. Female rats in the control group were fed and watered normally. Female rats in the PCOS group were given high-fat diets and letrozole ($1\%$ CMC solution) CMC by gavage at a dose of 1 mg/kg body weight per day. After 23 days of continuous administration, vaginal exfoliated cells lost their periodicity in the PCOS state. Rat ovarian tissue total ribonucleic acid (RNA) was isolated, which was prepared into first-strand complementary deoxyribonucleic acid (cDNA) through reverse transcription, followed by quantification through reverse transcription polymerase chain reaction (RT-PCR). The results were used to determine the difference in the NE and A1AT messenger RNA (mRNA) levels between PCOS and normal control groups and investigate how A1AT and NE are associated with the development of PCOS. The A1AT and NE mRNA levels in rat ovaries were detected by performing RT-PCR in the laboratory at Norman Bethune College of Medicine Jilin University in 2017.
## Statistical analysis
Fasting venous blood (5 mL) was extracted in the early morning, preserved for 30 min (minutes) at 37°C, and then centrifuged at 3,000 rpm for 15 min. The expression of serum A1AT, NE, IL-1β, and IL-8 was determined by performing ELISA. The ELISA kit was purchased from Millipore Corporation (Billerica, MA), and the assay was performed following the manufacturer’s instructions. The data were recorded using the HDM_9602G ELISA analyzer (Plantier Technology Co., Ltd., Beijing, China). The Statistical Package for the Social Sciences, version 16.0 (SPSS Inc., Chicago, IL) software was used for data processing, with statistical significance determined at $p \leq 0.05.$ The relationships between A1AT and variable values, as well as the relationship between A1AT and NE, were made for Spearman analysis.
Molecular docking of A1AT with NE, IL-8, and IL-1β was simulated using the insight II 2005 ZDOCK software docking program. The operating parameters were as follows: RMSD cutoff=6, angular step size=6, interface cutoff=9, top poses=2,000, and maximal cluster number=60. The complexes with the highest RDOCK scores were optimized with the RDOCK module. The interactions between residues on the interfaces of the obtained complexes were calculated using the LigPlot+ v.1.4 software (UCL Business PLC [UCLB]; London, UK). This study was conducted at the Reproductive Center, The Second Hospital of Jilin University, Changchun (China), from June 2021 to July 2022.
## Results
The results of the RT-PCR analysis showed that the A1AT and NE levels in the ovaries of PCOS rats differed significantly from those in normal controls (NCs). The relative A1AT levels in the ovaries of the PCOS rats were significantly lower than those in NCs, as determined by RT-PCR analysis ($p \leq 0.05$, Figure 1A). The relative NE expression in the ovaries of PCOS rats was significantly higher than that in normal controls ($p \leq 0.05$, Figure 1B).
**Figure 1:** *- Expression of tissue and serum A1AT, NE, IL-1β, and IL-8 levels between polycystic ovarian syndrome (PCOS) cases and normal controls. A-B: The relative alpha1-antitrypsin (A1AT) and neutrophil elastase (NE) mRNA levels in the ovaries of PCOS and normal control rats. C-F: Serum A1AT, interleukin (IL)-1β, IL-8, and NE expression in PCOS cases and controls. *p<0.05, **p<0.01, ***p<0.001*
Alpha1-antitrypsin levels among PCOS cases differed significantly compared to those in normal controls, and the results of ELISA showed that A1AT levels in the peripheral blood of PCOS cases were significantly lower than those in NCs ($p \leq 0.05$, Figure 1C). The expression of serum IL-8 was significantly higher in PCOS patients ($p \leq 0.05$, Figure 1D), and IL-1β was also significantly higher in PCOS patients ($p \leq 0.001$, Figure 1E). Serum levels of NE, one of the most important substrates of A1AT, were higher in PCOS patients, and the NE/A1AT ratio was also higher, which was significantly different from that of normal controls ($p \leq 0.001$, Figure 1F).
Serum NE, IL-1β, and IL-8 expressions were negatively related to the A1AT expression among PCOS cases. The A1AT expression was negatively related to NE (R= -0.750, $p \leq 0.01$). The A1AT was negatively related to IL-8 (R= -0.758, $p \leq 0.01$). The A1AT was negatively related to IL-1β (R= -0.704, $p \leq 0.01$). Neutrophil elastase was positively related to IL-8 ($R = 0.787$, $p \leq 0.01$). The expression of NE was positively related to IL-1β ($R = 0.826$, $p \leq 0.01$). The expression of IL-8 was positively related to IL-1β ($R = 0.744$, $p \leq 0.01$). The results are presented in Table 1 and Figure 2.
The Insight II ZDOCK molecular docking simulations showed that A1AT has direct interaction sites for NE, IL-1β, and IL-8. The simulated molecular structural docking of A1AT with NE, IL-1β, and IL-8 was performed to investigate the interaction of A1AT with pro-inflammatory factors NE, IL-1β, and IL-8. The crystal structure data were obtained from the Protein Data Bank (PDB) database (A1AT PDB ID: 1KCT; IL8 PDB ID: 3IL8; IL-1β PDB ID: 2I1B; NE PDB ID: 3Q76). The molecular docking calculations were performed using the ZDOCK module of the Insight II software. Each couple of complexes was calculated by the ZDOCK module to obtain 2,000 conformations. The complexes with the highest RDOCK scores were selected as the final results by optimizing the RDOCK module of the Insight II software. As shown in Figure 3, the docking results indicated that the active central residue of A1AT can be combined with the active sites for pro-inflammatory factors NE, IL-1β, and IL-8 to form many polar interactions. Furthermore, the residues PRO361 and LYS365 in the loop of the A1AT active center were involved in the formation of polar intermolecular interactions between the 2 complex molecules, indicating that these 2 residues were essential for A1AT to inhibit IL-1β and IL-8.
**Figure 3:** *- The residues involved in polar interactions are labeled and shown in the stick renders. The interactions between residues are shown in yellow dashed lines. A) The complex of NE (purple) and alpha1-antitrypsin (A1AT) (blue). B) The complex of interleukin (IL)-8 (red) and alpha1-antitrypsin (blue). C) The complex of IL-1β (yellow) and A1AT (blue).*
The complexes obtained by molecular docking were analyzed using the LigPlot software to further investigate the key residues of A1AT that interact with NE, IL-1β, and IL-8. As shown in Figure 4B, besides forming polar interactions, the complexes of A1AT and IL-8 have many residues involved in the formation of hydrophobic intermolecular interactions (A1AT: PRO362, LEU254, LYS259, VAL364, SER237, ALA284, THR215, ILE229, PRO255, ASP207, HIS209, and MET358; IL-8: PRO16 PRO53, SER14, LYS15, GLU29, SER30, and ALA35). These polar and hydrophobic interactions have critical effects on stable complex formation. Both the A1AT/IL-1β complex and the A1AT/NE complex also form many polar and hydrophobic intermolecular interactions (Figure 4 A-C). A1AT binds to NE, IL-1β, and IL-8, while inhibiting their activities through their direct interactions with active site residues.
**Figure 4:** *- Calculated interactions between the residues on the interfaces of the neutrophil elastase and alpha1-antitrypsin (A1AT) complex, the interleukin (IL)-8 and A1AT complex, and the IL-1β and A1AT complex.*
## Discussion
Obese patients often exhibit metabolic abnormalities, oxidative stress, and chronic low-grade inflammation, all of which increase pro-inflammatory factors, such as monocyte neutrophil chemotactic factor protein (MCP-1) and tumor necrosis factor (TNF-α), while decreasing anti-inflammatory factors such as adiponectin. The levels of the pro-inflammatory factors MCP-1 and TNF-α were significantly lower in normal individuals than that in PCOS cases. 14,15 Obese patients with PCOS often have chronic adipose inflammation and insulin resistance. Chronic low-grade adipose tissue inflammation might also be mechanistically associated with metabolic disorders and complications of organ tissues among obese and overweight individuals. The interaction of various pro-/anti-inflammatory signals greatly increases the difficulty in treating PCOS. 16 Leptin levels are high in obese individuals, and adipose inflammation is associated with leptin resistance. Leptin is negatively correlated with A1AT, and an increase in leptin levels inhibits A1AT expression, exacerbating — along with the inflammatory response — the lower A1AT levels in obese patients. 17,18 However, the complications associated with molecular events for the initiation of immune cell infiltration in adipose tissue, which in turn affect the production of inflammatory cytokines, are unknown. 19 Adipose tissue is a metabolic endocrine organ with high activity. It strongly influences PCOS progression by secreting pro-inflammatory factors. 20 Therefore, in this study, we only investigated obese PCOS patients, who inherently have more adipose tissue.
A macrophage is a type of immunocyte. It is most abundant in adipose tissues, and it strongly affects the inflammation of these tissues. Due to their sensitivity to the surrounding environment, macrophages possess various phenotypes: namely anti-inflammation and pro-inflammation. In healthy adipose tissues, macrophages are polarized to the anti-inflammatory M2 type to maintain adipose tissue homeostasis. However, in obese adipose tissue, macrophages are polarized to the pro-inflammatory M1 type; these changes induce the production of pro-inflammatory factors, which impairs insulin signaling and promotes insulin resistance. 21 Impairment of macrophage mitochondria leads to an obesity-mediated inflammatory response in macrophages, alongside systemic insulin resistance. Mitochondrial dysfunction induces IL-1β release, thus decreasing insulin sensitivity in the insulin-targeting cells through paracrine signaling and their infiltration in circulation. Insulin resistance and the associated hyperinsulinemia can induce the endocrine and reproductive features of PCOS. 22 *Obesity is* accompanied by a decrease in A1AT expression. A1AT can be generated by the liver to inhibit NE, while NE represents the neutrophil protease produced during inflammation. 23 Such an imbalance in A1AT-NE affects inflammatory responses. An increase in the NE/A1AT ratio increases the production of inflammatory factors. Neutrophil elastase causes the accumulation of pro-inflammatory cytokines and decreases the ratio of NE to A1AT, thus inhibiting the accumulation of pro-inflammatory factors. 24,25 PCOS cases showed higher levels of pro-inflammatory factors than normal controls, indicating the critical effect of the increase in the levels of pro-inflammatory factors on the pathogenic mechanism of PCOS26. These findings reflect those of similar studies. The A1AT mRNA levels in the ovaries of PCOS rats were lower than those in the ovaries of normal controls, while the expression of NE mRNA was higher than that of normal controls. Polycystic ovary syndrome cases showing comorbid obesity had significantly lower A1AT levels and significantly higher NE levels. An increase in the NE/A1AT ratio might contribute to the accumulation of pro-inflammatory factors, such as IL-1β and IL-8. This, in turn, might be associated with the pathogenic mechanism of PCOS.
Alpha1-antitrypsin can also reduce the levels of pro-inflammatory factors such as NE, IL-1β, and IL-8. In a rat model of peritoneal infiltration, Lewis et al 27 found that A1AT not only inhibited neutrophil migration by inhibiting IL-8 secretion by leukocytes, but it also inhibited the migration and aggregation of macrophages, thus impeding immune infiltration of the graft. For alleviating acute inflammation in pancreatic island transplantation, Koulmanda et al 28 studied gene expression in lymph nodes using real-time quantitative fluorescence PCR and found that A1AT inhibited the synthesis of acute-phase reactants and pro-inflammatory factors (such as IL-1, IL-6, TNF, IFN-g, and TNF-α), increased the levels of anti-inflammatory factors, and altered the immune balance in pancreatic lymph nodes.
The results of molecular docking showed that A1AT has direct interaction sites for NE, IL-1β, and IL-8: such as A1AT binds to and directly interacts with NE, IL-1β, and IL-8 through the active sites of the residues and inhibits the pro-inflammatory factors NE, IL-1β, and IL-8. By suppressing the expression of NE, the ratio of NE to A1AT can be further decreased along with the levels of the pro-inflammatory factors IL-8 and IL-1; this, in turn, can improve the therapeutic effects on PCOS.
## Study limitations
Polycystic ovary syndrome treatment has not yet been standardized since the pathogenic mechanism of PCOS remains unknown. The treatment of PCOS is usually selected based on clinical presentation and expected outcomes. In this study, we determined the link between low-grade chronic inflammation among obese PCOS patients and the pathogenic mechanism of PCOS. Alpha1-antitrypsin levels are low in obese PCOS patients, and A1AT deficiency might lead to the accumulation of pro-inflammatory factors, including NE, IL-1β, and IL-8, which are involved in the PCOS-related pathogenic mechanism.
In conclusion, A1AT might improve PCOS treatment by inhibiting pro-inflammatory factors NE, IL-1β, and IL-8 while improving the therapeutic outcome for PCOS.
Further studies need to determine whether A1AT can alleviate the symptoms of PCOS by improving excess androgen production, mitigating menstrual disorders, mitigating metabolic diseases, resisting endometrium, and enhancing fertility. Targeting A1AT to alleviate low-grade chronic inflammation among PCOS patients to improve their condition might be effective and represents a significant advancement in the treatment of PCOS. Thus, this study provided a new strategy for treating PCOS.
## References
1. Teede HJ, Misso ML, Boyle JA, Garad RM, McAllister V, Downes L. **Translation and implementation of the Australian‐led PCOS guideline: clinical summary and translation resources from the International Evidence‐based Guideline for the Assessment and Management of Polycystic Ovary Syndrome**. *Med J Aust* (2018) **209** S3-S8
2. Khan MJ, Ullah A, Basit S.. **Genetic basis of polycystic ovary syndrome (PCOS): current perspectives**. *Appl Clin Genet* (2019) **12** 249-260. PMID: 31920361
3. Cena H, Chiovato L, Obesity Nappi RE.. **polycystic ovary syndrome, and infertility: A new avenue for GLP-1 receptor agonists**. *J Clin Endocrinol Metab* (2020) **105** e2695-e2709. PMID: 32442310
4. Witchel SF, Plant TM.. **Intertwined reproductive endocrinology: Puberty and polycystic ovary syndrome**. *Curr Opin Endocr Metab Res* (2020) **14** 127-136. PMID: 33102929
5. Rostamtabar M, Esmaeilzadeh S, Tourani M, Rahmani A, Baee M, Shirafkan F. **Pathophysiological roles of chronic low-grade inflammation mediators in polycystic ovary syndrome**. *J Cell Physiol* (2021) **236** 824-838. PMID: 32617971
6. Patel S.. **Polycystic ovary syndrome (PCOS), an inflammatory, systemic, lifestyle endocrinopathy**. *J Steroid Biochem Mol Biol* (2018) **182** 27-36. PMID: 29678491
7. Lalonde ME, Koyuturk I, Brochu D, Jabbour J, Gilbert M, Durocher Y.. **Production of α2,6-sialylated and non-fucosylated recombinant alpha-1-antitrypsin in CHO cells**. *J Biotechnol* (2020) **307** 87-97. PMID: 31697975
8. Ando Y, Kuroda A, Kusama K, Matsutani T, Matsuda A, Tamura K.. **Impact of serine protease inhibitor alpha1-antitrypsin on expression of endoplasmic reticulum stress-induced proinflammatory factors in adipocytes**. *Biochem Biophys Rep* (2021) **26** 100967. PMID: 33732904
9. Madyaningrana K, Vijayan V, Nikolin C, Aljabri A, Tumpara S, Korenbaum E. **Alpha1-antitrypsin counteracts heme-induced endothelial cell inflammatory activation, autophagy dysfunction and death**. *Redox Biol* (2021) **46** 102060. PMID: 34246063
10. Yoshida K, Kusama K, Fukushima Y, Ohmaru-Nakanishi T, Kato K, Tamura K.. **Alpha-1 antitrypsin-induced endoplasmic reticulum stress promotes invasion by extravillous trophoblasts**. *Int J Mol Sci* (2021) **46** 102060
11. Otto-Buczkowska E, Grzyb K, Jainta N.. **Polycystic ovary syndrome (PCOS) and the accompanying disorders of glucose homeostasis among girls at the time of puberty**. *Pediatr Endocrinol Diabetes Metab* (2018) **24** 40-44. PMID: 30083660
12. Xue GB, Zheng WL, Wang LH, Lu LY.. **Alpha 1-antitrypsin. A novel biomarker for obesity in humans**. *Saudi Med J* (2013) **34** 34-39. PMID: 23299157
13. Wang MX, Yin Q, Xu X.. **A rat model of polycystic ovary syndrome with insulin resistance induced by letrozole combined with high fat diet**. *Med Sci Monit* (2020) **26** e922136. PMID: 32448863
14. Longo M, Zatterale F, Naderi J, Parrillo L, Formisano P, Raciti GA. **Adipose tissue dysfunction as determinant of obesity-associated metabolic complications**. *Int J Mol Sci* (2019) **20** 2358. PMID: 31085992
15. Trim W, Turner JE, Thompson D.. **Parallels in immunometabolic adipose tissue dysfunction with ageing and obesity**. *Front Immunol* (2018) **9** 169. PMID: 29479350
16. Kawai T, Autieri MV, Scalia R.. **Adipose tissue inflammation and metabolic dysfunction in obesity**. *Am J Physiol Cell Physiol* (2021) **320** C375-C391. PMID: 33356944
17. Obradovic M, Sudar-Milovanovic E, Soskic S, Essack M, Arya S, Stewart AJ. **Leptin and obesity: role and clinical implication**. *Front Endocrinol (Lausanne)* (2021) **12** 585887. PMID: 34084149
18. Beneventi F, Locatelli E, De Amici M, Cavagnoli C, Bellingeri C, De Maggio I. **Maternal and fetal Leptin and interleukin 33 concentrations in pregnancy complicated by obesity and preeclampsia**. *J Matern Fetal Neonatal Med* (2019) 1-7
19. Dimou NL, Papadimitriou N, Mariosa D, Johansson M, Brennan P, Peters U. **Circulating adipokine concentrations and risk of five obesity-related cancers: A Mendelian randomization study**. *Int J Cancer* (2020) **148** 1625-1636. PMID: 33038280
20. Zhang X, Xu X, Li P, Zhou F, Kong L, Qiu J. **TMT based proteomic analysis of human follicular fluid from overweight/obese and normal-weight patients with polycystic ovary syndrome**. *Front Endocrinol (Lausanne)* (2019) **10** 821. PMID: 31983920
21. Xu L, Yan X, Zhao Y, Wang J, Liu B, Yu S. **Macrophage polarization mediated by mitochondrial dysfunction induces adipose tissue inflammation in obesity**. *Int J Mol Sci* (2022) **23** 9252. PMID: 36012516
22. Zhang J, Bao Y, Zhou X, Zheng L.. **Polycystic ovary syndrome and mitochondrial dysfunction**. *Reprod Biol Endocrinol* (2019) **17** 67. PMID: 31420039
23. Mansuy-Aubert V, Zhou QL, Xie X, Gong Z, Huang JY, Khan AR. **Imbalance between neutrophil elastase and its inhibitor α1-antitrypsin in obesity alters insulin sensitivity, inflammation, and energy expenditure**. *Cell Metab* (2013) **17** 534-548. PMID: 23562077
24. Krotova K, Khodayari N, Oshins R, Aslanidi G, Brantly ML.. **Neutrophil elastase promotes macrophage cell adhesion and cytokine production through the integrin-Src kinases pathway**. *Sci Rep* (2020) **10** 15874. PMID: 32981934
25. Hunt AMD, Glasgow AMA, Humphreys H, Greene CM.. **Alpha-1 antitrypsin-a target for microRNA-based therapeutic development for cystic fibrosis**. *Int J Mol Sci* (2020) **21** 836. PMID: 32012925
26. Rudnicka E, Suchta K, Grymowicz M, Calik-Ksepka A, Smolarczyk K, Duszewska AM. **Chronic low grade inflammation in pathogenesis of PCOS**. *Int J Mol Sci* (2021) **22** 3789. PMID: 33917519
27. Lewis EC, Shapiro L, Bowers OJ, Dinarello CA.. **Alpha1-antitrypsin monotherapy prolongs islet allograft survival in mice**. *Proc Natl Acad Sci U S A* (2005) **102** 12153-12158. PMID: 16093309
28. Koulmanda M, Bhasin M, Hoffman L, Fan Z, Qipo A, Shi H. **Curative and beta cell regenerative effects of alpha1-antitrypsin treatment in autoimmune diabetic NOD mice**. *Proc Natl Acad Sci U S A* (2008) **105** 16242-16247.. PMID: 18852471
|
---
title: Bacteriological profile of wound swab and their antibiogram pattern in a tertiary
care hospital, Saudi Arabia
authors:
- Azzah S. Alharbi
journal: Saudi Medical Journal
year: 2022
pmcid: PMC9994516
doi: 10.15537/smj.2022.43.12.20220681
license: CC BY 4.0
---
# Bacteriological profile of wound swab and their antibiogram pattern in a tertiary care hospital, Saudi Arabia
## Body
The disruption in the skin’s defense barrier in term of epithelial continuity loss with or without subcutaneous tissue exposure (such as wound) creates a wet, warm, and nutrient-rich milieu that is favorable for pathogens colonization and growth. 1-3 A wound’s progression to infection probably contributed to the numerous pathogens and host factors. 4 A variety of microbial pathogens, including fungi, bacteria, parasites, and viruses can infect the wound. 5 Of which, Escherichia coli, *Staphylococcus aureus* (S. aureus), Klebsiella spp. ( species), Pseudomonas aeruginosa, and Acinetobacter spp. are among the most prevalent microorganisms isolated from both monomicrobial and polymicrobial wound infection. 4,6 Infected wounds may hinder the healing process and lead to serious complications with substantial impact on the quality of life. 6,7 *It is* among the most acquired infections at hospitals which has contributed majorly to prolonged hospitalization, and higher costs and is associated with considerable morbidity and mortality rates especially in the developing world. 6-8 Although, the current burden of wound infection in Saudi Arabia has not been comprehensively estimated yet, it is still expected to be high. Elevated prevalence of diabetes, obesity, ischemic heart disease amongst the Saudi population as well as untrained wound care at home- which is frequently influenced by traditional medical views-may impair the healing process and raise the risk of wound infection. 9-11 Diagnosis of the infection relies on the wound examination by an experienced clinician, which is further confirmed by infection biomarkers and microbiological analysis. 12 *While this* diagnostic approach can yield useful data, it is time-consuming and dependent on the clinician’s level of expertise. Therefore, antimicrobial agents sometimes are initiated empirically which may contribute to the emergence of antimicrobial resistant (AMR) pathogens with an additional economic and clinical burden. 8 Antimicrobial resistant pathogens poses a global health challenge particularly in developing world, where infection rate is elevated and financial constrains limit the broad usage of newer and high pricing quality assured antimicrobials. 13 In Africa for example, therapeutic guidelines for infections depend primarily on the use of empiric antimicrobials, without support of culture results. 14 Noting that most of the health care providers lake updated data on AMR. 15 In Saudi Arabia, recent research has shown that antimicrobial overuse, inadequate duration of its use, and the use of broad spectrum antimicrobials are prevalent practices among physicians. 16 Thus, wound infection caused by drug resistance pathogen is commonly reported from developing world. 8,17 *It is* evident that regular monitoring of the pathogenic organisms and their antimicrobial susceptibility profile are crucial for guiding empiric antimicrobial therapy of wound infection in health institutions. Very little is currently known on the bacteria and AMR profiles of wound infection from different regions of Saudi Arabia. To fill such a gap, this study was carried out to assess the microbial profile of wound infections and their antimicrobial susceptibility pattern at King Abdulaziz University Hospital (KAUH), Jeddah. King Abdulaziz University *Hospital is* one of the largest governmental referral and teaching healthcare hospitals in Saudi Arabia’s Western region, with a capacity of 876 beds for diagnostic and therapeutic purposes for patients with different characteristics. 18
## Abstract
### Objectives:
To assess the microbial profile of wound infection and their antibiogram pattern.
### Methods:
A retrospective study was carried out at King Abdulaziz University Hospital, Jeddah Saudi Arabia between December 2021 and July 2022 comprising data related to demographic, microbial profile and antibiotic sensitivity pattern of wound infection–suspected cases.
### Results:
A total of 305 wound swabs were collected; of which $56.1\%$ showed microbial growth. Among 187 microbial isolates, $62\%$ were gram-negative bacteria, $30.5\%$ were gram-positive bacteria and $7.5\%$ were fungi. Staphylococcus aureus was the prevailing isolates $17.1\%$, followed by *Klebsiella pneumoniae* and Pseudomonas aeruginosa, each with $13.9\%$ and *Escherichia coli* with 12.8 %. Providencia sp with $0.1\%$ was the least isolated bacteria. Out of 173 bacterial isolates, $46.8\%$ were sensitive to antimicrobial agents tested, while $53.2\%$ were resistant to one and more drug tested. Of these isolates, $22\%$ were found to be the MDR bacteria. The highest MDR percentages was noted among *Acinetobacter baumannii* ($70\%$) followed by *Klebsiella pneumoniae* ($53.9\%$), *Escherichia coli* ($25\%$) and *Pseudomonas aeruginosa* ($19.2\%$) and the least by ($12.5\%$) by Staphylococcus aureus.
### Conclusion:
The microbial isolation rates from wound infection was high, with *Staphylococcus aureus* being the most prevalent. Considerable antimicrobial resistance rate to the commonly used antibiotics was discovered. Thus, regular monitoring of microbial profile and their antimicrobial sensitivity pattern in the study region in attempt to contain antimicrobial resistance is highly recommended.
## Methods
The current study utilizes a retrospective-descriptive research approach carried out between December 2021 and July 2022 in which culture results of wound swab specimens over sixth months period -from January to June 2022 -at the Microbiology Department in KAUH were retrieved. The study protocol was approved by the Research Ethics Committee at KAU, with a Reference Number of 116-22 and conducted in accordance with the Declaration of Helsinki.
Data related to demographic (age, gender, and nationality), type of microorganism involved and antibiotic sensitivity / resistance pattern of wound infection– suspected cases, were retrieved from the medical records. Patients who were taking antibiotics or had recently taken antibiotics during the previous 2 weeks at the time of sample collection were excluded. Patients presenting inadequate demography and history of antimicrobial use were excluded.
The specimens were collected from the individuals with clinical evidence of wound infections (such as; swelling, redness, pain, the presence of pus with or without odor, high grade fever and rigors) upon physician request. Prior sample collection, the edges of wound were cleaned and the surface exudates were removed by washing with physiological sterile solution, using Levine’s technique. 13 This process is essential for the removal of environmental microbes contaminating wound surface. Samples were then aseptically obtained from wounds by rotating a sterile cotton swab under adequate pressure, without touching the nearby skin. Within 30 minutes of collection, samples were transported to a microbiological lab by reinserting swabs into test tubes filled with 0.5 mL of sterile normal saline. After that, specimens were processed and cultured following standard techniques used in medical microbiology lab. 19 For pathogen identification, colonies formed were further processed using morphology, gram staining, and biochemical reaction. 19 Antibiotic susceptibility testing of the detected isolates were performed using the Kirby Bauer disc diffusion method and observations were interpreted in accordance with guidelines set by the National Committee for Clinical Laboratory Standards. 20 A pathogen that is resistant towards 2 or more classes of antibiotics is termed a multidrug-resistant pathogen (MDR). 21 By dividing the number of susceptible/ resistant isolates by the whole number of tested isolates, the sensitivity/resistance rates of specific bacterial isolates to each tested antibiotic agent was calculated. In the case of the fungal samples, the guidelines do not require antifungal sensitivity testing as the treatment is standard and determined by the physician.
## Systematic analysis
All retrieved data were initially recorded into an Excel sheet (Microsoft Corporation, Redmond, WA) and exported to IBM SPSS Statistics for Windows, version 20 (IBM Corp., Armonk, N.Y., USA) for statistical analysis. Frequency and percentages were used to present categorical data. Chi square or fisher exact tests were performed to compare the culture positivity, proportion of bacterial isolates and resistance pattern with patients’ gender and nationality. Analysis was considered statistically significant at a p-value of ≤0.05.
## Results
Overall in this study, 305 wound specimens were obtained from 305 individuals who had a clinical signs of infection; of these, $45.9\%$ were female and $54.1\%$ were male. The study population ranged in age from 1 to 95 years, with a mean of 41.37 (SD ± 25.28) years. Approximately $31\%$ of the samples were collected from 60 year old or more, $56.1\%$ showed a microbial growth, while the rest were culture negative. Both female ($49.7\%$) and males ($50.2\%$) had nearly comparable infection rates. The incidence of microbial infections was significantly higher in the age group of 60 years old or more ($67.7\%$), followed by the age group of 41-59 ($59.4\%$), 19-40 ($57.5\%$), and 0-18 ($35.7\%$). The age, gender and nationality distribution of subjects included in this study is provided in Table 1.
**Table 1**
| Characteristics | Positive culture n (%) | Negative culture n (%) | Total n (%) | P-value |
| --- | --- | --- | --- | --- |
| Gender | | | | 0.082 |
| Female | 85 (60.7) | 55 (39.3) | 140 (45.9) | |
| Male | 86 (52.1) | 79 (47.9) | 165 (54.1) | |
| Total | 171 (56.1) | 134 (43.9) | 305 (100) | |
| Nationality | | | | 0.441 |
| Saudi | 88 (55.3) | 71(44.7) | 159 (52.1) | |
| Non Saudi | 83 (56.8) | 63 (43.2) | 146 (47.9) | |
| Total | 171 (56.1) | 134 (43.9) | 305 (100) | |
| Age groups (years) | | | | 0.001* |
| 0 to 18 | 25 (35.7) | 45 (64.3) | 70 (23.0) | |
| 19-40 | 42 (57.5) | 32 (42.5) | 74 (24.3) | |
| 41-59 | 41 (59.4) | 28 (40.6) | 69 (22.6) | |
| ≥ 60 | 63 (67.7) | 30 (32.3) | 93 (30.5) | |
| Total | 171 (56.1) | 134 (43.9) | 305 (100) | |
## Bacterial profile
Out of 171 culture positives, 155 ($90.6\%$) had single bacterial isolates whereas 16 ($9.4\%$) showed a mixed growth of 2 or more of different bacteria, so total microbial isolates was 187 (Appendix 1 & 2). Among 187 microbial isolates, 116 ($62\%$) were gram-negative bacteria, 57 ($30.5\%$) were gram-positive bacteria and 14 ($7.5\%$) were fungi. Staphylococcus aureus (S. aureus) was the prevailing isolates $17.1\%$ ($\frac{32}{187}$), followed by *Klebsiella pneumoniae* and Pseudomonas aeruginosa, each with 13.9 % ($\frac{26}{187}$) and *Escherichia coli* with 12.8 % ($\frac{24}{187}$). Providencia sp. with $0.1\%$ ($\frac{1}{187}$) was the least isolated bacteria Table 2. The age, gender, nationality distribution of common microbial isolates from wound are shown in Table 2
**Table 2**
| Microbial species | Gender | Gender.1 | Gender.2 | Nationality | Nationality.1 | Age group | Age group.1 | Age group.2 | Age group.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Microbial species | Total n(%) | Female n (%) | Male n (%) | Saudi n (%) | Non Saudi n (%) | 0 to 18 n (%) | 19-40 n (%) | 41-59 n (%) | ≥60 n (%) |
| Gram positive bacteria | | | | | | | | | |
| Staphylococcus aureus | 32 (17.1) | 18(9.6) | 14(7.5) | 27 (14.4) | 5 (2.7) | 5 (2.7) | 18(9.6) | 5(2.7) | 4 (2.1) |
| Staphylococcus epidermidis | 2 (1.1) | 0 (0) | 2 (1.1) | 2 (1.1) | 0 (0) | 0 (0) | 0 (0) | 1(0.5) | 1(0.5) |
| Streptococcus agalactiae | 6 (3.2) | 2 (1.1) | 4 (2.1) | 5 (2.7) | 1(0.5) | 1 (0.5) | 1(0.5) | 2(1.1) | 2(1.1) |
| Streptococcus pyogenes | 3 (1.6) | 0 (0) | 3 (1.6) | 1 (0.5) | 2 (2.7) | 0 (0) | 1 (0.5) | 1(0.5) | 1(0.5) |
| Enterococcus faecalis | 8 (4.3) | 3 (1.6) | 5 (2.7) | 4 (2.2) | 4 (2.15) | 1 (0.5) | 1 (0.5) | 3(1.6) | 3(1.6) |
| Enterococcus faecium | 3 (1.6) | 1(0.5) | 2 (1.1) | 1 (0.5) | 2 (1.1) | 2 (1.1) | 0 (0) | 1 (0.5) | 0 (0) |
| Enterococcus gallinarum | 3 (1.6) | 1(0.5) | 2 (1.1) | 3 (1.6) | 0 (0) | 0 (0) | 1 (0.5) | 0 (0) | 2 (1.1) |
| Total | 57 (30.5) | 25 (13.4) | 32 (17.1) | 43 (23.0) | 14 (7.48) | 9 (4.8) | 22 (11.8) | 13 (7.0) | 13 (7.0) |
| Gram negative bacteria | | | | | | | | | |
| Klebsiella pneumoniae | 26 (13.9) | 11 (5.9) | 15 (8.0) | 8 (4.3) | 18 (9.6) | 4 (2.2) | 6 (3.2) | 3 (1.6) | 13 (7) |
| Pseudomonas aeruginosa | 26 (13.9) | 16 (8.6) | 10 (5.4) | 15 (8.0) | 11(5.9) | 6 (3.2) | 4(2.2) | 8 (4.3) | 8 (4.3) |
| Escherichia coli | 24 (12.8) | 10 (5.4) | 14 (7.5) | 13 (7.0) | 11(5.9) | 2(1.1) | 7 (3.7) | 2 (1.1) | 13 (7.0) |
| Acinetobacter baumannii | 10 (5.3) | 3 (1.6) | 7 (3.5) | 6 (3.2) | 4 (2.1) | 0(0) | 1 (0.5) | 5 (2.7) | 4 (2.1) |
| Serratia marcescens | 8 (4.3) | 4 (2.2) | 4 (2.2) | 6 (3.2) | 2 (1.1) | 0 (0) | 2 (1.1) | 5 (2.7) | 1 (0.5) |
| Morganella morganii | 4 (2.1) | 1(0.5) | 3 (1.6) | 2 (1.1) | 2 (1.1) | 0 (0) | 0 (0) | 1 (0.5) | 3 (1.6) |
| Enterobacter cloacae | 6 (3.2) | 3 (1.6) | 3(1.6) | 0 (0) | 6 (3.2) | 2 (1.1) | 1 (0.5) | 1 (0.5) | 2(1.1) |
| Enterobacter aerogenes | 2 (1.1) | 2(1.1) | 0 (0) | 0 (0) | 2 (1.1) | 0 (0) | 2 (1.1) | 0 (0) | 0 (0) |
| Citrobacter freundii | 3 (1.6) | 0 (0) | 3 (1.6) | 0 (0) | 3 (1.6) | 0 (0) | 0 (0) | 1(0.5) | 2 (1.1) |
| Proteus mirabilis | 3 (1.6) | 2 (1.1) | 1(0.5) | 2 (1.1) | 1 (0.5) | 0 (0) | 0 (0) | 1 (0.5) | 2 (1.1) |
| Stenotrophomonas maltophilia | 3 (1.6) | 2 (1.1) | 1 (0.5) | 0 (0) | 3 (1.6) | 1 (0.5) | 0 (0) | 0 (0) | 2 (1.1) |
| Providencia sp. | 1 (0.5) | 1 (0.5) | 0 (0) | 0 (0) | 1 (0.5) | 0 (0) | 0 (0) | 1 (0.5) | 0 (0) |
| Total | 116 (62.0) | 55 (29.4) | 61 (32.6) | 52 (27.8) | 64 (34.2) | 15 (8.0) | 23 (12.3) | 28 (14.9) | 50 (26.7) |
| Fungi | 14 (7.5) | 10 (5.4) | 4 (2.2) | 5 (2.7) | 9 (4.8) | 2 (1.1) | 0 (0) | 2 (1.1) | 10 (5.4) |
| Total | 187 (100) | 90 (48.1) | 97(51.9) | 100 (53.5) | 87 (46.5) | 26 (13.9) | 45 (24.0) | 43 (23.0) | 73 (39.0) |
## Antimicrobial profile
Species-specific resistance analysis showed that S. aureus isolates were relatively sensitive to oxacillin, erythromycin, ciprofloxacin and trimethoprim-sulfamethoxazole with resistance rate of $29\%$, $22.6\%$, $12.5\%$ and 12.5. However, S. aureus was sensitive to clindamycin with a low resistance rate ($3.2\%$). The majority of *Klebsiella pneumoniae* isolates were resistant to meropenem and imipenem ($92.3\%$, each), ciprofloxacin ($68\%$), and amikacin ($66.7\%$). On the contrary, *Klebsiella pneumoniae* was susceptible to Piperacillin tazobactam, Amoxicillin/clavulanic acid and cefazoline with $12.5\%$ resistance each. Of 24 tested *Pseudomonas aeruginosa* isolates, resistant to ciprofloxacin and Piperacillin tazobactam were seen with rate of $37.5\%$ and $29\%$, however, only $16.7\%$ showed a resistance to gentamicin. Most of *Escherichia coli* were resistant to ciprofloxacin ($81\%$). A $100\%$ resistance towards meropenem, imipenem, cefepime and ceftazidime were seen in Acinetobacter baumannii. All isolates of *Streptococcus agalactiae* ($$n = 6$$) and S. pyogenes ($$n = 3$$) were sensitive to all tested antibiotics (clindamycin, erythromycin and penicillin). All isolates of Enterobacter aerogenes ($$n = 2$$) and *Citrobacter freundii* ($$n = 3$$) were sensitive to Ciprofloxacin, Gentamicin and trimethoprim-sulfamethoxazole while the isolates of *Stenotrophomonas maltophilia* ($$n = 3$$) were all sensitive to trimethoprim-sulfamethoxazole. A detailed overview of antibiotic sensitivity pattern of both gram positive and gram negative bacteria are presented in Table 3 and Table 4.
## Multi drug resistant pattern
Out of 173 bacterial isolates, 81 ($46.8\%$) were sensitive to antimicrobial agents tested, while 92 ($53.2\%$) were resistant to one and more drug tested. Of these isolates, 38 ($22\%$) were found to be the MDR bacteria. The overall MDR rate among gram-negative bacterial isolates $29.3\%$ ($\frac{34}{116}$) was higher than gram positive ones $7\%$ ($\frac{4}{57}$). The highest MDR percentages was noted among *Acinetobacter baumannii* (A. baumanii) ($70\%$) followed by *Klebsiella pneumoniae* ($53.9\%$), *Escherichia coli* ($25\%$), and *Pseudomonas aeruginosa* ($19.2\%$) and the least by ($12.5\%$) by S. aureus Table 5. Microbial resistance was not statistically significantly different by patient age ($$p \leq 0.192$$), gender ($$p \leq 0.625$$) and nationality ($$p \leq 0.101$$) Table 6.
## Discussion
Infections of the wound can prolong hospitalization and increase mortality rates by 70–$80\%$. 22 Clinical management of such infections are based on 2 essential factors, antibiotic therapy and wound care. 23 The antibiotic administration is usually initiated empirically, which possibly contributes to the development of antimicrobial resistant pathogens. 8 In developing countries, Saudi Arabia in particular, periodic analysis of the local epidemiology and antimicrobial susceptibility pattern of the involved pathogens-often underestimated- is required for effective application of empirical therapy and limiting the spread of antibiotic resistance. As part of routine microbiology laboratory analysis, culture methods are primarily used to identify and isolate potential microorganisms from swabs, and other types of specimens to determine their species and antimicrobial sensitivities, as a guide for effective therapy.
In the current retrospective analysis, 20 microbial species were recovered from 171 patients with clinical evidence of wound infection, yielding a $56.1\%$ isolation rate. These results match those observed in previous studies from Nepal ($57.4\%$), Bahir Dar ($53\%$), and Gondar ($52\%$). 13,24 It seems apparent that infection of the wound poses a significant clinical concern. In most of cases ($90.6\%$), only single bacterial species dominated the wounds’ microbial population. These results reflect those of Mohammed et al, 24 Upereti et al, 25 KC et al 26 and Maharjan et al 5 who also found that single bacterial species colonized $81.7\%$, $97.3\%$, $98\%$, and $96.1\%$ of wounds culture. The proportion of polymicrobial infection observed in this investigation is far below those observed by Yeong et al, 27 who reported a higher wound prevalence of polymicrobial resistant bacteria, but they are broadly consistent with earlier research. 5,24,26 Infection levels were highest among patients over the age of 60 ($69.6\%$), followed by those aged 41-59 ($59.5\%$). This may be due to age related alterations in both arm of immunity, the innate and adaptive immune systems, which reduce their ability to combat infection. 28 Gram-negative bacteria were more prevalent ($62\%$) than Gram-positive bacteria ($30.5\%$), supporting the findings of earlier research in Saudi Arabia and other countries. 8,29-32 However, *Staphylococcus aureus* was the predominant isolate followed by Pseudomonas aeruginosa, Klebsiella pneumoniae, and Escherichia coli. This trend is in agreement with those reported by El-Saed et al, 29 Shimekaw et al, 13 Rai et al 22 and others. 4,5,33 This result may be explained by the fact that most of these microbial isolates are part of skin and gut normal flora, so they are easily spread when there are breaks or cuts in the skin or soft tissue. Another possible explanation for this is that these isolates frequently found in health care environment as a contaminant. 4,34 Presently, $22\%$ of bacterial isolates were multi drug resistant. This is in agreement with the earlier study in which MDR bacteria account for $14\%$-$22\%$ of wound infection, however much lower than that of previously reported rates from Ethiopia with $76.1\%$ -$95.5\%$, and Bangladesh 66-$69\%$. 17,24,33,34 *This is* may be due to variations in type of isolated pathogens, characteristics of study population, insufficient access to effective medications, ineffective treatment plans, low treatment adherence, poorly managed infection control programs, as well as irrational and inappropriate use of antimicrobial medications in these countries. 35 *This is* maybe due to variations in type of isolated pathogens, characteristics of the study population as well as irrational and inappropriate use of antimicrobial medications in these countries of multiple sectors at Saudi Arabia to limit the growth, spread, and emergence of MDR pathogens. 36 Regarding species specific MDR pattern, $70\%$ of A. baumanii and $53.9\%$ of *Klebsiella pneumoniae* showed MDR followed by *Escherichia coli* ($25\%$), *Pseudomonas aeruginosa* ($19.2\%$) and *Staphylococcus aureus* ($12.2\%$). This trend of species specific MDR profile was lower when compared to those reported from other developing countries where the close monitoring and tracking of antimicrobial resistance is questionable. 17,24,33,37 This study found that prevalence of MDR microbial isolates was independent of age, gender, and nationality of patients. The results obtained showed a high resistance rates of the common isolated gram negative bacteria to meropenem, imipenem, cefepime and ceftazidime with (93-$100\%$), ciprofloxacin (68-$81\%$), and amikacin ($66.7\%$). In particular, *Klebsiella pneumoniae* demonstrated a highest resistance towards meropenem, imipenem ($92.3\%$, each), ciprofloxacin ($68\%$), and amikacin ($66.7\%$). This outcome is contrary to that of Tarana et al 4 who reported a high sensitivity of klebsiella to imipenem ($83.3\%$) and amikacin ($66.7\%$). Sisay et al 38 [2019] stated in their systematic review that *Escherichia coli* exhibited a relatively low resistance rate towards ciprofloxacin ($27\%$). This differs from the findings presented here where $81\%$ of *Escherichia coli* were resistant to ciprofloxacin. A $100\%$ A. baumanii showed a resistance towards meropenem, imipenem, cefepime and ceftazidime. These finding are partially confirmed by other studies which showed the resistance of A. baumanii towards amikacin in $70.6\%$ and imipenem in $83.3\%$, 8,39 however, the study of Puca et al 8 and Guan et al 30 was highly sensitive to amikacin ($96.7\%$) and imipenem ($100\%$). The observed disparity in bacterial susceptibility profile could be related to the variation in the level of irrational antibiotic use. Thus, precise comparisons of antibiotic susceptibility profile across different nations are difficult.
## Study limitations
Since the study was a retrospective, an in-depth data on the patients profile was not available due to the improper documentation and storage. As the data about the patient’s pathologies were missing. Numbers tested for some bacterial isolates and antibiotic combinations were small, limiting interpretation. Moreover, the study was single centred carried out in a small size of sample and for a short period of time, which was another limitation. However, a comprehensive work-up of pathogenic isolates and antimicrobial sensitivity profiles for wound infections in our institution were developed, which can be used as a guide for the appropriate usage of empiric antimicrobial therapy. Including more samples in a multicenter study would have yielded more significant results.
In conclusion, the microbial isolation rate from wound infection was high. The prevailing microbial isolates in the present study were Staphylococcus aureus, Klebsiella pneumoniae, *Pseudomonas aeruginosa* and Escherichia coli. Gram-negative wound pathogens are isolated at higher rates than Gram-positive ones. High resistance to one or more of antimicrobial agents was reported with a considerable proportion of them displayed MDR. Therefore, periodic surveillance of microbial profile and their antimicrobial sensitivity pattern in the study region is essential for efficient wound infection management with appropriate antibiotics, in attempt to contain antimicrobial resistance.
## References
1. Maillard J-Y, Kampf G, Cooper R.. **Antimicrobial stewardship of antiseptics that are pertinent to wounds: The need for a united approach**. *JAC Antimicrob Resist* (2021.0) **3** dlab027. PMID: 34223101
2. Simões D, Miguel SP, Ribeiro MP, Coutinho P, Mendonça AG, Correia IJ.. **Recent advances on antimicrobial wound dressing: A review**. *Eur J Pharm Biopharm* (2018.0) **127** 130-141. PMID: 29462687
3. Negut I, Grumezescu V, Grumezescu AM.. **Treatment strategies for infected wounds**. *Molecules* (2018.0) **23** 2392. PMID: 30231567
4. Tarana MN, Fardows J, Farhana N, Khatun R, Akter S.. **Bacteriological profile of wound swab and their antimicrobial susceptibility pattern in Shaheed Suhrawardy Medical College, Dhaka**. *J Shaheed Suhrawardy Med Coll* (2019.0) **11** 65-68
5. Maharjan N, Mahawal B.. **Bacteriological profile of wound infection and antibiotic susceptibility pattern of various isolates in a tertiary care center**. *J Lumbini Med Coll* (2020.0) **8** 218-224
6. Mahat P, Manandhar S, Baidya R.. **Bacteriological profile of wound infection and antibiotic susceptibility pattern of the isolates**. *J Microbiol Exp* (2017.0) **4** 126-133
7. Pallavali RR, Degati VL, Lomada D, Reddy MC, Durbaka VRP.. **Isolation and in vitro evaluation of bacteriophages against MDR-bacterial isolates from septic wound infections**. *PloS one* (2017.0) **12** e0179245. PMID: 28719657
8. Puca V, Marulli RZ, Grande R, Vitale I, Niro A, Molinaro G. **Microbial species isolated from infected wounds and antimicrobial resistance analysis: Data emerging from a three-years retrospective study**. *Antibiotics* (2021.0) **10** 1162. PMID: 34680743
9. Malaekah HM, Alotaibi AE, Alsebail RA, Alelawi GT, Alsarrani RH, Banjar WM.. **Wound care knowledge and perception of the Saudi general population in Riyadh Region**. *Adv Wound Care (New Rochelle)* (2021.0) **10** 293-300. PMID: 32602817
10. Althumiri NA, Basyouni MH, AlMousa N, AlJuwaysim MF, Almubark RA, BinDhim NF. **Obesity in Saudi Arabia in 2020: prevalence, distribution, and its current association with various health conditions**. *Healthcare (Basel)* **2021** 311
11. Alwin Robert A, Abdulaziz Al Dawish M, Braham R, Ali Musallam M, Abdullah Al Hayek A, Hazza Al Kahtany N.. **Type 2 diabetes mellitus in Saudi Arabia: major challenges and possible solutions**. *Curr Diabetes Rev* (2017.0) **13** 59-64. PMID: 26813972
12. Li S, Renick P, Senkowsky J, Nair A, Tang L.. **Diagnostics for wound infections**. *Adv Wound Care (New Rochelle)* (2021.0) **10** 317-27. PMID: 32496977
13. Shimekaw M, Tigabu A, Tessema B.. **Bacterial profile, antimicrobial susceptibility pattern, and associated risk factors among patients with wound infections at Debre Markos Referral Hospital, Northwest, Ethiopia**. *Int J Low Extrem Wounds* (2022.0) **21** 182-192. PMID: 32594808
14. Lai PS, Bebell LM, Meney C, Valeri L, White MC.. **Epidemiology of antibiotic-resistant wound infections from six countries in Africa**. *BMJ Glob Health 2018* **2** e000475
15. Pearson M, Chandler C.. **Knowing antimicrobial resistance in practice: a multi-country qualitative study with human and animal healthcare professionals**. *Global health action* (2019.0) **12** 1599560. PMID: 31294679
16. Baraka MA, Alboghdadly A, Alshawwa S, Elnour AA, Alsultan H, Alsalman T. **Perspectives of healthcare professionals regarding factors associated with antimicrobial resistance (AMR) and their consequences: a cross sectional study in Eastern province of Saudi Arabia**. *Antibiotics* (2021.0) **10** 878. PMID: 34356799
17. Alam MM, Islam MN, Hawlader MDH, Ahmed S, Wahab A, Islam M. **Prevalence of multidrug resistance bacterial isolates from infected wound patients in Dhaka, Bangladesh: a cross-sectional study**. *Int J Surg Open* (2021.0) **28** 56-62
18. Albishi W, Albeshri MA, Mortada HH, Alzahrani K, Alharbi R, Aljuhani F. **Awareness and level of knowledge about surgical site infections and risks of wound infection among medical physicians in King Abdulaziz University Hospital: Cross-sectional study**. *Interact J Med Res* (2019.0) **8** e12769. PMID: 30839280
19. Isaac S, Jennings D.. **Microbial culture**. (2020.0) 96-106
20. Weinstein MP, Lewis JS.. **The clinical and laboratory standards institute subcommittee on antimicrobial susceptibility testing: background, organization, functions, and processes**. *J Clin Microbiol* (2020.0) **58** e01864-e01919. PMID: 31915289
21. Marin C, Lorenzo-Rebenaque L, Laso O, Villora-Gonzalez J, Vega S.. **Pet reptiles: a potential source of transmission of multidrug-resistant Salmonella**. *Front Vet Sci* (2021.0) **7** 613718. PMID: 33490138
22. Rai S, Yadav UN, Pant ND, Yakha JK, Tripathi PP, Poudel A. **Bacteriological profile and antimicrobial susceptibility patterns of bacteria isolated from pus/wound swab samples from children attending a tertiary care hospital in Kathmandu, Nepal**. *Int J Microbiol* (2017.0) 2017
23. Jeschke MG, Phelan HA, Wolf S, Romanowski K, Rehou S, Saetamal A. **State of the science burn research: burns in the elderly**. *J Burn Care Res* (2020.0) **41** 65-83. PMID: 31999337
24. Mohammed A, Seid ME, Gebrecherkos T, Tiruneh M, Moges F.. **Bacterial isolates and their antimicrobial susceptibility patterns of wound infections among inpatients and outpatients attending the University of Gondar Referral Hospital, Northwest Ethiopia**. *International journal of microbiology.* (2017.0) 2017
25. Upreti N, Rayamajhee B, Sherchan SP, Choudhari MK, Banjara MR.. **Prevalence of methicillin resistant Staphylococcus aureus, multidrug resistant and extended spectrum β-lactamase producing gram negative bacilli causing wound infections at a tertiary care hospital of Nepal**. *Antimicrob Resist Infect Control* (2018.0) **7** 1-10. PMID: 29312658
26. Kc R., Shrestha A., Sharma V.. **Bacteriological study of wound infection and antibiotic susceptibility pattern of the isolates**. *Nepal J Sci Technol* (2013.0) **14** 143-150
27. Yeong E-K, Sheng W-H, Hsueh P-R, Hsieh S-M, Huang H-F, Ko A-T. **The wound microbiology and the outcomes of the systemic antibiotic prophylaxis in a mass burn casualty incident**. *J Burn Care Res* (2020.0) **41** 95-103. PMID: 31999335
28. Bajaj V, Gadi N, Spihlman AP, Wu SC, Choi CH, Aging Moulton VR.. **immunity, and COVID-19: how age influences the host immune response to coronavirus infections?**. *Front Physiol* (2021.0) **11** 571416. PMID: 33510644
29. El-Saed A, Balkhy HH, Alshamrani MM, Aljohani S, Alsaedi A, Al Nasser W. **High contribution and impact of resistant gram negative pathogens causing surgical site infections at a multi-hospital healthcare system in Saudi Arabia, 2007–2016**. *BMC Infect Dis* (2020.0) **20** 1-9
30. Guan H, Dong W, Lu Y, Jiang M, Zhang D, Aobuliaximu Y. **Distribution and antibiotic resistance patterns of pathogenic bacteria in patients with chronic cutaneous wounds in China**. *Front Med (Lausanne)* (2021.0) **8** 609584. PMID: 33816517
31. Rahim K, Saleha S, Zhu X, Huo L, Basit A, Franco OL.. **Bacterial contribution in chronicity of wounds**. *Microb Ecol* (2017.0) **73** 710-721. PMID: 27742997
32. Wu Y-K, Cheng N-C, Cheng C-M.. **Biofilms in chronic wounds: pathogenesis and diagnosis**. *Trends Biotechnol* (2019.0) **37** 505-517. PMID: 30497871
33. Mulu W, Abera B, Yimer M, Hailu T, Ayele H, Abate D.. **Bacterial agents and antibiotic resistance profiles of infections from different sites that occurred among patients at Debre Markos Referral Hospital, Ethiopia: a cross-sectional study**. *BMC Research Notes* (2017.0) **10** 1-9. PMID: 28057050
34. Chen Y, Chau J, Yoon J, Rapid Hladky J.. **label-free pathogen identification system for multidrug-resistant bacterial wound infection detection on military members in the battlefield**. *PLoS One* (2022.0) **17** e0267945. PMID: 35511776
35. Ayukekbong JA, Ntemgwa M, Atabe AN.. **The threat of antimicrobial resistance in developing countries: causes and control strategies**. *Antimicrob Resist Infect Control* (2017.0) **6** 1-8
36. Borgio JF, Rasdan AS, Sonbol B, Alhamid G, Almandil NB, Abdulazeez S.. **Emerging Status of Multidrug-Resistant Bacteria and Fungi in the Arabian Peninsula**. *Biology (Basel)* (2021.0) **10** 1144. PMID: 34827138
37. Ikhimiukor OO, Odih EE, Donado-Godoy P, Okeke IN.. **A bottom-up view of antimicrobial resistance transmission in developing countries**. *Nat Microbiol* (2022.0) **7** 757-765. PMID: 35637328
38. Sisay M, Worku T, Edessa D.. **Microbial epidemiology and antimicrobial resistance patterns of wound infection in Ethiopia: a meta-analysis of laboratory-based cross-sectional studies**. *BMC Pharmacol Toxicol* (2019.0) **20** 1-19. PMID: 30611293
39. Li L, Dai JX, Xu L, Chen ZH, Li XY, Liu M. **Antimicrobial resistance and pathogen distribution in hospitalized burn patients: a multicenter study in Southeast China**. *Medicine (Baltimore)* (2018.0) **97** e11977. PMID: 30142829
|
---
title: Parents’ awareness in the Kingdom of Saudi Arabia regarding failure to thrive
authors:
- Reem A. Alshammari
- Osama S. Alnezari
- Waleed A. Alhirabi
- Salm J. Alaamer
- Abdulmajeed S. Alsadun
- Abdulmalik F. Alhmazani
- Yousef F. Bakrshoom
- Ali H. Alharbi
journal: Saudi Medical Journal
year: 2022
pmcid: PMC9994523
doi: 10.15537/smj.2022.43.12.20220511
license: CC BY 4.0
---
# Parents’ awareness in the Kingdom of Saudi Arabia regarding failure to thrive
## Body
When height and weight measures are below the fifth percentile on the growth chart or fall down 2 or more major growth percentiles, it is considered to be physically failing to thrive. 1,2 A child who is not developing in accordance with expectations is referred to as failing to thrive (FTT) in the pediatric community. 3 The failure to thrive is linked to abnormal growth and development. The condition generally results from inadequate nutrition, though on occasions there may be alternate or additional contributory factors. 4 For instance, malabsorption disorders like cystic fibrosis celiac disease or severe allergies are also significant causes of FTT. Additionally, people who have a genetic condition or congenital cardiac disease may need more calories than they think. 5,6 Any such condition where a child needs more calories than assumed can in turn lead to FTT, too. In extreme cases, moreover, parental/guardian neglect or abuse can also cause FTT, with food either not provided or purposely withheld from an infant. 7-11 The most common symptoms of FTT are poor weight gain, irritability, along with easy and excessive fatigue and sleepiness. In turn, generalized development delay can be one of the most significant consequences of FTT. 12 Diagnosis in most cases involves regular measurements, observation and follow-ups for the concerned child. The factors which are needed to be considered include the child’s age, general health, past medical history, as well as the severity of their symptoms. 4,13 A team of healthcare professionals, such as social workers, dietitians, physical therapists, geneticists, and other experts, are generally involved in treatment. When children do not get better and grow back to normal after therapy interventions, cooperation between many providers is crucial. 4 *As a* result of the many effects and complications resulting from growth failure (FTT), including delayed growth and maturation, revealing the awareness of parents in Saudi Arabia on growth failure, its factors, causes and effects, is critical to protecting the health of mothers, children and infants. In addition, more studies are needed in Saudi Arabia that gauge parents’ awareness of failure to thrive carried out in Saudi Arabia that try to gauge how aware parents are of growth failure. As a result, we made the decision to assess the level of parental awareness on growth failure across all of Saudi Arabia and look into the potential influence of covariates (social and demographic) as determinants of the level of awareness of parents, both mother and father, aged between 18 and 60, and on the national level.
## Abstract
### Objectives:
To assess the level of parental Awareness about growth failure across all of Saudi Arabia and look into the potential influence of covariates (social and demographic) as determinants of the level of knowledge of parents, both mother and father, aged between 18 and 60, and on the national level.
### Methods:
A cross-sectional study involving a survey of 4,404 parents, aged between 18 and 60 years, in all administrative regions within Saudi Arabia was undertaken From March 2022 to May 2022.
### Results:
The majority of participants had average awareness of FTT, and differences in region, as well as in educational and professional levels, had an impact on this awareness, with participants from the western region who hold bachelor’s degree and employees have a better level of awareness than others.
### Conclusion:
To raise awareness, the whole public, especially parents, has to be continuously informed on failure to thrive in children and how to deal with it through educational programs and awareness campaigns.
## Methods
The sample size was estimated using the Raosoft Sample Size Calculator, taking into account the expected response of $50\%$, the margin of error of $5\%$, and the confidence interval of $95\%$. As the minimum needed sample size is 385, and for a representative sample of survey respondents with minimal bias, we multiplied the sample size and added a $35\%$ increment. Following the application of exclusion criteria, a representative sample ($$n = 4$$,404) of parents participated in the survey from all regions of Saudi Arabia.
The inclusion criteria were parents, whether mother or father, aged between 18 and 60 years, in all administrative regions within the Kingdom. While the general population, and under the age of 18 and over 60 years of age is excluded. The study was carried out from March to May 2022.
A structured questionnaire was created by the researchers under the supervision of a pediatric teaching assistant and consultant pediatrician after they carried out a literature study and sought advice from other professionals in the field. The main survey included 6 questions (age, gender, nationality, region, level of education, and occupation) inquiring regarding the sociodemographic characteristics of the participating parents and 18 questions measuring the participating parents’ levels of FTT awareness. It contained (a general definition of growth failure, its causes, symptoms, factors and management, in addition to child nutrition).
## Statistical analysis
Following data extraction, it was then revised, coded and fed into statistical software for data analysis. Namely, SPSS version 22 (IBMCorp, Armonk, NY, USA). Descriptive analysis based on frequency and percent distribution was undertaken for all variables, including demographic data, and awareness items. In respect awareness items, 10 questions were surveyed, a correct answer scored 3 points. A total summation of the discrete scores of the different items was then calculated. Correct answers received a value of 3, incorrect 0. The average awareness was then calculated for each participant in the sample. Participants whose mean score was below 1.5 were considered to have poor awareness. Those who scored 1.5-2.5 were considered to have intermediate awareness, while good awareness was achieved if a participant scored 2.5 or above. Finally, all statistical analysis was done using 2 tailed tests. A Chi-squared test was used to calculate the p-value. A p-value of under 0.05 was considered statistically significant. The study results were presented via tables, graphs, and pie charts.
The questionnaire was pre-tested on 10 persons, randomly selected from different age groups, prior to the onset of the actual data collection process. The knowledge scale’s reliability test, which was based on the scoring result, produced an acceptable result with a Cronbach’s alpha of 0.720. Any problems that were identified in the pre-test were dealt with by amending the questions such that they became more concise and intelligible. The questionnaire was presented once to each respondent.
Ethical approval was obtained from the Medical Research Ethics Committee at Hail University (No: H-2022-155). Informed consent was obtained from all respondents at the start of the questionnaire.
## Results
A total of 4,404 participants from different regions of Saudi Arabia responded to the survey. Responses were collected from March 2022 to May 2022. All participants completed the survey, giving a response rate of $100\%$. The demographic information collected included age, gender, region, nationality, educational level, and occupation.
Table 1 displays the distribution of social and demographic data for the participants. As it appears that the distribution of participants at various ages is equal, as was intended to obtain responses from various ages in an equal manner. The majority of the participants were Saudi women from the western region who had attended university and were currently working, along with Table 2 showing the distribution of their level of awareness regarding failure to thrive. As expected, the majority of participants had an intermediate level of awareness, and social and demographic co-factors had a part in the level of awareness that was noticed. The χ 2 analysis for awareness level and the socio-demographic variables showed significant ($p \leq 0.05$) associations, except in respect age and gender. Considering the regional differences in awareness levels, a statistically significant difference between the participants’ educational status and their awareness level was discovered ($$p \leq 0.000$$). In terms of the participants’ education level and awareness levels, parents with university degrees tend to have higher awareness levels. On the other hand, parents who had not received education were more likely to have low awareness levels. The participants’ education level and level of awareness were found to differ statistically ($$p \leq 0.048$$). There was a statistically significant difference between the nationality of participants and their awareness level with regard to these 2 variables ($$p \leq 0.008$$). As well there was a statistically significant difference between the functional status of the participants and their awareness level when it came to the occupation status of the participants and their levels of awareness ($$p \leq 0.048$$).
In respect the regional differences in awareness levels, $1.4\%$ of participants from the Central region had a good awareness level. The Southern region has an awareness level of $0.4\%$ and $0.3\%$ in the Northern region. The samples from the Eastern and Western regions contained no participants with good awareness levels Figure 1.
**Figure 1:** *- The regional distribution of the awareness level regarding failure to thrive.*
Regarding to the participants’ educational level and their awareness level, good awareness levels were mostly found in parents who have university education level. On the other hand, poor awareness levels were most common among non-university educated parents. Figure 2.
**Figure 2:** *- The educational distribution of the awareness level regarding failure to thrive.*
Regarding to the participant nationality and their awareness level, $1.2\%$ of Saudi national parents, compared with $0.2\%$ non-Saudi national parents, showed a good awareness level. Furthermore, $60\%$ of Saudis compared with $3\%$ of non-Saudis had an intermediate awareness level, while $30.1\%$ of Saudis and $1.4\%$ of non-Saudis had a poor awareness level (Figure 3A). While the occupation status of the participants and their awareness levels it was described in Figure 3B. The good awareness levels were identified among $1.2\%$ of employed parents compared with $0.9\%$ of unemployed parents. Intermediate awareness levels were reported in $38\%$ of employed parents compared with $27\%$ of unemployed parents. Last, poor awareness levels were found among $17.1\%$ of employed parents and $14.6\%$ of unemployed parents.
**Figure 3:** *- The nationality and occupation status distribution of the awareness level regarding failure to thrive. A) Nationality distribution of the level of awareness of failure to thrive, B) Occupational distribution of the level of awareness of failure to thrive.*
Table 3 shows the questions by which participants’ awareness of growth failure and the factors which cause it was measured.
**Table 3**
| Factors | The correct answers | n | % |
| --- | --- | --- | --- |
| How do you know that your child is gaining the ideal weight for his age? | Go to medical visits and examine it and take growth indicators periodically | 1499 | 34.0 |
| Have you seen sources to explain the nutritional programs appropriate for your children | Yes | 2948 | 66.9 |
| Do you think that family history has anything to do with poor child’s weight? | No | 694 | 15.8 |
| What is the common cause of poor normal weight gain? | Malnutrition | 2324 | 52.8 |
| What are the factors that may lead to poor weight of the child? | Premature birth | 1834 | 19.7 |
| What are the factors that may lead to poor weight of the child? | Double weight at birth | 1718 | 18.6 |
| What are the factors that may lead to poor weight of the child? | Chronic diseases | 2692 | 29.0 |
| What are the factors that may lead to poor weight of the child? | Teen mother | 916 | 9.9 |
| What are the factors that may lead to poor weight of the child? | Educational level for child care | 875 | 9.4 |
| In Your opinion What is the right time to break the baby about milk feeding? | At the age of 2 years | 2455 | 55.7 |
| What is the most appropriate time to make food? | At the age of 4-6 months | 2564 | 58.2 |
| How much do you think your child needs to feed (rice, vegetables, fruit, white meat) before a year? | More than 50% | 1461 | 33.2 |
| How much do you think your child needs to feed (juice or milk domestic record) before a year? | More than 50% | 1182 | 26.8 |
| How can the number of meals and their quantity during the day can vary between babies and bulldozers? | The baby is given a few foods and a quantity, fish, textures, and gradual diversity with age | 2184 | 49.6 |
| Is it permissible to have the oldest age of the year and his meal with family? | Yes | 2730 | 62.0 |
| When a child loses or loses weight, what is appropriate to do, and in your opinion, a successful outcome for treating the condition? | Go to children’s clinic for medical consultation | 362 | 8.2 |
| The neglect of the child or child abuse can lead to weight decrease? | Yes | 3753 | 85.2 |
| Thyroid activity can lead to insufficient weight gain? | Yes | 2722 | 61.8 |
| What is the first signs of growth failure? | Weight loss | 499 | 11.3 |
| Failure to treat growth failure can affect mental abilities? | Yes | 3058 | 69.4 |
| Delay in giving vaccinations to the child can leads to failure to thrive? | No | 1043 | 36.3 |
| In the event of severe growth failure, is it possible for short stature and small head circumference to occur with weight loss? | Yes | 2829 | 64.2 |
## Discussion
The Saudi population’s knowledge of FTT and its causes was examined in this study. The objective was to comprehend the current level of community awareness and how to raise it further. This was important to do because parental thoughts and behaviors towards their children’s health and development are greatly improved by awareness. Since normal growth is an indication of good health in children. 14 A good degree of parental awareness is helpful in monitoring growth, early discovery of the causes of poor development, and early intervention, all of which increase the likelihood that the kid will have good health. Second, a prior study by Hoddinott et al 15 demonstrated that growth failure has a major impact on crucial factors, including family formation, reproduction, men’s pay rates, the prevention of poverty, education, and cognitive success. This emphasizes the significance of fostering linear growth from conception to age 2 and enhancing childhood nutrition because these actions benefit both individuals and their families for the rest of their lives. 15 The importance of this study is that asses the level of awareness of parents on a national scale to test parental awareness on a national scale.
When investigating the parents’ awareness levels, through a set of questions, it was found that most of the participants had moderate levels of awareness on failure to thrive in children, which indicates that raising public awareness of failure to thrive is necessary in Saudi Arabia, where many regions call for particular attention. In addition, the studies’ findings indicated that there are variations in awareness levels between educational levels, between Saudis and non-Saudis, and between employees and non-employees, which calls for additional and deeper research for deeper study on the subject, and more an effort to raise awareness on all fronts. Especially since there are no previous studies that presented changes in the levels of community awareness in the past, to monitor levels of awareness in the future and work on them.
When determining the participants’ degree of awareness, a series of questions was put to them that brought to light several crucial details that could significantly alter the health of the child. The reason of FTT is discovered by testing, imaging, and endoscopy in less than $1.4\%$ of instances. 16 Therefore, these assessments should only be performed on children who exhibit obvious signs of an organic condition and those who do not grow after receiving behavioral or dietary therapies. 17 The results of the study indicated that $52.8\%$ of parents think that malnutrition is the most common cause of FTT. Meanwhile, regarding the type of food provided to the child during the first year, our study found that $43.9\%$ of infants receive less than $50\%$ of juice and milk artificial sweeteners perhaps. It’s crucial to comprehend this since excessive consumption of nutrient-poor liquids, including so-called “fruit” juice, which is primarily flavored sugar water, causes satiety before nutrient-rich foods can be consumed. 18 The study results shows that overall FTT awareness levels need to be improved in Saudi Arabia, with several regions showing a particular need for attention. Additionally, the majority of parents in this survey stated that they base their estimation of their child’s weight on how they perceive their child’s body shape and rely on seeing their child’s body shape to do so. New, culturally appropriate models must be developed in order to relate to the child’s weight status in the context of the Saudi community. The results also highlight there is a need to correct the misapprehension that family history is related to the child’s potential to gain weight. There is also a need to start introducing food at age of 4-6 months, rather than the common misconception of 9 months that most participants highlighted. Moreover, more FTT awareness programs are also needed. These factors are however merely the tip of the iceberg in respect improving FTT awareness levels in Saudi Arabia. The major emphasis must be on combating the misinformation that many parents’ encounter, by providing them with better access to verified healthcare sources and limiting the spread of less credible materials. The study results also indicate that most parents are unaware of the ideal weight for their children. Most ($52.7\%$) determined their child’s weight from observation of their body shape and growth, rather than arranging medical visits and assessing growth indicators periodically. Most ($51.8\%$) of those surveyed from the Eastern region displayed a poorer FTT awareness level than was the case with participants from the other regions. The region with the highest awareness levels was the Central region, where $7.6\%$ of those surveyed had good awareness. The present study therefore accords with other research in regard the geographic differences in awareness levels which exist between Saudi regions. The study also accords with Al-Qahtani et al 18 who claimed, that the majority of parents base their assessment of their children’s weight gain on how their children’s bodies seem. Furthermore, the study found that a correlation exists between educational level and awareness level. That is, the higher the participant’s educational level, the better their awareness level. The study also found that, when faced with poor weight gain in their child, $8.3\%$ of parents first respond by purchasing multiple vitamins from the pharmacy, though without a specific idea of what deficiencies need tackling seemed to be Saudi. Furthermore, $85.2\%$ of the parents surveyed agree that child abuse or neglect can cause FTT, due to the possibility of clinical bonding difficulties in infants with FTT. Therefore, pediatricians should think on seeking out further advice from mental health specialists who can help them assess the bond between an infant and its caregiver. 19
## Study limitations
A cross-sectional method precluded the drawing of any inference on the causality between variables. Moreover, awareness levels were also categorized in 3 classes. This, however, may obscure differences between the 3 levels, so any generalizing of the study results should be carried out with caution. Notwithstanding these limitations, this study addresses a crucial health issue for the Saudi society. Its strengths lie in the random stratifying sampling methodology and the large general pediatric population that was obtained.
In conclusion, according to the investigation into the parents’ awareness levels, the majority of the parents who had a bachelor’s degree and were had occupations. This suggests that cognition has a strong foundation and that education and occupation play a big part in it. It is advised to undertake in-depth studies on the levels of awareness in each region and to conduct future research as there were disparities in the levels of awareness between the regions of the Kingdom, but the explanation for these differences is unclear. Besides that, the study’s findings indicate that attention should be provided to the ongoing activation of educational programs and awareness campaigns for various age groups via the Internet and the media alongside one another in health centers, public spaces, workplaces, schools, and universities in order to raise the level of awareness of FTT in the Saudi community.
## References
1. **Failure to Thrive. [Updated 2022; cited 2022 Oct 7]. Available from**
2. Smith AE, Badireddy M.. **Failure to thrive. Shakespeare Survey: 72: Shakespeare and War**. *StatPearls* (2022.0) 183-99
3. Kumeliauskas L, Fruetel K, Holroyd-Leduc JM.. **Evaluation of older adults hospitalized with a diagnosis of failure to thrive**. *Can Geriatr J* (2013.0) **16** 49-53. PMID: 23737929
4. **Failure to Thrive (FTT) [Updated 2022 Sept; cited 2022 Oct 26]. Available from**
5. Jones CE, Desai H, Fogel JL, Negrin KA, Torzone A, Willette S. **Disruptions in the development of feeding for infants with congenital heart disease**. *Cardiol Young* (2021.0) **31** 589-596. PMID: 33303052
6. Luca AC, Miron IC, Mîndru DE, Curpăn AS, Stan RC, Țarcă E. **Optimal nutrition parameters for neonates and infants with congenital heart disease**. *Nutrients* (2022.0) **14** 1671. PMID: 35458233
7. Pratt KJ, Greer AG.. **Debating parental responsibility for childhood obesity: ethical and legal considerations**. *Bariatr Nurs Surg Patient Care* (2012.0) **7** 146-149
8. van Kalleveen MW, de Meij T, Plötz FB.. **Clinical spectrum of paediatric coeliac disease: A 10-year single-centre experience**. *Eur J Pediatr* (2018.0) **177** 593-602. PMID: 29392394
9. Agyemang A, Nowak-Wegrzyn A.. **Food protein-induced enterocolitis syndrome: a comprehensive review**. *Clin Rev Allergy Immunol* (2019.0) **57** 261-271. PMID: 30734159
10. Bazacliu C, Neu J.. **Necrotizing enterocolitis: long term complications**. *Curr Pediatr Rev* (2019.0) **15** 115-124. PMID: 30864508
11. Kim Y, Wang SE. **Jiang Y hui. Epigenetic therapy of Prader–Willi syndrome**. *Transl Res* (2019.0) **208** 105-118. PMID: 30904443
12. Cole SZ, Lanham JS.. **Failure to thrive: an update**. *Am Fam Physician.* (2011.0) **83** 829-834. PMID: 21524049
13. Levi-Soskin N, Shaoul R, Kohen H, Jbara A, Dori D.. **Model-based diagnosis with FTTell: Assessing the potential for pediatric failure to thrive (FTT) during the perinatal stage**. *Lecture Notes in Business Information Processing* (2019.0) **359** 37-47
14. Haymond M, Kappelgaard AM, Czernichow P, Biller BM, Takano K, Kiess W. **Early recognition of growth abnormalities permitting early intervention**. *Acta Paediatr* (2013.0) **102** 787-796. PMID: 23586744
15. Hoddinott J, Behrman JR, Maluccio JA, Melgar P, Quisumbing AR, Ramirez-Zea M. **Adult consequences of growth failure in early childhood**. *Am J Clin Nutr* (2013.0) **98** 1170-1178. PMID: 24004889
16. Larson-Nath C, Biank VF.. **Clinical review of failure to thrive in pediatric patients**. *Pediatr Ann 2016* **45** e46-e49
17. Al-Qahtani SM, Alsultan BS, Awdah NA, Alshehri MA, Alqahtani AS, al Assiri KM. **Parental perception and knowledge about ideal weight among preschool-aged children in Abha city, southwestern Saudi Arabia**. *Saudi J Obes* (2017.0) **5** 85
18. Goh LH, How CH, Ng KH.. **Failure to thrive in babies and toddlers**. *Singapore Med J* (2016.0) **57** 287-291. PMID: 27353148
19. Nangia S, Tiwari S.. **Failure to thrive**. *Indian J Pediatr* (2013.0) **80** 585-589. PMID: 23604606
|
---
title: Challenges in the Management of and Biologic Use in Incarcerated Patients With
Inflammatory Bowel Disease
authors:
- Sarah Barbina
- John Romano
- Erin Forster
journal: Crohn's & Colitis 360
year: 2023
pmcid: PMC9994588
doi: 10.1093/crocol/otad002
license: CC BY 4.0
---
# Challenges in the Management of and Biologic Use in Incarcerated Patients With Inflammatory Bowel Disease
## Abstract
### Background
Therapy and management of inflammatory bowel disease (IBD) require commitment from both the provider and patient to ensure optimal disease management. Prior studies show vulnerable patient populations with chronic medical conditions and compromised access to health care, such as incarcerated patients, suffer as a result. After an extensive literature review, there are no studies outlining the unique challenges associated with managing prisoners with IBD.
### Methods
A detailed retrospective chart review of 3 incarcerated patients cared for at a tertiary referral center with an integrated patient-centered IBD medical home (PCMH) and a review of literature was performed.
### Results
All 3 patients were African American males in their 30s with severe disease phenotypes requiring biologic therapy. All patients had challenges with medication adherence and missed appointments related to inconsistent access to clinic. Two of the 3 cases depicted better patient-reported outcomes through frequent engagement with the PCMH.
### Conclusions
It is evident there are care gaps and opportunities to optimize care delivery for this vulnerable population. It is important to further study optimal care delivery techniques such as medication selection, though interstate variation in correctional services poses challenges. Efforts can be made to focus on regular and reliable access to medical care, especially for those who are chronically ill.
## Introduction
Inflammatory bowel disease (IBD) is a relapsing and remitting inflammatory condition describing ulcerative colitis and Crohn’s disease (CD). The incidence is rising with 6.8 million people worldwide living with IBD.1 Therapies for moderate severity IBD involve either injection or infusion biologic therapy, though oral options are more available. Shared decision-making (SDM) is at the center of drug selection. However, the use of biologics requires frequent healthcare contact for optimal outcomes, a challenging reality for vulnerable populations such as those currently incarcerated.2 The highest age-standardized prevalence rate of IBD worldwide is in North America, a continent that includes the United States, which is known for the highest incarceration rate worldwide and the largest number of prisoners. Fortunately, our therapeutic options for the treatment of IBD are also increasing. SDM discussions between patient and provider are at the center of drug selection, and final choices are often influenced by route of administration and frequency of dosing. Vulnerable populations, such as those currently incarcerated, may require special considerations to ensure optimal outcomes.
In review of the literature, there are no studies outlining the unique challenges associated with managing IBD in these patients. We aim to describe the clinical course of several incarcerated patients at our tertiary referral center and the obstacles impacting their clinical course. This study describes 3 specific patients and the evolution of their SDM and the efficacy of their disease management.
## Methods
A retrospective case series with data extraction from electronic medical records from December 2019 to present and literature review was performed.
## Case 1
A 30-year-old incarcerated African American (AA) male with a history of an abdominal gunshot wound was admitted from jail with diarrhea and hypovolemia. Computed tomography (CT) showed terminal ileal inflammation. Colonoscopy was consistent with Crohn’s ileitis. Ustekinumab (UST) was selected as initial therapy and infused on the day of discharge. This choice was based on leveraging the weight-based initial dose and ability to ensure adherence, with confirmation of future dosing through nurse-led injection appointments, and consistent access to medication with patient assistance programs. Unfortunately, due to transportation issues, he had multiple missed doses with a gap of as long as 6 months. However, he was reloaded with an IV dose, and with intensive social work support to coordinate logistics, he received one maintenance dose prior to being released from his detention center. Unfortunately, he remains lost to follow-up by both the clinic and specialty pharmacy, a challenge not uncommon in relation to the correctional system and recidivism.
## Case 2
A 35-year-old incarcerated AA male was admitted to the hospital several times for hematochezia with multiple unrevealing endoscopic evaluations. He was readmitted for a gastroenterology (GI) bleed, and magnetic resonance enterography (MRE) and fecal calprotectin were again unremarkable. Repeat colonoscopy showed severe inflammation of the terminal ileum (TI) with patchy mild–moderate inflammation of colon and pathology consistent with idiopathic IBD. He was started on IV steroids and discharged on an oral prednisone taper for 9 weeks. Initial planned outpatient therapy was 5 mg/kg infliximab, but he experienced a delayed start requiring an additional steroid taper. Infliximab was started 4 months later at standard dosing. He unfortunately was late for his second dose and missed his third dose due to hospitalization for ongoing symptoms. During that admission, his anemia was so severe that a blood transfusion was required. At that time, the decision was made to reinduce with an increased dose at 10 mg/kg every 4 weeks with the first dose on the day of discharge. Since that time, he has been late for multiple infusions due to prison-related scheduling issues. After requiring his fifth prolonged prednisone taper in 1 year, he underwent infliximab reload at 10 mg/kg. He again was late for his every 4-week maintenance infusions. Since being released from prison, he has been on time for infliximab infusions 10 mg/kg every 8 weeks with reported improvement in his diarrhea and hematochezia. Subsequent endoscopy revealed resolution of the TI disease with patchy mild colitis in only 1 region of the colon. His dosing regimen was optimized to 10 mg/kg every 4 weeks and clinically he has been doing well.
## Case 3
A 31-year-old incarcerated AA male complained of hematochezia and fever requiring admission to the hospital and was diagnosed with *Clostridium difficile* colitis. CT scan and colonoscopy showed left-sided colitis. Following treatment with oral vancomycin, outpatient colonoscopy was consistent with residual proctosigmoiditis. Through SDM, he was started on mesalamine enemas but had difficulty retaining them and decision was made to start on UST for ease of dosing and avoidance of per-rectum therapies per patient preference. The UST was infused at the clinic during a scheduled visit. He missed multiple doses due to inconsistent transport to clinic for nurse-led administration of medication. He was then released from custody and off all therapy until developing C. difficile infection requiring hospitalization. He was treated with vancomycin and then resumed on PO and PR mesalamine as an outpatient. However, upon reincarceration with questionable access to medication, he developed worsening symptoms and was started on sulfasalazine. Repeat colonoscopy showed Mayo 3 pancolitis with pathology confirming moderate inflammation. He resumed UST therapy with a standard loading dose given at a clinic appointment and 90 mg SC every 8 weeks consistently while incarcerated. The patient has since been released from the detention center and has a steady job. He has been in frequent contact with the PCMH and the behavioral health social worker who assists him in coming to appointments and receiving his medication in a timely fashion from the specialty pharmacy. Clinically, he is doing well and is planned for endoscopic evaluation shortly once his insurance is valid. Additional biochemical evaluation is pending given the cost associated with self-pay laboratory studies.
## Discussion
All 3 patients were incarcerated AA males who experienced difficulty in obtaining routine care and consistent access to biologic therapy for IBD. Two out of the 3 patients were on primarily injection-based chronic therapies to best coincide with planned healthcare system encounters—either clinic or nurse visits. These visits can be coordinated with preidentified correctional facility personnel in advance but do require anonymity to avoid jailbreak attempts. Care delivered in the context of a medical home utilizes social workers/care coordinators to navigate these potential logistic challenges. It is evident there are care gaps as shown by loss to follow-up related to correctional transfers, recidivism, and missed doses of medication—even for those medications chosen for their relative dosing infrequency (every 8 weeks). Consideration should be given toward favoring IV “reload” doses over shortening dosing frequency to avoid gaps in administration. Two of the 3 cases clearly depict high healthcare utilization but do reflect that with the assistance of a medical home and frequent contact with correctional facilities, one can overcome barriers to ensure appropriate care for better clinical outcomes. It is important to further study the best way to deliver IBD care, specifically biologics, to this vulnerable population.
Although there is no research or case reports on IBD management in incarcerated individuals, there is literature on difficulty of chronic disease management for other diagnoses: $60\%$ of inmates with a diagnosis requiring routine lab work had never undergone a blood draw.3 One qualitative study stated that 1 patient with CD was unable to receive proper ileostomy bags and therefore suffered from leaking bags.4 Furthermore, a study showed only $38\%$ of patients with diabetes were given their prescribed aspirin.5 Similarly, IBD patients require more than just consistent medication administration; standard of care requires regular lab monitoring, vaccine administration, and cancer screenings.6 As seen in all 3 patients, concomitant vitamin and mineral deficiencies are commonly requiring repletion. Weekly rather than daily Vitamin D supplementation could be utilized given less-demanding dosing regimens along with parenteral rather than oral formulations of iron for similar reasons. Here we suggest biologic medications well suited for infrequent and subcutaneous administration and utility of a PCMH for the coordination of care for incarcerated patients. Therapeutic drug monitoring can be challenging to coordinate but should be utilized at least reactively in times of clinical decompensation and could be performed during nurse-led injection visits as needed.
## Conclusion
For incarcerated patients, the successful administration of biologic therapy for maximal medical effectiveness requires operationalizing SDM. For those with compromised access to care, as is seen in the inmate population, it is of paramount importance to attempt to standardize and optimize care pathways. For example, choosing therapies with similar efficacy but less-demanding monitoring or administration schedules may be ideal for those in correctional facilities. As seen in this patient population, erratic access to care and medications, despite the selection of the least frequently administered, noninfusion-based therapies, compromised efficacy, and ultimately resulted in worse outcomes. Advocacy efforts with organizations supporting incarcerated individuals focusing on regular and reliable access to medical care are needed. SDM conversations are not limited to those patients who live a life beyond bars.
**Table 1.**
| Unnamed: 0 | Case 1 | Case 2 | Case 3 |
| --- | --- | --- | --- |
| Age | 38 | 35 | 31 |
| Race | African American | African American | African American |
| Gender | Male | Male | Male |
| IBD type | Crohn’s disease | Crohn’s disease | Ulcerative colitis |
| Extraintestinal manifestations | Primary sclerosing cholangitis, arthralgias | None reported | None reported |
| Comorbidities | Gunshot wound with large bowel resection | Anemia, saddle pulmonary embolism | Recurrent clostridium difficile infection |
| Vitamin and mineral deficiencies | Iron, Vit D deficient | Vit D, B12, and Iron deficient | Iron, Vit D deficient |
| Current therapy | Ustekinumab 90 mg every 8 weeks | Infliximab 1000 mg (10 mg/kg) every 4 weeks | Ustekinumab 90 mg every 8 weeks |
| Missed therapy dose documented | Yes | Yes | Yes |
| Therapeutic drug monitoring performed | No | Yes | No |
| Drug level | | 13 | |
| Antibodies present | | No | |
| Hospital admissions since initial diagnosis | No | Yes | Yes |
## Funding
None declared.
## Conflict of Interest
Erin Forster holds the position of Associate Editor for Crohn’s & Colitis 360 and has been recused from reviewing or making decisions for the manuscript. No coauthors have any financial conflicts of interest to disclose.
## Data Availability
Data not publicly available.
## References
1. **The global, regional, and national burden of inflammatory bowel disease in 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017**. *Lancet Gastroenterol Hepatol* (2020) **5** 17-30. PMID: 31648971
2. Hoseyni H, Xu Y, Zhou H.. **Therapeutic drug monitoring of biologics for inflammatory bowel disease: an answer to optimized treatment?**. *J Clin Pharmacol.* (2018) **58** 864-876. PMID: 29462502
3. Wilper AP, Woolhandler S, Boyd JW. **The health and health care of US prisoners: results of a nationwide survey**. *Am J Public Health.* (2009) **99** 666-672. PMID: 19150898
4. Condon L, Hek G, Harris F. **Users’ views of prison health services: a qualitative study.**. *J Adv Nurse.* (2007) **58** 216-226
5. Clark BC, Grossman E, White MC, Goldenson J, Tulsky JP.. **Diabetes care in the San Francisco County Jail**. *Am J Public Health.* (2006) **96** 1571-1574. PMID: 16873757
6. Moscandrew M, Mahadevan U, Kane S.. **General health maintenance in IBD**. *Inflamm Bowel Dis.* (2009) **15** 1399-1409. PMID: 19591135
|
---
title: 'Determinants of self-reported health status during COVID-19 lockdown among
surveyed Ecuadorian population: A cross sectional study'
authors:
- Iván Dueñas-Espín
- Constanza Jacques-Aviñó
- Verónica Egas-Reyes
- Sara Larrea
- Ana Lucía Torres-Castillo
- Patricio Trujillo
- Andrés Peralta
journal: PLOS ONE
year: 2023
pmcid: PMC9994680
doi: 10.1371/journal.pone.0275698
license: CC BY 4.0
---
# Determinants of self-reported health status during COVID-19 lockdown among surveyed Ecuadorian population: A cross sectional study
## Abstract
### Objective
To examine the associations of sociodemographic, socioeconomic, and behavioral factors with depression, anxiety, and self-reported health status during the COVID-19 lockdown in Ecuador. We also assessed the differences in these associations between women and men.
### Design, setting, and participants
We conducted a cross-sectional survey between July to October 2020 to adults who were living in Ecuador between March to October 2020. All data were collected through an online survey. We ran descriptive and bivariate analyses and fitted sex-stratified multivariate logistic regression models to assess the association between explanatory variables and self-reported health status.
### Results
1801 women and 1123 men completed the survey. Their median (IQR) age was 34 (27–44) years, most participants had a university education ($84\%$) and a full-time public or private job ($63\%$); $16\%$ of participants had poor health self-perception. Poor self-perceived health was associated with being female, having solely public healthcare system access, perceiving housing conditions as inadequate, living with cohabitants requiring care, perceiving difficulties in coping with work or managing household chores, COVID-19 infection, chronic disease, and depression symptoms were significantly and independently associated with poor self-reported health status. For women, self-employment, having solely public healthcare system access, perceiving housing conditions as inadequate, having cohabitants requiring care, having very high difficulties to cope with household chores, having COVID-19, and having a chronic disease increased the likelihood of having poor self-reported health status. For men, poor or inadequate housing, presence of any chronic disease, and depression increased the likelihood of having poor self-reported health status.
### Conclusion
Being female, having solely public healthcare system access, perceiving housing conditions as inadequate, living with cohabitants requiring care, perceiving difficulties in coping with work or managing household chores, COVID-19 infection, chronic disease, and depression symptoms were significantly and independently associated with poor self-reported health status in Ecuadorian population.
## Introduction
Globally, lockdown’s impact on physical and mental health during the COVID-19 pandemic has been well-documented [1–6]. In several countries worldwide, lockdowns cause a high incidence of anxiety, depression, post-traumatic stress disorder, psychological distress, and other types of stress in the general population [7–9]. These conditions seem to affect women, children, adolescents, the elderly, and populations experiencing socioeconomic deprivation [7–10]. However, few studies have examined the specific impact of lockdown measures in the context of rampant social and economic inequalities and weak states with low emergency response capacities.
Specifically, in the Latin American and Caribbean regions, accentuated impacts from lockdown and the COVID-19 pandemic have generated several undesired effects such as the formation of COVID-19 hotspots exacerbated by weak social protection structures, fragmented health systems, and deep inequalities [11]. Although the region’s development process was facing serious structural limitations before the pandemic, it is expected that COVID-19 will cause a worse recession in the region, with subsequent contraction of regional gross domestic product (GDP) [12].
Similar to other Latin American countries, Ecuador was severely affected by the COVID-19 pandemic [13]. In 2020, the country reached the second highest rate of confirmed cases in South America [10] and the ninth place worldwide in the number of deaths per million people [14]. In April 2020, Ecuador’s case called global attention to disturbing images of corpses piling up in the streets of Guayaquil [15]. In the same year, $24\%$ of the urban population and $49\%$ of the rural population lived under the poverty line [16]. Moreover, only approximately $30\%$ of the economically active population had an adequate job, that is, employed persons who, during a reference week, received labor income equal to or greater than the minimum wage and worked equal to or greater than 40 hours a week, regardless of the desire and availability to work additional hours [17]. Fewer than $60\%$ of rural households had access to the Internet, and fewer than $20\%$ owned a computer [18].
Despite the precarious economic situation of most of the population, the measures taken by the Ecuadorian government to prevent COVID-19’s spread were mainly restrictive interventions, such as social distancing policies and mandatory lockdowns enforced by the police and military forces [13]. The Ecuadorian government’s response to the global emergency was also characterized by poor epidemiological surveillance, lack of access to the public health system, corruption scandals, null community participation, and insufficient social support [19, 20]. In June 2021, the Ecuadorian government implemented a plan for massive vaccinations.
Thus, for more than a year, people living in Ecuador had to cope with the fear of contracting COVID-19, compounded by movement and rights restrictions, an education system that was only allowed to operate online, a collapsed health system unable to provide care for both COVID-19 and other health conditions, and an economic recession [21]. Moreover, despite some social protection measures, such as food kits adopted by local governments, NGOs, churches, and civil society organizations, many basic needs went unfulfilled owing to lockdown restrictions, such as access to medicines for chronic patients [22]. Several fields of public health and social care were neglected by Ecuadorian authorities, leading to a profound worsening of health across all social classes, and the management of social risks, exacerbated by the pandemic, fell to families, particularly women. In this sense, the impact has been especially significant for women, children, teenagers, and people with disabilities [23].
These limited economic and social aid policies have resulted in significant reductions in the coverage of health supplies and medications for chronic patients, maternal health, and sexual and reproductive health as well as unequal vaccine access [24]. In this context, a few studies conducted in Ecuador reported an increased prevalence of psychological distress symptoms [25]. Some studies and anecdotal data point to an increase in many factors that are determinants of poor mental health during a health emergency, such as the burden of unpaid domestic labor for women, gender-based violence [13, 19], child abuse [26], lack of access to medicine for prior chronic diseases, poor housing conditions, and overcrowding [27].
After a brief revision from literature in Medline, we found several papers studying the social impact from the COVID-19 lockdown in Ecuadorian population. Regarding its impact on lifestyles, one study [28] found that teachers were not ready for the sudden shift to emergency remote teaching. Another study [29] found that stress was associated with poorer diet quality. Therefore, the confinement affected various areas of the lives of citizens.
Regarding the impact of lockdown on mental health of Ecuadorian population, one study [30] found that burnout has a mediating effect between job motivation and turnover intention, and that female and male workers’ burnout and turnover intentions levels are different when intrinsic motivation is present. Otherwise, a multicenter study [31] showed that the higher perception of stress, the less self-care activities are adopted, and in turn the lower the beneficial effects on well-being.
Regarding knowledge, attitudes and practices towards COVID-19, a paper [32] found that participants reported high levels of adoption of preventive practices; importantly, unemployed individuals, househusbands/housewives, or manual laborers, as well as those with an elementary school education, have lower levels of knowledge about COVID-19.
In the mental health area, a paper [33] found that cognitive emotion regulation strategies on anxiety and depression was moderated by the sex of participants and the time of assessment. Moreover, a study [34] found that age was significantly correlated with all the psychological variables; importantly, females presented higher levels of stress, especially those who have home care responsibilities.
Thus, despite that several papers have been published, it is not clear how the determinants of self-reported and mental health affected the general population during lockdown; and, specifically, how the lockdown circumstances affected to men and women differentially.
Self-reported health status is an indicator of people’s health, the use of health services, and mortality [35]. Moreover, scientific evidence supports the idea that self-reported health differs according to social class and job insecurity [36], with important differences by sex and gender [37]. It is therefore relevant to analyze confinement’s effects on self-perceived health, particularly because of its potential to explain and interpret inequities as explanatory factors of health in the lockdown context, as has been shown by other studies in the region [1].
Therefore, and to examine the association between self-reported health status and its associated factors during Ecuador’s COVID-19 lockdown, we conducted a cross-sectional survey of between July and to October 2020 to adults who were living in Ecuador between March to October 2020. As a secondary objective, we aimed to assess the differences in these associations between women and men.
## Design, population, and sample
This was a cross-sectional study based on an online survey (see S1 File). Participants were recruited through online platforms and social media using convenience and snowball sampling. Participants were aged 18 years or older and lived in Ecuador between March and October 2020.
## Survey and measurements
We conducted a cross-sectional survey of between July and to October 2020 to adults who were living in Ecuador between March to October 2020. The survey was created by a group of experts including psychologists, statisticians, and epidemiologists, and was previously applied in different countries as part of a wider study carried out by a group of researchers from the Institut Universitari d’Atenció Primària IDIAPJGol (Spain), FIOCRUZ Brasilia, Brazil, from the School of Public Health of the University of Chile, and from the Instituto National Public Health Mexico, School of Public Health of Mexico. Data were collected using the Survey Monkey® platform hosted by IDIAPJGol. The survey questions were worded according to the cultural and language particularities of Ecuador.
## Dependent variable
The dependent variable was self-reported health status, with five response options on a Likert scale, which was then dichotomized into good self-perceived health (very good and good) and poor self-perceived health (fair, poor, and very poor), which has been used for similar purposes in other studies [1, 27, 35].
## Independent variables
We employed the following independent variables in the survey: participants’ demographics (sex, age, and location); socioeconomic status (education level, employment status, access to health services), living conditions (housing area, total number of cohabitants, number of cohabitants who require care, age of cohabitants, and perception of the type of housing’s adequacy for lockdown, suffering violence (during the lockdown), difficulties in coping with work or managing household chores, health-oriented behaviors (physical activity during lockdown as well as alcohol, cigarette, illicit drugs, and sugary drinks consumption); COVID-19 related experiences and perceptions (having had COVID-19, degree of concern of being infected with SARS-CoV-2), having chronic diseases, and general health status (prior chronic illnesses, use of medicines).
Anxiety was measured using the Generalized Anxiety Disorder Scale (GAD-7) [38] and was categorized as normal, mild, moderate, and severe, and depression was assessed using the Patient Health Questionnaire (PHQ-9) [39] and was categorized as none/minimal, mild, moderate, and moderately severe.
## Statistical analyses and sample considerations
Assuming an alpha risk of $5\%$ and a beta risk of $20\%$, it was necessary to recruit at least 1235 individuals to estimate with a confidence level of $95\%$ and a precision of +/- 1.5 percentage units; a population percentage having fair or poor general health will predictably be around $7\%$ [40]. The necessary replacement percentage was predicted to be $10\%$. We employed the GRANMO sample calculator version 7.12 [41].
Descriptive statistics were performed using percentages for categorical variables and medians and interquartile ranges (IQR) for discrete and non-normally distributed variables. We tested normality by checking the histograms. We performed Chi2 to compare differences in proportions of explanatory variables across the two categories of health self-perception and the U-Mann-Whitney test to assess differences in discrete or non-normally distributed explanatory variables across health self-perception categories. We then estimated the crude and adjusted odds ratios (aOR) of regular or poor self-perception of health status for each explanatory variable and its categories.
We then fitted multivariate logistic regression models to evaluate the independent association between each explanatory variable (age, sex, education level, educational level, employment status, access to health services, social security, perception of the type of housing’s adequacy for lockdown, housing area, number of cohabitants who require care, physical activity during lockdown, alcohol consumption, degree of concern of being infected with SARS-CoV-2, difficulties in coping with the job or taking care of household chores, healthy or socially-active activities during lockdown, violence or abuse during lockdown, diseases, symptoms and medications, anxiety, depression, and use of antidepressants), and health status self-perception. First, we built a saturated model that included all individual covariates. Then, based on the researchers’ criteria, we eliminated covariates with Wald test p-value>0.25 from the model [42], and $95\%$ confidence intervals ($95\%$CI) of the aOR and their corresponding p-values were calculated. Once the parsimonious model was obtained, we compared both models and chose the “final” model, according to its level of significance from the likelihood ratio test. Considering that the percentage of missing data was <$23\%$, we employed a complete case analysis to estimate statistical associations (for further details see S1 Table).
Analyses were stratified by sex; respondents who had a non-binary gender identity or did not identify with other categories were excluded from the analysis, because the group was too small ($$n = 4$$). Sex was tested as an effect modifier and a confounder.
To test for potential effect modification, we performed several secondary analyses to assess the sensitivity of our estimates with our assumptions regarding biases as well as to test for model misspecifications. We ran the final model excluding (i) high- and low-educated subjects, (ii) those with chronic diseases, (iii) those with severe anxiety, and (iv) those with severe depression.
Statistically significant differences were considered when the p-value was <0.05; all analyses were performed using Stata 16.1 (Statistical Software Stata: Release 16.1 College Station, TX: StataCorp LP).
## Ethics approval
This study was approved by the Research Ethics Committee on Human Beings (CEISH) of the Ministry of Public Health of Ecuador (code number MSP-CGDES-2020-0129-O) with the authorization to obtain an online informed consent before the start of the survey. Minors were not included in the study.
## Descriptive results
We analyzed the information of 2924 people. The participant characteristics are presented in Table 1. Their median (IQR) age was 34 (27–44) years, and most patients were female ($68\%$). Regarding sociodemographic characteristics, most participants had a university education ($84\%$) and had a full-time public or private job ($63\%$).
**Table 1**
| Variable a | Whole sample n = 2924 |
| --- | --- |
| Age in years of life, median (IQR) | 34 (27 to 44) |
| Female, n % | 1801 (68) |
| Education level | |
| Educational level lower than university education, n % | 408 (16) |
| University educational level or higher, n % | 2191 (84) |
| Employment status | |
| Public or private full job, n (%) | 1601 (63) |
| Self-employment, n (%) | 315 (13) |
| Unpaid work, retired or student, n (%) | 612 (24) |
| Access to health services | |
| Social securityb, n (%) | 1517 (61) |
| Private health insurance, n (%) | 598 (24) |
| Public health services user, n (%) | 375 (15) |
| Perception of the adequacy of the type of housing to lockdown | |
| Moderately to well adequate, n (%) | 2204 (86) |
| Little or not adequate, n (%) | 348 (14) |
| Housing area | |
| <50 m2, n (%) | 243 (10) |
| 50 to 80 m2, n (%) | 488 (19) |
| 80 to 100 m2, n (%) | 557 (22) |
| 100 to 120 m2, n (%) | 477 (19) |
| ≥120 m2, n (%) | 779 (31) |
| Number of cohabitants, median (IQR) | 4 (2 to 5) |
| Number of cohabitants who require care, median (IQR) | 2 (1 to 3) |
| Number of cohabitants <18 years old, median (IQR) | 2 (1 to 3) |
| Physical activity during lockdown | |
| Not performing, n (%) | 366 (16) |
| Increased performing, n (%) | 588 (25) |
| The same performing than before lockdown, n (%) | 492 (21) |
| Reduced performing, n (%) | 871 (38) |
| Alcohol consumption | |
| Increase of alcohol consumption during lockdown, n (%) | 111 (5) |
| Any alcohol consumption during lockdown, n (%) | 825 (36) |
| Any cigarette consumption during lockdown, n (%) | 229 (10) |
| Any illicit drugs consumption during lockdown, n (%) | 83 (4) |
| Any consumption of sugary drinks, n (%) | 1593 (67) |
| Concerns arising from the pandemic: degree of concern of being infected with SARS-CoV-2 | |
| Not worried, n (%) | 69 (3) |
| A little worried, n (%) | 295 (13) |
| Moderately worried, n (%) | 781 (33) |
| Quite worried, n (%) | 654 (28) |
| Very worried, n (%) | 533 (23) |
| Very high difficulties to cope with the job or take care of household chores, n (%) | 109 (5) |
| New health activities during lockdown, n (%) | 1187 (51) |
| Suffer any type of violence or abuse during lockdown, n (%) | 316 (14) |
| Diseases, symptoms, and medications | |
| Have or had COVID-19 | 266 (11) |
| Presence of any chronic disease | 789 (34) |
| Anxiety symptoms as measured by GAD-7 questionnaire | |
| No anxiety (<5 points), n (%) | 539 (23) |
| Mild anxiety (5 to <10 points), n (%) | 801 (35) |
| Moderate anxiety (10 to <15 points), n (%) | 579 (25) |
| Severe anxiety (≥15 points), n (%) | 384 (17) |
| Any anxiety level (≥5 points), n (%) | 1740 (76) |
| Depression symptoms as measured by PHQ9 questionnaire | |
| No depression (<5 points), n (%) | 708 (31) |
| Mild depression (5 to <10 points), n (%) | 700 (30) |
| Moderate depression (10 to <15 points), n (%) | 473 (21) |
| Moderately severe depression (15 to <20 points), n (%) | 256 (11) |
| Severe depression (≥20 points), n (%) | 161 (7) |
| Any depression level (≥5 points), n (%) | 1590 (69) |
| Any use of antidepressants, n (%) | 225 (10) |
| Poor or regular health self-perception | 386 (16) |
Sixteen percent of the participants had regular or poor health self-perception status. In the whole sample, the prevalence of severe anxiety was $17\%$ and severe depression was $7\%$; nevertheless, $76\%$ had any anxiety level and $69\%$ had any depression level (Table 1). When comparing between both sex categories (Table 2), moderate to severe anxiety levels (10 to ≥15 points of the GAD-7 questionnaire) were reported in $38\%$ of the women and $29\%$ of the men; and, moderate to severe depression levels (10 to ≥20 points of the PHQ9 questionnaire) were reported in $35\%$ of the women and $26\%$ of the men.
**Table 2**
| Variable | Women n = 1801 | Men n = 854 | p-value |
| --- | --- | --- | --- |
| Age in years of life, median (IQR) | 33 (26 to 42) | 37 (29 to 49) | 0.001 |
| Education level | | | |
| Educational level lower than university education, n % | 280 (17) | 119 (15) | 0.332 |
| University educational level or higher, n % | 1415 (83) | 675 (85) | 0.332 |
| Employment status | | | |
| Public or private full job, n (%) | 1005 (61) | 522 (67) | <0.001 |
| Self-employment, n (%) | 192 (12) | 110 (14) | <0.001 |
| Unpaid work, retired or student, n (%) | 449 (27) | 143 (19) | <0.001 |
| Access to health services | | | |
| Social security, n (%) | 979 (60) | 482 (63) | 0.561 |
| Private health insurance, n (%) | 396 (24) | 176 (23) | 0.561 |
| Public health services user, n (%) | 246 (15) | 111 (14) | 0.561 |
| Perception of the adequacy of the type of housing to lockdown | | | |
| Moderately to well adequate, n (%) | 1439 (86) | 679 (87) | 0.786 |
| Little or not adequate, n (%) | 226 (14) | 103 (13) | 0.786 |
| Housing area | | | |
| <50 m2, n (%) | 144 (9) | 85 (11) | 0.005 |
| 50 to 80 m2, n (%) | 341 (17) | 130 (17) | 0.005 |
| 80 to 100 m2, n (%) | 384 (23) | 152 (20) | 0.005 |
| 100 to 120 m2, n (%) | 312 (19) | 147 (19) | 0.005 |
| ≥120 m2, n (%) | 476 (29) | 265 (34) | 0.005 |
| Number of cohabitants, median (IQR) | 4 (3 to 5) | 4 (2 to 4) | 0.012 |
| Number of cohabitants who require care, median (IQR) | 2 (1 to 3) | 1 (1 to 3) | <0.001 |
| Number of cohabitants <18 years old, median (IQR) | 2 (1 to 3) | 2 (1 to 3) | 0.776 |
| Physical activity during lockdown | | | |
| Not performing, n (%) | 275 (18) | 78 (11) | <0.001 |
| Increased performing, n (%) | 401 (27) | 164 (23) | <0.001 |
| The same performing than before lockdown, n (%) | 311 (21) | 156 (22) | <0.001 |
| Reduced performing, n (%) | 522 (35) | 315 (44) | <0.001 |
| Alcohol consumption | | | |
| Increase of alcohol consumption during lockdown, n (%) | 401 (27) | 164 (23) | 0.071 |
| Any alcohol consumption during lockdown, n (%) | 462 (31) | 325 (45) | <0.001 |
| Any cigarette consumption during lockdown, n (%) | 115 (8) | 103 (14) | <0.001 |
| Any illicit drugs consumption during lockdown, n (%) | 47 (3) | 36 (5) | 0.025 |
| Any consumption of sugary drinks, n (%) | 1012 (67) | 514 (72) | 0.041 |
| Concerns arising from the pandemic: degree of concern of being infected with SARS-CoV-2 | | | |
| Not worried, n (%) | 35 (2) | 32 (5) | 0.003 |
| A little worried, n (%) | 190 (13) | 94 (13) | 0.003 |
| Moderately worried, n (%) | 493 (32) | 252 (35) | 0.003 |
| Quite worried, n (%) | 425 (28) | 202 (28) | 0.003 |
| Very worried, n (%) | 376 (25) | 136 (19) | 0.003 |
| Very high difficulties to cope with the job or take care of household chores, n (%) | 90 (6) | 14 (2) | <0.001 |
| New health activities during lockdown, n (%) | 779 (52) | 353 (49) | 0.336 |
| Suffer any type of violence or abuse during lockdown, n (%) | 212 (14) | 97 (14) | 0.826 |
| Diseases, symptoms, and medications | | | |
| Have or had COVID-19 | 172 (11) | 83 (11) | 0.746 |
| Presence of any chronic disease | 512 (33) | 240 (34) | 0.873 |
| Anxiety symptoms as measured by GAD-7 questionnaire | | | |
| No anxiety (<5 points), n (%) | 297 (20) | 217 (30) | <0.001 |
| Mild anxiety (5 to <10 points), n (%) | 513 (34) | 244 (34) | <0.001 |
| Moderate to severe anxiety (10 to ≥15 points), n (%) | 686 (38) | 250 (29) | <0.001 |
| Any anxiety level (≥5 points), n (%) | 1181 (79) | 489 (69) | <0.001 |
| Depression symptoms as measured by PHQ9 questionnaire | | | |
| No depression (<5 points), n (%) | 401 (27) | 271 (38) | <0.001 |
| Mild depression (5 to <10 points), n (%) | 454 (30) | 213 (30) | <0.001 |
| Moderate to severe depression (10 to ≥20 points), n (%) | 638 (35) | 226 (26) | <0.001 |
| Any depression level (≥5 points), n (%) | 1092 (73) | 439 (62) | <0.001 |
| Any use of antidepressants, n (%) | 155 (10) | 65 (9) | 0.370 |
| Poor or regular health self-perception | 284 (17) | 91 (12) | 0.001 |
## Determinants of health self-perception
When comparing the characteristics between those participants with excellent/good vs. regular/poor health self-perception, there was a lower percentage of participants with a perception that the type of housing’s adequacy for lockdown was poor or inadequate ($12\%$ vs. $24\%$, $p \leq 0.01$), a lower percentage of participants with perception of greater difficulties in coping with work or taking care of household chores ($3\%$ vs. $14\%$, $p \leq 0.001$), a lower percentage of participants with a history of COVID-19 ($9\%$ vs. $21\%$, $p \leq 0.001$), and a lower percentage of participants with chronic diseases ($28\%$ vs. $70\%$, $p \leq 0.001$). Anxiety and depression symptoms were less frequent among participants with good self-perception (Table 3).
**Table 3**
| Variable | Good or excellent health self-perception (n = 2111) | Regular or poor health self-perception (n = 386) | p-value |
| --- | --- | --- | --- |
| Age in years of life, median (IQR) | 35 (27 to 45) | 34 (27 to 43) | 0.502 |
| Female, n % | 1350 (67) | 284 (76) | 0.001 |
| Education level | | | |
| University educational level or higher, n % | 1775 (86) | 303 (80) | 0.007 |
| Employment status | | | |
| Public or private full job, n (%) | 1296 (64) | 232 (64) | 0.006 |
| Self-employment, n (%) | 271 (13) | 29 (8) | 0.006 |
| Unpaid work, retired or student, n (%) | 460 (23) | 100 (28) | 0.006 |
| Access to health services | | | |
| Social securitya, n (%) | 1218 (61) | 221 (62) | <0.001 |
| Private health insurance, n (%) | 499 (25) | 61 (17) | <0.001 |
| Public health services user, n (%) | 273 (14) | 77 (22) | <0.001 |
| Perception of the adequacy of the type of housing to lockdown | | | |
| Moderately to well adequate, n (%) | 1860 (88) | 293 (76) | <0.001 |
| Little or not adequate, n (%) | 249 (12) | 92 (24) | <0.001 |
| Housing area | | | |
| <50 m2, n (%) | 181 (9) | 56 (15) | <0.001 |
| 50 to 80 m2, n (%) | 394 (19) | 84 (22) | <0.001 |
| 80 to 100 m2, n (%) | 445 (21) | 98 (26) | <0.001 |
| 100 to 120 m2, n (%) | 406 (19) | 57 (15) | <0.001 |
| ≥120 m2, n (%) | 674 (32) | 87 (23) | <0.001 |
| Number of cohabitants, median (IQR) | 4 (2 to 5) | 4 (3 to 5) | 0.039 |
| Number of cohabitants who require care, median (IQR) | 2 (1 to 3) | 2 (1 to 3) | <0.001 |
| Number of cohabitants <18 years old, median (IQR) | 2 (1 to 3) | 2 (1 to 3) | <0.084 |
| Physical activity during lockdown | | | |
| Not performing, n (%) | 274 (14) | 87 (25) | <0.001 |
| Increased performing, n (%) | 521 (27) | 67 (19) | <0.001 |
| The same performing than before lockdown, n (%) | 433 (22) | 57 (16) | <0.001 |
| Reduced performing, n (%) | 730 (37) | 136 (39) | <0.001 |
| Alcohol consumption | | | |
| Increase of alcohol consumption during lockdown, n (%) | 88 (5) | 22 (6) | 0.136 |
| Any alcohol consumption during lockdown, n (%) | 724 (37) | 98 (28) | 0.002 |
| Any cigarette consumption during lockdown, n (%) | 193 (10) | 36 (10) | 0.734 |
| Any illicit drugs consumption during lockdown, n (%) | 68 (3) | 15 (4) | 0.427 |
| Any consumption of sugary drinks, n (%) | 1338 (68) | 246 (71) | 0.369 |
| Concerns arising from the pandemic: degree of concern of being infected with SARS-CoV-2 | | | |
| Not worried, n (%) | 60 (3) | 9 (3) | <0.001 |
| A little worried, n (%) | 250 (13) | 45 (13) | <0.001 |
| Moderately worried, n (%) | 703 (36) | 75 (21) | <0.001 |
| Quite worried, n (%) | 533 (27) | 118 (34) | <0.001 |
| Very worried, n (%) | 425 (22) | 103 (29) | <0.001 |
| Very high difficulties to cope with the job or take care of household chores, n (%) | 57 (3) | 50 (14) | <0.001 |
| New healthy or socially active activities during lockdown, n (%) | 1039 (53) | 143 (41) | |
| Suffer any type of violence or abuse during lockdown, n (%) | 224 (12) | 80 (23) | <0.001 |
| Diseases, symptoms, and medications | | | |
| Have or had COVID-19 | 185 (9) | 79 (21) | <0.001 |
| Presence of any chronic disease | 551 (28) | 237 (70) | <0.001 |
| Anxiety symptoms as measured by GAD questionnaire, median (IQR) | 7 (4 to 12) | 13 (8 to 17) | <0.001 |
| No anxiety (<5 points), n (%) | 504 (26) | 32 (9) | <0.001 |
| Mild anxiety (5 to <10 points), n (%) | 727 (37) | 73 (21) | <0.001 |
| Moderate anxiety (10 to <15 points), n (%) | 464 (24) | 112 (33) | <0.001 |
| Severe anxiety (≥15 points), n (%) | 255 (13) | 125 (37) | <0.001 |
| Any anxiety level (≥5 points), n (%) | 1429 (73) | 903 (89) | <0.001 |
| Depression symptoms as measured by PHQ9 questionnaire, median (IQR) | 7 (3 to 11) | 13 (8 to 18) | <0.001 |
| No depression (<5 points), n (%) | 662 (34) | 43 (13) | <0.001 |
| Mild depression (5 to <10 points), n (%) | 625 (32) | 74 (22) | <0.001 |
| Moderate depression (10 to <15 points), n (%) | 385 (20) | 87 (26) | <0.001 |
| Moderately severe depression (15 to <20 points), n (%) | 180 (9) | 74 (22) | <0.001 |
| Severe depression (≥20 points), n (%) | 94 (5) | 63 (18) | <0.001 |
| Any depression level (≥5 points), n (%) | 1284 (66) | 298 (88) | <0.001 |
| Any use of antidepressants, n (%) | 157 (8) | 65 (19) | <0.001 |
The multivariate analyses (Table 4) showed that being female (aOR = 1.5, $95\%$ CI:1.1 to 2.2), having solely public healthcare system access (aOR = 1.9, $95\%$ CI: 1.2 to 2.9), perceiving housing as inadequate to cope with lockdown (aOR = 2.2, $95\%$ CI:1.4 to 3.4). Furthermore, perceiving very high difficulties in coping with work or managing household chores was associated with poor health self-perception (aOR = 2.7, $95\%$ CI:1.5 to 5.0). The odds of poor self-reported health status were as high as the increase in the number of cohabitants who required care (aOR = 1.2, $95\%$ CI:1.1 1.3). Furthermore, having had a diagnosis of COVID-19 or having had COVID-19 symptoms (aOR = 3.1, $95\%$CI:2.0–4.7), and suffering from chronic diseases (aOR = 6.9, $95\%$ CI:4.9 to 9.7), having severe depression (aOR 5.9, $95\%$CI:3.1–11.2), were independently associated with poor health self-perception; specifically, there was a “dose-response” association between increasing depression severity and regular or poor self-perception of health; specifically, there was a $60\%$ ($65\%$ CI:$59\%$–$83\%$, p-for-trend <0.001) increase in the odds of poor self-reported health status for each change to a higher depressive category.
**Table 4**
| Variable | Crude | p-value | Saturated | p-value.1 | Parsimonious | p-value.2 |
| --- | --- | --- | --- | --- | --- | --- |
| Female (male is the ref.) | 1.5 (1.2 to 2.0) | 0.001 | 1.7 (1.1 to 2.5) | 0.013 | 1.5 (1.1 to 2.2) | 0.023 |
| Education level | | | | | | |
| University educational level or higher (lower than university education is the ref.) | 0.7 (0.5 to 0.9) | 0.007 | 0.8 (0.5 to 1.3) | 0.351 | - | - |
| Employment status | | | | | | |
| Public or private full job (ref.) | 1 | - | 1 | - | 1 | - |
| Self-employment | 0.6 (0.4 to 0.9) | 0.013 | 0.4 (0.2 to 0.8) | 0.008 | 0.5 (0.3 to 0.8) | 0.011 |
| Unpaid work, retired or student | 1.2 (0.9 to 1.6) | 0.139 | 0.9 (0.6 to 1.4) | 0.574 | 0.8 (0.5 to 1.2) | 0.359 |
| Access to health services | | | | | | |
| Social securitya (ref.) | 1 | - | 1 | - | 1 | - |
| Private health insurance | 0.7 (0.5 to 0.9) | 0.010 | 0.7 (0.4 to 1.1) | 0.134 | 0.7 (0.5 to 1.1) | 0.162 |
| Public health services user | 1.6 (1.2 to 2.1) | 0.003 | 1.8 (11 to 2.9) | 0.022 | 1.9 (1.2 to 2.9) | 0.009 |
| Perception of the adequacy of the type of housing to lockdown | | | | | | |
| Little or not adequate (Moderately to well adequate is ref.) | 2.4 (1.8 to 3.1) | <0.001 | 2.1 (1.3 to 3.3) | 0.003 | 2.2 (1.4 to 3.4) | <0.001 |
| Housing area | | | | | | |
| <50 m2 (ref) | 1 | - | 1 | - | - | - |
| 50 to 80 m2 | 0.7 (0.5 to 1.0) | 0.056 | 0.6 (0.3 to 1.3) | 0.215 | - | - |
| 80 to 100 m2 | 0.7 (0.5 to 1.0) | 0.073 | 0.7 (0.4 to 1.5) | 0.363 | - | - |
| 100 to 120 m2 | 0.5 (0.3 to 0.7) | <0.001 | 0.6 (0.3 to 1.2) | 0.174 | - | - |
| ≥120 m2 | 0.4 (0.2 to 0.4) | <0.001 | 0.5 (0.3 to 1.1) | 0.096 | - | - |
| Number of cohabitants who require care (per each increase in one cohabitant) | 1.3 (1.2 to 1.4) | <0.001 | 1.2 (1.1 to 1.4) | 0.002 | 1.2 (1.1 to 1.3) | 0.004 |
| Physical activity during lockdown | | | | | | |
| Increased performing (any increase or no performing is ref.) | 0.7 (0.5 to 0.9) | 0.004 | 0.9 (0.6 to 1.5) | 0.793 | - | - |
| Alcohol consumption | | | | | | |
| Increase of alcohol consumption during lockdown, n (%) | 1.4 (0.9 to 2.3) | 0.138 | 1.3 (0.6 to 2.6) | 0.525 | - | - |
| Concerns arising from the pandemic: degree of concern of being infected with SARS-CoV-2 | | | | | | |
| Nothing worried (ref) | 1 | - | 1 | - | 1 | - |
| A little worried | 1.2 (0.6 to 2.6) | 0.642 | 0.6 (0.2 to 1.7) | 0.331 | - | - |
| Moderately worried, n (%) | 0.7 (0.3 to 1.5) | 0.367 | 0.8 (0.3 to 2.3) | 0.697 | - | - |
| Quite worried, n (%) | 1.5 (0.7 to 3.1) | 0.295 | 1.2 (0.4 to 3.4) | 0.680 | - | - |
| Very worried, n (%) | 1.7 (0.8 to 3.4) | 0.200 | 0.8 (0.3 to 2.3) | 0.680 | - | - |
| Very high difficulties to cope with the job or take care of household chores (not having is the ref.) | 5.6 (3.7 to 8.3) | <0.001 | 2.6 (1.4 to 5.0) | 0.004 | 2.7 (1.5 to 5.0) | 0.002 |
| New healthy or socially active activities during lockdown (not having is the ref.) | 0.6 (0.5 to 0.8) | <0.001 | 0.7 (0.5 to 1.1) | 0.109 | - | - |
| Suffer any type of violence or abuse during lockdown (not having is the ref.) | 2.22 (1.7 to 3.0) | <0.001 | 1.29 (0.8 to 2.0) | 0.247 | - | - |
| Diseases, symptoms, and medications | | | | | | |
| Have or had COVID-19 (not having is the ref.) | 2.7 (2.0 to 3.6) | <0.001 | 3.1 (2.0 to 4.9) | <0.001 | 3.1 (2.0 to 4.7) | <0.001 |
| Presence of any chronic disease (not having is the ref.) | 6.1 (4.7 to 7.8) | <0.001 | 6.9 (4.8 to 10.0) | <0.001 | 6.9 (4.9 to 9.7) | <0.001 |
| Anxiety symptoms as measured by GAD-7 questionnaire | | | | | | |
| No anxiety (<5 points) (ref.) | 1 | - | 1 | - | - | - |
| Mild anxiety (5 to <10 points) | 1.6 (1.0 to 2.4) | 0.037 | 1.1 (0.6 to 2.1) | 0.723 | - | - |
| Moderate anxiety (10 to <15 points) | 3.8 (2.5 to 5.7) | <0.001 | 1.2 (0.6 to 2.5) | 0.545 | - | - |
| Severe anxiety (≥15 points) | 7.7 (5.1 to 11.7) | <0.001 | 1.2 (0.6 to 2.7) | 0.631 | - | - |
| Depression symptoms as measured by PHQ-9 questionnaire, median (IQR) | | | | | | |
| No depression (<5 points) (ref.) | 1 | - | 1 | - | 1 | - |
| Mild depression (5 to <10 points) | 1.8 (1.2 to 2.7) | 0.003 | 1.1 (0.6 to 2.1) | 0.666 | 1.4 (0.9 to 2.3) | 0.162 |
| Moderate depression (10 to <15 points) | 3.5 (2.4 to 5.1) | <0.001 | 2.5 (1.3 to 4.6) | 0.005 | 3.2 (2.0 to 5.3 | <0.001 |
| Moderately severe depression (15 to <20 points) | 6.3 (4.2 to 9.5) | <0.001 | 2.7 (1.3 to 5.5) | 0.008 | 3.8 (2.2 to 6.7) | <0.001 |
| Severe depression (≥20 points) | 10.3 (6.6 to 16.0) | <0.001 | 4.3 (1.8 to 10.0) | 0.001 | 5.9 (3.1 to 11.2) | <0.001 |
| p-for-trend | | | | | 1.6 (1.39 to 1.8) | <0.001 |
| Any use of antidepressants (not using is the ref.) | 2.6 (1.9 to 3.6) | <0.001 | 1.3 (0.8 to 2.2) | 0.270 | - | - |
After sensitivity analyses (S2 Table), we found similar estimates when running the final (parsimonious) model when excluding (i) high- and low-educated subjects, (ii) those with chronic diseases, (iii) those with severe anxiety, and (iv) those with severe depression.
## Differences between women and men
When comparing the variables between women and men (Table 2), we found that, women had significantly greater percentages of: (i) unpaid work, being retired, or being a student ($27\%$ in women vs. $19\%$ in men, $p \leq 0.001$), (ii) being extremely worried about being infected with SARS-CoV-2 ($25\%$ vs. $19\%$, $p \leq 0.001$), (iii) extreme difficulty in coping with work or managing household chores ($6\%$ vs. $2\%$, $p \leq 0.001$), (iv) severe anxiety ($19\%$ vs. $12\%$, $p \leq 0.001$), (v) severe depression ($8\%$ vs. $5\%$, $p \leq 0.001$); and (vi) poor or regular health self-perception ($17\%$ vs. $12\%$, $p \leq 0.001$).
When we stratified the multivariate analyses of health self-perception by sex, we found differences in the determinants between men and women (see S3 Table). Specifically, for women, and considering the full-time job category as the reference, self-employment was significantly associated with a poorer self-reported health status (aOR = 0.6, $95\%$CI:0.2 to 0.8); moreover, there were $30\%$ higher odds of poor self-reported health status per each extra cohabitant who required care (aOR = 1.3, $95\%$ CI:1.1 to 1.5). Those having or having had COVID-19 had $330\%$ higher odds of regular or poor self-reported health status (aOR = 4.3, $95\%$ CI:2.6 to 7.1) when compared with their counterparts; and those reporting any chronic disease had a greater likelihood of having poor self-reported health status (aOR = 8.1, $95\%$ CI:5.3 to 12.1). We did not find such strong associations in men. For men, by contrast, we found that those perceiving housing’s adequacy for lockdown as poor or inadequate had greater odds of regular or poor self-reported status than women (aOR = 3.2, $95\%$ CI:1.4 to 7.1), and depression was much more associated with regular or poor self-reported health status than for women (aOR = 1.70, $95\%$ CI:1.32 to 2.22).
## Discussion
Our main findings were that the determinants of regular or poor self-reported health status among Ecuadorian-surveyed adult persons were: (i) being female by comparison with being male, (ii) perceiving housing conditions as inadequate for coping with the lockdown, (iii) living with people who require care, (v) perceiving extreme difficulties in coping with both work exigencies or managing household chores, (vi) a history of COVID-19 infection, (vii) presence of chronic diseases, and (viii) depressive symptoms. Similar to other studies, the complex Ecuadorian context during lockdown [10, 13–16, 18] helps explain the accentuated impact of such factors on the self-reported health status of the Ecuadorian population.
The factors associated with self-reported health differed by sex/gender. Specifically, women had a greater negative impact on their self-reported health when they received only public health services, had inadequate housing for lockdown, an increasing number of cohabitants who required care in the family, extreme perceived difficulties coping with work or managing household chores, having COVID-19, the presence of chronic disease, and increasing depressive symptoms. In men, the determinants were inadequate housing type, presence of chronic disease, and increasing depressive symptoms. Interestingly, in women, COVID-19’s effect and the presence of chronic diseases were more accentuated than in men as in other studies have been found [43]; while in men, the housing inadequacy and increasing depressive symptoms were more accentuated than in women, as was found in other contexts such as Spain [27]. We could not demonstrate a significant interaction or effect modification by sex/gender on the association between the associated factors and self-reported health.
Furthermore, the percentage of poor or regular health self-perception was $16\%$, which was greater among women ($17\%$) than among men ($12\%$). The prevalence of poor self-reported health status ($16\%$) is greater than that found in other contexts during lockdown [40]. A comparison between our data and the results of an occupational health survey conducted among workers in Quito and Guayaquil before the pandemic, where the authors found a prevalence of $11\%$ for self-perceived poor health status [36], suggests that the lockdown has significantly worsened the levels of self-perceived health in Ecuador. This is possibly because of the weakened social protection system, reflected on the lack of social support policies during this pandemic phase. Given that safe and adequate housing is essential to protect people from environmental conditions, create social ties, and establish life projects, housing deprivation and the lack of an adequate urban environment have significant health consequences for both sexes [44].
These findings highlight the impact of gender inequality on the burden of care and domestic work, and its negative effect on women’s health. As other researchers have shown [7, 8], being a woman was a risk factor for increased mental distress during lockdown. Furthermore, our findings add data on unpaid care and domestic work as one of the mechanisms through which lockdowns affect women’s health in particular ways [45]. Men did not feel this impact, which is probably related to the assignment of traditional gender roles, in which men are usually not responsible for care and domestic work.
Although it is true female had a poorer perception of self-reported health; both, men and women suffered a significant impact on their health, especially in the area of mental health. The fact that we found a worse self-perceived health in women compared to men is coincident with the results obtained from previous studies [46, 47].
Although mortality from COVID-19 has been consistently higher in men [48], scientific literature shows that women tend to have worse living conditions and use health services more frequently; they also have a greater number of disease diagnoses. A possible explanation is that women have historically suffered the greatest burden of social inequalities and have assumed more home responsibilities, which, compounded by the added responsibilities during confinement [49], has entailed a greater stress load. In addition, our findings corroborate that women have a greater burden of caring for dependent people, a fact explained by the assignment of traditional gender roles that persist in patriarchal systems, such as the Ecuadorian society. At the same time, these results show the poorness of the social protection system to deal with care as a social risk managed by family ties and networks, instead of public services. Both forms of response were eliminated during lockdown, so that care fell to women from nuclear families.
Importantly, women suffer more from the lack of proper access to tele-education for their children, given the cultural tendency to assign women as those responsible for educating children, despite their having to cope with everyday duties such as productive work. The regional difficulties in educating children could explain the even more important health effect of caring for household children. Specifically, in Latin America and the Caribbean, schools have been closed for an average of 37 weeks since March 2020. In Ecuador, it has been 40 weeks. In addition, only $39\%$ of primary school students can read a simple text [50], and only $37\%$ of households have Internet access, which means that six out of ten children cannot study through digital platforms. The situation is more serious for children in rural areas, where only $16\%$ of households have this service [51].
Males were affected too; as in other studies’ findings, there is poorer self-reported health in men when inadequate housing conditions are perceived [52, 53], and when there are depressive symptomatology [54]. These findings could be explained by: First, the fact that men are more prone to discomfort because of housing conditions, which is in turn related to gender roles and its impact on self-reported health. In traditional patriarchal systems, men are seen as the primary family breadwinners, and not being able to meet a desired standard could affect men’s health more than women’s. Second, according to previous studies [54], potential explanations of the accentuation of poor self-reported health by depression are related with the fact that the presence of symptoms of depression have been associated with the occurrence of severe sexual functioning disorders; and third, the intensity of other fears, not measured in this survey, could be correlated with depressive and sexual disorders.
As a result of overcrowding, couples experience financial and family stressors, with an increase in the number of conflicts during sustained social isolation and physical proximity, particularly among young and newly-formed intimate relationships. Moreover, with housing insecurity and housing conditions [55] inadequate to tackle the lockdown, a poor self-rated health status is expected. In that sense, we corroborated that living in poor housing conditions and having low income and/or poor labor conditions–the social determinants of health–affect more vulnerable populations [27, 56].
In addition, we found that $38\%$ of women and $29\%$ of men reported moderate to severe anxiety, and moderate to severe depression levels were reported in $35\%$ of women and $26\%$ of men. We corroborated that there were gender differences in depression and anxiety, as well as differences in the quantity of (subclinical) depressive symptoms [19]. Prior studies found that the levels of such mental health problems were much lower than our findings. Specifically, in one study in Spain [27], $31\%$ of women and $18\%$ of men reported moderate to severe anxiety, and moderate to severe depression levels were reported in $29\%$ of women and $17\%$ of men. It is possible that specific contexts determine reactions to the lockdown. We believe that the Ecuadorian population felt a profound health impact owing to the poor and uncoordinated pandemic response from national and local authorities.
Moreover, the frequency of anxiety and depression levels was greater in females than in males in the study population. However, the association between depression symptoms and regular-to-poor health self-perception was stronger in men than in women. It is coincident with a study [54] that found that the fear of contracting the COVID-19 infection, the fear of the health condition of loved ones, depressed mood, and exposition to media reports worsened their mental health. Furthermore, another study found that psychosocial factors explained the highest proportion of the variance in anxiety symptoms, being even higher in men than in women. Also, we found that depression and adverse housing conditions were more associated to a poorer self-perceived health in men than in women, maybe explained by previous findings that depressive symptomatology has a greater impact on men’s health when compared to women in terms of suicide attempts [57]. While suicide attempt rates are similar between men and women, males have an almost threefold higher risk of dying by suicide than females [58]. This higher mortality among men could be explained by various factors, including the use of more lethal means (firearms and hanging methods), whereas drug intoxication is more frequent in women. Young men may be less predisposed to help-seeking behaviors as an attempt to exhibit masculine behaviors, and their tendency to adopt avoidance strategies may make it more difficult for them to cope with emotional and behavioral problems [49]. In that regard, specific pandemic and lockdown policies should use a gender approach to identify those at risk and intervene [27].
Regarding protective variables, perceived social support was independently associated with lower anxiety while intimate partner violence was further associated with higher anxiety symptoms and this pattern was consistent in men and women.
Serious questioning of the Ecuadorian Public Health System and its manner of operating was a serious pandemic effect. Some participants reported being part of or knowing someone who was close to the health system. The lack of response to emergencies (which involved life or death in many cases), lack of basic information regarding COVID-19, serious difficulties in communicating basic information to the population, and lack of psychological support spaces for front-line professionals were the most common problems. Through proposals from civil society and academia the population gained access to spaces for psychological support and crisis intervention [59]. The evidence on the lockdown’s gendered impact on self-perceived health found here is a strong indicator of how deep-rooted patriarchal gender beliefs affect the health of the Ecuadorian population. Even though we have found only one study in Ecuador on teleworking’s impact on people’s lives, especially women, there are clear impacts from increases in teleworker numbers in the sectors where it was implemented, in the codification of work schedules and conciliation with family life, as well as significant specific effects on unions and teleworking health and safety [20].
The pandemic’s impact is also unknown in areas related to the additional burdens and impacts resulting from teleworking on women, who often bear the bulk of household care work; protection of labor rights and workers in legislative frameworks; and judgments or constitutional revisions relating to teleworking laws. Specifically, in a study it was found that $76\%$ of the women surveyed indicate an increase in workload, and $56\%$ dedicate themselves to their children’s schoolwork [20]. Furthermore, women who spent long hours on housework and childcare were more likely to report increased levels of psychological distress, and women were more likely than men to reduce working hours and adapt employment schedules because of increased unpaid care time [60]. In that regard, our results can be used to better justify formulating regulations that guarantee a maximum of eight daily working hours [20]. Continued gender inequality in divisions of unpaid care work during lockdown may put women at a greater risk of psychological distress, which is a consequence of gender biases in divisions of labor and their impact on psychological health [60]. Teleworking also raised questions about the use of the physical household spaces, which in many cases implied no temporal limits between hours of outside and inside home labor. In these cases, women mostly assume these new daily dynamics, resulting in extreme fatigue, anxiety, and sadness.
The COVID-19 prevalence was around $11\%$, which is similar to published (and non-published) reports of the pandemic’s evolution in Ecuador during the surveyed months [61]. It is important to enhance this finding because it helps understanding of this population’s context during the specific survey period.
This study has several limitations. The survey was only available online and was mainly completed by highly educated respondents, and it may have excluded people without digital access. As it happened with many studies during lockdown, an electronic survey had to be applied to obtain information in that setting. The advantage of this approach was that relevant information could be obtained in a time were performing research had many challenges. The disadvantage was that access to the survey could be limited to some populations (higher socioeconomic status, younger age, etc.). Thus, we speculate that the associations and inequalities would be even greater if it included more vulnerable people. Non-representative responses are a handicap of online surveys, since they do not capture the responses of those who lack access and/or Internet skills (e.g., the elderly, those with lower education, or those in remote locations). In addition, we had more responses from women than from men. This requires strategies to ensure greater male participation. However, as the analyses were stratified by sex, the main results were compared with those of the reference group. We plan to conduct another survey and a qualitative study several months after this first survey. This will be carried out using the same method, as it is being applied in other Latin American countries and Spain, to compare the results in different populations.
## Conclusions
Being female, having only access to the public healthcare system, having a perception of inadequate housing, living with cohabitants who require care, perceiving very high difficulties in coping with work or managing household chores, having or having had COVID-19, the presence of chronic diseases, and depressive symptoms are associated with a poorer self-reported health status in Ecuadorian population. Conversely, self-employment and having private health insurance were significantly and independently associated with better self-reported health status. For women, self-employment, having solely public healthcare system access, perceiving housing conditions as inadequate, having cohabitants requiring care, having very high difficulties to cope with household chores, having COVID-19, and having a chronic disease increased the likelihood of having poor self-reported health status. For men, poor or inadequate housing, presence of any chronic disease, and depression increased the likelihood of having poor self-reported health status.
## References
1. López-Contreras N, López-Jiménez T, Horna-Campos OJ, Mazzeie M, Anigstein MS, Jacques-Aviñó C. **Impacto del confinamiento por la COVID-19 en la salud autopercibida en Chile según género**. *Gac Sanit.* (2022.0)
2. Gloster AT, Lamnisos D, Lubenko J, Presti G, Squatrito V, Constantinou M. **Impact of COVID-19 pandemic on mental health: An international study**. *PLoS One.* (2020.0) **15** 1-20. DOI: 10.1371/journal.pone.0244809
3. Moore S, Public BMC, Moore ES, Lambert J, Grey E, Gillison F. **Life in lockdown: a longitudinal study investigating the impact of the UK COVID ‑ 19 lockdown measures on lifestyle behaviours and mental health**. *BMC Public Health* (2022.0) 1-15. DOI: 10.1186/s12889-022-13888-1
4. O’Connor RC, Wetherall K, Cleare S, McClelland H, Melson AJ, Niedzwiedz CL. **Mental health and well-being during the COVID-19 pandemic: Longitudinal analyses of adults in the UK COVID-19 Mental Health & Wellbeing study**. *Br J Psychiatry* (2021.0) **218** 326-33. PMID: 33081860
5. Wechsler TF, Schmidmeier M, Biehl S, Gerczuk J, Maria F, Cerda G. **Individual changes in stress, depression, anxiety, pathological worry, posttraumatic stress, and health anxiety from before to during the COVID ‑ 19 pandemic in adults from Southeastern Germany**. *BMC Psychiatry* (2022.0) 1-20. DOI: 10.1186/s12888-022-04148-y
6. Holmes EA O, ’Connor RC, Perry VH, Tracey I, Wessely S, Arseneault L. **Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science**. *The Lancet Psychiatry* (2020.0) **7** 547-60. DOI: 10.1016/S2215-0366(20)30168-1
7. Xiong J, Lipsitz O, Nasri F, Lui LMW, Gill H, Phan L. **Impact of COVID-19 pandemic on mental health in the general population: A systematic review**. *J Affect Disord* (2020.0) **277** 55-64. DOI: 10.1016/j.jad.2020.08.001
8. Panchal U, Salazar de Pablo G, Franco M, Moreno C, Parellada M, Arango C. **The impact of COVID-19 lockdown on child and adolescent mental health: systematic review. Eur Child Adolesc Psychiatry**. *Internet]* (2021.0). DOI: 10.1007/s00787-021-01856-w
9. Colucci E, Nadeau S, Higgins J, Kehayia E, Poldma T, Saj A. **COVID-19 lockdowns’ effects on the quality of life, perceived health and well-being of healthy elderly individuals: A longitudinal comparison of pre-lockdown and lockdown states of well-being. Arch Gerontol Geriatr**. *Internet]* (2022.0) **99** 104606. DOI: 10.1016/j.archger.2021.104606
10. Hallo A, Rojas A, Hallo C. **Perspective from Ecuador, the Second Country with More Confirmed Cases of Coronavirus Disease 2019 in South America: A Review**. *Cureus.* (2020.0) **12** 10-5. DOI: 10.7759/cureus.7452
11. Unidas Naciones. **El impacto del COVID-19 en América Latina y el Caribe**. *Of Nac Unidas para la Coord Asuntos Humanit -OCHA* (2020.0) 1-29
12. 12CEPAL CE para AL e C. CEPALSTAT. Bases de Datos y Publicaciones Estadísticas [Internet]. CEPAL. Organizacion de las Naciones Unidas. ONU. 2022 [cited 2022 Aug 16]. p. 1. Available from: https://statistics.cepal.org/portal/cepalstat/index.html?lang=es. *Bases de Datos y Publicaciones Estadísticas* (2022.0) 1
13. Torres I, López-Cevallos D. **In the name of COVID-19: Legitimizing the exclusion of community participation in Ecuador’s health policy**. *Health Promot Int.* (2021.0) **36** 1324-33. DOI: 10.1093/heapro/daaa139
14. Roser M, Ritchie H, Ortiz-Ospina E, Hassell J. **Excess mortality during the Coronavirus pandemic (COVID-19)—Our World in Data**. *OurWorldInData.org* (2020.0)
15. Chauca R.. **Covid-19 in ecuador: Political fragility and vulnerability of public health**. *Hist Ciencias, Saude—Manguinhos.* (2021.0) **28** 587-91
16. **Encuesta Nacional de Empleo, Desempleo y Subempleo (ENEMDU), junio 2021 Encuesta**. *Boletín Técnico N° 09-2021-ENEMDU.* (2021.0)
17. **Instituto Nacional de Estadísticas y Censos INEC**. *Estadísticas Laborales–Abril 2022*
18. 18Encuesta Nacional Multipropósito de Hogares, 2020 (Seguimiento al Plan Nacional de Desarrollo).
2021;. *Encuesta Nacional Multipropósito de Hogares, 2020 (Seguimiento al Plan Nacional de Desarrollo).* (2021.0)
19. Salk RH, Hyde JS, Abramson LY. **Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms**. *Psychol Bull.* (2017.0) **143** 783-822. DOI: 10.1037/bul0000102
20. Trujillo GO, Ventura AT, Bolaños SB. *Teletrabajo y pandemia en ecuador* (2021.0)
21. King K, Samaniego P, Carranza C. **Facing covid-19 in Ecuador: a blueprint for monetary policy and food sovereignty**. *Rev la régulation.* (2021.0) 1-13
22. Barone MTU, Villarroel D, de Luca PV, Harnik SB, Lima BL de S, Wieselberg RJP. **COVID-19 impact on people with diabetes in South and Central America (SACA region)**. *Diabetes Res Clin Pract* (2020.0) **166**. DOI: 10.1016/j.diabres.2020.108301
23. **Organización para la Cooperación y Desarrollo Económico [OCDE]. Impacto social del COVID-19 en Ecuador: desafíos y respuestas [Social impact of COVID-19 in Ecuador: challenges and responses]**. *Mak Dev Happen* (2020.0) **4** 2-21
24. Suárez-Rodríguez GL, Salazar-Loor J, Rivas-Condo J, Rodríguez-Morales AJ, Navarro J-C, Ramírez-Iglesias JR. **Routine Immunization Programs for Children during the COVID-19 Pandemic in Ecuador, 2020—Hidden Effects, Predictable Consequences**. *Vaccines.* (2022.0) **10** 857. DOI: 10.3390/vaccines10060857
25. Tusev A, Tonon L, Capella M. **Efectos Iniciales en la Salud Mental por la Pandemia de Covid-19 en algunas Provincias de Ecuador**. *Investigatio* (2020.0) **15** 11-24
26. Honda C, Yoshioka-Maeda K, Iwasaki-Motegi R. **Child abuse and neglect prevention by public health nurses during the COVID-19 pandemic in Japan**. *J Adv Nurs* (2020.0) **76** 2792-3. DOI: 10.1111/jan.14526
27. Jacques-Avinõ C, López-Jiménez T, Medina-Perucha L, De Bont J, Goncąlves AQ, Duarte-Salles T. **Gender-based approach on the social impact and mental health in Spain during COVID-19 lockdown: A cross-sectional study**. *BMJ Open* (2020.0) **10** 1-10. DOI: 10.1136/bmjopen-2020-044617
28. Estrella F.. **Ecuadorian university English teachers’ reflections on emergency remote teaching during the COVID-19 pandemic**. *Int J Educ Res Open.* (2022.0) **3**
29. Abril-Ulloa V, Santos SPL dos, Morejón-Terán YA, Carpio-Arias TV, Espinoza-Fajardo AC, Vinueza-Veloz MF. **Stress and Diet Quality Among Ecuadorian Adults During the COVID-19 Pandemic. A Cross-Sectional Study**. *Front Nutr* (2022.0) **9** 1-7. DOI: 10.3389/fnut.2022.924076
30. Paredes-Aguirre MI, Barriga Medina HR, Campoverde Aguirre RE, Melo Vargas ER, Armijos Yambay MB. **Job Motivation, Burnout and Turnover Intention during the COVID-19 Pandemic: Are There Differences between Female and Male Workers?**. *Healthc.* (2022.0) **10**. DOI: 10.3390/healthcare10091662
31. Luis E, Bermejo-Martins E, Martinez M, Sarrionandia A, Cortes C, Oliveros EY. **Relationship between self-care activities, stress and well-being during COVID-19 lockdown: A cross-cultural mediation model**. *BMJ Open* (2021.0) **11**. DOI: 10.1136/bmjopen-2020-048469
32. Bates BR, Moncayo AL, Costales JA, Herrera-Cespedes CA, Grijalva MJ. **Knowledge, Attitudes, and Practices Towards COVID-19 Among Ecuadorians During the Outbreak: An Online Cross-Sectional Survey**. *J Community Health* (2020.0) **45** 1158-67. DOI: 10.1007/s10900-020-00916-7
33. Rodas JA, Jara-Rizzo MF, Greene CM, Moreta-Herrera R, Oleas D. **Cognitive emotion regulation strategies and psychological distress during lockdown due to COVID-19**. *Int J Psychol* (2022.0) **57** 315-24. DOI: 10.1002/ijop.12818
34. Hidalgo-Andrade P, Hermosa-Bosano C, Paz C. **Teachers’ mental health and self-reported coping strategies during the covid-19 pandemic in ecuador: A mixed-methods study**. *Psychol Res Behav Manag* (2021.0) **14** 933-44. DOI: 10.2147/PRBM.S314844
35. McGee DL, Liao Y, Cao G, Cooper RS. **Self-reported health status and mortality in a multiethnic us cohort**. *Am J Epidemiol* (1999.0) **149** 41-6. DOI: 10.1093/oxfordjournals.aje.a009725
36. Borrell C, Muntaner C, Benach J, Artazcoz L. **Social class and self-reported health status among men and women: What is the role of work organisation, household material standards and household labour?**. *Soc Sci Med.* (2004.0) **58** 1869-87. DOI: 10.1016/S0277-9536(03)00408-8
37. Klein SL, Morgan R. **The impact of sex and gender on immunotherapy outcomes**. *Biol Sex Differ* (2020.0) **11** 1-13. PMID: 31900228
38. Spitzer RL, Kroenke K, Williams JBW, Löwe B. **A brief measure for assessing generalized anxiety disorder: The GAD-7**. *Arch Intern Med* (2006.0) **166** 1092-7. PMID: 16717171
39. Kroenke K, Spitzer RL, Williams JBW. **The PHQ-9: Validity of a brief depression severity measure**. *J Gen Intern Med* (2001.0) **16** 606-13. DOI: 10.1046/j.1525-1497.2001.016009606.x
40. Fountoulakis KN, Apostolidou MK, Atsiova MB, Filippidou AK, Florou AK, Gousiou DS. **Self-reported changes in anxiety, depression and suicidality during the COVID-19 lockdown in Greece**. *J Affect Disord* (2021.0) **279** 624-9. DOI: 10.1016/j.jad.2020.10.061
41. Marrugat J, Vila J, Pavesi M, Sanz F. **[Estimation of the sample size in clinical and epidemiological investigations]**. *Med Clin (Barc)* (1998.0) **111** 267-76. PMID: 9789243
42. Frank E Harrell. **Regression Modeling Strategies**. *Springer* (2003.0) **45** 170-170
43. Oginni OA, Oloniniyi IO, Ibigbami O, Ugo V, Amiola A, Ogunbajo A. **Depressive and anxiety symptoms and COVID-19-related factors among men and women in Nigeria**. *PLoS One* (2021.0) **16** 1-18. DOI: 10.1371/journal.pone.0256690
44. Peralta A, Espinel-Flores V, Gotsens M, Pérez G, Benach J, Marí-Dell’Olmo M. **Developing a deprivation index to study geographical health inequalities in ecuador**. *Rev Saude Publica* (2019.0) **53** 1-14. DOI: 10.11606/s1518-8787.2019053001410
45. Amparo Armas, Jackeline Contreras, Alison Vásconez. *La Economía del cuidado, el trabajo no remunerado y remunerado en Ecuador* (2009.0) 78
46. Czymara CS, Langenkamp A, Cano T. **Cause for concerns: gender inequality in experiencing the COVID-19 lockdown in Germany**. *Eur Soc* (2021.0) **23** S68-81. DOI: 10.1080/14616696.2020.1808692
47. Dang HAH, Viet Nguyen C. **Gender inequality during the COVID-19 pandemic: Income, expenditure, savings, and job loss**. *World Dev* (2021.0) **140** 105296. DOI: 10.1016/j.worlddev.2020.105296
48. Bienvenu LA, Noonan J, Wang X, Peter K. **Higher mortality of COVID-19 in males: Sex differences in immune response and cardiovascular comorbidities**. *Cardiovasc Res* (2020.0) **116** 2197-206. DOI: 10.1093/cvr/cvaa284
49. Moré P.. **Care work and the coronavirus crisis: The invisible labour that sustains life**. *Rev Esp Sociol* (2020.0) **29** 737-45
50. **United Nations Children’s Fund—UNICEF-**. *América Latina & El Caribe* (2022.0) 0-2
51. 51COVID-19: Cómo asegurar el aprendizaje de los niños sin acceso a Internet | UNICEF Ecuador [Internet]. [cited 2022
Jun
29]. Available from: https://www.unicef.org/ecuador/historias/covid-19-cómo-asegurar-el-aprendizaje-de-los-niños-sin-acceso-internet. *Cómo asegurar el aprendizaje de los niños sin acceso a Internet | UNICEF Ecuador* (2022.0)
52. Åberg Yngwe M, Fritzell J, Lundberg O, Diderichsen F, Burström B. **Exploring relative deprivation: Is social comparison a mechanism in the relation between income and health?**. *Soc Sci Med* (2003.0) **57** 1463-73. DOI: 10.1016/s0277-9536(02)00541-5
53. Monica Åberg Yngwe Naoki Kondo, Sara Hägg Ichiro Kawachi. **Relative deprivation and mortality–a longitudinal study in a Swedish population of 4,7 million, 1990–2006**. *BMC Public Heal* (2012.0) **12** 1-7
54. Szuster E, Pawlikowska-Gorzelańczyk A, Kostrzewska P, Mandera-Grygierzec A, Rusiecka A, Biernikiewicz M. **Mental and Sexual Health of Men in Times of COVID-19 Lockdown**. *Int J Environ Res Public Health* (2022.0) **19**. DOI: 10.3390/ijerph192215327
55. Housing Shaw M.. **and public health**. *Annu Rev Public Health* (2004.0) **25** 397-418. PMID: 15015927
56. Marí-Dell’Olmo M, Novoa AM, Camprubí L, Peralta A, Vásquez-Vera H, Bosch J. **Housing Policies and Health Inequalities**. *Int J Heal Serv* (2017.0) **47** 207-32. DOI: 10.1177/0020731416684292
57. Wilhelm Am KA. **Men and depression**. *Aust Fam Physician* (2009.0) **38** 102-5. PMID: 19283248
58. Miranda-Mendizabal A, Castellví P, Parés-Badell O, Alayo I, Almenara J, Alonso I. **Gender differences in suicidal behavior in adolescents and young adults: systematic review and meta-analysis of longitudinal studies**. *Int J Public Health* (2019.0) **64** 265-83. DOI: 10.1007/s00038-018-1196-1
59. Egas Reyes V, Torres Castillo AL, Hidalgo Jiménez N, Andrade Zapata S, Salao Sterckx E. **Salud mental, virtualidad y pandemia: Experiencias de Psico Escucha PUCE**. *Mundos Plur—Rev Latinoam Políticas y Acción Pública.* (2020.0) **7** 55-74
60. Xue B, McMunn A. **Gender differences in unpaid care work and psychological distress in the UK Covid-19 lockdown**. *PLoS One* (2021.0) **16** 1-15. DOI: 10.1371/journal.pone.0247959
61. **Actualización de casos de coronavirus en Ecuador–Ministerio de Salud Pública**. *Ministerio De Salud Publica* (2020.0) 1
|
---
title: 'Relationship between oral health and prognosis in patients with empyema: Single
center retrospective study with propensity score matching analysis'
authors:
- Eiji Iwata
- Teruaki Nishiuma
- Suya Hori
- Keiko Sugiura
- Masato Taki
- Shuntaro Tokunaga
- Junya Kusumoto
- Takumi Hasegawa
- Akira Tachibana
- Masaya Akashi
journal: PLOS ONE
year: 2023
pmcid: PMC9994691
doi: 10.1371/journal.pone.0282191
license: CC BY 4.0
---
# Relationship between oral health and prognosis in patients with empyema: Single center retrospective study with propensity score matching analysis
## Abstract
### Background
Empyema is a life-threatening infection often caused by oral microbiota. To the best of our knowledge, no reports have investigated the association between the objective assessment of oral health and prognosis in patients with empyema.
### Materials and methods
A total of 63 patients with empyema who required hospitalization at a single institution were included in this retrospective study. We compared non-survivors and survivors to assess risk factors for death at three months, including the Renal, age, pus, infection, diet (RAPID) score, and Oral Health Assessment Tool (OHAT) score. Furthermore, to minimize the background bias of the OHAT high-score and low-score groups determined based on the cut-off value, we also analyzed the association between the OHAT score and death at 3 months using the propensity score matching method.
### Results
The 3-month mortality rate was $20.6\%$ (13 patients). Multivariate analysis showed that a RAPID score ≥5 points (odds ratio (OR) 8.74) and an OHAT score ≥7 points (OR 13.91) were significantly associated with death at 3 months. In the propensity score analysis, a significant association was found between a high OHAT score (≥7 points) and death at 3 months ($$P \leq 0.019$$).
### Conclusion
Our results indicated that oral health assessed using the OHAT score may be a potential independent prognostic factor in patients with empyema. Similar to the RAPID score, the OHAT score may become an important indicator for the treatment of empyema.
## 1. Introduction
Empyema is defined as the presence of bacteria or pus in the pleural cavity and has common clinical symptoms, including dyspnea, fever, chest pain, and cough [1]. In recent years, the incidence of empyema has increased steadily worldwide, with a high mortality rate of 10–$30\%$ despite the advancements in antibiotic therapy and widespread chest tube drainage [2–4]. Physicians must understand the risk factors, clinical features, and severity of diseases with high mortality. A clinical risk score for predicting death can facilitate the formulation of early management strategies. In 2014, the RAPID score was developed as a clinical risk score of pleural infections, including empyema [5]. This score is composed of the following five patient characteristics and clinical data: renal, age, purulence, infection source, and dietary factors. Recent reports have revealed an association between the RAPID score and 3-month death rate (low risk: 0–2 points, medium risk: 3–4 points, high risk: 5–7 points) in patients with empyema [6,7].
The most common cause of empyema is bacterial pneumonia, which is associated with oral health, including the number of oral bacteria [8,9]. Bacteria breach the visceral pleura to establish an infected parapneumonic effusion, resulting in empyema. Many studies have reported that oral bacteria, including Streptococcus spp., Staphylococcus aureus, and Fusobacterium spp. Have been detected as the main causative bacteria of empyema [10–12]. In addition, a recent report presented direct genetic evidence that some bacteria in empyema are derived from the oral flora [13]. Therefore, oral health may be associated with the onset or prognosis of empyema. To the best of our knowledge, no reports have investigated the association between the objective assessment of oral health and prognosis in patients with empyema. The Oral Health Assessment Tool (OHAT) score system is widely recognized as an objective tool for assessing oral health [14–17]. In 2015, this scoring system was developed for non-dental professionals such as nurses and allied health personal care attendants [14]. This score consists of the following eight categories on a 3-point scale: lips, tongue, gums and mucosa, saliva, natural teeth, dentures, oral cleanliness, and dental pain, with higher total scores indicating poorer overall oral health. Recently, many researchers, including dentists, have used the OHAT score to evaluate the oral health of patients in various fields [15–17]. This study aimed to investigate the association between oral health assessed using the OHAT score and prognosis in patients with empyema.
## 2.1. Patients
This study included 63 patients hospitalized for empyema treatment between January 2017 and July 2022 at Kakogawa Central City Hospital. Light’s classification was used to diagnose empyema [18]. In brief, 1) aspiration of grossly purulent material on thoracentesis and 2) at least one of the following: a) thoracentesis fluid with a positive Gram stain or culture, b) pleural fluid glucose <40 mg/dL, c) pH <7.2, or d)- lactate dehydrogenase >1000 IU/L [18]. The exclusion criteria were as follows: patients under 20 years old, those who did not undergo pleural puncture for some reason, those who did not wish to participate after the publication of this study, and those with missing data that were needed in this study. Patients with confirmed empyema underwent various tests such as blood tests, and were treated with antibiotics and chest tube drainage. They also underwent dental examinations, including panoramic dental radiography and oral photography, within days after hospitalization and dental treatments, if needed, during hospitalization.
This study was performed in accordance with the 1964 Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Boards (IRB) of Kakogawa Central City Hospital (Authorization number: 2020–46). The ethics committee approved the study and gave administrative permissions to access the data used in this study. As this was a retrospective study, the research plan was published on the homepage of the hospital according to the instructions of the IRB in accordance with the guaranteed opt-out opportunity.
## 2.2. Study design
The present study is a retrospective cohort study. Patients were divided into two groups: non-survivors and survivors at 3 months. The following variables from medical records were investigated: [1] patient factors (sex, presence of dysphagia, compromised-host, smoking history); [2] clinical findings factors, such as CRP, WBC, blood urea nitrogen (BUN), age, purulence of pleural fluid, infection source (community-acquired/hospital-acquired), serum albumin, OHAT score, and etiology (monomicrobial/polymicrobial/no growth); and [3] treatment methods. Dysphagia was defined as coughing when taking a meal or decreasing swallowing ability on evaluation by physicians and speech-language-hearing therapists [7]. Data on treatment and outcomes were also evaluated for each patient during hospitalization. A compromised-host was defined as a patient with any of the following diseases: rheumatoid arthritis, chronic kidney disease, malignancy, diabetes, cardiovascular diseases, neurological diseases, and steroid use. We used two clinical risk scores: RAPID (total score; min:0 point, max:7 points) and OHAT (total score; min:0 point, max:16 points). The RAPID score was based on five common parameters (Table 1) [6]. Based on the results of the dental examinations, the presence of teeth with poor prognosis was retrospectively investigated using panoramic dental radiography. They were defined as teeth with abnormal radiographic findings (e.g., apical radiolucency larger than 3 mm in diameter, alveolar bone loss around more than half of the root, untreated root remnants, or vertically fractured roots) [19,20]. Medical records were used whether those teeth were extracted. Pleural fluid was collected by pleural puncture at the time of admission, and microbiological examinations were performed. Anaerobic containers were used to collect pleural fluid to detect anaerobic bacteria, and Gram staining and pleural fluid cultures were performed. Blood agar (Kohjin Bio Co., Ltd., Saitama, Japan) and chocolate agar media (Kohjin Bio Co., Ltd.) were used to detect general bacteria. Anaero Columbia agar medium with hemin and vitamin K1 (Nippon Becton Dickinson Co., Ltd., Tokyo, Japan) was used to detect anaerobic bacteria; any anaerobic bacteria were then cultivated at 35°C and $9\%$ CO2. The causative pathogens were then identified in the pleural fluid culture.
**Table 1**
| RAPID score | Unnamed: 1 | Unnamed: 2 |
| --- | --- | --- |
| Parameter | Measure | Score |
| Renal BUN (mg/dL) | <14 | 0 |
| Age (years)Purulence of pleural fluidInfection sourceDietary factors Alb (g/dL)Risk categories | 14–22.4>22.4<50>70PurulentNon-purulentCommunity-acquiredHospital-acquired≥2.7<2.7Score 0–2Score 3–4Score 5–7 | 12012010101Low riskMedium riskHigh risk |
## 2.3. OHAT score
The OHAT score consists of eight categories with three possible scores (0 = healthy, 1 = some changes, and 2 = unhealthy) (Table 2) [14]. The total score is the sum of the various sub-scores. Based on the results of the dental examinations, including oral photographs and medical records, OHAT score of each patient was retrospectively evaluated by two observers (EI and KS). EI is an oral and maxillofacial surgeon with ≥ 10 years of experience, and KS is a dental hygienist with ≥ 10 years of experience. The OHAT-J, which includes images of each category and point scale in Japanese, is well-known among dentists and dental hygienists in Japan [21,22]. In this study, the dentist (EI) and dental hygienist (KS) evaluated the OHAT score after visual training and calibration by using this picture (S1 Data). Finally, the OHAT score of each patient was determined through discussion among the observers.
**Table 2**
| OHAT score | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 |
| --- | --- | --- | --- |
| Category | 0 = healthy | 1 = changes | 2 = unhealthy |
| Lips | Smooth, pink, moist | Dry, chapped, or red, at corners | Swelling or lump, white/red/ulcerated patch; bleeding/ulcerated at corners |
| TongueGums and tissuesSalivaNatural teethDenturesOral cleanlinessDental pain | Normal, moist, rough, pinkPink, moist, smooth, no bleedingMoist tissues, watery and free flowing salivaNo decayed or broken teeth/rootsNo broken areas or teeth, dentures regularly worn and namedClean and no foodParticles or tartar in mouth or denturesNo behavioral, verbal, or physical signs of dental pain | Patchy, fissured, red, coatedDry, shiny, rough, red, swollen, one ulcer/sore spot under denturesDry sticky tissues, little saliva present, resident thinks they have a dry mouth1–3 decayed or broken teeth/roots or very worn-down teeth1 broken area/tooth or dentures only worn for 1–2 h daily, or dentures not named or looseFood particles/tartar/plaque in 1–2 areas of the mouth or on a small area of dentures or halitosis (bad breath)Verbal and/or behavioral signs of pain present, such as pulling at face, chewing lips, not eating, aggression | White/red patches, ulcerated, swollenSwollen, bleeding ulcers, white/red patches, generalized redness under denturesTissue parched and red, very little/no saliva present, saliva thick, resident thinks they have a dry mouth≥4 decayed or broken teeth/roots, or very worn-down teeth, or <4 teethMore than 1 broken area/tooth, denture missing or not worn, loose and needs denture adhesive, or not namedFood particles/tartar/plaque in most areas of the mouth or on most areas of dentures or severe halitosis (bad breath)Physical pain signs (swelling of cheek or gum, broken, ulcers) present, as well as verbal and/or behavioral signs (pulling at face, not eating, aggression) |
## 2.4. Statistical analyses
Statistical analyses were performed using SPSS (version 26.0; IBM Corp., Armonk, NY, USA) and Ekuseru-Toukei 2012 (Social Survey Research Information Co., Ltd., Tokyo, Japan). A receiver operating characteristic (ROC) curve was used to determine the cut-off values for the RAPID and OHAT scores. The area under the ROC curve was used to measure the accuracy of discrimination. The area under the ROC curve was used to measure the accuracy of discrimination (range, 0.5 to 1). The association of each variable with death at 3 months was analyzed using the non-parametric Mann-Whitney U test for ordinal variables and either the Fisher’s exact test or the chi-squared test was used for categorical variables. Statistical significance was set at $P \leq 0.05.$ The selected variables were included in a multiple logistic regression model using the forced-entry method. Odds ratios (Ors) and $95\%$ confidence intervals (Cis) were calculated. Furthermore, to minimize selection bias associated with the comparison of retrospective data analysis, propensity score matching was performed between the high and low OHAT score groups using cut-off values. Subsequently, propensity score-matched cases [36] were evaluated to determine an association between a high OHAT score and death at 3 months. Reliability assessments for the stability of OHAT scores were assessed in a test-retest of observers using Cohen’s kappa statistic for the individual categories and intraclass correlation (ICC) for the total OHAT score [14]. The Kappa statistic indicated the degree of departure between the actual observed and chance percentage agreement and was not weighted. In interpreting the Kappa statistic, values of 0.41–0.60 were considered moderate, 0.61–0.80 substantial, and 0.81–1.0 almost perfect agreement [14].
## 3. Results
The 3-month mortality rate was $20.6\%$ (13 out of 63 patients). The median age of non-survivors was 84.0 years and that of survivors was 72.0 years, which showed a significant difference ($P \leq 0.001$). All non-survivors and 42 of the 50 survivors were male.
Table 3 shows patient characteristics and the results of the univariate analysis. In the univariate analysis, the rate of dysphagia ($$P \leq 0.047$$), RAPID score ($P \leq 0.001$), and OHAT score ($P \leq 0.001$) were significantly higher in non-survivors than in survivors. Of the five factors assessed by the RAPID score, BUN level, and age were significantly higher in non-survivors than in survivors, while serum albumin level was lower in non-survivors than in survivors. Of the eight categories of OHAT score, lip ($P \leq 0.001$), tongue ($$P \leq 0.021$$), gums and tissues ($$P \leq 0.007$$), and saliva ($$P \leq 0.014$$) were unhealthier in non-survivors than survivors. More than half of the non-survivors ($69.2\%$) and survivors ($54.0\%$) had teeth with a poor prognosis. Of them, some non-survivors ($22.2\%$) and survivors ($55.6\%$) underwent extraction of those teeth. Non-survivors tended to have a lower frequency of oral care than survivors ($$P \leq 0.135$$).
**Table 3**
| Variables | Unnamed: 1 | Non-survivors Survivors(n = 13) (n = 50) | Non-survivors Survivors(n = 13) (n = 50).1 | P value |
| --- | --- | --- | --- | --- |
| Sex | Male | 13 (100.0%) | 42 (84.0%) | 0.188 b |
| Dysphagia | Yes | 12 (92.3%) | 31 (62.0%) | 0.047* b |
| Compromised host | Yes | 6 (46.2%) | 19 (38.0%) | 0.752 b |
| Smoking history | Yes | 10 (76.9%) | 47 (74. 0%) | 1.000 b |
| CRP (mg/dL) | Median (range) | 23.7 (7.1–30.3) | 21.4 (2.1–41.0) | 0.425 a |
| WBC (10 3 /μL) | Median (range) | 15.6 (10.8–24.8) | 15.4 (5.5–39.2) | 0.663 a |
| RAPID score | Median (range) | 5.0 (2–6) | 3.0 (0–5) | <0.001* a |
| BUN (mg/dL) Age (years) | Median (range)Median (range) | 43.4 (9.4–95.4)84.0 (65–96) | 16.3 (4.6–67.5)72.0 (43–90) | <0.001* a<0.001* a |
| Purulence of pleural fluid | PurulentNon-purulent | 13 (100.0%)0 (0.0%) | 42 (87.3%)8 (12.7%) | 0.188 b |
| Infection source | Community-acquired | 12 (92.3%) | 45 (90.0%) | 1.000 b |
| | Hospital-acquired | 1 (7.7%) | 5 (10.0%) | |
| Serum albumin (g/dL) | Median (range) | 2.1 (1.2–2.9) | 2.5 (1.7–4.2) | 0.025* a |
| OHAT score | Median (range) | 7.0 (4–13) | 5.0 (0–11) | <0.001* a |
| Lips | Median (range) | 1.0 (0–2) | 0.0 (0–2) | <0.001* a |
| Tongue | Median (range) | 1.0 (0–2) | 1.0 (0–2) | 0.021* a |
| Gums and tissues | Median (range) | 1.0 (0–2) | 0.5 (0–2) | 0.007* a |
| Saliva Natural teeth | Median (range)Median (range) | 1.0 (0–2)1.0 (0–2) | 0.0 (0–2)1.0 (0–2) | 0.014* a0.697 a |
| Teeth with a poor prognosis | Presence | 9 (69.2%) | 27 (54.0%) | 0.365 b |
| With tooth extraction | Yes (/presence) | 2/9 (22.2%) | 15/27 (55.6%) | 0.128 b |
| Dentures Oral cleanliness Frequency of oral care Dental pain | Median (range)Median (range)Median (range)Median (range) | 0.0 (0–2)2.0 (0–2)2.0 (0–3)0.0 (0) | 0.0 (0–2)2.0 (0–2)2.0 (1–4)0.0 (0–2) | 0.913 a0.055 a0.135 a0.384 a |
| Etiology | Monomicrobial | 5 (38.5%) | 22 (44.0%) | 1.000 b |
| | Polymicrobial | 2 (15.3%) | 11 (22.0%) | |
| | No growth | 6 (46.2%) | 17 (34.0%) | |
| Treatment | Antibiotic therapy only | 4 (30.8%) | 4 (8.0%) | 0.120 c |
| | + drainage | 2 (15.4%) | 5 (10.0%) | |
| | + drainage, urokinase | 7 (53.8%) | 39 (78.0%) | |
| | + surgery | 0 (0.0%) | 2 (4.0%) | |
RAPID score ≥5 points had a sensitivity of $61.5\%$, a specificity of $88.0\%$, and an area under curve (AUC) of 0.81 (Fig 1A). OHAT score ≥7 points had a sensitivity of $76.9\%$, a specificity of $74.0\%$, and an AUC of 0.79 (Fig 1B). After applying a logistic regression model and forced entry method, we found that RAPID score ≥5 points (OR 8.74) and an OHAT score ≥7 points (OR 13.91) were significant risk factors for death at 3 months (Table 4). Table 5 includes the intra- and inter-rater reliability data for the OHAT scores. Intra-rater reliability ranged from $84.1\%$ for oral cleanliness to $100\%$ for dental pain. Kappa statistics were in the range considered substantially perfect (0.61–0.80) for saliva and oral cleanliness, and for all other categories in the range of 0.81–1.00 (almost perfect). Inter-rater reliability ranged from $82.5\%$ for oral cleanliness to $100\%$ for dental pain. Kappa statistics were in the range of substantially perfect (0.61–0.80) for lips, saliva, and oral cleanliness, and for all other categories in the range of 0.81–1.00 (almost perfect). The ICC for the OHAT total scores was 0.94 for intra-rater and 0.92 for inter-rater reliability.
**Fig 1:** *(A) The ROC curve for accuracy of RAPID score. The AUC for our model was 0.810 ($95\%$ confidence interval 0.675 to 0.945). (B) The ROC curve for accuracy of OHAT score. The AUC for our model was 0.790 ($95\%$ confidence interval 0.664 to 0.91.* TABLE_PLACEHOLDER:Table 4 TABLE_PLACEHOLDER:Table 5 Table 6 shows the results of the pleural fluid culture test. In both groups, oral bacteria were detected in many patients. The most frequently detected bacteria were Streptococcus species, followed by facultative anaerobic Staphylococcus spp., obligate anaerobic Prevotella spp., Parvimonas micra, and Porphyromonas gingivalis.
**Table 6**
| Non-survivors | No. | Survivors | No..1 |
| --- | --- | --- | --- |
| [Facultative anaerobic bacteria] | 5 | [Facultative anaerobic bacteria] | 31 |
| Streptococcus spp. | 3 | Streptococcus spp. | 22 |
| S. constellatus | (2) | S. intermedius | (10) |
| S. intermedius | (1) | S. anginosus | (4) |
| Psudomonas aeruginosa | 1 | S. constellatus | (4) |
| Aspergillus fumigatus | 1 | S. agalactiae | (2) |
| | | S. gordonii | (1) |
| | | S. mitis | (1) |
| | | Staphylococcus spp. | 5 |
| | | S. aureus | (2) |
| | | MRSA | (2) |
| | | MSSA | (1) |
| | | Psudomonas aeruginosa | 2 |
| | | Citrobacter koseri | 1 |
| | | Enterococcus faecalis | 1 |
| [Obligate anaerobic bacteria] | 5 | [Obligate anaerobic bacteria] | 13 |
| Prevotella spp. | 2 | Parvimonas micra | 3 |
| P. buccae | (1) | Prevotella spp. | 3 |
| P. disiens | (1) | P. buccae | (1) |
| Parvimonas micra | 1 | P. disiens | (1) |
| Porphyromonas gingivalis | 1 | P. denticola | (1) |
| Fusobacterium nucleatum | 1 | Porphyromonas gingivalis | 3 |
| | | Veillonella spp. | 1 |
| | | Bacteroides vulgatus | 1 |
| | | Fusobacterium nucleatum | 1 |
| | | Finegoldia magna | 1 |
We compared patient characteristics between the OHAT high-score (≥ 7 points) and low-score (< 7 points) groups (Table 7). Propensity score matching was performed for an unbiased analysis of the OHAT score using seven variables (sex, dysphagia, compromised host, smoking history, CRP, WBC, and RAPID score). After propensity score matching, the characteristics of the two groups were balanced in seven variables, and the rate of non-survivors was significantly higher in the OHAT high-score group than in the low-score group ($$P \leq 0.019$$) (Table 8).
## 4. Discussion
In this study, we investigated the risk factors for death at 3 months in patients with empyema. Multivariate analyses showed that a RAPID score ≥5 points (OR 8.74) and an OHAT score ≥7 points (OR 13.91) were significantly associated with death at 3 months. Additionally, using cut-off values, propensity score analysis between the high and low OHAT score groups revealed a significant association between OHAT high score (≥7 points) and death at 3 months ($$P \leq 0.019$$).
The 3-month mortality rate in this study was $20.6\%$, which was slightly higher than or similar to that reported in previous studies [2–5,7]. In addition to the RAPID and OHAT scores, univariate analysis showed that age ($P \leq 0.001$) out of RAPID score and presence of dysphagia ($$P \leq 0.047$$) were significantly associated with death at 3 months in patients with empyema. Pneumonia is the third leading cause of death in Japan ($9.2\%$), and the ratio of aspiration pneumonia to total cases of pneumonia increases with age (50–59 years: approximately $30\%$; 60–69 years: approximately $50\%$; 70–79 years: approximately $70\%$; 80–89 years: approximately $80\%$; over 90 years: approximately $90\%$) [23]. *In* general, 20–$40\%$ of hospitalized patients with pneumonia have pleural effusion, and $10\%$ progress to acute empyema [18]. A previous study reported a significant relationship between dysphagia and death at 3 months in patients with empyema [13].
Previous studies reported that the RAPID score enables the prediction of death in patients with pleural infections, including empyema at 3 months [5–7], indicating that patients with RAPID scores ≥5 were at a high risk of death at 3 months [6,7]. These results are in line with those of the present study. Of the five factors contributing to this score, BUN levels, age, and serum albumin levels were significantly different between survivors and non-survivors. High BUN levels indicate dehydration, which is expected to negatively affect the patient prognosis. A previous report showed that high BUN levels in the RAPID score were associated with death at 3 months in patients with empyema (median 53 mg/dL vs 19 mg/dL; $P \leq 0.01$) [7] similarly to this study (median 43.4 mg/dL vs 16.3 mg/dL; $P \leq 0.001$). Serum albumin is a reliable marker of nutritional status [24], and a previous study showed an association between low serum albumin levels and infection [25]. Additionally, Sakai et al. reported that preoperative serum albumin level is a valid predictor of complications following surgery for acute empyema (incidence of high-level group vs low-level group = $39\%$ vs $8\%$; $$P \leq 0.012$$) [26].
The OHAT score has been used in various fields [15–17], and its inter-rater reliability has been discussed. Several researchers have used Cohen’s kappa statistics to investigate the reliability and validity of the OHAT score per category in many fields [14,27,28], concluding that the OHAT score is a reliable and valid screening assessment tool for their research subjects. We also analyzed the intra- and inter-rater reliabilities of the OHAT scores per category using Cohen’s kappa statistics in this study. The results were either almost perfect or moderately perfect and were reliable for evaluation. Furthermore, Nishizawa et al. investigated the association between the OHAT score and aspiration pneumonia [29]. They set the OHAT score cut-off value to 4 points, and few patients with aspiration pneumonia had OHAT scores of ≥7 points, which was the cut-off value determined by using ROC curve in the present study. This difference may indicate the patient’s general medical condition during each disease.
Empyema is caused by obligate anaerobic bacteria such as Prevotella spp., Peptostreptococcus, or *Fusobacterium nucleatum* (30–$40\%$ responsible for mixed infections) in addition to *Streptococcus pneumoniae* and *Staphylococcus aureus* [10–12,30,31]. The detection frequency of the *Streptococcus anginosus* group (S. anginosus, S. constellatus, and S. intermedius), which resides in the oral cavity, is also high. Particularly in empyema, polymicrobial infections with obligate anaerobic bacteria are common [32]. In the present study, *Streptococcus pneumoniae* was not detected, and there was no significant difference between monomicrobial and polymicrobial patients, unlike the results of a previous study in which a significant difference was found [13]. The most frequently detected bacteria were Streptococcus anginosus, followed by Staphylococcus spp., Prevotella spp., Parvimonas micra, and Porphyromonas gingivalis. Therefore, most of the causative bacteria were derived from the oral flora. Teeth and periodontal tissues can be a route of bacterial invasion [33]. There are two possible pathways for the onset of empyema: first, descending mediastinitis by dental infection (via the cervical tissue space) spreading into the thoracic cavity; and second, the route by which bacteria reach the thoracic cavity via hematogenous circulation [33]. In this study, two out of nine non-survivors with teeth with a poor prognosis ($22.2\%$) and 15 of 27 survivors ($55.6\%$) underwent tooth extraction. Non-survivors tended to have a lower frequency of oral care than survivors ($$P \leq 0.135$$). Many non-survivors did not wish to improve their oral health by extracting teeth with a poor prognosis or frequent oral care, regardless of their higher OHAT score than survivors. Therefore, our dental education, might have been insufficient, especially for the OHAT high-score groups. Patients with a high OHAT score and who leave teeth with a poor prognosis untreated may have a low interest in oral health. Dentists should educate patients on the importance of improving oral health to improve the prognosis of empyema.
This study had several limitations. First, there is a possibility of unknown confounding factors as this was a retrospective study; for example, degree of underlying disease (e.g., presence of chronic obstructive pulmonary disease) or degree of smoking history (e.g., Brinkman index). Although, a propensity score matching analysis was performed to decrease the effect of confounding factors as much as possible, the possibility of selection bias could not be completely excluded. Second, the sample size was small, which might have introduced biases in the data selection and analyses. Third, no bacteria were detected in the pleural fluid cultures of several patients. One possible reason is that some patients may have undergone pleural puncture after antibiotic administration. Whether antibiotics were administered before hospitalization is unknown. However, strict limitations of antibiotic administration in clinics or other hospitals before admission to our hospital may be difficult because empyema is a severe disease that can result in death, and early management is important. Additionally, we did not use quantitative polymerase chain reaction or next-generation sequencing to detect the causative bacteria. Finally, the content of dental treatment may have affected the prognosis of patients with empyema. In this study, the dental treatment methods, including the standard of tooth extraction and frequency of oral care, were not unified. However, there was no significant difference between non-survivors and survivors in the rate of patients with tooth extraction and frequency of oral care. In the future, prospective studies are necessary to identify useful prognostic factors for patients with empyema.
## 5. Conclusion
This is the first report to investigate the association between the objective assessment of oral health and the prognosis of empyema. Our results indicate that oral health assessed using the OHAT score may be a potential independent prognostic factor in patients with empyema. However, these findings should be carefully considered because of the retrospective study design. In a prospective study, we should eliminate confounding factors as much as possible by excluding patients with the administration of antibiotics before pleural puncture and unifying dental treatment methods, such as standard of tooth extraction and frequency of oral care.
## References
1. Corcoran JP, Wrightson JM, Belcher E, DeCamp MM, Feller-Kopman D. **Pleural infection: past, present, and future directions**. *Lancet Respir Med* (2015.0) **3** 563-577. DOI: 10.1016/S2213-2600(15)00185-X
2. Asai N, Suematsu H, Hagihara M, Nishiyama N, Kato H. **The etiology and bacteriology of healthcare-associated empyema are quite different from those of community-acquired empyema**. *J Infect Chemother* (2017.0) **23** 661-667. DOI: 10.1016/j.jiac.2017.04.011
3. Bostock IC, Sheikh F, Millington TM, Finley DJ, Phillips JD. **Contemporary outcomes of surgical management of complex thoracic infections**. *J Thorac Dis* (2018.0) **10** 5421-5427. DOI: 10.21037/jtd.2018.08.43
4. Lin CW, Huang KY, Lin CH, Wang BY, Kor CT. **Video-Assisted Thoracoscopic Surgery in Community-Acquired Thoracic Empyema: Analysis of Risk Factors for Mortality**. *Surg Infect (Larchmt)* (2022.0) **23** 191-198. DOI: 10.1089/sur.2021.191
5. Rahman NM, Kahan BC, Miller RF, Gleeson FV, Nunn AJ. **A clinical score (RAPID) to identify those at risk for poor outcome at presentation in patients with pleural infection**. *Chest* (2014.0) **145** 848-855. DOI: 10.1378/chest.13-1558
6. Corcoran JP, Psallidas I, Gerry S, Piccolo F, Koegelenberg CF. **Prospective validation of the RAPID clinical risk prediction score in adult patients with pleural infection: the PILOT study**. *Eur Respir J* (2020.0) **56** 2000130. DOI: 10.1183/13993003.00130-2020
7. Yamazaki A, Ito A, Ishida T, Washio Y. **Polymicrobial etiology as a prognostic factor for empyema in addition to the renal, age, purulence, infection source, and dietary factors score**. *Respir Investig* (2019.0) **57** 574-581. DOI: 10.1016/j.resinv.2019.06.008
8. Kikutani T, Tamura F, Tashiro H, Yoshida M, Konishi K. **Relationship between oral bacteria count and pneumonia onset in elderly nursing home residents**. *Geriatr Gerontol Int* (2015.0) **15** 417-421. DOI: 10.1111/ggi.12286
9. Scannapieco FA. **Pneumonia in nonambulatory patients. The role of oral bacteria and oral hygiene**. *J Am Dent Assoc* (2006.0) **137** 21S-25S. DOI: 10.14219/jada.archive.2006.0400
10. Ishiguro T, Takayanagi N, Ikeya T, Yoshioka H, Yanagisawa T. **Isolation of Candida species is an important clue for suspecting gastrointestinal tract perforation as a cause of empyema**. *Intern Med* (2010.0) **49** 1957-1964. DOI: 10.2169/internalmedicine.49.3667
11. Dyrhovden R, Nygaard RM, Patel R, Ulvestad E, Kommedal Ø. **The bacterial aetiology of pleural empyema. A descriptive and comparative metagenomic study**. *Clin Microbiol Infect* (2019.0) **25** 981-986. DOI: 10.1016/j.cmi.2018.11.030
12. Maskell NA, Batt S, Hedley EL, Davies CW, Gillespie SH. **The bacteriology of pleural infection by genetic and standard methods and its mortality significance**. *Am J Respir Crit Care Med* (2006.0) **174** 817-23. DOI: 10.1164/rccm.200601-074OC
13. Katsuda R, Inubushi J, Tobata H, Eguchi T, Terada K. **Genetic Homology between Bacteria Isolated from Pulmonary Abscesses or Pyothorax and Bacteria from the Oral Cavity**. *Microbiol Spectr* (2022.0) **10** e0097421. DOI: 10.1128/spectrum.00974-21
14. Chalmers JM, King PL, Spencer AJ, Wright FA, Carter KD. **The oral health assessment tool—validity and reliability**. *Aust Dent J* (2005.0) **50** 191-199. DOI: 10.1111/j.1834-7819.2005.tb00360.x
15. Ogawa T, Koike M, Nakahama M, Kato S. **Poor Oral Health Is a Factor that Attenuates the Effect of Rehabilitation in Older Male Patients with Fractures**. *J Frailty Aging* (2022.0) **11** 324-328. DOI: 10.14283/jfa.2021.54
16. Furuya J, Suzuki H, Hidaka R, Matsubara C, Motomatsu Y. **Association between oral health and advisability of oral feeding in advanced cancer patients receiving palliative care: a cross-sectional study**. *Support Care Cancer* (2022.0) **30** 5779-5788. DOI: 10.1007/s00520-022-06984-w
17. Nomoto A, Shimizu A, Ohno T, Tohara H, Hashidume M. **Poor oral health and anorexia in older rehabilitation patients**. *Gerodontology* (2022.0) **39** 59-66. DOI: 10.1111/ger.12600
18. Light RW. **Parapneumonic effusions and empyema**. *Proc Am Thorac Soc* (2006.0) **3** 75-80. DOI: 10.1513/pats.200510-113JH
19. Paul RA, Tamse A, Rosenberg E. **Cracked and broken teeth—definitions, differential diagnosis and treatment**. *Refuat Hapeh Vehashinayim* (1993.0) **24** 7-12
20. Huumonen Sisko, Ørstavik Dag. **Radiological aspects of apical periodontitis**. *Endodontic Topics* (2002.0) **1** 3-25
21. Matsuo M, Nakagawa K. **Creation of oral health assessment tool Japanese version (OHAT-J) and examination of its reliability and validity**. *J. Jpn. Soc. Disability Oral Health* (2016.0) **37** 1-7
22. 22Oral Health Assessment Tool (OHAT) Japanese version. Available from: https://www.ohcw-tmd.com/research.
23. Teramoto S, Fukuchi Y, Sasaki H, Sato K, Sekizawa K. **High incidence of aspiration pneumonia in community- and hospital-acquired pneumonia in hospitalized patients: a multicenter, prospective study in Japan**. *J Am Geriatr Soc* (2008.0) **56** 577-579. DOI: 10.1111/j.1532-5415.2008.01597.x
24. Cabrerizo S, Cuadras D, Gomez-Busto F, Artaza-Artabe I, Marı´n-Ciancas F. **Serum albumin and health in older people: review and meta-analysis**. *Maturitas* (2015.0) **81** 17-27. DOI: 10.1016/j.maturitas.2015.02.009
25. Pimlott BJ, Jones CA, Beaupre LA, Johnston DW, Majumdar SR. **Prognostic impact of pre-operative albumin on short-term mortality and complications in patients with hip fracture**. *Arch Gerontol Geriatr* (2011.0) **53** 90-94. DOI: 10.1016/j.archger.2010.06.018
26. Sakai T, Sano A, Azuma Y, Koezuka S, Otsuka H. **Preoperative undernutrition predicts postoperative complications of acute empyema**. *Health Sci Rep* (2021.0) **4** e232. DOI: 10.1002/hsr2.232
27. Klotz AL, Zajac M, Ehret J, Hassel AJ, Rammelsberg P. **Development of a German version of the Oral Health Assessment Tool**. *Aging Clin Exp Res* (2020.0) **32** 165-172. DOI: 10.1007/s40520-019-01158-x
28. Simpelaere IS, Van Nuffelen G, Vanderwegen J, Wouters K, De Bodt M. **Oral health screening: feasibility and reliability of the oral health assessment tool as used by speech pathologists**. *Int Dent J* (2016.0) **66** 178-189. DOI: 10.1111/idj.12220
29. Nishizawa T, Niikura Y, Akasaka K, Watanabe M, Kurai D. **Pilot study for risk assessment of aspiration pneumonia based on oral bacteria levels and serum biomarkers**. *BMC Infect Dis* (2019.0) **19** 761. DOI: 10.1186/s12879-019-4327-2
30. Chen KY, Hsueh PR, Liaw YS, Yang PC, Luh KT. **A 10-year experience with bacteriology of acute thoracic empyema: emphasis on Klebsiella pneumoniae in patients with diabetes mellitus**. *Chest* (2000.0) **117** 1685-1689. DOI: 10.1378/chest.117.6.1685
31. Light RW. **Pleural Diseases Fourth Edition**. *Lippincott Williams & Wilkins* (2001.0) 151-181
32. Ahmed RA, Marrie TJ, Huang JQ. **Thoracic empyema in patients with community-acquired pneumonia**. *Am J Med* (2006.0) **119** 877-883. DOI: 10.1016/j.amjmed.2006.03.042
33. Reyes L, Herrera D, Kozarov E, Roldán S, Progulske-Fox A. **Periodontal bacterial invasion and infection: contribution to atherosclerotic pathology**. *J Clin Periodontol* (2013.0) **14** S30-50
|
---
title: Tirap controls Mycobacterium tuberculosis phagosomal acidification
authors:
- Imène Belhaouane
- Amine Pochet
- Jonathan Chatagnon
- Eik Hoffmann
- Christophe J. Queval
- Nathalie Deboosère
- Céline Boidin-Wichlacz
- Laleh Majlessi
- Valentin Sencio
- Séverine Heumel
- Alexandre Vandeputte
- Elisabeth Werkmeister
- Laurence Fievez
- Fabrice Bureau
- Yves Rouillé
- François Trottein
- Mathias Chamaillard
- Priscille Brodin
- Arnaud Machelart
journal: PLOS Pathogens
year: 2023
pmcid: PMC9994722
doi: 10.1371/journal.ppat.1011192
license: CC BY 4.0
---
# Tirap controls Mycobacterium tuberculosis phagosomal acidification
## Abstract
Progression of tuberculosis is tightly linked to a disordered immune balance, resulting in inability of the host to restrict intracellular bacterial replication and its subsequent dissemination. The immune response is mainly characterized by an orchestrated recruitment of inflammatory cells secreting cytokines. This response results from the activation of innate immunity receptors that trigger downstream intracellular signaling pathways involving adaptor proteins such as the TIR-containing adaptor protein (Tirap). In humans, resistance to tuberculosis is associated with a loss-of-function in Tirap. Here, we explore how genetic deficiency in Tirap impacts resistance to *Mycobacterium tuberculosis* (Mtb) infection in a mouse model and ex vivo. Interestingly, compared to wild type littermates, *Tirap heterozygous* mice were more resistant to Mtb infection. Upon investigation at the cellular level, we observed that mycobacteria were not able to replicate in Tirap-deficient macrophages compared to wild type counterparts. We next showed that Mtb infection induced Tirap expression which prevented phagosomal acidification and rupture. We further demonstrate that the Tirap-mediated anti-tuberculosis effect occurs through a Cish-dependent signaling pathway. Our findings provide new molecular evidence about how Mtb manipulates innate immune signaling to enable intracellular replication and survival of the pathogen, thus paving the way for host-directed approaches to treat tuberculosis.
## Author summary
The efficiency of *Mycobacterium tuberculosis* in establishing its replicative niche relies on its capacity to manipulate host factors. The aim of our study was to identify the impact of a genetic deficiency in Tirap, a key factor of the innate immunity response, on the evolution of in vivo and ex vivo tuberculosis infections. Here, we show that mice heterozygous for a truncated Tirap protein were more resistant to the infection, displaying less bacterial burden in the lungs. At the cellular level, we identified a repression of Cish-mediated phagosomal acidification in Tirap-deficient macrophages resulting in an enhanced bactericidal activity. Our results provide new insights into how Mtb infection promotes Tirap expression to ensure its survival.
## Introduction
In a given population, individuals differ from each other in many of their genes. Infectious diseases have been major threats to health and survival throughout the history of human evolution. Natural selection is thereby expected to act significantly on host defense genes, particularly on innate immunity genes, whose products mediate the host interaction with the microbial environment [1]. Toll like receptors (TLRs) play a central role in the coordination of innate and adaptive immunity. TLRs can recognize a distinct range of conserved microbial components. The TLR-mediated detection of microbes activates a signalling cascade that leads to the initiation of an immunoregulatory response. The variability of this immune response is genetically controlled. In humans, genetic deficits and polymorphisms associated with TLR pathways are often described as influencing the course of many infections [2–4].
Among the various immune actors involved in this process, the tirap gene, located on chromosome 11q24.2, encodes for the Tirap/Mal (TIR-containing adaptor protein/MyD88 adaptor-like) protein, which has a C-terminal TIR domain acting as a sorting and bridging adaptor between TLR2, TLR4 and TLR9 to bring the TLR adaptor MyD88 (myeloid differentiation primary response 88) into the immune pathway [5,6]. The signalling cascade initiated upon TLR stimulation leads to the activation of transcription factors resulting in the production of pro-inflammatory cytokines such as Interleukin (IL)-6, IL-12 and Tumour Necrosis *Factor alpha* (TNF-α) [7]. Tirap knock-out mice have been reported to be more susceptible to *Escherichia coli* [8] and *Klebsiella pneumonia* [9] infection. Furthermore, *Tirap is* the most polymorphic of all the adaptor proteins. In human populations, more than 30 SNPs (single nucleotide polymorphism) have been described in the Tirap protein and the surrounding region (8 of which are in its coding region) [10]. *These* genetic variations have been associated with both disease susceptibility and resistance, suggesting paradoxical functions of Tirap to contain infections [11].
Contrasting roles of *Tirap* genetic variations have been particularly reported in patients facing tuberculosis (TB) [12–15]. TB is a human infectious lung disease that kills more than 1.5 million people each year [16]. Mycobacterium tuberculosis (Mtb), the bacterium responsible for the disease, is a successful pathogen, which, upon inhalation, is able to infect host alveolar macrophages and other cell types. The bacillus will then massively manipulate endocytic trafficking of macrophages to ensure its survival [17]. One of these survival strategies is the manipulation of endosomal and lysosomal host compartments, which prevents the acidification of Mtb-containing vacuoles by blocking phagosome maturation [18]. Ultimately, Mtb can escape from the vacuole and reside inside the cell cytoplasm [19]. Therefore, Mtb has developed an extensive set of mechanisms to promote its survival and replicate intracellularly during its prolonged stay in the alveolar macrophages.
Epidemiological studies have identified a role of *Tirap* genetic variation and more precisely the S180L mutation in TB susceptibility [20–23]. Amino acid substitution of a serine by a leucine at position 180 leads to altered NF-κB signalling and lastly protection against exaggerated inflammation [10]. *The* gene may have this polymorphism as homozygous ser/ser or leu/leu or heterozygous as ser/leu motif. The S180L (C539T) has been reported to be protective against several diseases but its role in TB susceptibility has been largely debated. For example, Capparelli et al. demonstrated that S180L heterozygosity confers resistance against TB in individuals [22], whereas another study showed the completely opposite effect of S180L [21]. Even though a meta-analysis concluded that S180L polymorphism is significantly correlated with reduced risk of TB infection [15], further investigations are needed to understand the roles of this protein in the control of TB.
In this work, we compared the ability of wild type (WT/Tirap+/+), heterozygous (Tirap+/-) or homozygous knock-out (Tirap-/-) mice for Tirap to control Mtb infection. Interestingly, we observed that heterozygous mice are the more resistant to infection. At the infected macrophage level, we observed in Tirap KO cells that the bacterium was unable to control Cish-mediated intra-endocytic processes, corroborating with reduced Mtb replication. Together these results suggest that a partial loss-of-function in the Tirap protein protects against Mtb infection.
## In vivo model of Tirap heterozygosity protects from Mtb infection
C57BL/6 Tirap-deficient mice (Tirap-/-) have been historically generated by gene targeting leading to the depletion of two exons coding for the TIR domain and the expression of non-functional Tirap protein [24]. In accordance with the objective of this work, we generated a mouse model for Tirap heterozygosity (Tirap+/-) by cross-breeding wild type (Tirap+/+) and knock-out (Tirap-/-) mice (S1 Fig). To determine the physiological relevance of a total or a partial loss of Tirap function during TB infection, we inoculated intranasally these mice with a high dose (1 x 105 colony-forming units (CFU)) of the virulent Mtb H37Rv strain. During the experiment, no apparent differences in body weight or animal behavior were observed. 28 days post-infection, mice were sacrificed and lungs were harvested for histopathology, immunopathology and to study bacterial burden. Interestingly, determination of pulmonary mycobacterial loads showed a significant increase in Tirap+/+ and Tirap-/- mice compared to Tirap+/- littermates suggesting that the latter are more resistant to infection (Fig 1A). To gain insight into the pathogenesis established during infection, we analyzed lung sections, cytokine production and immune cell recruitment to the lungs.
**Fig 1:** *Comparative study of bacterial load, recruited immune cells and cytokine expression in lungs of infected Tirap +/+, +/- and -/- mice.C57BL/6 WT (+/+), Tirap+/- (+/-) and Tirap-/- (-/-) mice were intranasally infected with Mtb H37Rv (105 CFU/20μL). 28 days post-infection, mice were sacrificed to study their pathological state. (A) Mycobacterial load was determined by plating lung lysates and counting Colony-Forming Units (CFU) 2 weeks after plating. Shown are mean ± SEM of at least 9 infected mice per condition of three independent experiments. (B) Histograms showing relative cytokine amounts in lung lysates of infected +/+, +/- and -/- mice compared to mock-infected mice. Shown are mean ± SEM of 7 infected mice per condition of one representative out of two independent experiments. (C) Toluidine blue staining of non-infected (control) and infected (28 days post-infection) slice lungs (5 μm) from WT, Tirap+/- and Tirap-/- mice. Bar: 10 mm. (D) Histogram depicting changes of immune cells populations during Mtb H37Rv infection, as determined by flow cytometry using specific cell surface markers. Cell numbers were normalized to the total cell number analyzed in the whole lung. Shown are mean ± SEM of cells obtained from three individual mice per group representative of two independent experiments. dpi: days post-infection, ns: non-significant, * P value < 0.05, ** P value < 0.01, as determined by one-way ANOVA test.*
First, the inflammatory cytokine profiles in lungs of infected Tirap+/- and Tirap-/- mice show a significant decrease in the amount of TNFα, IL1β and IFNγ compared to wild type animals (Fig 1B). A decrease in the transcription of IL12p40 and TNFα was also detected in lungs of Tirap-deficient mice relative to wild type mice (S2 Fig). Even if anatomopathological examination between the different Tirap genotypes revealed no difference in the overall aspect of the lungs and the lesions induced by the infection (Fig 1C), we compared the recruitment of immune cells by flow cytometry. While the overall basal number of immune cells present in the lungs of each genotype was not significantly different (S1 Fig), the examination of innate immune cell profiles 28 days post-infection revealed a noticeable increase in neutrophils in the lungs of Tirap-/- mice (Fig 1D). In contrast, the number of CD8 T cells, CD4 T cells and B lymphocytes increased in lungs of infected Tirap+/- mice, but not in Tirap+/+ and Tirap-/- mice, suggesting a more effective adaptive immune response, which could explain the resistance of heterozygous mice to Mtb infection (Fig 1D).
Taken together, our results show that Tirap+/- mice exhibit more pronounced control of Mtb infection. These observations are consistent with scientific evidence in human showing that Tirap SNP heterozygosity is associated with protection. Given the observations at the tissue level, we went on to study Mtb replication in cellulo to determine the role of Tirap at the host cell level.
## Tirap deficiency impairs Mtb replication inside macrophages
To evaluate the contribution of Tirap within the main intracellular niche of Mtb, we first assessed its effect on Mtb replication in primary murine macrophages. We first ensured that the proliferative capacity of the BMDMs was not altered by Tirap deficiency. As shown in S1 Fig, the three types of macrophages exhibited the same growth kinetics over a period of 4 days. Bone marrow-derived macrophages (BMDMs) derived from each genotype of mice were then infected with a GFP-expressing Mtb H37Rv strain (MOI (multiplicity of infection) of 2) and intracellular bacterial growth was quantified at day 4 post-infection. Cells were grown in 384-well plates and their nuclei were labeled enabling analysis by an automated confocal microscopy approach using in-house multiparametric image analysis. This allowed acquisition and examination of hundreds of images generating robust and reproducible data sets [25]. We applied segmentation algorithms to input images, allowing us to distinguish nuclei and intracellular bacteria and to determine the percentage of infected cells and the intracellular bacterial load per cell. Tirap deficiency did not impair Mtb uptake by macrophages, as the percentages of Mtb-infected BMDMs 3 h post-infection (hpi) were similar (Fig 2A). We then compared the intracellular area of Mtb per infected cell, which directly correlates with the bacterial load [18]. At 4 days post-infection (dpi), increase of bacterial load was lower in Tirap+/- BMDMs than Tirap+/+ BMDMs, which is consistent with the phenotype observed in mice (Fig 2A). Intriguingly, fully deficient BMDMs also showed a lower bacterial load than Tirap+/+ BMDMs and a similar load compared to that of Tirap+/- BMDMs. The phenotype was confirmed by CFU plating of infected macrophages (Fig 2B). Briefly, while only heterozygous mice are associated with resistance, both heterozygous and homozygous Tirap-deficient macrophages prevented Mtb replication.
**Fig 2:** *Tirap expression and localization in Mtb-infected BMDMs: Impact on bacterial growth and cytokine expression.(A) BMDMs were grown in 384-well plates, infected with Mtb H37Rv-GFP (MOI of 2) and analyzed by automated confocal microscopy. Shown are representative images of Tirap+/+, Tirap+/- and Tirap-/- BMDMs at 3 hpi (upper panel) and 4 dpi (lower panel). Segmentation algorithms were applied to input images to detect nuclei labeled by Hoechst 33342 (cyan) and the GFP signal of Mtb H37Rv (green) to determine infection rate 3 hpi (upper histogram) and replication fold increase from 3 hpi to 4 dpi (lower histogram). Shown are mean ± SEM of at least 8 analyzed wells per condition of one representative out of three independent experiments. Bar: 50 μm. (B) BMDMs were grown in 24-well plates, infected with Mtb H37Rv (MOI of 2) and lysed at 2, 24, 48 and 72 hpi. Serial dilutions of lysates were plated out to determine bacterial load by counting CFUs. Shown are mean ± SEM of 4 wells per condition of one representative out of two independent experiments. (C) Histograms showing relative cytokine amounts in supernatant of infected WT, Tirap+/- and Tirap-/- BMDMs 96 hpi compared to non-infected cells. Shown are mean ± SEM of three infected wells. (D) Histogram showing mean ± SEM of relative Tirap gene expression. WT, Tirap+/- and Tirap-/- BMDMs were infected with Mtb H37Rv at MOI of 2 for 3 h. Non-infected (NI) WT cells served as control. Transcription of Tirap was assessed by quantitative RT-PCR and normalized to the expression of Gapdh, used in all samples as a housekeeping gene. (E) RAW264.7 cells were transfected with mCherry-Tirap vector expressing lentivirus for 48 h prior to infection with Mtb H37Rv-GFP. Confocal microscopy imaging shows subcellular localization of Tirap (m-cherry) and Mtb H37Rv-GFP (green). Bar: 5 μm. NI: non-infected, Inf: infected, hpi: hours post-infection, dpi: days post-infection, ns: non-significant, * P value < 0.05, ** P value < 0.01, *** P value < 0.001, **** P value < 0.0001, as determined by one-way ANOVA test.*
We next compared the inflammatory response initiated after BMDM infection and observed that, similarly to in vivo results, TNFα and IL1β production was lower in Tirap+/- and Tirap-/- macrophages compared to infected WT cells (Fig 2C). Therefore, at the gene expression level, Tnf-α, Il1β, Il6, Il12p40 and Nos2 were significantly lower in deficient cells compared to the WT (S2 Fig). Surprisingly in our model, a lower inflammatory response is associated with resistance to infection. The contradictory phenotype observed between Tirap-/- mice and macrophages could be explained by the recruitment of neutrophils in the mouse tissue, which would compensate bacterial replication.
We then sought to understand why Tirap promotes Mtb replication inside macrophages. First, we measured Tirap expression in infected BMDMs by quantitative RT-PCR. While Tirap was expressed at steady state in non-infected (NI) Tirap+/+ and Tirap+/- BMDMs at a similar rate, we observed a pronounced difference during Mtb infection at 3 hpi (MOI of 2). Indeed, Tirap expression increased by three-fold in Tirap+/+ BMDMs upon infection (Fig 2D). Moreover, no induction of the transcription of Tirap was observed in heterozygous cells upon Mtb infection. As expected, Tirap-/- macrophages lacked expression of Tirap. This suggests that bacteria engage Tirap and/or its downstream signaling pathways to replicate inside macrophages.
Second, we aimed at analyzing the cellular distribution of Tirap. To this end, murine RAW264.7 macrophages were transfected with a fluorescently-coupled mCherry-Tirap lentiviral vector prior to infection with Mtb H37Rv-GFP (Fig 2E). Using confocal microscopy, we found that the Tirap signal localized close to bacteria, indicating that the adaptor might be recruited to the Mtb-containing vacuole (MCV) during infection.
Taken together, these data suggest that the increased expression of Tirap and its recruitment to the MCV during infection is the first evidence that Mtb has the capacity to dampen host defense mechanisms via Tirap. Moreover, our results align with the intracellular replication of Mtb and the activation of inflammatory responses. To investigate this further, we compared the intracellular trafficking pathways of Mtb in different types of macrophages.
## Tirap prevents acidification of the Mtb-containing vacuole (MCV) allowing bacteria to reach their replicative niche
To investigate phagosomal maturation, we first monitored the fusion of MCVs with lysosomes by fluorescence microscopy using the pH-sensitive LysoTracker dye. It has been previously demonstrated that the intensity of LysoTracker labeling directly correlates with the acidification of Mtb vacuoles [18]. Upon Mtb infection, LysoTracker intensity was significantly higher in Tirap+/- and Tirap-/- cells than in WT cells (Fig 3A). These results suggest that *Tirap is* involved in the blockade of phagosomal acidification that is required for Mtb replication.
**Fig 3:** *Tirap implication in phagosomal maturation and lipid droplet formation.Typical images and related quantifications (representative of two independent experiments) of phagosome acidification (A), phagosome rupture (B) and lipid droplet formation (C) of Mtb H37Rv infected BMDMs treated (D) or not with ConA. (A) DAPI-labelled cell nuclei are shown in blue, Mtb H37Rv-DsRed in red and acidic-pH-sensitive LysoTracker staining in green. The LysoTracker signal was set to the minimum in non-infected controls. Bar: 10 μm. Histograms showing mean ± SEM obtained from 7 wells of the percentage of bacteria displaying a Lysotracker signal within the cells. (B) Representative images showing cells displaying CCF4-AM staining (green) or CCF4 staining (blue) corresponding to phagosomal rupture. Histograms showing mean ± SEM obtained from 7 wells of the percentage of BMDMs detected with phagosomal rupture 24 hpi. Bar: 5 μm. (C) Representative images showing LD in Mtb infected cells. DAPI-labelled cell nuclei are in blue, Mtb H37Rv-GFP is in green, and LD staining (LipidTox) is in red. Bar: 5 μm. Histogram showing the average number ± SEM of LD per cell at 96 hpi obtained from at least 6 wells. LD: lipid droplets, * P value < 0.05, ** P value < 0.01, *** P value < 0.001, **** P value < 0.0001, as determined by one-way ANOVA test.*
Although several studies have demonstrated that MCVs remain features of endosomes and early phagolysosomes (for comprehensive review [26]), others reported that the phagosome membrane rapidly undergoes progressive disruption through the action of the canonical bacterial virulence factors phthiocerol dimycocerosate (PDIM) and ESX-1 [27,28]. This phagosomal permeabilization allows Mtb to access cytosolic components that may contribute to mycobacterial replication and to induce cell necrosis for further dissemination [19,29]. This led us to assess whether Tirap deficiency could interfere with Mtb-induced phagosomal rupture. We used the FRET CCF4-AM dye, a cell permeable β-lactamase substrate that shifts its emission wavelength from green to blue when converted into its negatively charged metabolite CCF4 [30]. As Mtb expresses membrane-associated β-lactamase BlaC, the green-to-blue ratio of the dye has been often used as a surrogate marker correlating with the ability of mycobacteria to escape their phagosomal vacuoles and to reach the cytosol [19,31]. At 24 hpi, the percentage of Mtb phagosomal rupture in Tirap+/- and Tirap-/- infected BMDMs was lower than in Tirap+/+ BMDMs, correlating with the increased acidification in Tirap-deficient cells compared to WT cells (Fig 3B). It is also established that Mtb persistence and replication rely on host metabolic homeostasis shift and availability of host nutrients in the cytosol. Among the latter, lipid droplets (LD) are a key reservoir of neutral host lipids that are important carbon sources required during Mtb replication [32]. Whereas large amounts of LD were present upon Mtb infection at 4 dpi in WT BMDMs, the number of LD spots in Mtb-infected Tirap-deficient cells (both heterozygous and homozygous) was almost absent (Fig 3C). This suggests that in Tirap-expressing cells, Mtb has likely more access to host nutrients that fuel intracellular replication than in Tirap-deficient cells.
Taken together, these observations showed that in WT macrophages, Tirap impacts Mtb trafficking thereby promoting bacterial replication. Indeed, *Tirap is* required for the control of phagosomal acidification that is necessary for phagosomal escape, intracellular replication and reprogramming of host metabolism. To determine whether there is a direct link between phagosome acidification, phagosomal rupture and LD formation, we followed Mtb intracellular trafficking in macrophages treated with the phagosomal acidification inhibitor concanamycin A (conA). ConA specifically inhibits vacuolar type H+-ATPase and thereby prevents the functions of acidic organelles [33]. Interestingly, the blockade of vacuolar acidification in Tirap-deficient macrophages results in a phenotype similar to that of WT with increased Mtb phagosomal escape and a higher number of LD in infected cells (Fig 3D). These results support that control of phagosomal acidification is dependent on a Tirap-mediated pathway.
## Tirap induces Cish signalling pathway
To better understand how a lack of Tirap results in significant containment of Mtb growth in macrophages, we compared the expression profiles of Mtb-infected WT and Tirap-deficient BMDMs by RNAseq. As Tirap+/- and Tirap-/- BMDMs displayed the same resistance phenotype, we performed an analysis to discriminate genes that are commonly modulated in these two backgrounds compared to WT cells during infection (Fig 3A). To fine-tune our analysis, we excluded all genes already modulated in non-infected cells (light grey dots on Fig 4B). Within 3 hpi, the transcriptional signature in Tirap-deficient BMDMs differed markedly from WT BMDMs with 38 downregulated genes (blue dots) and 62 upregulated genes (orange dots) in Tirap-deficient BMDMs (Fig 4A and 4B) (S1 Table for the detailed list).
**Fig 4:** *Identification of differentially regulated genes between +/+, +/- and -/- BMDMs during Mtb infection.(A) Total RNA was extracted from non-infected and infected (3 h) WT, Tirap+/- and Tirap-/- BMDMs and sequenced with the Illumina system and Deseq2 analysis as described in the Materials and Methods section. (B) Volcano plot of RNAseq data from non-infected versus infected cells showing the adjusted p-value (false discovery rate, FDR -log10) versus fold change, FC (log2). The 100 genes with an FDR < 0.01 and FC > 2 are shown in blue and orange for downregulated and upregulated genes, respectively. (C) Histograms showing mean ± SEM of gene expression levels in BMDMs from WT, Tirap+/- and Tirap-/- mice 3 hpi. Cish and Gmcsf expression was quantified by quantitative RT-PCR. Stat5 expression level is reported from RNAseq data. (D) Schema of the different subcellular events studied in infected RAW264.7 cells silenced for Cish gene expression. (E) RAW264.7 cells were grown in 384-well plates, infected with Mtb H37Rv (MOI of 2) and analyzed by automated confocal microscopy. Histograms show the quantification of Cish gene expression and different phenotypic subcellular events during Mtb infection of WT (Cont) and silenced (Cish-shRNA) RAW264.7 cells. ** P value < 0.01, *** P value < 0.001, **** P value < 0.0001, as determined by one-way ANOVA test.*
*Most* genes whose expression was inhibited (e.g. Il6, Il1α, Il1β, Ifnα or Nos2) are known to be involved in the inflammatory response. Interestingly, Cish was previously reported by our team and others to play a role in TB pathogenesis [18]. We confirmed by quantitative RT-PCR that Cish expression was 4- and 21-times higher in infected WT BMDMs compared to Tirap+/- and Tirap-/- BMDMs during infection, respectively (Fig 4C). Data showed that during Mtb infection, *Cish is* overexpressed and *Cish is* recruited to MCVs. Sequestration of Cish close to Mtb triggers the ubiquitin-mediated proteasomal degradation of the proton v-ATPase, thus inhibiting the acidification of MCVs and allowing replication of bacteria. Inhibition of Cish expression in Tirap-deficient cells during infection could interfere with v-ATPase degradation, and this might explain the capacity of Tirap-deficient cells to control bacterial growth by inducing acidification of MCVs.
In our previous study we showed that infection of macrophages with Mtb leads to secretion of Granulocyte-Macrophage Colony-Stimulating Factor (GM-CSF) inducing STAT5-mediated expression of Cish [18]. To determine whether Tirap participates in intracellular Mtb survival by inducing GM-CSF-dependent STAT5 signaling, we compared *Gmcsf* gene expression during infection between WT and Tirap-deficient BMDMs. In agreement with the expression pattern of Stat5, we showed that Gmcsf expression was higher in WT BMDMs when compared to expression levels in Tirap-deficient cells (Fig 4C). This observation strongly suggests that Tirap acts as a modulator of macrophage defense by triggering STAT5 signaling via GM-CSF secretion. This also supports the hypothesis that Mtb uses Tirap to block the maturation of the vacuole by triggering Cish activity in mutant cells.
To further test this hypothesis, we studied the phenotype of Cish-deficient macrophages to examine whether they act like Tirap-deficient macrophages during Mtb infection. Thus, we investigated the intracellular trafficking of Mtb in RAW264.7 macrophages that were transfected with a lentiviral vector encoding Cish-shRNA (Fig 4D). The results show that, similarly to Tirap-deficient macrophages, strong inhibition of Cish transcription throughout Mtb infection is associated with decreased bacterial replication, increased phagosomal acidification, blocked phagosomal rupture and reduced amounts of LD in macrophages (Fig 4E). Together, our results strongly suggest that the phenotype of Tirap-deficient macrophages is related to Cish-dependent signaling.
## Tirap affects secretion of effectors by intracellular Mtb
Our results show that there is a clear evidence that Tirap regulates phagosomal rupture and LD formation. As these events are associated with the secretion of bacterial effectors, we examined the impact of the Tirap genotype on the secretion of ESAT-6 and Antigen 85 (Ag85a) by Mtb within macrophages. We used a previously described multiplexed quantitation tool, which is based on the recognition of effectors of MHC class II epitopes by highly discriminative T cell receptors of a panel of T cell hybridomas [34]. The latter were engineered to express a fluorescent reporter driven by the IL-2 promoter (Fig 5A). The results suggested that the secretion of ESAT-6 was slightly decreased in Tirap+/- and Tirap-/- macrophages compared to WT cells (Fig 5A). Less Ag85a was also detected in Tirap-/- cells. Together with the lack of replication difference in deficient macrophages infected with Mtb H37Ra (Fig 5B), an attenuated strain described to secrete fewer effectors [35], our results suggest that the Tirap-associated phenotype in macrophages could be associated with the expression and secretion of virulence effectors by Mtb. This TB antigen specific hybridoma-based detection assay has the advantage to be a correlate of effectors exported into the cytosol, but it is an indirect detection tool dependent on MHCII antigen processing. In our model, we cannot exclude that Tirap deficiency does not play a role in macrophage antigen presentation. This question needs further in-depth examination, which is beyond the scope of the current study.
**Fig 5:** *Tirap implication in Mtb effectors secretion.(A) BMDMs were seeded into 96-well plates and loaded with homologous (pos) or control (-) peptides or infected with Mtb H37Rv-DsRed. 24 hours post incubation, cells were washed and co-cultured with transduced anti-Ag85 or anti-Esat6 reporter T-cell hybridomas. These were then analyzed by automated confocal microscopy. Shown are representative images of DAPI labelled T-cells (blue) and Antigen specific T-cells (green). Histograms showing mean ± SEM of percentage of ESAT-6 or Ag85 specific T-cells obtained from 5 analysed wells. (B) BMDMs from WT, Tirap+/- and Tirap-/- mice were infected with an attenuated strain of Mtb (H37Ra-GFP) (MOI of 2) and analyzed by automated confocal microscopy. The histogram shows the replication fold increase from 3 hpi to 4 dpi. Shown are mean ± SEM obtained from at least 12 analyzed wells per condition. (C) WT (+/+), knocked-out (-/-) for Myd88, TLR2, TLR4 and TLR9 BMDMs were infected with H37Rv-GFP (MOI of 2) and analyzed by automated confocal microscopy. The histogram shows the replication fold increase from 3 hpi to 4 dpi. Shown are mean ± SEM obtained from at least 4 analyzed wells per condition of one representative out of two independent experiments. *** P value < 0.001 as determined by one-way ANOVA test.*
## Tirap-associated phenotype is independent of Myd88
We finally considered the impact of Tirap being recruited to the MCV on its implication in signaling pathways of intracellularly expressed TLRs. We investigated the impact of TLR2, TLR4, TLR9 and MyD88 deficiency on Mtb replication (Fig 5C). Knock-out BMDMs were infected with Mtb H37Rv-GFP and bacterial growth was quantified at 4 dpi. No replication differences were observed in cells deficient for TLR2, TLR4 and Myd88 compared to WT macrophages. However, we found that TLR9 deficiency restricts Mtb excessive growth and replication at a rate comparable to Tirap deficiency. TLR9 is known to localize to endosomes as well as to phagolysosomes, where it could be triggered by mycobacterial DNA after pathogen uptake. This led us to consider TLR9 as an important candidate pattern-recognition receptor that might account for the Tirap-dependence of host resistance to Mtb. This strongly suggests that the impact of Tirap on Mtb replication could be linked to TLR9 activation in a MyD88-independent signaling pathway, but further investigations are clearly needed. As a matter of fact, Myd88-independent TLR9 signalling was already reported in other studies [36,37].
## Discussion
TLRs are pattern recognition receptors, which sense invading pathogens by recognizing pathogen associated molecular patterns (PAMPs). TLRs recognize several PAMPS of Mtb, such as lipoprotein, lipomannan, lipoarabinomannan and the heat shock protein 65 [38]. PAMP recognition initiates signalling pathways through adaptor proteins (MyD88/Tirap) leading to activation of inflammation. Human studies have indicated that genetic variations in TLR signaling pathway genes regulate the cellular immune response and may influence susceptibility to TB in different populations [39]. Indeed, several studies have focused on the involvement of Tirap polymorphism during Mtb infection leading to conflicting conclusions [11].
In this work, we wanted to study the implication of a deficiency of the TIR domain of Tirap in the control of Mtb infection. To this end, we cross-bred WT and Tirap-/- C57BL/6 mice to generate heterozygous individuals in order to compare the ability of these three mouse genotypes to control intranasal infection with Mtb H37Rv. As previously observed, fully deficient mice control the infection at similar rates as observed in WT mice [40]. However, our results show that heterozygous Tirap-deficient mice are more resistant to infection. This related result illustrates the concept of heterozygote advantage, which corresponds to a more fit heterozygote phenotype compared to homozygotes. This concept suggests that natural selection will maintain polymorphism in the population [41]. This hypothesis could be consistent with human studies showing that heterozygosity for SNP S180L, a loss-of-function associated mutation [10], is also associated with protection against TB [14,22].
In these studies, the authors showed that heterozygous subjects display intermediate inflammatory levels, which is in line with our results.
In fully Tirap-deficient infected mice, we observed a significant increase in the number of recruited pulmonary neutrophils (Fig 6A for summary). This strong infiltration may be related to a compensatory and inappropriate response of the host. Sustained neutrophil influx is often associated with susceptibility to Mtb infection [42] especially in genetically susceptible individuals [43,44]. For example, Nair et al. showed that neutrophil neutralization protects against Mtb exacerbation in ACOD-1 deficient mice [45]. We also observed that heterozygous mice displayed a higher level of adaptive immune cells in lungs 28 days post-infection which could explain its phenotype. In vivo, these hypotheses need to be further investigated.
**Fig 6:** *Summary.(A) The outcome of Mtb infection is dependent on Tirap expression and depends on whether the mouse has homozygous (susceptibility) or heterozygous (resistance) Tirap KO phenotype. Although homozygous mice show the same susceptibility phenotype, the lungs of WT mice are characterized by a greater inflammatory state than the lungs of completely deficient mice. The latter, in turn, present a neutrophil-rich environment that may be permissive to Mtb replication. At the macrophage level, both homozygous and heterozygous deficiency in Tirap expression restrict intracellular Mtb growth. Efficient killing of the pathogen is promoted by inhibition formation of LD, prevention of production of inappropriate amounts of inflammatory cytokines and induction of a proper MCV maturation. (B) Infection of macrophages by Mtb, which leads to the recruitment of Tirap to Mtb-containing vacuoles under WT conditions, limits the acidification of MCVs, induces their rupture and allows Mtb access to the cytosol, where the generation of lipid droplets is induced and fuels the growth of pathogens in host cells. Although our model clearly shows the involvement of the Cish-STAT5 pathway in targeting MCV maturation, the molecular link with the involvement of Tirap remains to be explored.*
In order to decipher the molecular mechanisms involved in this phenotype, we then wanted to specify how *Mtb is* able to replicate in macrophages, one of the main reservoirs of Mtb replication in the lungs [18]. Using high-content microscopy and image-based analysis, we observed that Tirap deficiency did not affect bacterial uptake but reduced severely Mtb replication levels in heterozygous and full KO macrophages. These observations showed that resistance of heterozygous mice could be correlated with the macrophage phenotype, whereas the resistance of fully deficient macrophages was not reflected in mice. This latter point suggests that despite the difficulty of replication in macrophages, the neutrophil-rich environment and the difficulty in activating inflammatory responses could ultimately allow Mtb replication in the lungs.
Our findings also demonstrate that in wild type macrophages, Mtb infection induces overexpression of Tirap and its recruitment to the MCV. Interestingly, we followed the replication of Mtb in mutant macrophages for TLRs involved in Mtb detection, and we observed that only TLR9 deficiency impairs mycobacterial replication. Together with a human study showing that TLR9 polymorphism is associated with resistance against TB [46], we can expect that Mtb engaged a TLR9/Tirap signalling axis to enable its replication.
Clearly, this hypothesis requires further investigation. Mtb uses efficient strategies to escape eradication by macrophages, such as the recently confirmed escape from phagosomal vacuoles. It was demonstrated that restriction of phagosomal acidification is essential for mycobacterial phagosomal rupture and cytosolic contact [19]. Moreover, LD formation occurs during massive bacterial replication of virulent Mtb. Mtb is thought to trigger LD formation as a strategy to create a host lipid depot to be used as a carbon source reservoir to enable intracellular replication [47]. In our study, we showed that phagosomal acidification is higher in Tirap-deficient cells compared to WT cells. This phenomenon was associated with the absence of phagosomal rupture and LD formation (Fig 6A for summary). Interestingly, inhibition of intracellular acidification using ConA restores a WT phenotype in deficient cells exhibiting strong phagosomal rupture and LD formation. The ESX-1 type VII secretion system of Mtb governs numerous aspects of the host-pathogen interaction thought to be implicated in phagosomal maturation. The host cell factors governing these events are yet unexplored and require further investigation, which is of importance to determine whether Tirap may be one of them.
In order to identify the mechanism by which Mtb uses Tirap signalling to promote its replication, we performed a RNAseq analysis to compare gene expression between infected Tirap-deficient and WT macrophages. Among the hundred genes identified, the *Cish* gene was subject to an interesting downregulation in mutant macrophages. A previous study from our group showed that the v-ATPase is targeted for ubiquitination and proteasomal degradation during Mtb infection due to the expression of Cish [18]. V-ATPase degradation prevents MCV acidification and promotes Mtb replication. More precisely, Mtb infection leads to granulocyte-macrophage colony-stimulating factor (GM-CSF) secretion, inducing STAT5-mediated expression of Cish. Consistently, it was shown that inhibition of Cish expression led to reduced replication of Mtb in macrophages. Therefore, we monitored GM-CSF and STAT5 expression in Tirap-deficient cells. Interestingly, reduced rexpression of Cish was associated with a lower expression level of GM-CSF and STAT5 in both heterozygous and homozygous Tirap KO macrophages (Fig 6B for summary). To decipher whether reduced Cish expression could be associated with the phenotype observed in Tirap-deficient mice, Cish was knocked down in macrophages using a shRNA-encoding lentiviral vector. Interestingly, Cish inhibition produced results similar to those observed in Tirap-deficient cells regarding bacterial replication, phagosomal acidification, cytosolic access and LD formation. From those experiments, we conclude that Tirap and Cish deficiency induce similar phenotypes upon Mtb infection. More investigations are needed to determine if the two signalling are directly related.
Overall, our results show that only *Tirap heterozygous* mice are more resistant to Mtb infection, which correlates with a substantial number of studies showing that heterozygous polymorphism associated with a Tirap loss-of-function confers resistance to Mtb infection in humans.
However, a previous study in mice investigated the role of the Tirap S200L mutation (which correspond to the common S180L human variant) during Mtb infection [48]. The authors showed that this mutation impairs in vitro and in vivo responses to the infection, which is associated with an increased bacterial survival. This mutation attenuates IFNγ signaling that affect phagosomal maturation and autophagy. This research pint-points that the single nucleotide S200L polymorphism induces an opposite phenotype than the full lack of the *Tirap* gene observed in our work. Besides the fact that experimental procedures (e.g. higher MOI, IFN-γ macrophage activation, protocol of infection) are quite different between the two studies, it is of interest to look at the relevance of the two models. In our model we mainly wanted to investigate the impact of a total or partial lack of Tirap to decipher its fundamental role in the control of tuberculosis, which is relevant to understand the biology of the protein. The identification of host genetic markers remains useful to predict TB development and understand the immunopathogenesis of the disease. In this context, our work reveals the potential of Tirap inhibition as an attractive target for the development of new therapeutic strategies. The second model is also of interest as it mimics a common human variant of Tirap described to play a role on the control of the infection. It is well known that *Tirap is* involved in numerous intracellular processes that could be modulated by a single point mutation. Together, these two studies need to be considered as complementary, thus strengthening the importance that further investigations are needed to decipher the role of Tirap downstream signalling during Mtb infection.
## Ethics statement
All mice were maintained, and breeding was performed in the animal facility of the Pasteur Institute of Lille, France, together with heterozygous mice for TIRAP gene (Tirap+/-) generated there (agreement B59-350009). All experimental procedures were approved by the institutional ethical committee “Comité d’Ethique en Experimentation Animale (CEEA) 75, Nord Pas-de-Calais” and the “Education, Research and Innovation Ministry” (APAFIS#1 327 0232–2017061411305485 v6, approved on $\frac{14}{09}$/2018). All experiments were performed in accordance with relevant guidelines and regulations.
## Reagents
Cell nuclei were fluorescently labelled using DAPI (Sigma Aldrich) or Hoechst 33342 (ThermoFisher). Lysotracker Green DND-26, CCF4-AM and LipidTox Deep Red were obtained from ThermoFisher.
## Mice
C57BL/6NJ wild type and Tirap-/- mice were purchased from The Jackson Laboratory (Bar Harbor, ME, USA). Mice genotyping was performed using the KAPA Taq EXtra HotStart ReadyMix PCR Kit according to the manufacturer instructions (Biosystems). The following primer pairs were used to amplify a 900 bp DNA fragment of the wild type (a+b) and the mutant genes (b+c). Primer a CATCCTGTGTGGCTGTCTGTGAACCAT, Primer b TGGCCAATGTGTGAGCAAGTTCTGTGC, Primer c ATCGCCTTCTATCGCCTTCTTGACGAG.
## Murine bone marrow-derived macrophages (BMDMs)
Murine bone-marrow progenitors were obtained by sampling tibias and femur bones from 8 to 12-week-old C57BL/6NJ wild type, Tirap+/- and Tirap-/- mice. BMDMs were obtained by seeding 107 bone marrow cells in 75 cm2 flasks in RPMI 1640 *Glutamax medium* (Gibco) supplemented with $10\%$ heat-inactivated Fetal Bovine Serum (FBS, Gibco; RPMI-FBS) and $10\%$ L929 cell supernatant containing Macrophage Colony-Stimulating Factor (M-CSF). Fresh medium was added every 3–4 days. After 7 days incubation at 37°C in an atmosphere containing $5\%$ CO2, the BMDMs monolayer was rinsed with D-PBS and cells were harvested with Versene (Gibco). BMDM were resuspended into culture medium to be used for subsequent assays.
## Bacteria
Recombinant strains of Mtb H37Rv expressing an enhanced green fluorescent protein (GFP) or a red fluorescent protein DsRed [49] were cultured in Middlebrook 7H9 medium (Difco) supplemented with $10\%$ oleic acid-albumin-dextrose-catalase (OADC, Difco), $0.2\%$ glycerol (Euromedex), $0.05\%$ Tween 80 (Sigma-Aldrich) and 50 μg/ml hygromycin (ThermoFisher Scientific) or 25 μg/ml kanamycin (Sigma-Aldrich) for H37Rv-GFP or H37Rv-DsRed, respectively. Cultures were maintained for 14 days until the exponential phase was reached. Before cell infection, bacilli were washed with Dulbecco’s Phosphate Buffered Saline (DPBS, free from MgCl2 and CaCl2, Gibco), resuspended in 10 mL RPMI-FBS and centrifuged at 1000 RPM for 2 min at room temperature to remove bacterial aggregates. Bacterial titer of the suspension was determined by measuring the optical density (OD600 nm) and GFP or DsRed fluorescence on a Victor Multilabel Counter (Perkin Elmer). The bacterial suspension was diluted at the required titre in RPMI 1640 supplemented with $10\%$ FBS prior to infection. For in vivo studies, the non-fluorescent Mtb H37Rv strain were grown in Middlebrook 7H9 medium, as described previously [50,51].
## Infection of mice and determination of bacterial burden
8-12-week-old mice were inoculated with H37Rv via the intranasal route (i.n.) ( 105 CFU/20 μL) as previously described [52]. 28 days post infection, mice were euthanized, and lungs were harvested for bacterial burden evaluation by colony forming units (CFU) enumeration. Lungs were homogenized for 20 min in a tube containing 2.5 mm diameter glass beads and 1 ml of PBS using the MM 400 mixer mill (Retsch GmbH, Haan, Germany). Ten-fold serial dilutions (from 10−2 to 10−9) of each sample were plated onto 7H11 medium agar plate (Difco) supplemented with $10\%$ oleic acid-albumin-dextrose-catalase (OADC, Difco). After a 2-week growth period at 37°C, CFUs were determined at the appropriate dilution allowing optimal colonies enumeration.
## Lung histopathology
At the determined time-point, mice were euthanized, lungs were harvested perfused and soaked in $4\%$ formaldehyde ($10\%$ formalin solution, neutral buffered, HT501128, Sigma-Aldrich) for 24 hours at 4°C and then dehydrated in a series of ethanol solutions to visualize their internal structures. Specimens were then embedded and cut in sections down to 5 μm thickness. Slices were colored using toluidine blue ($0.1\%$) for 4 min after being dewaxed and rehydrated. Samples were examined using an optical microscope (Zeiss Axio lab A1) and a stereomicroscope (Zeiss Stemi 305) and a camera and the Zen 2011 module (Zeiss) for image analyses.
## Flow cytometry
A described previously [53], lungs were harvested, cut into small pieces and incubated for 1 hour at 37°C with a mix of DNAse I (100 μg/ml, Sigma-Aldrich) and collagenase (400 U/ml 1.6 mg/ml, Roche). Lung cells were washed and filtered through a 100 μM filter before being incubated with saturating doses of purified 2.4G2 (anti-mouse Fc receptor, ATCC) in 200 μL PBS $0.2\%$ BSA $0.02\%$ NaN3 (FACS buffer) for 20 minutes at 4°C to prevent antibody binding on the Fc receptor. Various fluorescent mAb combinations in FACS buffer were used to determine cell populations (Table 1). Acquisitions were done on FACScanto II cytofluorometer (Becton Dickinson) with the following mAbs from BD Biosciences: Fluorescein (FITC)-coupled HL3 (anti-CD11c), FITC-coupled 145-2C11 (anti-CD3), APC-coupled RB6-8C5 (anti-GR1), phycoerythrine (PE)-coupled RM4-5 (anti-CD4), PE-coupled E50-2440 (anti-SIGLEC-F), APC-coupled BM8 (anti-F$\frac{4}{80}$). APC-eF780-coupled M$\frac{1}{70}$ (antiCD11b) were purchased from eBiosciences and fixable viability dye Aqua (ThermoFisher) was used to gate viable cells. Gating strategies are summarized in S3 Fig.
**Table 1**
| Cell type | Phenotype |
| --- | --- |
| Neutrophils | CD11b+ Ly6G+ |
| Dendritic cells | CD11b+ CD11c+ F4/80- |
| Alveolar macrophages | F4/80+ SiglecF+ CD11c+ |
| Interstitial macrophages | F4/80+ SiglecF- CD11cint |
| CD4 T cells | CD3+ CD4+ |
| CD8 T cells | CD3+ CD8+ |
| B cells | CD3- B220+ MHCII+ |
## Infection for quantification of intracellular mycobacterial replication, phagosomal acidification, phagosomal rupture and Lipid Droplets (LD) formation
2x104 BMDM were seeded per well in 384-well plates. Cells were infected for 3 h with H37Rv-GFP at a MOI of 2. Cells were extensively washed with RPMI-FBS in order to remove extracellular Mtb and incubated at 37°C with $5\%$ CO2.
For intracellular mycobacterial replication assay, $10\%$ formalin solution (Sigma-Aldrich) containing 10 μg/mL Hoechst 33342 (Life-Technologies) was added to each well at 3 h and 96 hpi. Plates were incubated for 30 min, allowing nuclei staining and cell fixation. Cells were stored in DPBS until image acquisition.
To quantify phagosomal acidification 3 hpi, cells were incubated with 1mM LysoTracker Green DND-26 for 1.5h at 37°C with $5\%$ CO2. Cells were then fixed with a solution containing $10\%$ formalin solution and 10 μg/mL Hoechst 33342 [25].
To inhibit intracellular acidification, cells were incubated with 100 nM of Concanamycin A (ConA) (Sigma-Aldrich, C9705) 2 h before infection until the end of the assay.
For phagosomal rupture, 24 hpi, cells were stained with 8 μM CCF4-AM in EM buffer (120 mM NaCl2, 7 mM KCl, 1.8 mM CaCl2, 0.8 mM MgCl2, 5 mM glucose, 2.5 μM probenecid, and 25 mM Hepes, pH 7.3) for 1 h at room temperature in the dark. Cells were then washed three times using EM buffer before imaging.
For LD formation assay, cells were washed and fixed at 96 hpi, as previously described [25]. Cells were washed twice with DBPS and intracellular LD were stained with 25 μL per well of 2000-fold diluted HCS LipidTOX deep Red neutral lipid probe (Invitrogen) in DPBS for 30 min at room temperature.
## Image acquisition
Images were acquired using an automated fluorescent confocal microscope (In Cell analyzer 6000, GE) equipped with a 20X (NA 0.70) air lens or 60X (NA 1.2) water lens for Tirap localization, intracellular mycobacterial replication, phagosomal acidification, phagosomal rupture, LD formation and quantitation of intracellular Mtb secreted effectors assays. The confocal microscope was equipped with 405, 488, 561 and 642 nm excitation lasers. The emitted fluorescence was captured using a camera associated with a set of filters covering a detection wavelength ranging from 450 to 690 nm. Hoechst 33342-stained nuclei and CCF4-stained cells were detected using the 405 nm laser with a $\frac{450}{50}$-nm emission filter. Green signals corresponding to LysoTracker Green DND-26, CCF4-AM, ZsGreen+ Tcells, and H37Rv-GFP were recorded using 488 nm laser with $\frac{540}{75}$-nm emission filters. Red signals corresponding to H37Rv-DsRed was recorded using 561 nm laser with $\frac{600}{40}$-nm emission filters. LipidTOX signal was detected using 630-nm excitation and 690-nm emission wavelengths.
## Image analysis
Images from the automated confocal microscope were analyzed using multi-parameter scripts developed using Columbus system (version 2.3.1; PerkinElmer). Segmentation algorithms were applied to input images to detect nuclei and the signal of Mtb H37Rv to determine infection and replication rates. Briefly, the host cell segmentation was performed using two different Hoechst signal intensities—a strong intensity corresponding to the nucleus and a weak intensity in cytoplasm—with the algorithm “Find Nuclei” and “Find Cytoplasm”, as described previously [25]. GFP or DsRed signal intensities in a cell were used for the intracellular bacterial segmentation with the algorithm “Find Spots”. The identified intracellular bacteria were quantified as intracellular *Mtb area* with number of pixels. Subsequently, the population of infected cells was determined, and the increase of intracellular Mtb area, corresponding to intracellular mycobacterial replication, was calculated. For quantification of phagosomal acidification with Lysotracker Green DND-26, green signal intensity in a cell was used for the intracellular acidic compartment segmentation with the algorithm “Find Spots”.
## Assessment of intracellular mycobacterial load by CFU plating
BMDMs were grown at 1x105 cells/well in 24-well plates in RPMI supplemented with $10\%$ FBS and $10\%$ M-CSF. Macrophages were infected with Mtb H37Rv (MOI of 2). Extracellular bacteria were washed off at 3 hr post-infection. At indicated time points post-infection, cells were lysed and five-fold serial dilutions of each sample were plated onto 7H11 medium agar plates (Difco) supplemented with $10\%$ oleic acid-albumin-dextrose-catalase (OADC, Difco). After a 2-week growth period at 37°C, CFUs were determined at the appropriate dilution allowing optimal enumeration of colonies.
## Recognition of effectors MHC class II epitopes by highly discriminative T cell hybridomas
As previously described [34], BMDMs were seeded into 96-well plates at 1 × 105 cells/well in 100 μL of RPMI containing $10\%$ FBS and $10\%$ M-CSF. After overnight incubation, cells were loaded with 1 μg/mL of homologous or control peptides or infected with Mtb H37Rv-DsRed (MOI of 2). 24 hpi, cells were washed and co-cultured with 5 × 104 transduced anti-Ag85 or anti-Esat6 T cell hybridomas. After 24 h incubation, the non-adherent T cells were transferred into new 96-well plates. Cell nuclei were stained with 10 μg/mL of Hoechst 33342 (Sigma-Aldrich) for 30 min at 37°C. Image acquisitions were performed on an automated fluorescence confocal microscope (InCell 6500) using a 20x lens. Hoechst-labelled T cell nuclei were detected using a 405-nm excitation laser coupled with a $\frac{450}{50}$ detection filter, and ZsGreen+ antigen-activated T cells were detected using a 488-nm laser coupled with a $\frac{540}{75}$ detection filter.
## Construction of a lentivirus expressing TIRAP-mCherry
The coding sequence of TIRAP-GFP was amplified by PCR using primers 5’- TTTGAGATCTACCATGGCATCATCGACCTCC-3’ and 5’- TTTGCTCGAGTTACTTGTACAGCTCGTCCATG-3’ and the plasmid Tirap-GFP PMSCV2.2-KCD2, kindly provided by Jonathan Kagan, as a template. The PCR product was restricted with BglII and XhoI and inserted in the plasmid pRRL.sin.cPPT.SFFV/IRES-puro. WPRE plasmid [54], restricted with BamHI and XhoI. Then, the GFP coding sequence was excised from the resulting construct using BamHI and BsrGI, and exchanged for that of mCherry, which had been amplified by PCR using primers 5’- TTTGGGATCCACCGGTCGCCACCATGGTGAGCAAGGGCGA-3’ and 5’- TTTGGAGCTCTTACTTGTACAGCTCGTCCATGC -3’. A lentiviral vector stock was obtained by cotransfection of HEK-293T cells with the resulting plasmid (pRRL.sin.cPPT.SFFV/TIRAP-mCherry. IRES-puro. WPRE) and plasmids expressing HIV Gag-Pol and VSV-G at a ratio of 5:4:1. The culture medium was collected after 3 days at 33°C, filtered and used to transduce RAW264.7 cells for 48 hours to study Tirap intracellular distribution in Mtb infected cells. After 2 hours of infection with Mtb H37Rv-GFP, cells were imaged using a confocal microscope (Zeiss LSM880) equipped with a 40x objective and Zen imaging software (Zeiss, Germany). The images were analysed using ImageJ software.
## Lentiviral transduction
RAW264.7 cells were transduced with lentiviral vector expressing Cish targeting shRNA (NM_009895, openbiosystems) for 48 hours. Transduced cells were then selected with 10 μg/mL of puromycin. The inhibition of Cish transcription was validated by quantitative RT-PCR.
## RNA extraction
BMDMs were grown in 6-well plates and RNA was extracted using QIAzol lysis reagent and miRNeasy Mini Kit according to the manufacturer instructions (Qiagen). RNA concentration was determined using the GE SimpliNano device (GE Healthcare, UK). Remaining DNA in samples was digested using the amplification grade DNase I kit (Sigma-Aldrich, USA) for 6 min at RT. The reaction was stopped by heat inactivation for 10 min at 70°C.
Total RNA from lung tissues were extracted with the NucleoSpin RNA kit (Macherey-Nagel, Hoerdt, Germany).
## Quantitative RT-PCR
For BMDMs RNA extracts, cDNA synthesis was achieved by reverse transcription using the Superscript IV Vilo Mastermix kit (ThermoFisher, USA) following the manufacturer’s instructions. RNA from lung extract was reverse-transcribed with the High-Capacity cDNA Archive Kit (Life Technologies, USA). qPCR was performed using the applied biosystems SYBR Select Master Mix (Thermofischer) with 20 ng cDNA per sample and the appropriate primer pairs (Table 2). Gapdh was used as the reference housekeeping gene for normalization. qPCR reactions were measured by the QuantStudio 12K Flex system (Applied Biosystems, USA) using the following cycles: 2 min 50°C, 10 min 95°C followed by 40 cycles of 15 s 95°C, 30 s 60°C and 30 s 72°C. The target mRNA fold change was calculated based on the 2-ΔΔCt formula, where the *Gapdh* gene was used as the reference gene, and RNA from non-infected wild type BMDMs was used as the standard condition.
**Table 2**
| Genes | Forward | Reverse |
| --- | --- | --- |
| Arg1 | ATTGTGAAGAACCCACGGTCTG | ACTGTGGTCTCCACCCAGCA |
| Cish | CTAGACCCTGAGGGGGATCT | GGGTGCTGTCTCGAACTAGG |
| Gapdh | GCAAAGTGGAGATTGTTGCCA | GCCTTGACTGTGCCGTTGA |
| Gmcsf | TGCCTGTCACGTTGAATGAAGA | CCCGTAGACCCTGCTCGAATA |
| Ifng | CAACAGCAAGGCGAAAAAG | GTGGACCACTCGGATGAGCT |
| Il12p40 | GACCCTGCCCATTGAACTGGC | CAACGTTGCATCCTAGGATCG |
| Il1b | TCGTGCTGTCGGACCCATA | GTCGTTGCTTGGTTCTCCTTGT |
| Il6 | CAACCACGGCCTTCCCTACT | CCACGATTTCCCAGAGAACATG |
| Inos | CAGCTGGGCTGTACAAACCTT | CATTCGAAGTGAAGCGTTTCG |
| Tnfa | CATCTTCTCAAAATTCGAGTGACAA | TGGGAGTAGACAAGGTACAACCC |
| Tirap | CCTCCACTCCGTCCAAGAAG | TGAACCATCATAGAGGTGGCTTT |
## RNA sequencing
RNA quality was analyzed by the measurement of the RNA integrity number (RIN) with a bioanalyzer RNA 6000 Nano assay prior to sequencing. mRNA library preparation was realized following manufacturer’s recommendations (Ultra 2 mRNA kit from NEB). Final samples pooled library prep were sequenced on Novaseq6000 ILLUMINA with S1-200 cartridge (2x1600Millions of 100 bases reads) in one run, corresponding to 2x30Millions of reads per sample after demultiplexing. Quality of raw data was evaluated with FastQC. Poor quality sequences and adapters were trimmed or removed with the fastp tool, using default parameters, to retain only good quality paired reads. Illumina DRAGEN bio-IT Plateform (v3.8.4) was used for mapping on mm10 reference genome and for quantification using gencode vM25 annotation gtf file. Library orientation, library composition and coverage along transcripts were checked with Picard tools. Subsequent analyses were conducted with R software. Differential expression analysis was performed with the DESeq2 (v1.26.0) bioconductor package. Multiple hypothesis adjusted p-values were calculated with the Benjamini-Hochberg procedure to control FDR with a threshold of significance at 0.05. The cut-off for absolute log2-ratio was set at 2.
## ELISA assay
Cytokine production was measured from lung extracts (28 dpi) or BMDMs supernatants (4 dpi) accordingly following protocol recommendations of the manufacturer for IL-1β, IL-12p40, IFNγ (Invitrogen—Waltham, MA) and TNFα (R&D Systems—Minneapolis, MN).
## Statistics
All analyses and histograms were performed using GraphPad Prism 9 software. Significance of obtained results was tested using Ordinary one-way ANOVA. In S2 Fig, differences in the mean between two groups were analyzed using Student’s t-test. Indicated symbols of *, **, *** and **** denote $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.001$ and $p \leq 0.0001$ respectively.
## References
1. Barreiro LB, Ben-Ali M, Quach H, Laval G, Patin E, Pickrell JK. **Evolutionary dynamics of human Toll-like receptors and their different contributions to host defense.**. *PLoS Genet* (2009) **5** e1000562. DOI: 10.1371/journal.pgen.1000562
2. Noreen M, Arshad M. **Association of TLR1, TLR2, TLR4, TLR6, and TIRAP polymorphisms with disease susceptibility.**. *Immunol Res.* (2015) **62** 234-52. DOI: 10.1007/s12026-015-8640-6
3. D’Onofrio V, Monnier AA, Kremer C, Stappers MHT, Netea MG, Gyssens IC. **Lesion size is associated with genetic polymorphisms in TLR1, TLR6, and TIRAP genes in patients with major abscesses and diabetic foot infections**. *Eur J Clin Microbiol Infect Dis* (2020) **39** 353-60. DOI: 10.1007/s10096-019-03732-7
4. Collin M, Dickinson R, Bigley V. **Haematopoietic and immune defects associated with GATA2 mutation**. *Br J Haematol* (2015) **169** 173-87. DOI: 10.1111/bjh.13317
5. Rajpoot S, Wary KK, Ibbott R, Liu D, Saqib U, Thurston TLM. **TIRAP in the Mechanism of Inflammation.**. *Front Immunol* (2021) **12** 697588. DOI: 10.3389/fimmu.2021.697588
6. Zyzak J, Mitkiewicz M, Leszczyńska E, Reniewicz P, Moynagh PN, Siednienko J. **HSV-1/TLR9-Mediated IFNβ and TNFα Induction Is Mal-Dependent in Macrophages.**. *J Innate Immun.* (2020) **12** 387-98. PMID: 31851971
7. Takeda K, Akira S. **Microbial recognition by Toll-like receptors**. *J Dermatol Sci* (2004) **34** 73-82. DOI: 10.1016/j.jdermsci.2003.10.002
8. Jeyaseelan S, Manzer R, Young SK, Yamamoto M, Akira S, Mason RJ. **Toll-IL-1 receptor domain-containing adaptor protein is critical for early lung immune responses against Escherichia coli lipopolysaccharide and viable Escherichia coli**. *J Immunol* (2005) **175** 7484-95. DOI: 10.4049/jimmunol.175.11.7484
9. Jeyaseelan S, Young SK, Yamamoto M, Arndt PG, Akira S, Kolls JK. **Toll/IL-1R domain-containing adaptor protein (TIRAP) is a critical mediator of antibacterial defense in the lung against Klebsiella pneumoniae but not Pseudomonas aeruginosa.**. *J Immunol* (2006) **177** 538-47. DOI: 10.4049/jimmunol.177.1.538
10. Khor CC, Chapman SJ, Vannberg FO, Dunne A, Murphy C, Ling EY. **A Mal functional variant is associated with protection against invasive pneumococcal disease, bacteremia, malaria and tuberculosis**. *Nat Genet* (2007) **39** 523-8. DOI: 10.1038/ng1976
11. Belhaouane I, Hoffmann E, Chamaillard M, Brodin P, Machelart A. **Paradoxical Roles of the MAL/Tirap Adaptor in Pathologies.**. *Front Immunol.* (2020) **11** 569127. DOI: 10.3389/fimmu.2020.569127
12. Saranathan R, Sathyamurthi P, Thiruvengadam K, Murugesan S, Shivakumar S, Gomathi NS. **MAL adaptor (TIRAP) S180L polymorphism and severity of disease among tuberculosis patients.**. *Infect Genet Evol* (2020) **77** 104093. DOI: 10.1016/j.meegid.2019.104093
13. Liu Q, Li W, Li D, Feng Y, Tao C. **TIRAP C539T polymorphism contributes to tuberculosis susceptibility: evidence from a meta-analysis**. *Infect Genet Evol* (2014) **27** 32-9. DOI: 10.1016/j.meegid.2014.06.025
14. Castiblanco J, Varela DC, Castaño-Rodríguez N, Rojas-Villarraga A, Hincapié ME, Anaya JM. **S180L polymorphism is a common protective factor against developing tuberculosis and systemic lupus erythematosus**. *Infect Genet Evol* (2008) **8** 541-4. PMID: 18417424
15. Miao R, Li J, Sun Z, Xu F, Shen H. **Meta-analysis on the association of TIRAP S180L variant and tuberculosis susceptibility**. *Tuberculosis (Edinb).* (2011) **91** 268-72. DOI: 10.1016/j.tube.2011.01.006
16. 16WHO. Global Tuberculosis Report 2021. 2021.. *WHO. Global Tuberculosis Report* (2021)
17. Kinsella RL, Zhu DX, Harrison GA, Mayer Bridwell AE, Prusa J, Chavez SM. **Perspectives and Advances in the Understanding of Tuberculosis**. *Annu Rev Pathol* (2021) **16** 377-408. DOI: 10.1146/annurev-pathol-042120-032916
18. Queval CJ, Song OR, Carralot JP, Saliou JM, Bongiovanni A, Deloison G. **Mycobacterium tuberculosis Controls Phagosomal Acidification by Targeting CISH-Mediated Signaling**. *Cell Rep* (2017) **20** 3188-98. DOI: 10.1016/j.celrep.2017.08.101
19. Simeone R, Sayes F, Song O, Gröschel MI, Brodin P, Brosch R. **Cytosolic access of Mycobacterium tuberculosis: critical impact of phagosomal acidification control and demonstration of occurrence in vivo**. *PLoS Pathog* (2015) **11** e1004650. DOI: 10.1371/journal.ppat.1004650
20. Sánchez D, Lefebvre C, Rioux J, García LF, Barrera LF. **Evaluation of Toll-like receptor and adaptor molecule polymorphisms for susceptibility to tuberculosis in a Colombian population**. *Int J Immunogenet* (2012) **39** 216-23. DOI: 10.1111/j.1744-313X.2011.01077.x
21. Selvaraj P, Harishankar M, Singh B, Jawahar MS, Banurekha VV. **Toll-like receptor and TIRAP gene polymorphisms in pulmonary tuberculosis patients of South India**. *Tuberculosis (Edinb).* (2010) **90** 306-10. DOI: 10.1016/j.tube.2010.08.001
22. Capparelli R, De Chiara F, Di Matteo A, Medaglia C, Iannelli D. **The MyD88 rs6853 and TIRAP rs8177374 polymorphic sites are associated with resistance to human pulmonary tuberculosis**. *Genes Immun* (2013) **14** 504-11. DOI: 10.1038/gene.2013.48
23. Nejentsev S, Thye T, Szeszko JS, Stevens H, Balabanova Y, Chinbuah AM. **Analysis of association of the TIRAP (MAL) S180L variant and tuberculosis in three populations.**. *Nat Genet* (2008) **40** 261-2. DOI: 10.1038/ng0308-261
24. Yamamoto M, Sato S, Hemmi H, Sanjo H, Uematsu S, Kaisho T. **Essential role for TIRAP in activation of the signalling cascade shared by TLR2 and TLR4**. *Nature* (2002) **420** 324-9. DOI: 10.1038/nature01182
25. Deboosere N, Belhaouane I, Machelart A, Hoffmann E, Vandeputte A, Brodin P. **High-Content Analysis Monitoring Intracellular Trafficking and Replication of Mycobacterium tuberculosis Inside Host Cells**. *Methods Mol Biol* (2021) **2314** 649-702. DOI: 10.1007/978-1-0716-1460-0_29
26. Hoffmann E, Machelart A, Song OR, Brodin P. **Proteomics of Mycobacterium Infection: Moving towards a Better Understanding of Pathogen-Driven Immunomodulation**. *Front Immunol* (2018) **9** 86. DOI: 10.3389/fimmu.2018.00086
27. Barczak AK, Avraham R, Singh S, Luo SS, Zhang WR, Bray MA. **Systematic, multiparametric analysis of Mycobacterium tuberculosis intracellular infection offers insight into coordinated virulence**. *PLoS Pathog* (2017) **13** e1006363. DOI: 10.1371/journal.ppat.1006363
28. Augenstreich J, Arbues A, Simeone R, Haanappel E, Wegener A, Sayes F. **ESX-1 and phthiocerol dimycocerosates of Mycobacterium tuberculosis act in concert to cause phagosomal rupture and host cell apoptosis**. *Cell Microbiol* (2017) 19. DOI: 10.1111/cmi.12726
29. Conrad WH, Osman MM, Shanahan JK, Chu F, Takaki KK, Cameron J. **Mycobacterial ESX-1 secretion system mediates host cell lysis through bacterium contact-dependent gross membrane disruptions**. *Proc Natl Acad Sci U S A* (2017) **114** 1371-6. DOI: 10.1073/pnas.1620133114
30. Song OR, Deboosere N, Delorme V, Queval CJ, Deloison G, Werkmeister E. **Phenotypic assays for Mycobacterium tuberculosis infection**. *Cytometry A* (2017) **91** 983-94. DOI: 10.1002/cyto.a.23129
31. Simeone R, Bobard A, Lippmann J, Bitter W, Majlessi L, Brosch R. **Phagosomal rupture by Mycobacterium tuberculosis results in toxicity and host cell death**. *PLoS Pathog* (2012) **8** e1002507. DOI: 10.1371/journal.ppat.1002507
32. den Brok MH, Raaijmakers TK, Collado-Camps E, Adema GJ. **Lipid Droplets as Immune Modulators in Myeloid Cells**. *Trends Immunol* (2018) **39** 380-92. DOI: 10.1016/j.it.2018.01.012
33. Huss M, Ingenhorst G, König S, Gassel M, Dröse S, Zeeck A. *J Biol Chem* (2002) **277** 40544-8. PMID: 12186879
34. Sayes F, Blanc C, Ates LS, Deboosere N, Orgeur M, Le Chevalier F. **Multiplexed Quantitation of Intraphagocyte Mycobacterium tuberculosis Secreted Protein Effectors**. *Cell Rep* (2018) **23** 1072-84. DOI: 10.1016/j.celrep.2018.03.125
35. Ryndak M, Wang S, Smith I. **PhoP, a key player in Mycobacterium tuberculosis virulence**. *Trends Microbiol* (2008) **16** 528-34. DOI: 10.1016/j.tim.2008.08.006
36. Rogers GL, Suzuki M, Zolotukhin I, Markusic DM, Morel LM, Lee B. **Unique Roles of TLR9- and MyD88-**. *Dependent and -Independent Pathways in Adaptive Immune Responses to AAV-Mediated Gene Transfer. J Innate Immun* (2015) **7** 302-14. PMID: 25612611
37. Landrigan A, Wong MT, Utz PJ. **CpG and non-CpG oligodeoxynucleotides directly costimulate mouse and human CD4+ T cells through a TLR9- and MyD88-independent mechanism**. *J Immunol* (2011) **187** 3033-43. DOI: 10.4049/jimmunol.1003414
38. Stamm CE, Collins AC, Shiloh MU. **Sensing of Mycobacterium tuberculosis and consequences to both host and bacillus**. *Immunol Rev* (2015) **264** 204-19. DOI: 10.1111/imr.12263
39. Harishankar M, Selvaraj P, Bethunaickan R. *Influence of Genetic Polymorphism Towards Pulmonary Tuberculosis Susceptibility. Frontiers in Medicine* (2018) 5
40. Fremond CM, Togbe D, Doz E, Rose S, Vasseur V, Maillet I. **IL-1 receptor-mediated signal is an essential component of MyD88-dependent innate response to Mycobacterium tuberculosis infection**. *J Immunol* (2007) **179** 1178-89. DOI: 10.4049/jimmunol.179.2.1178
41. Sellis D, Callahan BJ, Petrov DA, Messer PW. **Heterozygote advantage as a natural consequence of adaptation in diploids**. *Proc Natl Acad Sci U S A* (2011) **108** 20666-71. DOI: 10.1073/pnas.1114573108
42. Dallenga T, Schaible UE. **Neutrophils in tuberculosis—first line of defence or booster of disease and targets for host-directed therapy?**. *Pathog Dis.* (2016) 74. DOI: 10.1093/femspd/ftw012
43. Yeremeev V, Linge I, Kondratieva T, Apt A. **Neutrophils exacerbate tuberculosis infection in genetically susceptible mice**. *Tuberculosis (Edinb).* (2015) **95** 447-51. DOI: 10.1016/j.tube.2015.03.007
44. Fonseca KL, Maceiras AR, Matos R, Simoes-Costa L, Sousa J, Cá B. **Deficiency in the glycosyltransferase Gcnt1 increases susceptibility to tuberculosis through a mechanism involving neutrophils**. *Mucosal Immunol* (2020) **13** 836-48. DOI: 10.1038/s41385-020-0277-7
45. Nair S, Huynh JP, Lampropoulou V, Loginicheva E, Esaulova E, Gounder AP. **Irg1 expression in myeloid cells prevents immunopathology during M. tuberculosis infection**. *J Exp Med* (2018) **215** 1035-45. DOI: 10.1084/jem.20180118
46. Chen Z, Wang W, Liang J, Wang J, Feng S, Zhang G. **Association between toll-like receptors 9 (TLR9) gene polymorphism and risk of pulmonary tuberculosis: meta-analysis.**. *BMC Pulm Med* (2015) **15** 57. DOI: 10.1186/s12890-015-0049-4
47. Knight M, Braverman J, Asfaha K, Gronert K, Stanley S. **Lipid droplet formation in Mycobacterium tuberculosis infected macrophages requires IFN-γ/HIF-1α signaling and supports host defense.**. *PLoS Pathog.* (2018) **14** e1006874. PMID: 29370315
48. Ní Cheallaigh C, Sheedy FJ, Harris J, Muñoz-Wolf N, Lee J, West K. **A Common Variant in the Adaptor Mal Regulates Interferon Gamma Signaling**. *Immunity* (2016) **44** 368-79. DOI: 10.1016/j.immuni.2016.01.019
49. Christophe T, Jackson M, Jeon HK, Fenistein D, Contreras-Dominguez M, Kim J. **High content screening identifies decaprenyl-phosphoribose 2’ epimerase as a target for intracellular antimycobacterial inhibitors.**. *PLoS Pathog* (2009) **5** e1000645. DOI: 10.1371/journal.ppat.1000645
50. Deboosère N, Iantomasi R, Queval CJ, Song OR, Deloison G, Jouny S. **LppM impact on the colonization of macrophages by Mycobacterium tuberculosis**. *Cell Microbiol* (2017) **19**. DOI: 10.1111/cmi.12619
51. Raze D, Verwaerde C, Deloison G, Werkmeister E, Coupin B, Loyens M. **Heparin-Binding Hemagglutinin Adhesin (HBHA) Is Involved in Intracytosolic Lipid Inclusions Formation in Mycobacteria.**. *Front Microbiol.* (2018) **9** 2258. DOI: 10.3389/fmicb.2018.02258
52. Machelart A, Salzano G, Li X, Demars A, Debrie AS, Menendez-Miranda M. **Intrinsic Antibacterial Activity of Nanoparticles Made of β-Cyclodextrins Potentiates Their Effect as Drug Nanocarriers against Tuberculosis**. *ACS Nano* (2019) **13** 3992-4007. PMID: 30822386
53. Hanot Mambres D, Machelart A, Potemberg G, De Trez C, Ryffel B, Letesson JJ. **Identification of Immune Effectors Essential to the Control of Primary and Secondary Intranasal Infection with Brucella melitensis in Mice**. *J Immunol* (2016) **196** 3780-93. DOI: 10.4049/jimmunol.1502265
54. Doyle T, Moncorgé O, Bonaventure B, Pollpeter D, Lussignol M, Tauziet M. **The interferon-inducible isoform of NCOA7 inhibits endosome-mediated viral entry.**. *Nat Microbiol* (2018) **3** 1369-76. DOI: 10.1038/s41564-018-0273-9
|
---
title: 'Adverse health correlates of intimate partner violence against older women:
Mining electronic health records'
authors:
- Serhan Yılmaz
- Erkan Gunay
- Da Hee Lee
- Kathleen Whiting
- Kristin Silver
- Mehmet Koyuturk
- Gunnur Karakurt
journal: PLOS ONE
year: 2023
pmcid: PMC9994723
doi: 10.1371/journal.pone.0281863
license: CC BY 4.0
---
# Adverse health correlates of intimate partner violence against older women: Mining electronic health records
## Abstract
Intimate partner violence (IPV) is often studied as a problem that predominantly affects younger women. However, studies show that older women are also frequently victims of abuse even though the physical effects of abuse are harder to detect. In this study, we mined the electronic health records (EHR) available through IBM Explorys to identify health correlates of IPV that are specific to older women. Our analyses suggested that diagnostic terms that are co-morbid with IPV in older women are dominated by substance abuse and associated toxicities. When we considered differential co-morbidity, i.e., terms that are significantly more associated with IPV in older women compared to younger women, we identified terms spanning mental health issues, musculoskeletal issues, neoplasms, and disorders of various organ systems including skin, ears, nose and throat. Our findings provide pointers for further investigation in understanding the health effects of IPV among older women, as well as potential markers that can be used for screening IPV.
## Introduction
Intimate Partner Violence (IPV) is a devastating public health problem and affects millions of women globally each year. According to recent statistics, about a quarter of women in the US experience severe physical violence from their partner in their lifetime [1]. This rate varies from 15 percent to 71 percent around the world [2]. IPV can be broadly defined as “abusive behaviors perpetrated by someone who is or was involved in an intimate relationship with the victim” [1]. IPV involves physical, emotional, sexual harm to the victim-survivor [1, 3]. Other common forms of abuse include psychological/emotional maltreatment through behaviors that causes emotional pain or injury with verbal threats, berating, harassment, or intimidation, economic deception, and willful negligence [4–6]. The reported adverse health effects of IPV extend from minor injuries and cuts, chronic conditions to acute severe injuries, and even death [7–10]. Past research additionally indicated that mental health-related issues such as depression, anxiety, post-traumatic stress disorder (PTSD), substance abuse, and suicide are widely observed among survivors of IPV [11, 12].
IPV has been studied mainly as a problem that predominantly affects younger women. The U.S. Department of Health and Human Services recommends IPV screening for women and girls ages 15–46. While much evidence exists documenting the most severe forms of relationship violence that are directed against women of childbearing age, older women are also vulnerable to IPV at an increasing rate [7, 13, 14]. Older adult women report that nonphysical abuse can also be harmful to the victim’s mental and physical health [15]. Neglect, defined as the failure of a caregiver to fulfill his/her duties, including behaviors such as withholding food or medication, also affects older women and can also have detrimental health effects [4, 6].
It can be challenging to detect the physical effects of IPV among older women, since they are naturally more prone to injury and ailment [8, 16]. Furthermore, as health declines over the years, health care providers may mistake the signs of abuse as normal wear and tear to physical and mental health. Limited research on the health of older female victims of partner violence shows that health problems reported by older women are concordant with the general population [10, 17]. Older women who report nonphysical abuse such as seclusion or exclusion, financial exploitation also report that these forms of abuse adversely affects their well being [5, 15, 18]. However, there is limited information on the specific health consequences of IPV on aging women [19–21]. In this paper, we aim to identify health correlates of IPV that are specifically common in older adult women.
A complicating factor for researchers and service professionals is the lack of coordination between the fields of IPV and older adult abuse. The lack of conceptual clarity on older women abuse intersecting with IPV presents many challenges to understanding victims’ experiences and providing necessary support [22]. Barriers to diagnosis and treatment include the victim’s fear of reprisal by the abuser, victim denial or shame, inexperience and lack of knowledge by health care personnel, and the ageist attitude of society [16]. Furthermore, older adult women who are being abused may not be as familiar with the language or concepts used to describe violence and may not have the willingness or ability to disclose such events [23]. Finally, the victim may feel too ashamed to admit the abuse is occurring or may be frightened by the prospect of living alone after many years of co-dependence [23]. All these barriers make this population harder to reach and may prevent the victim from asking for help or making any progress toward leaving their abuser. For these reasons, identification of potential health-related markers of IPV in older women can also be useful for clinicians, care providers, and service professional to identify potential signs of IPV and develop strategies to follow up accordingly.
We take a data-driven approach to identify the health correlates of IPV against older women. Specifically, we aim to answer the following questions: To answer these questions, we utilize electronic health records (EHRs) provided by the IBM Explorys Therapeutic Dataset [24]. IBM *Explorys is* a private Electronic Health Record (EHR) database, which pools data from more than 8 billion ambulatory visits to more than 40 US healthcare networks including diverse institutions and points of care [25]. It is a browser-based search engine with query options of various diagnostic categories based on ICD-$\frac{9}{10}$ codes. Cohorts include data on diagnoses, findings, and demographics. In this paper, we use diagnostic data we obtain by querying this tool. Throughout this paper, we refer to diagnoses, findings, and demographics returned by Explorys as “terms”.
Records for patients 18 years or older seen in multiple healthcare systems from 1999 to 2019 are included in the database. Data are standardized and normalized using common ontologies, searchable through a HIPAA-compliant, patient de-identified web application (Explore; Explorys Inc). The diversity of pooled data in IBM *Explorys is* aimed at reflecting the full real-world healthcare continuum, while the large patient cohort enhances statistical power. Moreover, it allows flexible queries to acquire data that represent a specific population (such as older women who suffer from IPV). To identify records that belong to older women, we query the database for women age 65 and over. Accordingly, we use the term “older adult” throughout the paper to indicate adults with the chronological age 65 and over.
While the richness of data and the flexibility of queries in IBM Explorys provide unprecedented opportunities for mining data to identify previously unreported associations, there are important computational and statistical challenges due to the employed privacy measures: (i) IBM Explorys does not provide access to individual records and allows querying of the records only in the form of number of records, and (ii) the number of records provided in query results are rounded to the nearest ten, posing further challenges to assess statistical significance because of the additional uncertainty due to. For these reasons, it is not straightforward to accurately identify associated diagnostic terms and/or conditions in a robust manner using IBM Explorys data.
Here, we develop a general framework that is designed to utilize EHR data (specifically from IBM Explorys) to identify conditions that exhibit stronger association with the condition of interest (intimate partnet violence) in one population (e.g., older women) as compared to another population (e.g., younger women). We refer to such conditions as differentially co-morbid. To address the challenges that stem from the privacy measures of Explorys while providing a robust and easy-to-interpret framework, we: (i) systematically quantify the association of each condition with a target condition of interest (e.g., IPV) in a data-agnostic manner, (ii) compute confidence intervals that take into account the overall rarity of the conditions and the errors to ensure statistical rigor, and (iii) classify the conditions into categories (e.g., high, medium, low prevalence) to provide easy to interpret results. This framework is illustrated in Fig 1.
**Fig 1:** *Flowchart illustrating our pipeline for mining electronic health records to identify health correlates of intimate partner violence against older women.(a) Generated cohorts for background and older populations. (b) Each cohort contains a frequency table indicating the number of records for each term. (c) 2 × 2 contingency tables (shown as Venn diagrams) are constructed for both background and older women populations and for each term t. (d) Using the contingency tables, intimate partner violence (IPV) prevalence scores are computed for both populations. (e) A differential prevalence score is computed for each term to uncover terms that are more associated with IPV in older women population compared to background (BG).*
## Data collection
IBM Explorys Therapeutic Dataset provides the Explorys Cohort Discovery tool which allows the submission of a query by specifying demographic criteria and/or keywords (for findings or diagnoses) to acquire a subpopulation. As a response, the cohort discovery tool forms a cohort that contains the number of records in the specified subpopulation for each finding and/or diagnosis terms in the database. Throughout this paper, we refer to these diagnoses as terms.
We investigate the potential health correlates of IPV in two populations: (i) Older women population of 65+ years of age and Background (BG) population of women 18–65 years of age. We query the Explorys Cohort Discovery tool to generate cohorts of interest (provided as S1 Data) corresponding to these two populations (Fig 1a), which are specified as follows:
## Querying and cohort formation
We ran all queries in June 2019. Each query result (i.e., cohort) X contains the following information: [1] Cohort size NX indicating the total number of records in X, [2] a list of terms T (there are around 18000 terms in the database), and [3] a frequency table fX that contains for each term t ∈ T the number of records fX(t) identified with t (Fig 1b). We provide the frequency tables of all cohorts in S1 Data. To denote the number of records in a population of interest Z (Senior or BG), we use the following notation:
## Constructing contingency tables
For each population of interest Z (Senior or BG) and term t, we construct a 2 × 2 contingency table (Fig 1c). This table contains the number of records in Z for all combinations of the existence and absence of IPV and term t variables, i.e.:
## Computing co-morbidity scores
For population Z (either senior or background), we consider a term t to be co-morbid if, in this population, IPV and term t are significantly more frequently observed together rather than separately. We quantify this using the co-morbidity score C(t|Z), which is defined as the log-odds ratio LOR(t, IPV|Z): LOR(t,IPV|Z)=log2(NZ(t,IPV)NZ(¬t,¬IPV)NZ(¬t,IPV)NZ(t,¬IPV))=log2(NZ(t,X))+log2((NZ-NZ(t)-NZ(IPV)+NZ(t,IPV))-log2(NZ(t)-NZ(t,IPV))-log2(NZ(IPV)-NZ(t,IPV)) [1] As shown in Fig 1d, LOR(t, IPV|Z) increases monotonically as the frequency of term t in Z∩IPV subpopulation goes up compared to the frequency of term t in Z\IPV subpopulation.
## Accounting for variance
To account for the variability in the estimation of LOR(t, IPV|Z), we compute a standard error SE(t, IPV|Z) as follows: SE(t,IPV|Z)=1NZ(t,IPV)+1NZ(t,¬IPV)+1NZ(¬t,IPV)+1NZ(¬t,¬IPV)ln[2] [2] Next, we compute 1—α level confidence interval as follows: LORmin(t,IPV|Z)=LOR(t,IPV|Z)-zαSE(t,IPV|Z)LORmax(t,IPV|Z)=LOR(t,IPV|Z)+zαSE(t,IPV|Z) [3] where zα is a critical value obtained from normal inverse cumulative distribution (e.g., zα = 1.96 for α = 0.05). Throughout this paper, we use α = 0.05 and $95\%$ confidence intervals to determine the statistical significance of the terms.
## Accounting for measurement error due to rounding
The confidence interval shown in Eq 3 accounts for variance but does not take into account the measurement error due to rounding of the number of records. For example, if Explorys returns the number of records NZ(t, IPV) = 10 for a term t, this indicates the actual number of records can be anywhere between 5 and 15. For terms with relatively low frequencies, this can potentially alter the log-odds ratio a substantial amount. In order to take the additional uncertainty due to rounding into account, we compute an augmented confidence interval using a Monte-Carlo simulation: First, we sample each number of records Nt from [Nt-5, Nt+5] uniformly at random and take 100 samples. Next, we use multiple imputation methods [26] to get an estimate of a standard error on LOR that accounts for the rounding of the counts as follows: The confidence intervals are then computed with standard errors corrected for the rounding of the numbers.
## Accounting for detection bias in health records
We suspect that EHR databases and hospital records may suffer from a detection bias due to difficulties in diagnosis (e.g., if a severe condition is detected, more medical tests may be performed which can lead to the detection of more terms. Otherwise, there is less attention and many terms go unnoticed). Thus, if not addressed, this bias could lead to an over-estimation in our co-morbidity scores (log-odds ratios) [27]. To help address this issue, we make an estimate μ of the detection bias on the log odds ratios by looking at the distribution of the co-morbidity scores across all terms (see Fig 2). Based on this estimation, we compute a corrected co-morbidity score C^(t|Z) that takes into account a detection bias of level μ as follows: C^(t|Z)=C(t|Z)-μ [4]
**Fig 2:** *The distributions of co-morbidity and differential co-morbidity scores in Senior and Background (BG) populations.(Left & Middle panels) The distribution of the raw co-morbidity scores (i.e., odds ratios) for all terms in Senior and Background populations. The geometric mean of the co-morbidity scores across all terms is used to determine the null level accounting for the detection bias (OR≈3 is the mean across all terms as opposed to the OR = 1 natural level). The null hypothesis thresholds to determine Minor/Moderate/High co-morbidity terms are marked on the histograms (OR = 3/5/10). (Right panel) The distribution of the differential co-morbidity scores for all terms. Since there is not a notable shift in the histogram, OR = 1 is considered as the null level for differential co-morbidity. OR: Odds ratio.*
Thus, we consider OR=μ as a more appropriate null hypothesis level (as opposed to “OR = 1” natural level) in the presence of a detection bias of magnitude μ. To estimate the magnitude of the detection bias μ, we consider the geometric average of the raw co-morbidity scores over all terms as a guideline (mean odds ratio is respectively: 3.16 and 3.18 for Senior and Background populations, Fig 2). Thus, we consider μ = 3 as the null level indicating no co-morbidity. Here, our reasoning is that if all terms exhibit a strong association (as indicated by the mean co-morbidity score), this association is likely not due to an inherent co-morbidity in the population, but rather is related to record keeping or the detection of the terms (for example, if a patient has a severe condition like IPV, more inquiry and more medical tests may be applied, thus leading to a greater fraction of the terms to be detected).
## Accounting for multiple comparisons
Our aim in this study is to identify terms with highest co-morbidity and estimate a lower bound on their effect size. For this purpose, we utilize confidence intervals (CIs) and rank the terms according to the lower bound of their interval. However, this process causes a multiple comparisons problem: After such a sorting is applied and top terms are taken, the confidence intervals are no longer valid (not valid in the sense that lower bound of the interval no longer imply statistical significance). To overcome this issue, we aim to bound the false discovery rate (FDR) of the findings (note that we aim to bound FDR as opposed to the family-wise error rate to avoid being overly stringent while determining the significant terms, thus we bound the average number of false discoveries and not the probability of making a false discovery). For this purpose, we utilize the Benjamini-Hochberg (BH) procedure [28] in combination with a more recent work [29] that correct the false coverage rate (FCR) of the confidence intervals for a given selection of significant items obtained from BH procedure (shortly BH-selected FCR corrected CIs). This process goes: Here, to avoid specifying a fixed null hypothesis (from which we could only learn that null hypothesis is satisfied or not), we extend this process for a series of null hypotheses: OR = μ*. As the null level μ*, we practically consider all levels (sampled logarithmically in 0.01 intervals) and apply BH-procedure for each level μ*. Next, for each term t, we find the highest μ(t) a term would be deemed significant (after BH correction) and construct the corresponding FCR corrected confidence intervals. Note that, the lower bound of these corrected intervals (equal to μ(t)) answers the question: Thus, this allows us to avoid relying on arbitrary significance thresholds (e.g., OR = 10), and allows us to answer questions like “Which terms would no longer be deemed significant if we selected the significance threshold to be OR = 10.1 instead?” simply by looking at the confidence intervals. To summarize, the process that we apply to take into account of multiple comparisons is as follows:
## Assessment of differential co-morbidity
One of the objectives of this study is to uncover terms that are frequently observed together with IPV in older women population more so than the background population. To this end, we compute a differential co-morbidity score DC(t) (Fig 1e) compared to the background (BG) population: DC(t)=C(t|Senior)-C(t|BG)=LOR(t,IPV|Senior)-LOR(t,IPV|BG) [5] Since the older women and background populations are independent, the standard error of the differential co-morbidity for term t can be computed as follows: σ(t)=SE2(t,IPV|Senior)+SE2(t,IPV|BG) [6] Using the standard error, we compute the corrected confidence intervals for the differential co-morbidity as detailed in “Accounting for multiple comparisons” section. Overall, we consider a term t to be differentially co-morbid with high confidence if DCmin(t) is greater than zero. Otherwise, we conclude that it has a low confidence level to make a judgement.
## Experimental setting
The datasets obtained from IBM Explorys system contain information about a total of 18863 terms. We assess the co-morbidity of these terms with IPV in the older women population as well as the background (BG) population. For each term t, we compute co-morbidity scores C(t|Senior), C(t|BG) and the differential co-morbidity score DC(t) for the difference between senior and background populations. For each co-morbidity score, we compute the corresponding $95\%$ augmented confidence intervals (corrected to bound the false discovery rate) to assess the statistical significance. We consider a co-morbidity score to be invalid if the confidence interval does not have a finite range (e.g., when the term frequency is zero in one or more cohorts).
To make the interpretation of the results easier, we consider three null levels for the co-morbidity scores (OR = $\frac{3}{5}$/10) and label the significant findings (at α = 0.05 level after accounting for FDR via BH-procedure) as respectively Minor/Moderate/Highly co-morbid terms. Note that, for Minor co-morbidity, the null level is selected to be OR = 3 (as opposed to OR = 1) to take into account the detection bias.
## Medical terms that are co-morbid in older victims of IPV
We identify 2057 and 5464 valid terms for older women (senior) and background populations respectively (2039 of these are valid for both). The difference in the number of valid terms is likely due to the difference in cohort sizes as the background population has around 2.5 times more number of records than the older women population (13164960 vs. 5253320).
First, we start by investigating the terms that are statistical significant (for null hypothesis OR = 1, α = 0.05, after FDR is bounded using BH-procedure) and we observe a rather interesting result: In both populations, almost all valid terms are deemed statistically significant (4664 terms for background, and 1681 terms for senior population). To investigate whether this is a result of a detection bias in our dataset, we examine the distribution of the co-morbidity scores across all terms in Fig 2. As it can be seen, the distribution of the co-morbidity scores are considerably shifted to the right and are approximately centered around OR = 3 for both senior and background populations (geometric mean for the odds ratio is respectively: 3.16 [1.42, 5.66] and 3.18 [1.84, 4.79] for senior and background populations). This suggests that OR = 3 can be considered as a more natural null level for assessing the co-morbidity with IPV in this dataset, explaining why there are so many significant terms when tested for OR = 1. Note that, we do not observe any notable shift in the distribution of the differential co-morbidity scores (Fig 2 right panel) since the effect of the bias seems approximately equal for senior and background populations which cancels out when we take the difference.
Overall, when we look at the terms with high co-morbidity, we identify respectively 199, 64 and 13 terms with minor, moderate and high co-morbidity in the senior population (and 905, 420 and 165 terms in the background population). Here, we mainly focus on the highly co-morbid terms in the senior population and report the top 20 terms in Table 1 sorted by the minimum bound of their $95\%$ confidence intervals (after they are corrected to bound the false coverage rate). We provide the remaining terms identified as co-morbid in S2 Data. For each term, we provide both the raw co-morbidity scores and their corrected versions where the expected portion of the association due to detection bias is removed (by dividing the raw co-morbidities to OR = 3).
**Table 1**
| Unnamed: 0 | Term Description | Co-morbidity in Senior population | Co-morbidity in Senior population.1 | Number of Records | Number of Records.1 | Number of Records.2 | Number of Records.3 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | Term Description | Raw | Corrected for detection bias | BG | IPV | Senior | SeniorIPV |
| 1.0 | History of abuse | 91.4 [27.9, 299.4] H | 30.5 [9.3, 99.8] H | 36660 | 310 | 2780 | 20 |
| 2.0 | Maltreatment syndromes | 194.7 [27.9, 1358.4] H | 64.9 [9.3, 452.8] H | 4390 | 100 | 660 | 10 |
| 3.0 | Poisoning caused by anticonvulsant | 50.4 [16.2, 156.6] H | 16.8 [5.4, 52.2] H | 26920 | 190 | 5100 | 20 |
| 4.0 | Poisoning caused by sedative AND/OR hypnotic | 46.3 [15.5, 138.3] H | 15.4 [5.2, 46.1] H | 28660 | 200 | 5650 | 20 |
| 5.0 | Continuous acute alcoholic intoxication in alcoholism | 85.8 [13.7, 535.5] H | 28.6 [4.6, 178.5] H | 7870 | 120 | 1400 | 10 |
| 6.0 | Continuous opioid dependence | 38.9 [12.9, 116.9] H | 13.0 [4.3, 39.0] H | 25590 | 140 | 6750 | 20 |
| 7.0 | Chronic post-traumatic stress disorder | 28.2 [12.1, 65.8] H | 9.4 [4.0, 21.9] H | 136510 | 680 | 14260 | 30 |
| 8.0 | History of physical abuse | 64.6 [10.8, 386.9] H | 21.5 [3.6, 129.0] H | 29000 | 290 | 1990 | 10 |
| 9.0 | Poisoning caused by central nervous system drug | 21.7 [10.8, 43.6] H | 7.2 [3.6, 14.5] H | 150480 | 820 | 25370 | 40 |
| 10.0 | Acute drug intoxication | 29.9 [10.5, 85.0] H | 10.0 [3.5, 28.3] H | 69550 | 420 | 8690 | 20 |
| 11.0 | Alcohol intoxication | 29.5 [10.5, 82.6] H | 9.8 [3.5, 27.5] H | 68940 | 420 | 8650 | 20 |
| 12.0 | Contusion of multiple sites | 20.8 [10.5, 41.1] H | 6.9 [3.5, 13.7] H | 55570 | 850 | 26750 | 40 |
| 13.0 | Pathological drug intoxication | 29.4 [10.3, 84.0] H | 9.8 [3.4, 28.0] H | 70250 | 430 | 8860 | 20 |
| 14.0 | Posttraumatic stress disorder | 22.1 [10.0, 48.7] M | 7.4 [3.3, 16.2] M | 161610 | 720 | 17980 | 30 |
| 15.0 | Toxic effect of ethyl alcohol | 27.3 [9.8, 75.7] M | 9.1 [3.3, 25.2] M | 73690 | 450 | 9480 | 20 |
| 16.0 | Poisoning caused by chemical substance | 18.5 [9.5, 36.3] M | 6.2 [3.2, 12.1] M | 154130 | 730 | 29160 | 40 |
| 17.0 | Poisoning caused by psychotropic agent | 26.4 [9.5, 73.8] M | 8.8 [3.2, 24.6] M | 60390 | 360 | 9730 | 20 |
| 18.0 | Alcohol abuse | 16.7 [9.3, 29.9] M | 5.6 [3.1, 10.0] M | 220590 | 1200 | 42040 | 50 |
| 19.0 | Drug abuse | 19.8 [9.1, 43.0] M | 6.6 [3.0, 14.3] M | 190400 | 1020 | 20100 | 30 |
| 20.0 | Nondependent alcohol abuse | 23.6 [8.7, 63.7] M | 7.9 [2.9, 21.2] M | 36180 | 230 | 11090 | 20 |
In Fig 3, we visualize the significant findings and compare their co-morbidities in senior and background population. We observe that while most of the terms that are highly co-morbid in senior population are also highly co-morbid in background population, there are some terms with notably higher co-morbidity in the senior population. Next, we focus on such terms exhibiting differential co-morbidity.
**Fig 3:** *X-axis and Y-axis indicate the minimum bounds of 95% augmented confidence intervals (corrected to bound the FDR) for IPV co-morbidity scores in Senior and Background (BG) populations i.e., OR(t, IPV|Senior), and OR(t, IPV|BG) respectively.The terms identified with High/Moderate/Minor co-morbidity in both populations are shown in red/yellow/blue regions respectively. The terms identified as differentially co-morbid (having significantly higher co-morbidity in Senior population) are marked with black circles. OR: Odds ratio, IPV: Intimate partner violence.*
## Terms with a higher co-morbidity in older victims of IPV compared to younger victims
We find that there are 162 terms with significant differential co-morbidity (exhibiting higher association with IPV in the older women population). Since there is a large number of findings and these consist of many similar terms (e.g., there is a term for “severe depression” and another for “severe major depression”), we manually annotate and group these based on their general categories and report a few selected term from each category in Table 2. Note that, while making this selection, we take into account of borderline cases by looking at their confidence intervals and also consider the overall co-morbidity of the terms in background and senior populations. Here, we mainly focus on the terms that exhibit significant co-morbidity in senior population in addition to being deferentially co-morbid (corresponding to upper right side in Fig 3). We provide the remaining terms and their assigned categories in S3 Data.
**Table 2**
| Category | Term Description | Corrected Co-morbidity in Population Z | Corrected Co-morbidity in Population Z.1 | Differential Co-morbidity | Number of Records | Number of Records.1 | Number of Records.2 | Number of Records.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Category | Term Description | Senior | Background (BG) | Senior vs. BG | BG | IPV | Senior | SeniorIPV |
| Drug Interactions | Poisoning caused by anticonvulsant | 16.8 [5.4, 52.2] H | 4.7 [3.6, 6.0] H | 3.61 [1.27, 10.25] | 26920 | 190 | 5100 | 20 |
| Miscellaneous | History of abuse | 30.5 [9.3, 99.8] H | 5.7 [4.6, 7.0] H | 5.38 [1.58, 18.32] | 36660 | 310 | 2780 | 20 |
| Substance Use Issues | Continuous opioid dependence | 13.0 [4.3, 39.0] H | 3.6 [2.7, 4.7] M | 3.64 [1.26, 10.51] | 25590 | 140 | 6750 | 20 |
| Substance Use Issues | Alcohol intoxication | 9.8 [3.5, 27.5] H | 4.1 [3.5, 4.9] H | 2.38 [1.02, 5.55] | 68940 | 420 | 8650 | 20 |
| Mental Health | Chronic post-traumatic stress disorder | 9.4 [4.0, 21.9] H | 3.5 [3.1, 4.0] M | 2.68 [1.24, 5.82] | 136510 | 680 | 14260 | 30 |
| Mental Health | Major depression in partial remission | 5.2 [2.1, 12.8] M | 1.2 [0.9, 1.7] | 4.30 [1.31, 14.15] | 31750 | 60 | 16870 | 20 |
| Mental Health | Adjustment disorder with mixed emotional features | 3.9 [1.6, 9.5] m | 1.1 [0.9, 1.4] | 3.62 [1.25, 10.44] | 77440 | 130 | 22080 | 20 |
| Mental Health | Generalized anxiety disorder | 1.9 [1.3, 2.8] m | 1.1 [1.0, 1.2] m | 1.70 [1.10, 2.63] | 578410 | 910 | 211620 | 80 |
| Mental Health | Chronic mood disorder | 1.9 [1.3, 2.9] m | 1.2 [1.1, 1.4] m | 1.55 [1.01, 2.39] | 473480 | 830 | 176180 | 70 |
| Mental Health | Anxiety disorder | 1.7 [1.2, 2.3] m | 1.2 [1.1, 1.3] m | 1.40 [1.02, 1.92] | 1718770 | 2420 | 624790 | 170 |
| Muscle Skeletal Issues | Injury of ligament of hand | 4.9 [1.9, 12.2] M | 2.0 [1.6, 2.3] m | 2.48 [1.02, 5.98] | 90940 | 270 | 17730 | 20 |
| Muscle Skeletal Issues | Synovitis | 1.8 [1.0, 3.0] m | 0.8 [0.6, 0.9] | 2.32 [1.19, 4.55] | 188300 | 220 | 101300 | 40 |
| Muscle Skeletal Issues | Acquired deformity of joint of foot | 1.4 [0.9, 2.2] | 0.5 [0.5, 0.7] | 2.64 [1.31, 5.33] | 176850 | 150 | 159380 | 50 |
| Disorders | Hypoglycemia | 2.1 [1.1, 3.8] m | 1.1 [0.9, 1.3] | 1.96 [1.00, 3.86] | 104830 | 170 | 64730 | 30 |
| Disorders | Developmental disorder | 2.3 [1.0, 5.0] m | 0.8 [0.7, 0.9] | 2.86 [1.12, 7.30] | 425210 | 510 | 36860 | 20 |
| Disorders | Nutritional disorder | 1.0 [0.8, 1.3] | 0.5 [0.5, 0.5] | 2.01 [1.31, 3.10] | 1289750 | 950 | 980750 | 170 |
| Disorders | Vitamin D deficiency | 0.9 [0.7, 1.2] | 0.5 [0.4, 0.5] | 1.99 [1.28, 3.10] | 911940 | 640 | 669660 | 120 |
| Skin Problem | Tinea pedis | 2.4 [1.1, 5.5] m | 0.8 [0.6, 1.0] | 3.22 [1.15, 9.03] | 67660 | 80 | 34850 | 20 |
| Infectious disease | Infectious disease of lung | 1.9 [1.1, 3.2] m | 0.9 [0.7, 1.1] | 2.09 [1.08, 4.03] | 78290 | 110 | 96360 | 40 |
| Women’s Health | Pelvic injury | 1.8 [1.1, 3.0] m | 1.0 [0.9, 1.1] | 1.76 [1.01, 3.07] | 612530 | 890 | 102340 | 40 |
| Neurological | Migraine | 1.6 [1.1, 2.4] m | 0.9 [0.8, 1.0] | 1.80 [1.12, 2.87] | 1122900 | 1390 | 208030 | 70 |
| Neoplasm/Tumor | Neoplasm of stomach | 1.7 [0.8, 3.6] | 0.4 [0.2, 0.6] | 4.75 [1.21, 18.72] | 35500 | 20 | 51160 | 20 |
| ENT issues | Posterior rhinorrhea | 1.4 [0.7, 2.9] | 0.5 [0.4, 0.6] | 3.10 [1.12, 8.56] | 96150 | 70 | 59130 | 20 |
| Inflammation | Pharyngitis | 1.3 [0.9, 1.8] | 0.7 [0.7, 0.8] | 1.75 [1.10, 2.78] | 1893860 | 1830 | 263480 | 70 |
## Discussion
Much of the past research on IPV is based on data from younger women. However, recent studies demonstrated that the older women in growing numbers are also often victims of physical and nonphysical forms of IPV (e.g. emotional, psychological and economic abuse) [7, 13, 14, 30, 31]. Our aim in this study was to investigate the health correlates of IPV among older women. We presented a general framework that is designed to utilize electronic health record (EHR) data to identify health correlates of a condition of interest (e.g., IPV) that is specific to a target population (e.g., older women). We mined the EHR data that is available through IBM Explorys, a database containing demographic and diagnostic information gathered from diverse institutions across the United States. The data is analyzed by systematically assessing associations of medical terms, computing confidence intervals that take into account the rounding errors, and classifying the terms into confidence levels.
Based on the analysis in our previous study [27] and looking at the distributions of the co-morbibidity scores (in Fig 2), we reason that the over-population of the terms deemed as significant using the standard approach (i.e., testing for OR = 1 at 0.05 level accounting for FDR) is likely because of a detection bias in the health records. The presence of a severe condition like IPV would naturally warrant more exploration during the screening and this can result in more terms to be detected (including those that would otherwise undetected). Thus, this can cause an artificial association with IPV that is not representative of the inherent population.
Our initial analyses regarding the co-morbid terms in Senior population indicated that substance abuse and poisoning associated with substances are significantly co-morbid with IPV in older women (Table 1). This finding is particularly strong in that 17 of the top 20 terms that are co-morbid with IPV are substance abuse related, while the remaining 3 are directly associated with abuse (history of abuse, maltreatment syndromes, and history of physical abuse). It is important to note that screening for substance abuse and medication overuse among older women with a history of IPV is critical since these terms are highly correlated.
In contrast to terms with significance co-morbidity with IPV, terms with significant differential co-morbidity with IPV (in older women as compared to the background population) were more diverse (Table 2). Specifically, we identified 161 diagnostic terms that exhibited a significantly stronger association with IPV in older women as compared to the background population. These terms included history of abuse, those related to mental health (21 terms) and substance use issues [5], neoplasm, tumors and growths (26 terms), musculoskeletal issues (25 terms), disorders (20 terms), skin problems (11 terms), ear, nose and throat issues (11 terms), inflammation [7], neurological conditions [6], immune problems [5], women’s health (OB-GYN) (5 terms), infectious disease (4 terms), procedures (4 terms), eye disease (3 terms), drug interactions (3 terms), acute conditions (2 terms) and other conditions (3 terms).
Our detailed analysis indicated that mental health conditions such as major depression in partial remission, adjustment disorder with mixed emotional features, chronic post-traumatic stress disorder, anxiety disorder, mood disorder are more likely to occur among older women who have been abused by their partners as compared to younger women. Also continuous opioid dependence, and alcohol intoxication were also found to be differentially co-morbid with IPV in older women as compared to the background population. Past research reports that IPV is associated with an increased likelihood of clinical depression and suicide attempts among women in general [11]. A systematic exploration of the predominant mental health conditions of older women abuse and psychological well-being demonstrates that depression, anxiety, and post-traumatic stress disorder are among prevalent problems [32]. Also, in-depth interviews conducted with abused women aged from 63 to 79 found that older abused women are more prone to symptoms related to mental health issues like anxiety,depression and negative view of self [33]. Similarly, clinical and case-controlled studies indicated poor mental health, particularly depression and dementia as common problems in geriatric clinics among abused older people [34]. It is possible that these conditions are partially related to the isolation of older adults [35, 36]. Moreover, the isolation coupled with IPV likely makes older people more prone to depression. Specifically, we observe that older victims of IPV suffer from major depression roughly 4 times more than younger women ($95\%$ confidence interval: [1.35, 11.71]).
Findings also indicated that musculoskeletal issues such as acquired deformity of joint of foot, acquired deformity of the lower limb, injury of ligament of hand, flexion deformity, polyarthropathy are terms that are more prevalent in the older IPV victims population as compared to the background population (Table 2). This is also consistent with prior research reporting that older adults may come into the emergency department due to fall injuries that could be linked to IPV. Although older women are naturally prone to musculoskeletal issues and osteoporosis resulting in loss of mobility and physical independence, this rate is even higher among older women with a history of IPV. It is possible that a physical trauma as a result of IPV may negatively impact the already vulnerable musculoskeletal system through scaring from an injury, inflammatory disease or hyperglycemia, which we also observed more frequently among older women with a history of abuse, doubling the risk of muscle musculoskeletal issues that are functionally limiting and physically debilitating [37–39]. Injuries are more common among terms that are more co-morbid in the older women population as compared to the background population (Table 2). This is also consistent with prior research reporting that older adults may come into the emergency department due to fall injuries that could be linked to IPV [5]. A Nationwide Emergency Department Sample from 2006 to 2009 revealed that there were approximately 28,000 ED visits per year due to IPV [40]. Older adults, in particular, may come into the ED due to fall injuries that could be linked to IPV [6, 12]. Therefore, the emergency department (ED) provides a valuable opportunity to identify and treat this at-risk population [6].
Another important category that emerged as differentially co-morbid with IPV in older women was neoplasms and tumors, with neoplasm of stomach showing significant differential co-morbidity. Past researchers including Cesario et al. [ 41] interviewed three hundred abused women to explore the link between cancer and IPV. They found that abused women reported 10 times higher levels of a diagnosis of cervical cancer than the general population. Past research also suggested a link between breast cancer and IPV [42]. Researchers also discovered that cancer patients with history of IPV were twice more likely to develop estrogen and/or progesterone negative tumor receptors than patients without IPV history [42]. As concordant with our mental health related findings (Table 2), IPV is frequently linked with higher levels of perceived stress, PTSD and depression [43]. These conditions have been thought to be linked to cancer progression by mediating the link through increasing the vulnerability through smoking, alcohol consumption, and obesity. Furthermore, cancer survivorship is negatively affected by IPV through delays in screenings, diagnosis and treatment as well as women’s ability to cope with and recover [44]. Consequently, while our finding on the high differential co-morbidity of neoplasm of stomach is a new finding that is not reported in the literature, there is strong support for multiple links between IPV and other cancers that warrants further investigation of the relationship between IPV and neoplasm of stomach in older women.
*The* generality of the diagnostic terms affecting multiple organ systems demonstrates the importance of Family Health and Wellness Clinics and Women’s Health Clinics as critical fields to detect IPV. In addition, the basic routine health care visits for most women are critical for a first line of defense against more serious IPV-related injury and ailment, especially considering the finding that $84\%$ of women who confide in someone about the abuse choose to tell their health care provider [45]. Past research also indicated that as older people need longer recovery time, health outcomes of abuse in later life could be more overwhelming [46]. Furthermore, as one of the front lines of treatment, the ED provides a safe environment for older adult victims to seek help. It can also serve as a point of contact for the effective distribution of referral information, as health care professionals have unique access in the ED to otherwise hard-to-reach victims [6, 9]. However, ED screening has some limitations. For example, screening windows for IPV in the ED may be too brief to determine the extent, forms and the effects of the IPV. It can also be difficult to conduct interventions in such a time-sensitive and public environment [17, 47]. Findings of this study reinforce the necessity to have screening measures at place in the emergency department for women of all ages, regardless of whether they present with trauma injuries. The high percentage of women who suffer emotional and physical abuse makes it imperative that interventions exist for women with history of IPV.
## Limitations
As discussed in the Introduction, there are multiple barriers to the reporting and identification of IPV in all women, which may be accentuated for older women. While one motivation for this study is to identify potential markers of IPV to help overcome these barriers, it is important to note that these barriers also impose limitations on the data we analyze in this study. To be more specific, the cases of IPV that are reported in the EHR database can be subject to detection bias, e.g., more severe cases of IPV may be over-represented in our cohorts. Thus the associations we identify here may be associated with severe IPV as opposed to more common forms of IPV and emotional abuse.
Another limitation of our findings is that they do not provide causal interpretations of the associations that are identified. Since the data is cross-sectional and is provided in summaries (i.e., no sample-specific data is available), our findings are limited to high-level associations. Since sample-specific data is not available, we are not able to perform cluster analyses or develop supervised models that can be test with cross-validation, posing limitations to the interpretation and validation of our findings. For these reasons, dedicated data collection efforts that target specific populations, take into account longitudinal patterns, and investigate causal relationships are needed to further characterize the mechanisms of these associations. Our findings can provide useful starting points for such studies.
## Conclusion
In conclusion, this study investigates the medical conditions (terms) that are associated with IPV in older women. There are many potential factors that may contribute to the increasing rates of reported violence amongst the older adult population. Clinicians must be aware of IPV for proper care of older adult patients, especially for those with suspicious symptoms. We expect that terms that are identified in this study could be useful for screening IPV in older women and facilitate timely interventions. Furthermore, the prevalence estimations provided in this study could give insight about the risk of IPV in both older and younger women populations. Evaluations on IPV could be conducted on all women that present to the Health Care System including the emergency department settings, family medicine department settings, women’s health clinics, and nursing homes or retirement communities. Such efforts can lead to reduced recurrence of violence, improved mental health and overall higher quality of life among this vulnerable population.
## References
1. 1Breiding M, Basile KC, Smith SG, Black MC, Mahendra RR. Intimate partner violence surveillance: Uniform definitions and recommended data elements. Version 2.0.”. 2015;.
2. Garcia-Moreno C, Jansen HA, Ellsberg M, Heise L, Watts CH. **Prevalence of intimate partner violence: findings from the WHO multi-country study on women’s health and domestic violence**. *The lancet* (2006.0) **368** 1260-1269. DOI: 10.1016/S0140-6736(06)69523-8
3. Smith S, Zhang X, Basile KC, Merrick MT, Jing W, Kresnow MJ. *The NationalIntimate Partner and Sexual Violence Survey(NISVS): 2015 Data Brief—Updated Release* (2018.0)
4. Hullick C, Carpenter CR, Critchlow R, Burkett E, Arendts G, Nagaraj G. **Abuse of the older person: Is this the case you missed last shift?**. *Emergency medicine Australasia: EMA* (2017.0) **29** 223. DOI: 10.1111/1742-6723.12756
5. Allison E, Ellis P, Wilson S. **Elder abuse and neglect: the emergency medicine perspective**. *European journal of emergency medicine: official journal of the European Society for Emergency Medicine* (1998.0) **5** 355-363. DOI: 10.1097/00063110-199809000-00013
6. Pathak N, Dhairyawan R, Tariq S. **The experience of intimate partner violence among older women: A narrative review**. *Maturitas* (2019.0) **121** 63-75. DOI: 10.1016/j.maturitas.2018.12.011
7. 7Black M, Basile K, Breiding M, Smith S, Walters M, Merrick M, et al.. National intimate partner and sexual violence survey: 2010 summary report; 2011.
8. Ho CS, Wong SY, Chiu MM, Ho R. **Global prevalence of elder abuse: A metaanalysis and meta-regression**. *East Asian archives of psychiatry* (2017.0) **27** 43. PMID: 28652497
9. Nelson HD, Nygren P, McInerney Y, Klein J. **Screening women and elderly adults for family and intimate partner violence: a review of the evidence for the US Preventive Services Task Force**. *Annals of Internal Medicine* (2004.0) **140** 387-396. DOI: 10.7326/0003-4819-140-5-200403020-00015
10. Campbell JC. **Health consequences of intimate partner violence**. *The lancet* (2002.0) **359** 1331-1336. DOI: 10.1016/S0140-6736(02)08336-8
11. Karakurt G, Smith D, Whiting J. **Impact of intimate partner violence on women’s mental health**. *Journal of family violence* (2014.0) **29** 693-702. DOI: 10.1007/s10896-014-9633-2
12. Nelson HD, Bougatsos C, Blazina I. **Screening women for intimate partner violence: a systematic review to update the US Preventive Services Task Force recommendation**. *Annals of internal medicine* (2012.0) **156** 796-808. DOI: 10.7326/0003-4819-156-11-201206050-00447
13. Taverner T, Baumbusch J, Taipale P. **Normalization of neglect: a grounded theory of RNs’ experiences as family caregivers of hospitalized seniors**. *Canadian Journal on Aging/La Revue canadienne du vieillissement* (2016.0) **35** 215-228. DOI: 10.1017/S0714980816000179
14. 14Gunay E, Lee DH, Karakurt G. Intimate Partner Violence among the Elderly in the City of Cleveland; 2015.
15. Dunlop DD, Song J, Arntson EK, Semanik PA, Lee J, Chang RW. **Sedentary time in US older adults associated with disability in activities of daily living independent of physical activity**. *Journal of Physical activity and Health* (2015.0) **12** 93-101. DOI: 10.1123/jpah.2013-0311
16. Hirst SP, Penney T, McNeill S, Boscart VM, Podnieks E, Sinha SK. **Best-practice guideline on the prevention of abuse and neglect of older adults**. *Canadian Journal on Aging/La Revue canadienne du vieillissement* (2016.0) **35** 242-260. DOI: 10.1017/S0714980816000209
17. Amstadter AB, Begle AM, Cisler JM, Hernandez MA, Muzzy W, Acierno R. **Prevalence and correlates of poor self-rated health in the United States: the national elder mistreatment study**. *The American Journal of Geriatric Psychiatry* (2010.0) **18** 615-623. DOI: 10.1097/JGP.0b013e3181ca7ef2
18. Beach SR, Liu PJ, DeLiema M, Iris M, Howe MJ, Conrad KJ. **Development of short-form measures to assess four types of elder mistreatment: Findings from an evidence-based study of APS elder abuse substantiation decisions**. *Journal of elder abuse & neglect* (2017.0) **29** 229-253. DOI: 10.1080/08946566.2017.1338171
19. Paranjape A, Sprauve-Holmes NE, Gaughan J, Kaslow NJ. **Lifetime exposure to family violence: implications for the health status of older African American women**. *Journal of Women’s Health* (2009.0) **18** 171-175. DOI: 10.1089/jwh.2008.0850
20. 20Neglect, Abuse and Violence Against Older Women. United Nations; 2013. Available from: https://www.un.org/esa/socdev/documents/ageing/neglect-abuse-violence-older-women.pdf.
21. Mouton CP, Rovi S, Furniss K, Lasser NL. **The associations between health and domestic violence in older women: results of a pilot study**. *Journal of women’s health & gender-based medicine* (1999.0) **8** 1173-1179. DOI: 10.1089/jwh.1.1999.8.1173
22. Crockett C, Brandl B, Dabby FC. **Survivors in the margins: The invisibility of violence against older women**. *Journal of Elder Abuse & Neglect* (2015.0) **27** 291-302. DOI: 10.1080/08946566.2015.1090361
23. Milberger S, Israel N, LeRoy B, Martin A, Potter L, Patchak-Schuster P. **Violence against women with physical disabilities**. *Violence and Victims* (2003.0) **18** 581. DOI: 10.1891/088667003780928080
24. 24IBM Explorys Therapeutic Dataset;. https://www.ibm.com/downloads/cas/NNPN9J9Q.
25. 25IBM Explorys EHR Solutions;. https://www.ibm.com/products/explorys-ehr-data-analysis-tools.
26. 26Liu X. Methods and applications of longitudinal data analysis. Elsevier; 2015.
27. Yılmaz S, Alghamdi B, Singuri S, Hacialiefendioglu AM, Özcan T, Koyutürk M. **Identifying health correlates of intimate partner violence against pregnant women**. *Health information science and systems* (2020.0) **8** 1-13. DOI: 10.1007/s13755-020-00124-6
28. Benjamini Y, Hochberg Y. **Controlling the false discovery rate: a practical and powerful approach to multiple testing**. *Journal of the Royal statistical society: series B (Methodological)* (1995.0) **57** 289-300
29. Benjamini Y, Yekutieli D. **False discovery rate–adjusted multiple confidence intervals for selected parameters**. *Journal of the American Statistical Association* (2005.0) **100** 71-81. DOI: 10.1198/016214504000001907
30. Karakurt G, Silver KE. **Emotional abuse in intimate relationships: The role of gender and age**. *Violence and victims* (2013.0) **28** 804-821. DOI: 10.1891/0886-6708.vv-d-12-00041
31. Mezey NJ, Post LA, Maxwell CD. **Redefining intimate partner violence: Women’s experiences with physical violence and non-physical abuse by age**. *International Journal of Sociology and Social Policy* (2002.0) **22** 122-154. DOI: 10.1108/01443330210790120
32. Comijs HC, Penninx BW, Knipscheer KP, van Tilburg W. **Psychological distress in victims of elder mistreatment: The effects of social support and coping**. *The Journals of Gerontology Series B: Psychological Sciences and Social Sciences* (1999.0) **54** P240-P245. DOI: 10.1093/geronb/54B.4.P240
33. McGarry J, Simpson C, Hinchliff-Smith K. **The impact of domestic abuse for older women: a review of the literature**. *Health & social care in the community* (2011.0) **19** 3-14. DOI: 10.1111/j.1365-2524.2010.00964.x
34. Dyer CB, Pavlik VN, Murphy KP, Hyman DJ. **The high prevalence of depression and dementia in elder abuse or neglect**. *Journal of the American Geriatrics Society* (2000.0) **48** 205-208. DOI: 10.1111/j.1532-5415.2000.tb03913.x
35. Frost MH, Willette K. **Risk for abuse/neglect: Documentation of assessment data and diagnoses**. *Journal of Gerontological Nursing* (1994.0) **20** 37-45. DOI: 10.3928/0098-9134-19940801-08
36. Gerino E, Caldarera AM, Curti L, Brustia P, Rollè L. **Intimate partner violence in the golden age: Systematic review of risk and protective factors**. *Frontiers in psychology* (2018.0) **9** 1595. DOI: 10.3389/fpsyg.2018.01595
37. Boulton AJ, Armstrong DG, Albert SF, Frykberg RG, Hellman R, Kirkman MS. **Comprehensive foot examination and risk assessment: a report of the task force of the foot care interest group of the American Diabetes Association, with endorsement by the American Association of Clinical Endocrinologists**. *Diabetes care* (2008.0) **31** 1679-1685. DOI: 10.2337/dc08-9021
38. Cheuy VA, Hastings MK, Commean PK, Mueller MJ. **Muscle and joint factors associated with forefoot deformity in the diabetic neuropathic foot**. *Foot & ankle international* (2016.0) **37** 514-521. DOI: 10.1177/1071100715621544
39. Gheno R, Cepparo JM, Rosca CE, Cotten A. **Musculoskeletal disorders in the elderly**. *Journal of clinical imaging science* (2012.0) **2**. DOI: 10.4103/2156-7514.99151
40. Davidov DM, Larrabee H, Davis SM. **United States emergency department visits coded for intimate partner violence**. *The Journal of emergency medicine* (2015.0) **48** 94-100. DOI: 10.1016/j.jemermed.2014.07.053
41. Cesario SK, McFarlane J, Nava A, Gilroy H, Maddoux J. **Linking Cancer and Intimate Partner Violence**. *Clinical journal of oncology nursing* (2014.0) **18**. DOI: 10.1188/14.CJON.65-73
42. Jetelina KK, Carr C, Murphy CC, Sadeghi N, S Lea J, Tiro JA. **The impact of intimate partner violence on breast and cervical cancer survivors in an integrated, safety-net setting**. *Journal of Cancer Survivorship* (2020.0) **14** 906-914. DOI: 10.1007/s11764-020-00902-x
43. Coker AL, Follingstad D, Garcia LS, Williams CM, Crawford TN, Bush HM. **Association of intimate partner violence and childhood sexual abuse with cancer-related well-being in women**. *Journal of Women’s Health* (2012.0) **21** 1180-1188. DOI: 10.1089/jwh.2012.3708
44. Modesitt SC, Gambrell AC, Cottrill HM, Hays LR, Walker R, Shelton BJ. **Adverse impact of a history of violence for women with breast, cervical, endometrial, or ovarian cancer**. *Obstetrics & Gynecology* (2006.0) **107** 1330-1336. DOI: 10.1097/01.AOG.0000217694.18062.91
45. Daoud N, Kraun L, Sergienko R, Batat N, Shoham-Vardi I, Davidovitch N. **Patterns of healthcare services utilization associated with intimate partner violence (IPV): Effects of IPV screening and receiving information on support services in a cohort of perinatal women**. *PloS one* (2020.0) **15** e0228088. DOI: 10.1371/journal.pone.0228088
46. Winterstein T, Eisikovits Z. **The experience of loneliness of battered old women**. *Journal of Women & Aging* (2005.0) **17** 3-19. DOI: 10.1300/J074v17n04_02
47. Ernst AA, Weiss SJ. **Intimate partner violence from the emergency medicine perspective**. *Women & health* (2002.0) **35** 71-81. DOI: 10.1300/J013v35n02_05
|
---
title: Bivariate power Lomax distribution with medical applications
authors:
- Maha E. Qura
- Aisha Fayomi
- Mutua Kilai
- Ehab M. Almetwally
journal: PLOS ONE
year: 2023
pmcid: PMC9994725
doi: 10.1371/journal.pone.0282581
license: CC BY 4.0
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# Bivariate power Lomax distribution with medical applications
## Abstract
In this paper, a bivariate power Lomax distribution based on Farlie-Gumbel-Morgenstern (FGM) copulas and univariate power Lomax distribution is proposed, which is referred to as BFGMPLx. It is a significant lifetime distribution for modeling bivariate lifetime data. The statistical properties of the proposed distribution, such as conditional distributions, conditional expectations, marginal distributions, moment-generating functions, product moments, positive quadrant dependence property, and Pearson’s correlation, have been studied. The reliability measures, such as the survival function, hazard rate function, mean residual life function, and vitality function, have also been discussed. The parameters of the model can be estimated through maximum likelihood and Bayesian estimation. Additionally, asymptotic confidence intervals and credible intervals of Bayesian’s highest posterior density are computed for the parameter model. Monte Carlo simulation analysis is used to estimate both the maximum likelihood and Bayesian estimators.
## 1 Introduction
The Lomax (Lx) [1], or Pareto II, distribution presented primarily for modeling business failure data. In statistical literature, many authors used this distribution to model reliability data set and life testing [2], income and wealth data sets [3], biological sciences [4], and data set from receiver operating characteristic (ROC) curves analysis [5].
Rady et al. [ 6] proposed power lomax (PLx) model as a new extension of the Lx distribution with an extra shape parameter and applied it to medical databases. The cumulative distribution function (CDF) and probability density function (PDF) of power lomax are F(x;γ,β,λ)=1-λγ(λ+xβ)-γ,γ,β,λ>0,x>0, [1] and f(x;γ,β,λ)=γβλγxβ-1(λ+xβ)-γ-1,γ,β,λ>0,x>0, [2] where γ, β is shape parameter and λ is scale parameter.
In statistical literature, there are various ways to construct bivariate distributions, and copulas are one of them (see Lai [7] and Nelsen [8]). Copulas are a useful tool for describing a bivariate distribution with dependence structure. They are defined by Nelsen [8] as a function that joins bivariate distribution functions with uniform [0, 1] margins. Sklar [9] presents the joint PDF and joint CDF for two marginal univariate distributions as follows: If F(xi) is the univariate CDF of Xi, $i = 1$, 2, the joint CDF and PDF, denoted by F(x1, x2) and f(x1, x2), are defined by the copula function, given as F(x1,x2)=Cθ(F1(x1),F2(x2)), [3] where θ is the dependence measures between X1 and X2, C is copula’s cdf, and c is copula’s pdf. f(x1,x2)=f1(x1)f2(x2)Cθ(f1(x1),f2(x2)). [ 4] Bivariate distribution studies can be advanced through the use of copulas, which model the relationship between two random variables. Popular copulas include Gaussian, Clayton, Farlie-Gumbel-Morgenstern, Gumbel, Frank, and Archimedean copulas. Further details can be found in the references, [10–17]. The choice of copula function depends on the type of dependence structure between the two random variables. For example, the *Gaussian copula* is used to model linear dependence while the *Clayton copula* is used to model positive dependence.
The Farlie-Gumbel-Morgenstern (FGM) have been characterized using Eqs [3] and [4]. Morgenstern [18] proposed a simple method for constructing a bivariate family of distributions using marginals. Farlie [19] proposed a generalization of Morgenstern’s method that is known as the Farlie-Gumbel-Morgenstern (FGM) family of distributions. The FGM copula offers several advantages in modeling bivariate distributions. One of the main advantages is its flexibility in capturing a wide range of dependence structures, from complete independence to perfect dependence [14]. Additionally, the FGM copula is capable of handling asymmetrical dependence, making it well-suited for modeling data with skewed or heavy-tailed distributions [18]. Moreover, the FGM copula allows for the construction of bivariate distributions with a wide range of marginals, including continuous and discrete marginals [8]. Furthermore, the FGM copula has a simple form, which makes it computationally efficient and easy to implement in practice [10]. A new bivariate model based on adaptive progressive hybrid censored has been introduced by [11]. Bivariate Chen distribution based on FGM copula has been obtained by [12]. The bivariate models based on copula function with application of accelerated life testing (ALT) has been suggested by [13]. These features make the FGM copula a useful tool for many real-world applications, particularly in the fields of finance, insurance, and medical research [20].
A lot of work has been done in bivariate distributions based on Morgenstern-type distributions. Gupta et al. [ 21] derived three- and five-parameter bivariate beta distributions from the Morgenstern system of curves and studied the distributions of the product and quotient of variates. Vaidyanathan et al. [ 22] proposed a bivariate Lindley distribution using the Morgenstern method and presented some properties. Almetwally et al. [ 23] proposed bivariate distributions called the FGM Bivariate Fréchet (FGMBF) and AMH Bivariate Fréchet (AMHBF) distributions using Farlie-Gumbel-Morgenstern (FGM) and Ali-Mikhail-Haq (AMH) copulas and univariate Fréchet distributions. El-Sherpieny et al. [ 24] proposed the bivariate FGM Weibull-G family, which is a new flexible bivariate generalized family of distributions based on the FGM copula. Muhammed et al. [ 25] presented the bivariate inverted Topp-Leone (BITL) distribution, which is derived from Farlie-Gumbel-Morgenstern, Ali-Mikhail-Haq, Plackett, and Clayton copulas. Abulebda et al. [ 26] introduced a new bivariate XGamma (BXG) distribution and investigated its statistical properties through examination of real data. Hassan et al. [ 27] proposed the bivariate generalized half-logistic distribution using the FGM copula to asses household financial affordability in the Kingdom of Saudi Arabia.
Our motivation for conducting this research is to The novelty of this research lies in the combination of the power Lomax distribution and the FGM copula. This new distribution addresses the limitations of prior work by offering improved robustness and accuracy in modeling heavy-tailed or skewed data. Additionally, the use of the FGM copula provides more flexibility in modeling of a variety of different types of dependence structures, including both positive and negative dependence between the two variables compared to traditional copula functions. This makes it a good choice for modeling data with complex relationships The paper is structured as follows: In section 2, the description and notation of BFGMPLx distribution is introduced. Section 3 discusses the statistical properties of BFGMPLx. In section 4, we study the positive (negative) quadrant dependence property of BFGMPLx. In section 5, different reliability measures for BFGMPLx are obtained. The maximum likelihood (ML) and Bayesian methods are used to estimate the parameters in BFGMPLx in section 6. Section 7 covers asymptotic and credible intervals. Simulation and an application to real data are provided in sections 8 and 9, respectively. Finally, the paper is concluded in section 10.
## 2 Bivariate Farlie-Gumble-Morgenstern power Lomax distribution
The joint CDF and PDF of the FGM copula [28] are given, respectively, by F(x1,x2)=F1(x1)F2(x2)[1+θ(1-F1(x1))(1-F2(x2))], [5] f(x1,x2)=f1(x1)f2(x2)[1+θ(1-2F1(x1))(1-2F2(x2))], [6] where −1 < θ < 1. The random variables say (X1, X2) follow the BFGMPLx distribution if its CDF is defined by F(x1,x2)=[1-λ1γ1(λ1+x1β1)-γ1][1-λ2γ2(λ2+x2β2)-γ2][1+θλ1γ1(λ1+x1β1)-γ1λ2γ2(λ2+x2β2)-γ2], [7] where λ1, β1, γ1, λ2, β2, γ2 > 0, −1 < θ < 1, and x1, x2 > 0. The corresponding PDF is f(x1,x2)=[γ1β1λ1γ1x1β1-1(λ1+x1β1)-γ1-1][γ2β2λ2γ2x2β2-1(λ2+x2β2)-γ2-1][1+θ(2λ1γ1(λ1+x1β1)-γ1-1)(2λ2γ2(λ2+x2β2)-γ2-1)]. [ 8] In Figs 1–3, we show the 3-dimension plots of the joint density of a BFGMPLx distribution for various parameter values.
**Fig 1:** *3-dimension of joint density of BFGMPLx.* **Fig 2:** *3-dimension of joint density of BFGMPLx.* **Fig 3:** *3-dimension of joint density of BFGMPLx.*
Sreelakshmi [29] introduced the relationship between copula and reliability copula which is defined as follows: R(x1,x2)=1-F1(x1)-F2(x2)+C(F1(x1),F2(x2)). [ 9] According to [9], The FGM reliability is R(x1,x2)=1-F1(x1)-F2(x2)+F1(x1)F2(x2)[1+θ(1-F1(x1))(1-F2(x2))].
The following is the reliability function for BFGMPLx distribution: R(x1,x2)=[λ1γ1(λ1+x1β1)-γ1][λ2γ2(λ2+x2β2)-γ2][1+θ(1-λ1γ1(λ1+x1β1)-γ1)(1-λ2γ2(λ2+x2β2)-γ2)]. [ 10]
## 3 Properties of BFGMPLx distribution
In this section, some important statistical properties of the BFGMPLx distribution are introduced such as marginal distributions, conditional distributions, conditional expectations, product moments and moment generating function.
## 3.1 The marginal distributions
The functions of marginal density for X1 and X2, respectively, f(x1;γ1,β1,λ1)=γ1β1λ1γ1x1β1-1(λ1+x1β1)-γ1-1,γ1,β1,λ1>0,x1>0. [ 11] f(x2;γ2,β2,λ2)=γ2β2λ2γ2x2β2-1(λ2+x2β2)-γ2-1,γ2,β2,λ2>0,x2>0, [12] which are Power Lomax distributed as shown in Eqs [11] and [12].
## 3.2 Conditional distribution
The distribution of conditional probability for X2 provided X1 is obtained as follows f(x2∣x1)=γ2β2λ2γ2x2β2-1(λ2+x2β2)-γ2-1[1+θ-2θF2(x2)-2θF1(x1)(1-2F2(x2))]. [ 13] The conditional CDF of X2 given X1 is as follows: F(x2∣x1)=F2(x2)[1+θ-θF2(x2)-2θF1(x1)(1-F2(x2))]. [ 14] The conditional probability distribution of X1 given X2 is derived as follows: f(x1∣x2)=γ1β1λ1γ1x1β1-1(λ1+x1β1)-γ1-1[1+θ-2θF2(x2)-2θF1(x1)(1-2F2(x2))]. [ 15] The conditional CDF of X1 given X2 is as follows: F(x1∣x2)=F1(x1)[1+θ-θF1(x1)-2θF2(x2)(1-F1(x1))]. [ 16]
The conditional expectation of X1 given X2 = x2 in BFGMPLx using the conditional density of X1 given X2 = x2 in [15] is calculated as E(x1∣x2)=λ11β1β1B(1β1,γ1-1β1)[1+θ-2θF2(x2)+(1-Λ12)(4θF2(x2)-2θ)], [17] where Λj=B(1βj,2γj-1βj)B(1βj,γj-1βj), $j = 1$, 2 and B(a,b) is beta function with a and b which are two real numbers greater than 0.
The above conditional expectation is non-linear in X2, as can be seen. In a similar manner it can be demonstrated that the conditional expectation of X2 given X1 = x1 is also non-linear in x1.
## 3.3 Generating random variables
By [14], a bivariate sample of the Power Lomax distribution based on the conditional method can be generated:
## 3.4 Moment generating function
Let (X1, X2) represent a random variable that its PDF defined in Eq [8]. The moment generating function of (X1, X2) is then obtained by Mx1,x2(t1,t2)=∑n1=0∞(t1)n1n1!n1β1λ1n1β1B(n1β1,γ1-n1β1)∑n2=0∞(t2)n2n2!n2β2λ2n2β2B(n2β2,γ2-n2β2)[1+θ-2θΩ2-2θΩ1+4θΩ1Ω2], [18] where Ωj=1-B(njβj,2γj-nβj)2B(njβj,γj-njβj) and $j = 1$, 2. Proof of moment generating function is given in S1 Appendix.
## 3.5 Product moments
If distribution of the random variable (X1, X2) is BFGMPLx, then its r1th and r2th joint moments around zero denoted by μr1r2′ can be presented as follows: μr1r2′=r1β1λ1r1β1B(r1β1,γ1-r1β1)r2β2λ2r2β2B(r2β2,γ2-r2β2)[1+θ-2θϒ2-2θϒ1+4θϒ1ϒ2], [19] where ϒj=1-B(rjβj,2γj-rjβj)2B(rjβj,γj-rjβj) and $j = 1$, 2. Proof the product moments is given in S1 Appendix.
From [45] in S1 Appendix, the covariance and correlation (ρ) between X1 and X2 are calculated as follows: cov(X1,X2)=1β1λ11β1B(1β1,γ1-1β1)1β2λ21β2B(1β2,γ2-1β2)θ[1-Λ1-Λ2+Λ1Λ2], [20] and ρ(X1,X2)=θ(1-Λ1-Λ2+Λ1Λ2)2β1B(2β1,γ1-2β1)B(1β1,γ1-1β1)2-12β2B(2β2,γ2-2β2)B(1β1,γ1-1β1)2-1. [ 21] We notice that ρ = 0 when θ = 0, this implies that X1 and X2 are independent.
## 4 Positive quadrant dependence
Positive quadrant dependence property of BFGMPLx is presented in this section. Positive quadrant dependence, a type of random variable dependence, was introduced by Lehmann [30]. Two random variables X1 and X2 are considered positive quadrant dependent (PQD) if Theorem 1: BFGMPLx is PQD (NQD) for positive (negative) value of θ.
Proof: Consider Pr(X1>x1,X2>x2)-Pr(X1>x1)Pr(X2>x2)=R(x1,x2)-R(x1)R(x2), [22] =[1+θ(1-λ1γ1(λ1+x1β1)-γ1)(1-λ2γ2(λ2+x2β2)-γ2)][λ1γ1(λ1+x1β1)-γ1][λ2γ2(λ2+x2β2)-γ2]-[λ1γ1(λ1+x1β1)-γ1][λ2γ2(λ2+x2β2)-γ2],=θ[λ1γ1(λ1+x1β1)-γ1][λ2γ2(λ2+x2β2)-γ2](1-λ1γ1(λ1+x1β1)-γ1)(1-λ2γ2(λ2+x2β2)-γ2)=θξ(x1,x2), [23] where ξ(x1,x2)=(1-λ1γ1(λ1+x1β1)-γ1)(1-λ2γ2(λ2+x2β2)-γ2)[λ1γ1(λ1+x1β1)-γ1][λ2γ2(λ2+x2β2)-γ2]=R(x1)R(x2)F1(x1)F2(x2), which for all values of x1 and x2 is always non-negative, because cdf and reliability function takes values ranging from zero to one. As a result, for positive values of θ, θξ(x1, x2) ≥ 0 ∀x1, x2. This demonstrates the condition stated in [22]. Therefore, BFGMPLx is PQD for positive values of θ. Likewise, for negative values of θ, θξ(x1, x2) ≤ 0∀x1, x2. As a result, inequality in [22] is reversed, hence for negative values of θ, BFGMPLx is NQD. Thus BFGMPLx has both positive and negative quadrant dependence.
## 5 Reliability measures
In this section, we derive reliability measures such as hazard rate, mean residual life, and vitality function in the context of BFGMPLx.
## 5.1 Hazard rate function
Using the definition of bivariate hazard rate function introduced by Basu [31], the hazard rate function of BFGMPLx is obtained as h(x1,x2)=γ1β1x1β1-1γ2β2x2β2-1(λ1+x1β1)(λ2+x2β2)[1+θ(2λ1γ1(λ1+x1β1)-γ1-1)(2λ2γ2(λ2+x2β2)-γ2-1)][1+θ(1-λ1γ1(λ1+x1β1)-γ1)(1-λ2γ2(λ2+x2β2)-γ2)]. [ 24] In Figs 4–6, we show the 3D plots of the joint hazard of a BFGMPLx distribution for various parameter values.
**Fig 4:** *3-dimension of joint hazard of BFGMPLx.* **Fig 5:** *3-dimension of joint hazard of BFGMPLx.* **Fig 6:** *3-dimension of joint hazard of BFGMPLx.*
Basu has a main constraint, which its definition is from R2 → R, i.e. h(x1, x2) is not a vector quantity. To overcome this constraint, Johnson et al. [ 32] and Sreelakshmi [29] introduced the bivariate hazard rate function in vector form as follows: h(x1,x2)=(-∂lnR(x1,x2)∂x1,-∂lnR(x1,x2)∂x2), [25] where R(.) denotes the bivariate reliability function. For FGM copula, Vaidyanathan [22] introduced -∂lnR(x1,x2)∂x1 as follows: -∂lnR(x1,x2)∂x1=h(x1)[1-([1-F(x1)]-1[(θF(x2))-1+1]-1)-1]. [ 26] From [10], we get -∂lnR(x1,x2)∂x1=γ1β1x1β1-1λ1+x1β1[1-A-1λ1γ1θ(1-λ2γ2(λ2+x2β2)-γ2)((λ1+x1β1)-γ1)], [27] -∂lnR(x1,x2)∂x2=γ2β2x2β2-1λ2+x2β2[1-A-1λ2γ2θ(1-λ1γ1(λ1+x1β1)-γ1)((λ2+x2β2)-γ2)], [28] where A=[1+θ(1-λ1γ1(λ1+x1β1)-γ1)(1-λ2γ2(λ2+x2β2)-γ2)].
The vector hazard rate function of BFGMPLx is obtained by substituting the above expressions in [25]. Eq [27] consists of two terms: the first term γ1β1x1β1-1λ1+x1β1 is the hazard rate of the power lomax distribution, which is an inverted bathtub (IBT) for β > 1 and is a decreasing hazard rate (DHR) for 0 < β ≤ 1, according to [6]. The second term [1-A-1λ1γ1θ(1-λ2γ2(λ2+x2β2)-γ2)((λ1+x1β1)-γ1)] is a positive increasing function for positive θ and is a negative decreasing function for negative θ. Thus, for positive and negative values of θ, BFGMPLx is J-HR and IHR for β > 1 and is Reversed J-HR and Bathtub for 0 < β ≤ 1. Eq [28] exhibits similar behavior. Fig 7 shows the shapes of the vector hazard rate function of BFGMPLx depending on x1 for several values of parameters.
**Fig 7:** *Possible shapes of the vector of the hrf for the BFGMPLx depending on x1 for several values of parameters.*
## 5.2 Mean residual life
The average remaining life of a unit after it has survived for a particular time t is denoted by mean residual life (MRL). Shanbag and Kotz [33] defined MRL for vector-valued random variables as: m(x1,x2)=(m1(x1,x2),m2(x1,x2)), [29] where m1(x1,x2)=E(X1-x1∣X1≥x1,X2≥x2), and m2(x1,x2)=E(X2-x2∣X1≥x1,X2≥x2).
The expressions for m1(x1, x2) and m2(x1, x2) in BFGMPLx are obtained as: m1(x1,x2)=λ11β1β1B(1β1,γ1-1β1)[1+θ(1-Λ1)(1-λ2γ2(λ2+x2β2)-γ2)][λ1γ1(λ1+x1β1)-γ1]A, [30] m2(x1,x2)=λ21β2β2B(1β2,γ2-1β2)[1+θ(1-Λ2)(1-λ1γ1(λ1+x1β1)-γ1)][λ2γ2(λ2+x2β2)-γ2]A. [31] Substituting [30] and [31] in [29] yields BFGMPLx’s MRL.
## 5.3 Vitality function
Sankaran and Nair [34] defined a bivariate vitality function for a two-component system as: υ(x1,x2)=(υ1(x1,x2),υ2(x1,x2)), [32] where υ1(x1,x2)=E(X1∣X1≥x1,X2≥x2), and υ2(x1,x2)=E(X2∣X1≥x1,X2≥x2).
Also, υi(x1, x2) is related to mi(x1, x2) by υi(x1,x2)=xi+mi(x1,x2),$i = 1$,2. [ 33] Here υ1(x1, x2) calculates the expected life time of first component as the sum of current age x1 and the average lifetime remaining to it, assuming the second component has survived past age x2. υ2(x1, x2) has a similar interpretation. Using Eqs [30] and [31] in [33], we obtain υ1(x1, x2) and υ2(x1, x2) of BFGMPLx as: υ1(x1,x2)=x1+λ11β1β1B(1β1,γ1-1β1)[1+θ(1-Λ1)(1-λ2γ2(λ2+x2β2)-γ2)][λ1γ1(λ1+x1β1)-γ1]A, [34] υ2(x1,x2)=x2+λ21β2β2B(1β2,γ2-1β2)[1+θ(1-Λ2)(1-λ1γ1(λ1+x1β1)-γ1)][λ2γ2(λ2+x2β2)-γ2]A [35] From [34] and [35], the vitality function of BFGMPLx can be obtained using [32].
## 6 Methods of estimation
In this section, we present two estimation methods for estimating the unknown parameters of the BFGMPLx distribution: maximum likelihood estimation (MLE) and Bayesian estimation.
## 6.1 Maximum likelihood estimation
Let (xi1, xi2), $i = 1$, 2, …, n denote random samples from BFGMPLx with parameters Θ = (β1, γ1, λ1, β2, γ2, λ2, θ). the log likelihood function lnL is obtained by using the density function given in [8], lnL=nlnγ1+nlnβ1+nγ1lnλ1+(β1-1)∑$i = 1$nln(xi1)-(γ1+1)∑$i = 1$nln(λ1+xi1β1)+nlnγ2+nlnβ2+nγ2lnλ2+(β2-1)∑$i = 1$nln(xi2)-(γ2+1)∑$i = 1$nln(λ2+xi2β2)+∑$i = 1$nln[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)], [36] We obtain the following likelihood equations by partially differentiating lnL with respect to the vector of parameters Θ and equating them to zero. The following are the first derivatives: ∂L(Θ)∂γl=nγl+nlnλl-∑$i = 1$nln(λl+xilβl)+∑$i = 1$n2θλlγl(λl+xilβl)-γl(2λjγj(λj+xijβj)-γj-1)(lnλl-ln(λl+xilβl))1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1), [37] ∂L(Θ)∂βl=nβl+∑$i = 1$nln(xil)-∑$i = 1$n(γl+1)xilβlln(xil)(λl+xilβl)-∑$i = 1$n2θγlλlγlxilβlln(xil)(λl+xilβl)-γl-1(2λjγj(λj+xijβj)-γj-1)1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1), [38] ∂L(Θ)∂λl=nγlλl-∑$i = 1$nγl+1(λl+xilβl)+∑$i = 1$n2θγlλlγl(λl+xilβl)-γl(2λjγj(λj+xijβj)-γj-1)(1λl-1(λl+xilβl))1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1), [39] ∂L(Θ)∂θ=∑$i = 1$n(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1), [40] where j = [1, 2], l = [1, 2]; j ≠ l, (for example $j = 1$ then $l = 2$).
## 6.2 Bayesian estimation
Here, we employ the symmetric loss functions to derive the Bayes estimators of the BFGMPLx distribution’s parameters. We must select an acceptable prior density function and hyper-parameter values that reflect our belief regarding the data. We use the symmetric square error loss function (SSELF) to get the estimates based on a complete sample and assume that the parameters of BFGMPLx distribution are independent. For the parameters γl, βl, and λl, we select gamma-independent priors, specifically, π1(γl)∝γlql-1e-wlγl,γl>0,ql,wl>0,π2(βl)∝βlrl-1e-ulβl,βl>0,rl,ul>0,π3(λl)∝λlol-1e-plλl,λl>0,ol,pl>0, while the copula parameter θ has uniform prior distribution where −1 < θ < 1 The joint prior Eq [41] as follows: π(Θ)∝γ1q1-1e-w1γ1β1r1-1e-u1β1λ1o1-1e-p1λ1γ2q2-1e-w2γ2β2r2-1e-u2β2λ2o2-1e-p2λ2, [41] The likelihood method’s estimate and variance-covariance matrix can be used to determine how to elicit the independent joint prior’s hyper-parameters. Gamma priors’ mean and variance can be used to represent the derived hyper-parameters. For more information see [35–37]. The parameters γl, βl, and λl where $l = 1$, 2, of BFGMPLx distribution should be well-known and positive. The likelihood function, Eq [43] is as follows: L(Θ)=γ1nβ1nλ1nγ1∏$i = 1$nxi1β1-1(λ1+xi1β1)-γ1-1γ2nβ2nλ2nγ2∏$i = 1$nxi2β2-1(λ2+xi2β2)-γ2-1∏$i = 1$n[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)], [42] and the joint posterior distribution can be expressed using the joint prior function Eq [41] and likelihood function Eq [43]. Consequently, the function of the Θ joint’s posterior density is Π(Θ|x1,x2)∝γ1n+q1-1β1n+r1-1λ1nγ1+o1-1e-w1γ1-u1β1-p1λ1γ2n+q2-1∏$i = 1$nxi1β1-1(λ1+xi1β1)-γ1-1∏$i = 1$nxi2β2-1(λ2+xi2β2)-γ2-1e-w2γ2-u2β2-p2λ2β2n+r2-1λ2nγ2+o2-1∏$i = 1$n[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)]. [ 43] The symmetric loss function is the squared-error loss function, abbreviated as SELF. The Bayesian estimator of Θ under SELF is then the average Θ˜=EΘ(Θ).
The expectation of loss functions are difficult to analyse by mathematical integration, hence the Markov Chain Monte Carlo (MCMC) approach will be utilised. Gibbs sampling and the broader Metropolis-within-Gibbs samplers are the two most significant MCMC algorithm sub-classes. Robert et al. [ 38] covered this algorithm. Similar to acceptance-rejection sampling, the Metropolis-Hastings (MH) method treats a candidate value derived from a proposal distribution as normal for each iteration of the process. The MH approach computes an appropriate transition in two phases, starting with Θi=Θ^i: In addition to ensuring that the goal density stays invariant, Θ, this well-stated transition density guarantees that the chain converges to its specific invariant density starting from any initial condition.
Using the posterior conditional density functions of the relevant parameters, this method’s basic idea is to provide posterior samples of the relevant parameters. The posterior density function of the relevant parameters is provided by Eq. ( 5.3). The posterior conditional density functions of γl, βl, and λl where $l = 1$, 2 can be constructed as follows from this equation: Π(γl|βl,λl,θ,x1,x2)∝γln+ql-1λlnγle-wlγl∏$i = 1$n(λl+xilβl)-γl-1[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)], Π(βl|γl,λl,θ,x1,x2)∝βln+rl-1e-ulβl∏$i = 1$nxilβl-1(λl+xilβl)-γl-1[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)], Π(λl|γl,βl,θ,x1,x2)∝λlnγl+ol-1e-plλl∏$i = 1$n(λl+xilβl)-γl-1[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)], Π(θ|γl,βl,λl,x1,x2)∝∏$i = 1$n[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)], where l is 1 and 2.
## 7 Confidence intervals
In this section, we present two different methods to construct confidence intervals (CI) for the unknown parameters of BFGMPLx distribution, which are asymptotic confidence intervals (ACI) and highest posterior density Bayesian estimation.
## 7.1 Asymptotic confidence intervals
The asymptotic normal distribution of the MLE is the most widely used technique for establishing confidence bounds for the parameters. With respect to the Fisher information matrix I(Θ), which is comprised of the negative second derivatives of the natural logarithm of the likelihood function evaluated at Θ^=(γ1^,β1^,λ1^,γ2^,β2^,λ2^,θ^), the asymptotic variance-covariance matrix of the MLE of the parameters, suppose that the asymptotic variance-covariance matrix of the parameter vector is I(Θ^)=-E[Iγ1^γ1^Iβ1^γ1^Iβ1^β1^Iλ1^γ1^Iλ1^β1^Iλ1^λ1^Iγ2^γ1^Iγ2^β1^Iγ2^λ1^Iγ2^γ2^Iβ2^γ1^Iβ2^β1^Iβ2^λ1^Iβ2^γ2^Iβ2^β2^Iλ2^γ1^Iλ2^β1^Iλ2^λ1^Iλ2^γ2^Iλ2^β2^Iλ2^λ2^Iθ^γ1^Iθ^β1^Iθ^λ1^Iθ^γ2^Iθ^β2^Iθ^λ2^Iθ^θ^] [44] where V(Θ^)=I-1(Θ^). Based on the asymptotic normality of the MLE, a 100(1 − α)% confidence interval for parameter Θ can be constructed as: γl^±Z0.025Iγ1^γ1^, βl^±Z0.025Iβl^βl^, λl^±Z0.025Iλl^λl^ and θ^±Z0.025Iθ^θ^, where $l = 1$, 2 and Z0.025 is the percentile of the standard normal distribution with right tail probability α2. The second derivatives of the likelihood function with respect to the parameters are as follows Iγlγl=∂2L(Θ)∂γl2=-nγl2+∑$i = 1$n{2θλlγl(λl+xilβl)-γl(2λjγj(λj+xijβj)-γj-1)((lnλl)2-(ln(λl+xilβl))2)1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)}-∑$i = 1$n{4θ2λl2γl(λl+xilβl)-2γl(2λjγj(λj+xijβj)-γj-1)2(lnλl-ln(λl+xilβl))2[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)]2} Iγlγj=∑$i = 1$n{4θλlγlλjγj(λl+xilβl)-γl(λj+xijβj)-γj(lnλl-ln(λl+xilβl))(lnλj-ln(λj+xijβj))1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)[1-θ(2λlγl(λl+xilβl)-γl-1)(2λjγj(λj+xijβj)-γj-1)1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)]}, Iγlβl=-∑$i = 1$nxilβlln(xil)(λl+xilβl)-∑$i = 1$n{2θλlγlxilln(xil)(λl+xilβl)-γl-1(2λjγj(λj+xijβj)-γj-1)(γl(lnλl-ln(λl+xilβl))+1)1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)}+∑$i = 1$n{4θ2γlλl2γlxilβlln(xil)(λl+xilβl)-2γl-1(2λjγj(λj+xijβj)-γj-1)2(lnλl-ln(λl+xilβl))[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)]2}, Iγlλl=nλl-∑$i = 1$n1(λl+xilβl)+∑$i = 1$n{(2λjγj(λj+xijβj)-γj-1)(λl+xilβl)-γl2θλlγl[(λl(lnλl-ln(λl+xilβl))+1)(1λl-1(λl+xilβl))]1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)}-∑$i = 1$n{4θ2γlλl2γl(λl+xilβl)-2γl(2λjγj(λj+xijβj)-γj-1)2(lnλl-ln(λl+xilβl))(1λl-1(λl+xilβl))[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)]2}, Iγlθ=∑$i = 1$n2λlγl(λl+xilβl)-γl(2λjγj(λj+xijβj)-γj-1)(lnλl-ln(λl+xilβl))1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)-∑$i = 1$n{2λlγl(λl+xilβl)-γl(2λlγl(λl+xilβl)-γl-1)(2λjγj(λj+xijβj)-γj-1)2(lnλl-ln(λl+xilβl))[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)]2}, Iβlβl=-nβl2-∑$i = 1$n(γl+1)xilβl(lnxil)2λl+xilβl-∑$i = 1$n{θγlλlγlxilβl(lnxil)2(λl+xilβl)-γl-1(2λjγj(λj+xijβj)-γj-1)(1-γl+1λl+xilβl)1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)}-∑$i = 1$n[2θγlλlγlxilβlln(xil)(λl+xilβl)-γl-1(2λjγj(λj+xijβj)-γj-1)]2[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)]2 Iβlβj=∑$i = 1$n4θγlγjλlγlλjγjxilβlxijβjln(xil)ln(xij)(λl+xilβl)-γl-1(λj+xijβj)-γj-11+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)[1-θ(2λlγl(λl+xilβl)-γl-1)(2λjγj(λj+xijβj)-γj-1)1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)], Iβlλl=(λl+1)xilβlln(xil)(λl+xilβl)2-∑$i = 1$n{2θγlλlγlxilβlln(xil)(λl+xilβl)-γl-1(2λjγj(λj+xijβj)-γj-1)(γlλl-(γl+1)(λl+xilβl))1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)}+∑$i = 1$n{4θ2γl2λl2γlxilβlln(xil)(λl+xilβl)-2γl-1(2λjγj(λj+xijβj)-γj-1)2(γlλl-1(λl+xilβl))[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)]2}, Iβlθ=-∑$i = 1$n2γlλlγlxilβlln(xil)(λl+xilβl)-γl-1(2λjγj(λj+xijβj)-γj-1)1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)+∑$i = 1$n{2θγlλlγlxilβlln(xil)(λl+xilβl)-γl-1(2λlγl(λl+xilβl)-γl-1)(2λjγj(λj+xijβj)-γj-1)2[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)]2}, Iλlλl=-nγlλl2+∑$i = 1$nγl+1(λl+xilβl)2+∑$i = 1$n{2θγlλlγl(λl+xilβl)-γl(2λjγj(λj+xijβj)-γj-1)(γl-1λl-γl+1(λl+xilβl))(1λl-1(λl+xilβl))1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)}-∑$i = 1$n{4θ2γl2λl2γl(λl+xilβl)-2γl(2λjγj(λj+xijβj)-γj-1)2(1λl-1(λl+xilβl))2[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)]2}, Iλlλj=-nγlλl2+∑$i = 1$nγl+1(λl+xilβl)2+∑$i = 1$n{2θγlλlγl(λl+xilβl)-γl(2λjγj(λj+xijβj)-γj-1)(1λl-1(λl+xilβl))(γl-1λl-γl+1(λl+xilβl))1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)}-∑$i = 1$n{4θ2γl2λl2γl(λl+xilβl)-2γl(2λjγj(λj+xijβj)-γj-1)2(1λl-1(λl+xilβl))2[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)]2}, Iλlθ=∑$i = 1$n2γlλlγl(λl+xilβl)-γl(2λjγj(λj+xijβj)-γj-1)(1λl-1(λl+xilβl))1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)-∑$i = 1$n{2γlλlγl(λl+xilβl)-γl(2λjγj(λj+xijβj)-γj-1)2(2λlγl(λl+xilβl)-γl-1)(1λl-1(λl+xilβl))[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)]2}, Iθθ=-∑$i = 1$n(2λ1γ1(λ1+xi1β1)-γ1-1)2(2λ2γ2(λ2+xi2β2)-γ2-1)2[1+θ(2λ1γ1(λ1+xi1β1)-γ1-1)(2λ2γ2(λ2+xi2β2)-γ2-1)]2.
## 7.2 Highest posterior density Bayesian estimation
The approach of Chen and Shao [39] is frequently used to construct highest posterior density (HPD) intervals for unknown distribution parameters in Bayesian estimation. For instance, two endpoints from the MCMC sampling outputs, the lower $5\%$ and upper $95\%$ percentiles, can be used to calculate a $90\%$ HPD interval. The following is how reliable Bayesian intervals of the parameters are obtained:
## 8 Simulation
The performance of MLE using the Newton—Raphson (NR) and Bayesian estimates using the Metropolis—Hastings methods are contrasted numerically in this section. For the parameters of the BFGMPLx distribution, the performance of the various techniques and the analytically deduced results may be evaluated exactly. The “maxLik” package was installed in R 4.2.2 programming, and after 1000 samples from BFGMPLx distribution had been gathered, the MLEs and $95\%$ ACI estimations of the parameter values were assessed. To acquire the Bayes point estimates and their HPD interval estimates of the same unknown parameters using the “coda” tool in the R programming language. For simulation study of BFGMPLx distribution, the values of the parameter can be defined as follows: In Table 1: γ1 = 0.6;β1 = 3, λ1 = 2.8, γ2 = 0.9, β2 = 2.5, λ2 = 0.7.
In Table 2: γ1 = 1.3, β1 = 5, λ1 = 1.5, γ2 = 0.9, β2 = 4, λ2 = 1.3.
In Table 3: γ1 = 0.8;β1 = 1.3, λ1 = 1.5, γ2 = 1.2, β2 = 1.5, λ2 = 2.
While θ is a copula parameter is changed in all tables as 0.4, and -0.5. The sample-sizes (n) are as follows 35, 50, 100, and 150. The simulation results of bias, mean squared error (MSE), and length of CI (LCI) (For MLE asymptotic CI (LACI), while Bayesian credible CI (LCCI)) on 5000 iteration of Monte Carlo simulation are shown in Tables 1–3. Nelsen [8] has explored the construction of a sample from a defined joint distribution and utilising a conditional technique to generate random variables. *The* generated samples are used to calculate the Bayesian estimates, together with the corresponding posterior symmetric loss function and Bayesian HPD intervals.
The following conclusions can be drawn from Tables 1–3:
## 9 Application
The fit models were compared using the conventional value of criteria (VC), which included the Akaike information criterion (AIVC), consistent AIVC (CAIVC), Bayesian information criterion (BIVC), Hannan-Quinn information criterion (HQIVC), Anderson-Darling value (ADV), Cramer-von Mises value (CMV), Kolmogorov-Smirnov distance (KSD), p-value of Kolmogorov-S (PVKS), and standard error (SE). Our main statistical objective was to analyse a genuine dataset that is important in various domains using a fitting approach model. In this regard, we contrasted the suggested BFGMPLx distribution’s fit with the BFGMWE and BFGMWITL mentioned by El-Sherpieny et al. [ 24] and Bivariate FGM Lomax-Claim (BFGMLC) distribution which was mentioned by Zhao et al. [ 40].
We have thought about the length of diabetes and serum creatinine in this part (SrCr). Since the patients’ diabetes was already known, we are predicting the complications that may result from it (using the values of SrCr, the data has been divided into two groups: groups with diabetic nephropathy (DN) (SrCr 1.4mg/dl) and groups with nondiabetic nephropathy (SrCr 1.4mg/dl)). SrCr reports were provided for each patient from the 200 patients whose reports were available. From January 2012 to August 2013, the pathology reports of these patients were gathered from the path lab of Dr. Lal. This data has been discussed by Grover et al. [ 41]. Duration of diabetes: 7.4, 9, 10, 11, 12, 13, 13.75, 14.92, 15.8286, 16.9333, 18, 19, 20, 21, 22, 23, 24, 26, 26.6. Serum Creatinine: 1.925, 1.5, 2, 1.6, 1.7, 1.7533, 1.54, 1.694, 1.8843, 1.8433, 1.832, 1.59, 1.7833, 1.2, 1.792, 1.5, 1.5033, 2, 2.14.
Table 4 lists the MLE, SE, KS distance and associated p-value, CVM and AD for the marginal distributions. The fitted pdf with histogram plot, fitted cdf with empirical cdf and P-P plot of PL distribution in Figs 8 and 9 for Duration of diabetes and Serum Creatinine respectively support the findings (KS-test) in Table 4 of our study.
**Table 4**
| Unnamed: 0 | Unnamed: 1 | Estimates | SE | CVM | AD | KSD | PVKS |
| --- | --- | --- | --- | --- | --- | --- | --- |
| X | γ | 11.3951 | 20.1213 | 0.0216 | 0.1659 | 0.0751 | 0.9996 |
| X | β | 3.2833 | 0.6177 | 0.0216 | 0.1659 | 0.0751 | 0.9996 |
| X | λ | 164764.8045 | 2918.7836 | 0.0216 | 0.1659 | 0.0751 | 0.9996 |
| Y | γ | 8.0453 | 33.3879 | 0.0248 | 0.1915 | 0.102 | 0.989 |
| Y | β | 9.6562 | 3.198 | 0.0248 | 0.1915 | 0.102 | 0.989 |
| Y | λ | 2377.1949 | 741.5255 | 0.0248 | 0.1915 | 0.102 | 0.989 |
Figs 8 and 9 show the fitted CDF with empirical CDF, fitted PDF with histogram plot, and PP-plot for the duration of diabetes and serum creatinine, respectively. In Table 5 goodness of fit measures for FGM copula have been obtained as kendall, sperman, tau, rho, statistics, and copula parameter θ with P-*Value is* 0.1339 > 0.05, then we accept the null hypothesises as the bivariate date is fit for FGM copula. The MLE with SE, and coefficient of variation (CV) for parameters of BFGMPLx, BFGMWE, BFGMWITL, and BFGMLC distributions have been discussed in Table 6. Table 7 obtained AIC, BIC, HQIC, and CAIC for bivariate models to select the best fit bivariate model of this data.
**Fig 8:** *Estimated cdf, pdf and pp-plot for duration of diabetes data.* **Fig 9:** *Estimated cdf, pdf and pp-plot for stress variable for serum creatinine data.* TABLE_PLACEHOLDER:Table 5 TABLE_PLACEHOLDER:Table 6 TABLE_PLACEHOLDER:Table 7 MCMC summarized results for parameters of BFGMPLx model has been obtained in Table 8. Fig 10 displays the trace plot with convergence line for the BFGMPLx distribution’s parameters without copula parameter. Fig 11 demonstrates the symmetric normal distribution of the posterior density histogram for MCMC findings. Fig 12 trace plot with convergence line and posterior density histogram for MCMC of copula parameter θ.
**Fig 10:** *Trace plot of MCMC results with convergence lines.* **Fig 11:** *Histogram plot with density posterior curve of MCMC results.* **Fig 12:** *Trace with convergence line and histogram plot with density posterior curve of MCMC result for copula parameter.* TABLE_PLACEHOLDER:Table 8
## 10 Conclusions
The bivariate power Lomax model, a new and more flexible extension of the bivariate distribution based on the FGM copula, is introduced in this paper. The suggested distribution is a significant and novel contribution to the field of bivariate modeling. It provides a robust and accurate tool for modeling heavy-tailed or skewed data and offers an improved way to model the dependence structure between two variables compared to traditional methods. Its fundamental mathematical characteristics are studied. Its hazard rate might be J-HR, IHR, Reversed J-HR, or Bathtube. It is demonstrated that the suggested model fits the positive (negative) quadrant dependence property. The parameter estimators, using likelihood and Bayesian methods, are derived, with the general conclusion that Bayesian estimation is better than its counterpart. A Monte Carlo simulation study is additionally offered to assess the behavior of the estimators. Asymptotic confidence intervals of the likelihood estimation and highest posterior density of the Bayesian estimation are derived for the parameters of this model. Finally, the significance and adaptability of the BFGMPLx distribution are discussed by analyzing medical data related to the duration of diabetes and serum creatinine. Analytical evaluations revealed that our BFGMPLx model had a satisfactory match when compared to other distributions.
## References
1. Lomax K. S. **Business failures: Another example of the analysis of failure**. *Journal of the American Statistical Association* (1954.0) **49** 847-852. DOI: 10.1080/01621459.1954.10501239
2. Hassan A. S., Al-Ghamdi A. S. **Optimum step stress accelerated life testing for Lomax distrbution**. *Journal of Applied Sciences Research* (2009.0) **5** 2153-2164
3. Harris C. M. **The Pareto distribution as a queue service discipline**. *Operations Research* (1968.0) **16** 307-313. DOI: 10.1287/opre.16.2.307
4. Holland O., Golaup A., Aghvami A. **Traffic characteristics oof aggregated module downloads for mobile terminal reconfiguration**. *IEE Proceedings-communications* (2006.0) **153** 683-690. DOI: 10.1049/ip-com:20045155
5. Campbell G., Ratnaparkhi M. V. **An application of lomax distributions in receiver operating characteristic (roc) curve analysis**. *Communications in Statistics–Theory and Methods* (1993.0) **22** 1681-1697. DOI: 10.1080/03610929308831110
6. Rady E. H. A., Hassanein W. A, Elhaddad T. A. **The power Lomax distribution with an application to bladder cancer data**. *SpringerPlus* (2016.0) **5** 1-22. DOI: 10.1186/s40064-016-3464-y
7. Lai C. D. **Constructions of continuous bivariate distributions**. *Journal of the Indian Society for Probability and Statistics* (2004.0) **8** 21-43
8. Nelsen R. B. *An introduction to copulas* (2006.0)
9. Sklar A. **Random variables, joint distributions, and copulas**. *Kybernetica* (1973.0) **9** 449-460
10. Joe H. *Multivariate models and dependence concepts* (1997.0)
11. El-Sherpieny E. S. A., Muhammed H. Z., Almetwally E. M. **Data Analysis by Adaptive Progressive Hybrid Censored Under Bivariate Model**. *Annals of Data Science* (2022.0) 1-42. DOI: 10.1007/s40745-022-00455-z
12. El-Sherpieny E. S. A., Muhammed H. Z., Almetwally E. M. **Bivariate Chen Distribution Based on Copula Function: Properties and Application of Diabetic Nephropathy**. *Journal of Statistical Theory and Practice* (2022.0) **16** 54. DOI: 10.1007/s42519-022-00275-7
13. El-Sherpieny E. S. A., Muhammed H. Z., Almetwally E. M. **Accelerated Life Testing for Bivariate Distributions based on Progressive Censored Samples with Random Removal**. *J. Stat. Appl. Probab* (2022.0) **11** 203-223
14. Gumbel E. J. **Bivariate exponential distributions**. *Journal of the American Statistical* (1960.0) **55** 698-707. DOI: 10.1080/01621459.1960.10483368
15. Frank M. **On the simultaneous associativity of functions**. *Journal of mathematical analysis and applications* (1979.0) **72** 352-360
16. Genest C., MacKay J. **Copulas, Archimax Distributions and Generation of Random Variates**. *Journal of Multivariate Analysis* (1986.0) **21** 100-120
17. Durante F., Sempi C. *Copulas: an introduction* (2017.0)
18. Morgenstern D. **Einfache beispiele zweidimensionaler verteilungen**. *Mitteilingsblatt fur Mathematische Statistik* (1956.0) **8** 234-235
19. Farlie D. J. G. **The performance of some correlation coefficients for a general bivariate distribution**. *Biometrika* (1960.0) **47** 307-323. DOI: 10.2307/2333302
20. Embrechts P., McNeil A. J., Straumann D. **Correlation and dependence in risk management: properties and pitfalls**. *Risk Management & Insurance Review* (2002.0) **5** 131-160
21. Gupta A. K., Wong C. F. **On three and five parameter bivariate beta distributions**. *Metrika* (1985.0) **32** 85-91. DOI: 10.1007/BF01897803
22. Vaidyanathan V. S, Sharon Varghese A. **Morgenstern type bivariate Lindley distribution**. *Statistics, Optimization & Information Computing* (2016.0) **4** 132-146
23. Almetwally E. M., Muhammed H. Z. **On a bivariate Fréchet distribution**. *Journal Statistics Appications Probability* (2020.0) **9** 1-21
24. El-Sherpieny E. S. A., Almetwally E. M., Muhammed H. Z. **Bivariate weibull-g family based on copula function: properties, bayesian and non-bayesian estimation and applications**. *Statistics, Optimization & Information Computing* (2022.0) **10** 678-709. DOI: 10.19139/soic-2310-5070-1129
25. Muhammed H. Z., El-Sherpieny E. S. A., Almetwally E. M. **Dependency measures for new bivariate models based on copula function**. *Information Sciences Letters* (2021.0) **10** 511-526. DOI: 10.18576/isl/100316
26. Abulebda M., Pathak A. K., Pandey A., Tyagi S. **On a Bivariate XGamma Distribution Derived from Copula**. *Statistica* (2022.0) **82** 15-40
27. Hassan M. K., Chesneau C. **Bivariate Generalized Half-Logistic Distribution: Properties and Its Application in Household Financial Affordability in KSA**. *Mathematical and Computational Applications* (2022.0) **27** 72. DOI: 10.3390/mca27040072
28. 28Conway, D. A. Farlie-Gumbel-Morgenstern Distributions. Wiley StatsRef: Statistics Reference Online. 2014.
29. Sreelakshmi N. **An introduction to copula-based bivariate reliability Concepts**. *Communications in Statistics-Theory and Methods* (2018.0) **47** 996-1012. DOI: 10.1080/03610926.2017.1316396
30. Lehmann E. L. **Some concepts of dependence**. *The Annals of Mathematical Statistics* (1966.0) **37** 1137-1153. DOI: 10.1214/aoms/1177699260
31. Basu A. P. **Bivariate failure rate**. *Journal of American Statistical Association* (1971.0) **66** 103-104. DOI: 10.1080/01621459.1971.10482228
32. Johnson N. L., Kotz S. **A vector multivariate hazard rate**. *Journal of Multivariate Analysis* (1975.0) **5** 53-66. DOI: 10.1016/0047-259X(75)90055-X
33. Shanbag D. N., Kotz S. **Some new approaches to multivariate probability distributions**. *Journal of multivariate analysis* (1987.0) **22** 189-211. DOI: 10.1016/0047-259X(87)90085-6
34. 34Sankaran P. G., Nair U. On bivariate vitality functions. In Proceeding of National Symposium on Distribution Theory, 1991.
35. Gupta R. D, Kundu D. **Generalized exponential distributions: statistical inferences**. *Journal Statistical Theory and Applications* (2002.0) **1** 101-118
36. Dey S., Singh S., Tripathi Y. M, Asgharzadeh A. **Estimation and prediction for a progressively censored generalized inverted exponential distribution**. *Statistical Methodology* (2016.0) **32** 185-202. DOI: 10.1016/j.stamet.2016.05.007
37. Hamdy A., Almetwally E. M. **Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical**. *Computational Journal of Mathematical and Statistical Sciences* (2023.0) **2** 31-51. DOI: 10.21608/cjmss.2023.185497.1001
38. Robert C. P., Casella G. *Monte Carlo statistical methods* (1999.0) 231-283
39. Chen M. H., Shao Q. M. **Monte Carlo estimation of Bayesian credible and HPD intervals**. *Journal of Computational and Graphical Statistics* (1999.0) **8** 69-92. DOI: 10.2307/1390921
40. Zhao J., Faqiri H., Ahmad Z., Emam W., Yusuf M., Sharawy A. M. **The lomax-claim model: bivariate extension and applications to financial data**. *Complexity* (2021.0) **2021** 1-17
41. Grover G., Sabharwal A., Mittal J. **Application of Multivariate and Bivariate Normal Distributions to Estimate Duration of Diabetes**. *International Journal of Statistics and Applications* (2014.0) **4** 46-57
|
---
title: Butyrate suppresses atherosclerotic inflammation by regulating macrophages
and polarization via GPR43/HDAC-miRNAs axis in ApoE−/− mice
authors:
- Huiyan Ma
- Libo Yang
- Yajuan Liu
- Ru Yan
- Rui Wang
- Peng Zhang
- Zhixia Bai
- Yuanyuan Liu
- Yi Ren
- Yiwei Li
- Xin Jiang
- Ting Wang
- Ping Ma
- Qining Zhang
- Aifei Li
- Mixue Guo
- Xiaoxia Zhang
- Shaobin Jia
- Hao Wang
journal: PLOS ONE
year: 2023
pmcid: PMC9994734
doi: 10.1371/journal.pone.0282685
license: CC BY 4.0
---
# Butyrate suppresses atherosclerotic inflammation by regulating macrophages and polarization via GPR43/HDAC-miRNAs axis in ApoE−/− mice
## Abstract
Chronic low-grade inflammation is regarded to an important signature of atherosclerosis (AS). Macrophage (Mψ) and related polarization have been demonstrated to play a crucial role in the occurrence and development of AS inflammation. Butyrate, a bioactive molecule produced by the intestinal flora, has been increasingly demonstrated to exhibit a vital role for regulating the inflammation in chronic metabolic diseases. However, the effectiveness and multiple anti-inflammation mechanisms of butyrate on AS still need to be further understood. ApoE−/− mice fed with high-fat diet as AS model were administered with sodium butyrate (NaB) for 14 weeks of treatment. Our results showed that the atherosclerotic lesion in the AS group was dramatically reduced after NaB intervention. Moreover, deteriorated routine parameters of AS including body weights (BWs), low-density lipoprotein (LDL-C), triglyceride (TG), total cholesterol (TC) were significantly reversed by NaB administration. Abnormal elevated plasma and aorta pro-inflammatory indicators including interleukin (IL)-1β, IL-6, IL-17A, tumor necrosis factor (TNF)-α and lipopolysaccharide (LPS), as well as reduced anti-inflammatory IL-10 in plasma were respectively rectified after NaB administration. Consistently, accumulated Mψ and associated imbalance of polarization in the arota were attenuated with NaB treatment. Importantly, we demonstrated that the suppression of Mψ and associated polarization of NaB was dependent on binding G-protein coupled receptor (GPR) and inhibiting histone deacetylase HDAC3. Moreover, we found that intestinal butyrate-producing bacteria, anti-inflammatory bacteria and intestinal tight junction protein zonula occludens-1 (ZO)-1 may contribute to this effectiveness. Intriguingly, according to transcriptome sequencing of atherosclerotic aorta, 29 elevated and 24 reduced miRNAs were found after NaB treatment, especially miR-7a-5p, suggesting that non-coding RNA may possess a potential role in the protection of NaB against AS. Correlation analysis showed that there were close complicated interactions among gut microbiota, inflammation and differential miRNAs. Collectively, this study revealed that dietary NaB may ameliorate atherosclerotic inflammation by regulating Mψ polarization via GPR43/HDAC-miRNAs axis in ApoE−/− mice.
## Introduction
Atherosclerosis (AS) represents a basis of cardiovascular diseases with the highest morbidity and mortality worldwide [1], of which the pathological features are mainly described as inflammation and lipid metabolism disturbance [2]. Vascular intimal macrophages (Mψs) are thought to be responsible for the maintenance of atherosclerotic inflammation in the development of AS [3].
Numerous studies have shown that Mψs and associated polarization play a vital role in the initiation and development of AS inflammation [4–6]. M1 Mψ is mainly involved in the promotion of inflammatory response. In contrast, M2 Mψ shows anti-inflammation effect and promotes tissue repair by producing anti-inflammatory factors such as interleukin (IL)-10. In the pathological conditions, accumulating Mψs along with subsequently polarization to M1, are responsible for aggravating AS inflammation [7]. Conversely, M2 Mψ are thought to secret anti-inflammatory IL-10 and collagen, contributing to the stability of AS plaques [8]. Thus, the regulation of Mψ and associated M1/M2 polarization may be a potential therapeutic strategy for AS.
Accumulating evidence suggests that gut dysbiosis is closely associated with exacerbation of AS progression [9, 10]. The formation and rupture of atherosclerotic plaque are related to the high levels of gut microbiota-derived lipopolysaccharide (LPS) and inflammatory cytokines in the circulation [11]. Short-chain fatty acids (SCFAs), mainly acetate, propionate and butyrate, are the main end-products of the bacterial fermentation of nondigestible dietary fibers within the lumen of the mammalian colon [12]. Compared to other SCFAs, emerging studies have demonstrated that butyrate presents an anti-inflammatory effect in chronic metabolic diseases [13, 14]. Butyrate and related butyrate-producing bacteria are inversely correlated with atherosclerotic lesion [15–17].
Butyrate exerts an anti-inflammatory effect mainly by both binding to G protein-coupled receptors (GPRs) and inhibiting histone deacetylases (HDACs) [12]. Butyrate-binding receptors, GPRs, which mainly include GPR41, GPR43, and GPR109a, can suppress the recruitment of inflammatory cells and the production of pro-inflammatory cytokines. As a major butyrate receptor, GPR43 highly expressed in Mψ may mediate anti-inflammatory response [16]. Studies from animal models have confirmed that sodium butyrate could suppress the activation of nuclear factor-κB (NF-κB) pathway via GPR43 and β-arrestin-2 to inhibit inflammation [15, 18, 19]. Peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors that exert significant impacts on metabolism-related pathways [20]. The elevation of PPARγ by butyrate was involved in the inhibition of NF-κB pathway, resulting in the improvement of inflammation [21]. Butyrate also affects the rate of neointima formation by reducing the activation of Nod-like receptor pyrin domain 3 (NLRP3) inflammasome [22]. Transcription factor specificity protein 1 (Sp1) is often compounded with histone deacetylases (HDACs) to regulate acetylation of target genes. Notably, butyrate as an inhibitor of HDACs influence the formation of the Sp1/HDAC complex [23].
MicroRNAs (miRNAs) play an critical role in the regulation of the development of AS [24]. MiRNAs are well-known for regulating gene expression at the post-transcriptional level by pairing with target sequences in the 3′ untranslated region of mRNAs. The endogenously expressed miRNAs play important roles in many physiological and pathological processes [25]. MiR-205 was epigenetically regulated by HDAC2 through an Sp1-mediated pathway [25]. MiR-7a/b protected against cardiac remodeling and hypoxia-induced injury in H9c2 cardiomyoblasts involving Sp1 and PARP-1 [26]. The bulk of evidence has demonstrated the central role of epigenetic machinery in Mψ polarization [27].
In the present study, we examined the effects of orally administered butyrate on AS progression and associated mechanisms including Mψ polarization in inflammation, gut microbiota, and the role of miRNAs in HFD-induced atherosclerotic Apolipoprotein E deficiency (ApoE−/−) mice.
## Animal experiments
All animal protocols used in this study were approved by the Ethics Committee of Ningxia Medical University (No. 2020–527). Thirty male Jackson (C57BL/6J) mice aged 8 weeks and weighing 18–22 g were purchased from Ningxia Medical Laboratory Animal Center. Thirty male ApoE−/− mice (8-week-old) were obtained from Vital River Laboratory Animal Technology Co., Ltd., Beijing, China. All the mice were maintained under standard, specific, and pathogen-free conditions in individual cages in a temperature-controlled room (ambient temperature 22 ± 1°C, air humidity 40–$70\%$) with a 12 h light/dark cycle in Ningxia Medical Laboratory Animal Center. A high-fat diet (HFD) with $0.5\%$ cholesterol (No. TP28520) was purchased from TROPHIC Animal Feed High-tech Co., Ltd., Nantong, China. The exact product description of HFD and normal diet were supported in S1 Table. Sodium butyrate (NaB, purity>98, No. V900464) was obtained from Sigma (St Louis, MO, USA).
## Experimental design
As shown in Fig 1A, after one week of adaption the mice were randomly assigned to 4 groups ($$n = 15$$/each group): control group (CON), CON treated with NaB group (CON+NaB), atherosclerosis group (AS) and AS treated with NaB group (AS+NaB). C57BL/6J mice in the CON or ApoE−/− mice in AS were respectively fed normal or HFD diet. Meanwhile, mice in CON and AS groups were administered normal saline, as well as mice in CON+NaB and AS+NaB groups were fed with NaB (200mg/kg, dissolved by normal saline) by gavage once daily. During the experiment, body weights (BWs) were monitored weekly and food intake was recorded every 2 days. After 14 weeks of feeding, stool samples were freshly obtained and immediately frozen at −80°C for the subsequent analysis. All mice were euthanized with $4\%$ sodium pentobarbital and associated indications were investigated. Blood samples were rapidly collected by orbital bleeding and centrifuged at 4°C (1,200 × g for 15 min) to obtain plasma samples, which were stored at −80°C for further study.
**Fig 1:** *NaB alleviated physiological parameters and serum lipids in diverse groups.(A) Schematic time diagram of the experimental design. (B) Body weights (BWs) of 4 groups. (C) Food intake. (D) Triglyceride (TG). (E) Total cholesterol (TC). (F) High-density lipoprotein (HDL-C). (G) Low-density lipoprotein (LDL-C). Data were expressed as mean ± SEM. *P<0.05, **P<0.01, ***P<0.001. ns, no significant difference. CON: control group; CON+NaB: sodium butyrate (NaB)-fed control group; AS: model group; AS+NaB: NaB-fed model group.*
## Histology and morphometry evaluations of atherosclerotic lesions
The pathological changes in AS were measured with en face oil red O staining, HE staining, and Masson’s trichrome staining. Images captured with Canon EOS 70D camera were analyzed using Image J 8.0 software (National Institutes of Health, United States). The lesion area index was calculated as the percentage of aortic lumen area covered by atherosclerotic lesions. The necrotic core was measured by Image-*Pro plus* software, and the ratio of the positive area to the total plaque area was calculated for statistical analysis as previously described [28]. Observers were blinded to the experimental groups.
## Flow cytometry
Mψs, the significant aortic inflammatory cells, were isolated from aortic tissues. Briefly, 1 g of aortic tissues was minced and suspended in 5 ml of Hanks balanced salt solution (HBSS) containing $0.1\%$ (w/v) collagenase type IV (Sigma, United States) for 20 min at 37°C. Next, the specimen was washed with RPMI1640 containing $2\%$ of fetal bovine serum (FBS) and then filtered through a 200-mesh nylon membrane. After centrifugation at 70 × g for 3 min at 4°C, the supernatants were discarded, and the pellets were resuspended in 3 ml HBSS. After the erythrocyte lysis, samples were centrifuged for 5 min at 500 × g, 4°C, and then washed 2 times. The final concentration was adjusted to 1 × 107 cells/ml. To stain Mψs, 1 μl of PE-anti-F$\frac{4}{80}$ antibody, APC-anti-iNOS antibody, BP450-anti-CD206 antibody, and FITC-anti-TLR4 antibody (Biolegend, United States) were simultaneously added in 100 μl of cell suspension and incubated on the ice in the dark for 30 min. The prepared samples were measured and analyzed using the Beckman Cyto FLEX flow cytometer (Beckman Bioscience, United States).
## Plasma LPS assay
The plasma LPS level in each group was examined using a Limulus amebocyte lysate kit (Xiamen Bioendo Technology Co., Ltd., Xiamen, China) according to the manufacturer’s instruction. Briefly, the plasma was diluted with endotoxin-free water (1:4). Then 50 μl of diluted plasma was put into each well in a 96-well plate. At the initial time point, 50 μl of the Limulus amebocyte lysate reagent was added to each well. The plate was incubated at 37°C for 30 min. Then, 100 μl of chromogenic substrate warmed to 37°C was added to each well, and the incubation was extended for an additional 6 min at 37°C. Finally, the reaction was stopped by adding 100 μl of $25\%$ solution of glacial acetic acid. Optical density at 545 nm was measured with a microplate reader (Thermo Scientific, United States).
## Inflammatory cytokines
Tumor necrosis factor (TNF)-α, interferon (IFN)-γ, IL-6, IL-1β, IL-17A, and IL-10 were respectively determined by RayBiotech (QAM-INT-1-1) chips (Quantibody® Mouse Interleukin Array) according to the manufacturer’s instructions.
## Measurements of plasma lipid profiles
Plasma levels of triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL-C) and low-density lipoprotein (LDL-C) were measured by an automatic biochemical analyzer (AU400 Olympus, Japan).
## Quantitative real-time PCR
Transcriptional mRNA levels of genes were performed by quantitative real-time PCR (qRT-PCR). After RNA was isolated from the aorta tissue, cDNA was synthesized using M-ML V reverse transcriptase (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer’s instructions. qPCR (ABI VII7 PCR System, Applied Biosystems; Thermo Fisher Scientific, Inc.) was conducted in a 20 μl reaction volume (10 μl SYBR Green Master Mix, 0.8 μl PCR Forward Primer (10 μM), 0.8 μl PCR Reverse Primer (10 μM), 0.4 μl ROX, 2 μl cDNA, and 6 μl nuclease-free water) with the following protocol: initiation at 95°C for 5 min, followed by 40 cycles of 95°C (5 sec) and 60°C (34 sec). GAPDH was used as a reference. The assay was performed in three replicate wells, and three parallel experiments for each sample were conducted. The 2-ΔΔCt methods were used to calculate relative RNA expression levels. Primers sequences were presented in S2 Table.
## Gut microbiota analysis
The mice in each group were transferred to fresh and sterilized cages after 14 weeks of treatment. The fresh feces of each group were individually collected and immediately frozen into liquid nitrogen, finally stored at −80°C until the DNA extraction. Cetyltrimethylammonium bromide (CTAB) method [29] was used to extract the genomic DNA of samples, and then the purity and concentration of the DNA were detected by agarose gel electrophoresis and Nanodrop one (Thermo Fisher, USA). Briefly, 16S rRNA genes were amplified by using V3-V4 regions bacterial primers (341F 5’- CCTAYGGGRBGCASCAG-3’ and 806R 5’- GG ACTACNNGGGTATCTAAT-3’). All PCR reactions were carried out with Phusion1 HighFidelity PCR Master Mix (New England Biolabs, USA). Sequencing libraries were generated using the Ion Plus Fragment Library Kit 48 rxns (Thermo Scientific, USA). The library quality was assessed on the Qubit@ 2.0 Fluorometer (Thermo Scientific, USA). The library was sequenced on an Illumina HiSeq 2500 platform (Illumina, USA) by Beijing Nuo He Zhi Yuan Technology Co., Ltd., China.
## Transcriptome sequencing and analysis
Whole aorta transcriptome library preparation and deep sequencing were conducted by Biomarker Technologies Co, Ltd. The purity was determined using an ultra-microspectrophotometer (optical density 260 nm, NanoDrop; Thermo Fisher Scientific, Inc.), $$n = 3$$/group. DESeq R package was used to identify the significantly dysregulated miRNAs with cut-off criteria: $P \leq 0.05$ and |log2 fold change|>1.
## GO and KEGG pathway analysis
To better understand the biological functions and potential mechanisms of miRNAs in the effectiveness of NaB on AS, GO enrichment and KEGG pathway analyses were employed on these predicted target genes of differentially expressed miRNAs. Briefly, GO analyses (www.geneontology.org) consisted of three components: biological process (BP), cellular component (CC), and molecular function (MF). KEGG analyses were carried out to investigate the potential significant pathways (http://www.genome.jp/kegg/).
## Statistical analysis
The data shown as the mean ± SEM were conducted with Prism 8.01 (GraphPad Software Inc., CA, United States). Two-way analysis of variance (ANOVA) followed by the Turkey multiple-comparison test was used to determine statistical difference between experimental groups. Correlation analysis was performed using the Spearman method. $P \leq 0.05$ was considered statistically significant.
## NaB alleviated physiological parameters and serum lipids
To assess whether the difference in diet intake contributes to the effects of NaB treatment, food consumption and body weights were monitored. The body weights in AS group showed a steady weight gain and subsequently increased after 14 weeks, compared to the CON group (Fig 1B). NaB treatment showed no effect on weight gain compared to AS group. In terms of food intake, the average intake of mice in each group was decreased during the intervention period, but without a significant difference (Fig 1C), suggesting that NaB administration showed no influence in energy intake.
After 14 weeks of treatment, the serum biochemical parameters of mice were respectively determined. Compared to the CON group, plasma levels of TG ($P \leq 0.05$; Fig 1D), TC ($P \leq 0.001$; Fig 1E), and LDL-C ($P \leq 0.001$; Fig 1G) in AS group were notably increased. After NaB administration, plasma TC, TG and LDL-C levels were significantly rectified (Fig 1D, 1E and 1G). A decrease trend of HDL-C (Fig 1F) in plasma was observed in AS group without significant difference compared with CON group. It also showed no significant difference in HDL-C level in supplementary NaB group during HFD. Moreover, there was no significant difference in serum lipids between CON and CON+NaB groups. Collectively, these data demonstrated that NaB could protect against dyslipidemia in atherosclerotic ApoE−/− mice.
## NaB consumption ameliorated atherosclerosis
To further elucidate the involvement of NaB in the amelioration of atherosclerosis, both en face analyses of the aorta and the cross-sectional analyses of the aortic sinus area were evaluated. Histopathologic staining including en face oil red O staining, oil red O staining, Masson’s trichrome staining and HE staining were used to measure atherosclerotic plaque, fibrosis, and pathological damage in the aortic root of the heart, respectively. As shown in Fig 2A, lesion area and necrotic core size in the aortic sinus were remarkably exacerbated in AS mice. The percentage of en face oil red O staining in the AS group was notably higher than that in the CON group ($P \leq 0.001$; Fig 2B). Similar aggregated results of oil red O staining ($P \leq 0.01$) and Masson’s trichrome staining ($P \leq 0.001$) were separately observed in AS model, compared to the CON group (Fig 2C and 2D). In addition, H&E staining of the aortic sinus revealed a significant increase in necrotic core size in the AS group (Fig 2E). After intervention with NaB, the necrotic core size was decreased significantly. Intriguingly, these pathological lesions in AS were attenuated with NaB administration ($P \leq 0.05$; Fig 2E). In addition, no lesion was found between the CON group and CON+NaB group (Fig 2B, 2C and 2D). Taken together, these results demonstrated that NaB intervention could ameliorate the atherosclerotic lesions.
**Fig 2:** *NaB consumption reduced aorta atherosclerosis.(A) Representative sections of the valve area of the aortic root of the heart were stained with en face oil red O staining, oil red O staining, Masson’s trichrome staining and hematoxylin&eosin staining, respectively, Quantitative analysis as lesion area/total area (%). (B) face oil red O staining. (C) oil red O staining. (D) Masson’s trichrome staining and (E) Relative necrotic core area expressed as percentage of the total plaque area. *P<0.05, **P<0.01, ***P<0.001. ns, no significant difference. The bar of 500 μm was presented in the right corner of (A).*
## Dietary NaB significantly reduced chronic inflammation in AS
Mounting scientific proofs over decades have suggested that atherosclerosis represents a chronic inflammatory disorder [30]. Accumulating evidences support that NaB possesses the ability to reduce the expressions of pro-inflammatory cytokines [31]. We further examined concentrations of pro-inflammatory cytokines including IL-1β, IL-6, IL-17A, TNF-α, IFN-γ, as well as anti-inflammatory IL-10 (Fig 3A), respectively. The results showed that plasma levels of pro-inflammatory IL-1β, IL-6, IL-17A and IFN-γ in the AS group were significantly increased compared to the CON group, but the anti-inflammatory IL-10 was notably decreased. After the dietary NaB intervention, the concentrations of IL-1β, IL-6, IL-17A and IFN-γ in plasma were decreased and anti-inflammatory IL-10 was increased compared with those in the AS group.
**Fig 3:** *Dietary NaB significantly reduced chronic inflammation in AS.(A) Plasma of mice from 4 groups were respectively collected for the determination of interleukin (IL)-1β, IL-6, IL-17A, IL-10, tumor necrosis factor (TNF)-α, interferon (IFN)-γ by RayBiotech chip detection. (B) RT-PCR was used to determine relative mRNA levels of IL-1β, IL-6, IL-17A, IL-10, TNF-a, and IFN-γ in aorta tissues. (C) Plasma lipopolysaccharide (LPS) levels in diverse groups were measured using a Limulus amebocyte lysate kit. *P<0.05, **P<0.01, ***P<0.001. ns, no significant difference.*
In parallel, mRNA levels of in situ aortic inflammatory TNF-α, IL-1β, IL-6 IL-17A, IFN-γ, and IL-10 were determined to evaluate the effects of dietary NaB on plaque inflammation in atherosclerotic mice (Fig 3B). Similar to the above plasma levels of inflammation, aggravated TNF-α, IL-1β, IL-6, IFN-γ (all $P \leq 0.05$) in aortic tissues were remarkably decreased after dietary NaB intervention. Moreover, a decrease trend of anti-inflammatory IL-10 in aorta was observed in AS group without significant difference compared to CON group, which also showed no significant difference after NaB treatment. These results suggested that dietary NaB treatment ameliorated the inflammation in atherosclerotic ApoE−/− mice.
## Dietary NaB reduced plasma LPS levels
LPS-mediated inflammation based on gut-heart axis has been thought to contribute to the AS aggravation [29]. Thus, we further tested the plasma LPS in AS. Plasma LPS levels in AS group were higher than those in the CON group ($P \leq 0.001$; Fig 3C), which was significantly decreased after NaB intervention ($P \leq 0.05$; Fig 3C), demonstrating that dietary NaB may ameliorate LPS-induced intestinal barrier dysfunction and subsequent translocated circulating endotoxemia.
## NaB regulated atherosclerotic Mψs and M1/M2 polarization in mice
Mψs and associated polarization play a vital role in the formation and progression of atherosclerotic lesions [32]. To further analyze the effects of NaB on aortic Mψs, aortic F$\frac{4}{80}$+TLR4+ Mψs were measured by flow cytometry (Fig 4A). The ratios of aortic F$\frac{4}{80}$+ cells and F$\frac{4}{80}$+TLR4+ cells were increased in AS group compared to CON group ($P \leq 0.01$; Fig 4B and 4C). However, the proportions of F$\frac{4}{80}$+ TLR4+ cells and F$\frac{4}{80}$+ cells were respectively lower after NaB treatment ($P \leq 0.05$; Fig 4B and 4C). As shown in Fig 4D, iNOS, M1 Mψs-associated marker, in aortic plaques of model group was increased significantly ($P \leq 0.05$; Fig 4E) compared with control group, and exhibited an obvious decrease with NaB treatment. Conversely, the expression of M2 Mψs marker CD206 in aorta of model group after dietary NaB intervention was elevated (Fig 4F and 4G), suggesting that NaB may alleviate AS via regulating total Mψs and polarization by suppressing M1 polarization and enhancing M2 activation.
**Fig 4:** *Effects of NaB on Mψ and associated polarization in ApoE−/− mice with AS by flow cytometry.(A) Flow cytometry analysis of aorta F4/80+TLR4+ Mψs in diverse groups. (B) The proportion of aorta F4/80+ Mψs. (C) The proportion of aorta F4/80+ TLR4+ Mψs. (D) Flow cytometry analysis of aorta F4/80+ iNOS+ M1 Mψs in diverse groups. (E) The proportion of aorta F4/80+ iNOS+ M1 Mψs. (F) Flow cytometry analysis of aorta F4/80+ CD206+ M2 Mψs in diverse groups. (G) The proportion of aorta F4/80+ CD206+ M2 Mψs. *P<0.05, **P<0.01, ***P<0.001. ns, no significant difference. All experiments were performed in triplicate. Mψ: macrophage; TLR4: Toll-like receptor 4.*
## NaB inhibited inflammation via GPR43 and HDAC mediated pathways
NaB exerts pleiotropic biological effects mainly by activating G protein-coupled receptors (GPRs) and inhibiting histone deacetylases (HDACs) [33]. As shown in Fig 5, compared with HFD-fed mice, NaB treatment elevated the transcriptional levels of GPR43, PPAR-γ, and β-arrestin-2, as well as down-regulated HDAC3, Sp1, NF-κB, and NLRP3. These results indicated that GPR43 and HDAC signaling pathway may probably involve in NaB-mediated regulation of inflammation in the atherosclerotic ApoE−/− mice.
**Fig 5:** *NaB inhibited inflammation via GPR43 and HDAC mediated pathway.RT-PCR was used to determine relative mRNA levels of HDAC1 (A); HDAC2 (B); HADC3 (C); Sp1 (D); PPAR-γ (E); GPR43 (F); β-arrestin-2 G); NF-κB (H) and NLRP3 (I) in aorta tissues. *P<0.05, **P<0.01, ***P<0.001. ns, no significant difference.*
## NaB consumption enriched intestinal butyrate-producing and anti-inflammatory bacteria
Growing evidence has demonstrated that gut dysbiosis is closely associated with the development of AS [29, 34–37]. To further confirm the effects of NaB on gut microbiota in diverse groups, bacterial community were investigated by 16S rRNA sequencing and analysis.
At the phylum level, microbial composition of all mice was dominated with Firmicutes (CON $44\%$, CON+NaB $45\%$, AS $73\%$, AS+NaB $43\%$) and Bacteroidetes (CON $35\%$, CON+NaB $42\%$, AS $0.05\%$, AS+NaB $15\%$) (Fig 6A). We found an obviously decreased abundance of Firmicutes ($P \leq 0.001$) and an increase trend of Bacteroidetes ($$P \leq 0.06$$) in AS+NaB group compared to the AS group (Fig 6C and 6D). The ratio of Firmicutes/Bacteroidetes (F/B) was increased ($P \leq 0.05$; Fig 6E) in the AS group, which was reversely decreased after the intervention of dietary NaB ($P \leq 0.05$; Fig 6E). Thus, the NaB had a major influence on the F/B ratio under the HFD feeding in the atherosclerotic mice. In addition, NaB also restored the increased abundance of Verrucomicrobiota in AS ($P \leq 0.01$; Fig 6F).
**Fig 6:** *NaB consumption modulated the composition of gut microbiota.(A-I) The phylum and the genus levels. (J) The mRNA expression level of ZO-1 in different groups. *P<0.05, **P<0.01, ***P<0.001. ns, no significant difference.*
The relative abundance of microbiota at the genus level was shown in Fig 6B. The relative abundance of Akkermansia and Faecalibaculum in the AS+NaB group were significantly increased compared to the AS group (all $P \leq 0.01$, Fig 6G and 6I). Additionally, Bifidobacterium in the CON+ NaB group was higher than those in the CON group ($P \leq 0.01$; Fig 6H). In brief, the data summarized here clearly indicated that exogenous butyrate altered the composition of the microbiota in AS. Importantly, butyrate-producing bacteria Faecalibaculum was increased in the AS+ NaB group compared to the AS group (Fig 6I). The above-mentioned results indicated that butyrate reduced atherosclerosis development by regulating butyrate-producing bacteria of gut microbiota.
To further assess the integrity of gut mucosal barrier after the above rectification of gut dysbiosis with NaB treatment, tight junction protein ZO-1 expression in diverse groups was determined (Fig 6J). Compared to the CON group, intestinal ZO-1 expression in AS group was significantly reduced, indicating that the integrity of gut mucosa was impaired in AS. However, gut mucosal ZO-1 level of AS mice showed a notable elevation after the supplementation with NaB, demonstrating that NaB administration may contribute to enhancing the integrity of the gut barrier ($P \leq 0.001$; Fig 6J).
## Correlation analysis among gut microbiota, inflammation and serum lipids
For the assessment of interactions among the differential bacteria microbiota, inflammatory indicators, serum lipids in AS, correlation analysis was performed in AS and AS treated with NaB (Fig 7). In brief, the abundance of Firmicutes was found to be positively associated with the levels of pro-inflammatory indicators (TNF-α, IL-1β, IL-6, IL-17A, IFN-γ, LPS) and serum lipids (TG, TC, LDL-C), but negatively correlated with IL-10 and HDL-C. However, the beneficial bacteria Bacteroidetes were negatively correlated with metabolic and pro-inflammatory indicators (TG, TC, LDL-C, LPS, IL-1β, IL-17A, TNF-α, IFN-γ). The abundance of butyrate-producing bacteria Faecalibaculum exhibited a positive correlation with IL-10, whereas negatively correlated with TC and LDL-C. Taken together, there were close and complicated interactions among gut bacteria, inflammation, and serum lipids in AS and AS treated with NaB.
**Fig 7:** *Altered gut microbiota in AS mice and their association with inflammatory indicators and serum lipids.The heat map showing the correlation of different microbial abundance with inflammatory indicators and serum lipids. The intensity of the color indicates the degree of correlation between the corresponding factor and each microbial species, which is obtained by Spearman’s correlation analysis. *P<0.05, **P<0.01, ***P<0.001.*
## NaB consumption notably modulated the aorta miRNAs
MicroRNAs (miRNAs) play an essential role in the regulation of atherosclerosis [24]. The cDNA and sRNA libraries of aortic tissue samples were sequenced. Moreover, counts of clean reads and mapped ratio of sequencing data were shown in Table 1. Under the NaB treatment, 53 miRNAs were identified to express differentially with the significance ($P \leq 0.05$; Table 2). Compared with the AS group, up-regulated 29 miRNAs and down-regulated 24 miRNAs in the AS+NaB group were shown in the cluster heatmap (Fig 8A) and volcano diagram (Fig 8B). The most significantly enriched KEGG pathways were shown in Fig 8C. For the miRNAs, endocytosis was the most significantly enriched pathway. GO analysis contained the biological process (BP), cellular component (CC), and molecular function (MF) for host linear transcripts. Based on the GO enrichment analysis of the trans targeted genes of miRNA, the most significantly enriched BP, CC, and MF were signal transduction, nucleus, and ATP binding (Fig 8D–8F), respectively. It was indicated that atherosclerotic miRNAs were different with or without NaB treatment, suggesting that NaB may play an important role in atherosclerosis-related GO terms such as transcription factor activity. As shown in Fig 8G, compared with the AS group, a total of 25 inflammation-associated miRNAs were found. MiR-7a-5p was up-regulated after the supplementation with NaB in comparison with the AS group ($P \leq 0.05$; Table 2). Increasing evidence supports that miR-7a-5p plays an important role in regulating the inflammatory process in inflammatory diseases [38]. MiR-7a-5p was identified as a protector of cardiac remodeling and hypoxia-induced injury in H9c2 cardiomyoblasts [26]. Subsequently, qRT-PCR of miR-7a-5p was identified in consistent with the RNA-sequencing results ($P \leq 0.01$; Fig 8H). Moreover, we found that miR-7a-5p was negatively correlated with metabolic and pro-inflammatory indicators (TG, TC, LDL-C, TNF-α, IL-1β, IL-6, IL-17A, IFN-γ, LPS) and positively associated with HDL-C and IL-10 (Fig 8I). Taken together, the attenuation of dietary NaB on AS may be probably dependent on miRNAs regulation.
**Fig 8:** *NaB altered the aorta transcriptome.(A) MiRNAs cluster heatmap with differential expression in AS and AS+NaB groups. (B) Differential-expression volcano diagram. (C) KEGG enrichment plot of differential miRNA host genes between AS and AS+NaB. (D-F) GO analysis of miRNA host genes between AS and AS+NaB. (G) Inflammation-related miRNAs. (H) The expression level of miR7a-5p was measured by real-time PCR. (I) Correlation analyses between the relative abundance of miR7a-5p and other related indicators. *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001.$ ns, no significant difference.* TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2
## Discussion
In the present study, we investigated the efficacy of dietary NaB intervention on chronic AS. At the end of this experiment, we demonstrated that NaB could ameliorate the AS, as well as further revealed that the effectiveness was mainly attributed to the suppression of atherosclerotic inflammation by regulating Mψs polarization via GPR43/ HDAC-miRNAs axis in ApoE−/− mice.
ApoE-/- mice fed with HFD, a classical mouse model of AS, were used to test the hypothesis that NaB protect against AS. Since ApoE-/- mice have a C57BL/6J genetic background, C57BL/6J mice were used as a CON group. Moreover, in parallel with previous studies, our results demonstrated that long-term NaB administration could ameliorate AS in mice [39, 40].
Dyslipidemias including hypercholesterolemia and hyperlipidemia, can further enhance the risk for atherosclerotic CVDs [41]. NaB has been thought to show a beneficial effect on the lipid metabolism in diverse chronic diseases [1]. Consistently, in this study, decreases of TG, LDL-C, TC levels, but an increase of HDL-C in AS with dietary NaB treatment demonstrated that NaB could attenuate lipid disturbance in AS development.
Mψs exerts a predominant inflammatory role in AS lesion formation as well as plaque rupture [42]. In the present study, the proportions of F$\frac{4}{80}$+ cells and F$\frac{4}{80}$+ TLR4+ cells were all significantly decreased with the dietary NaB administration, suggesting that the anti-inflammation effect of NaB was ascribed to the inhibition of inflammatory Mψs. Decreased levels of M1 iNOS, and elevated M2 CD206 indicated that NaB possessed the ability to ameliorate inflammation of AS via regulating Mψ M1/M2 polarization.
Concomitant with the reduction of pro-inflammatory Mψs, NaB administration also decreased the levels of pro-inflammatory cytokines (TNF-α, IL-1β, IL-6, IL-17A, IFN-γ), but elevated anti-inflammatory IL-10. A similar study found that combination of pro-inflammatory cytokines led to chronic inflammatory response in the arterial wall, which is thought to promote disease progression characterized by atherosclerotic plaque buildup [43]. Furthermore, the dramatic changes in Mψ described above may be responsible for the release of pro-inflammatory cytokines by LPS-TLR4-NF-κB/Nod 3-like receptor protein (NLRP3) inflammasome signaling [44]. However, whether other immune cells such as regulatory T cells (Tregs), Th17 cells and myeloid suppressor cells (MDSCs) are involved in the anti-inflammatory effects of the NaB treatment on AS still needs to be further explored.
After translocation into arteries, LPS links gut microbiota and pathogen-induced systemic inflammation, subsequently binds to TLR4 of Mψs, leading to an inflammatory cascade that ultimately aggravates AS [11]. Endotoxemia causes the activation of M1 Mψs, which promote the formation of AS foam cells [45]. Elevated plasma LPS level in AS indicated impaired gut barrier with abnormal integrity and permeability in consistent with previous reports [37, 46]. Importantly, this elevated plasma LPS of AS group was conversely changed by NaB intervention, suggesting that the effectiveness of NaB on chronic inflammation in AS was partially due to the reduction of LPS translocation.
Among the numerous pathogenesis in AS, gut dysbiosis is increasingly thought to be critical in the inflammation of AS [47]. We found that NaB could notably change gut microbial composition by improving anti-inflammatory related bacteria (Bacteroidetes, Verrucomicrobiota, Akkermansia, Bifidobacterium) and butyrate-producing bacteria (Faecalibaculum), but decreasing Firmicutes and F/B ratio, suggesting that the protection of NaB against the inflammation of AS may partially attribute to the rectification of gut dysbiosis. Akkermansia and Bifidobacterium are conductive to the reduction of LPS leakage via protecting the gut mucosal barrier function [48, 49]. Bifidobacterium plays a synergistic effect in the improvement of inflammation to further alleviated the atherosclerosis [50]. Akkermansia is implicated in declining aortic lesions and atherosclerosis [51]. Akkermansia can also stimulate goblet cells to secret mucus and elevate the expression of gut junction proteins [52]. In addition to these microbiota, butyrate-producing bacteria *Faecalibaculum is* also lack in atherosclerotic CVD [53].
Positive correlations of increased Firmicutes with metabolic indicators (TG, TC, LDL-C) demonstrated that these pathogenic bacteria were related to the lipid dysmetabolism of AS. In contrast, Bacteroidetes were negatively correlated with these indicators. These findings indicated that gut microbiota may play crucial role in etiology of dyslipidemia. However, the underlying mechanisms of gut microbiota affects blood lipid levels remains unclear. It has been addressed that gut bacteria could generate SCFAs, modulating hepatic and/or systemic lipid and glucose metabolism via the activation of nuclear or GPCRs [54, 55]. Meanwhile, gut microbiota also modulate the metabolism of bile acids which is the main end-product of cholesterol [56].
Gut microbiota is closely associated with the integrity and permeability of gut barrier which is indicated by the tight junction proteins [53, 57]. In our study, the improvement of tight junction protein ZO-1 revealed that oral NaB intervention may contribute to the attenuation of the integrity of gut barrier, thereby reducing LPS translocation and ultimately suppressing atherosclerotic chronic inflammation.
Apart from the above restorement of NaB on gut dysbosis-related inflammation, NaB also directly serve as a novel anti-inflammation approach by binding to the GPR43 [16, 58]. Thus, we speculated and proved that NaB regulated inflammatory Mψs and their polarization through GPR43-β-arrestin-2-mediated pathways by increasing the levels of M2 (CD206, IL-10 and PPARγ) and reducing M1 indicators (iNOS, TNF-α and NF-κB/NLRP3).
As an inhibitor of HDACs, NaB can regulate the inflammation through the acetylation regulation of inflammatory gene expression [59]. In the present study, LPS significantly increased the accumulation of HDAC1-3/Sp1 and reduced PPARγ acetylation in Mψs. However, dietary NaB restored PPARγ acetylation and expression, PPARγ further repressed pro-inflammatory NF-κB /NLRP3 pathways. In parallel with our study, Saemann et al. has demonstrated that butyrate could inhibit the secretion of TNF-α and the activation of NF-κB and up-regulate the expression of anti-inflammatory factors IL-10 in LPS-activated mononuclear cells and neutrophils via HDAC inhibition [60]. Moreover, butyrate also increases the expression of PPARγ, which act as an E3 ubiquitin ligase of NF-κB/p65 to promote its degradation [61]. Here, we demonstrated that NaB may prevent atherosclerotic chronic inflammation through the HDAC/Sp1/PPARγ/NF-κB or NLRP3 signaling pathway, but the exact mechanism still needs to be further investigated in vitro experiment.
MiRNAs have emerged as evolutionarily conserved, noncoding small RNAs that serve as important regulators and fine-tuners of a range of pathophysiological cellular effects and molecular signaling pathways involved in atherosclerosis [62]. In recent years, there has been increased interesting in the role of miRNAs on macrophage polarization which mainly rely on the regulation of vital signaling pathways [63, 64]. Inhibition of miRNA-155 attenuates AS via reducing M1 Mψ polarization and inflammatory responses in mice [64]. MiRNA-130a suppression can protect against atherosclerosis through inhibiting inflammation by regulating the PPARγ/NF-κB expression [65]. In addition, miRNAs are closely related to HDACs in different human chronic diseases and cancerogenic pathways [66]. To date, many miRNAs have been found directly targeted by HDACs in chronic metabolic diseases [67–69]. Intriguingly, in our study, 25 differential inflammation-related miRNAs candidates were found by transcriptomic analysis after long-term dietary NaB supplementation. Especially, miR-7a-5p was identified and proved to be closely interacted with the inflammation. Due to the complex relationships about roles of above differential miRNAs in AS with NaB treatment, the underlying mechanisms need to be further investigated.
## Conclusion
Our study provides a new evidence that butyrate could ameliorate the progression of inflammation in atherosclerosis through regulating macrophage polarization via GPR43-related and HDAC/PPAR-γ/NF-κB/NLRP3/miRNAs signal pathways. Schematic mechanism is shown in Fig 9.
**Fig 9:** *Schematic diagram of anti-inflammation effect of NaB in AS.*
## References
1. Du Y, Li X, Su C, Xi M, Zhang X, Jiang Z. **Butyrate protects against high-fat diet-induced atherosclerosis via up-regulating ABCA1 expression in apolipoprotein E-deficiency mice**. *Brit J Pharmacol* (2020) **177** 1754-72. DOI: 10.1111/bph.14933
2. Troseid M, Andersen GO, Broch K, Hov JR. **The gut microbiome in coronary artery disease and heart failure: Current knowledge and future directions**. *Ebiomedicine* (2020) **52** 102649. DOI: 10.1016/j.ebiom.2020.102649
3. Cai Y, Wen J, Ma S, Mai Z, Zhan Q, Wang Y. **Huang-Lian-Jie-Du Decoction Attenuates Atherosclerosis and Increases Plaque Stability in High-Fat Diet-Induced ApoE() Mice by Inhibiting M1 Macrophage Polarization and Promoting M2 Macrophage Polarization**. *Front Physiol* (2021) **12** 666449. DOI: 10.3389/fphys.2021.666449
4. Leipner J, Dederichs TS, von Ehr A, Rauterberg S, Ehlert C, Merz J. **Myeloid cell-specific Irf5 deficiency stabilizes atherosclerotic plaques in Apoe() mice**. *Mol Metab* (2021) **53** 101250. DOI: 10.1016/j.molmet.2021.101250
5. Kiepura A, Stachyra K, Wisniewska A, Kus K, Czepiel K, Suski M. **The Anti-Atherosclerotic Action of FFAR4 Agonist TUG-891 in ApoE-Knockout Mice Is Associated with Increased Macrophage Polarization towards M2 Phenotype**. *Int J Mol Sci* (2021) **22**. DOI: 10.3390/ijms22189772
6. Zhou J, Liu L, Wu P, Zhao L, Wu Y. **Fusobacterium nucleatum Accelerates Atherosclerosis via Macrophage-Driven Aberrant Proinflammatory Response and Lipid Metabolism**. *Front Microbiol* (2022) **13** 798685. PMID: 35359716
7. Wang C, Ma C, Gong L, Guo Y, Fu K, Zhang Y. **Macrophage Polarization and Its Role in Liver Disease**. *Front Immunol* (2021) **12** 803037. DOI: 10.3389/fimmu.2021.803037
8. Park SH. **Regulation of Macrophage Activation and Differentiation in Atherosclerosis**. *J Lipid Atheroscler* (2021) **10** 251-67. DOI: 10.12997/jla.2021.10.3.251
9. Sanchez-Rodriguez E, Egea-Zorrilla A, Plaza-Diaz J, Aragon-Vela J, Munoz-Quezada S, Tercedor-Sanchez L. **The Gut Microbiota and Its Implication in the Development of Atherosclerosis and Related Cardiovascular Diseases**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12030605
10. Troseid M, Andersen GO, Broch K, Hov JR. **The gut microbiome in coronary artery disease and heart failure: Current knowledge and future directions**. *Ebiomedicine* (2020) **52** 102649. DOI: 10.1016/j.ebiom.2020.102649
11. Zhu L, Zhang D, Zhu H, Zhu J, Weng S, Dong L. **Berberine treatment increases Akkermansia in the gut and improves high-fat diet-induced atherosclerosis in Apoe() mice**. *Atherosclerosis* (2018) **268** 117-26. DOI: 10.1016/j.atherosclerosis.2017.11.023
12. He J, Zhang P, Shen L, Niu L, Tan Y, Chen L. **Short-Chain Fatty Acids and Their Association with Signalling Pathways in Inflammation, Glucose and Lipid Metabolism**. *Int J Mol Sci* (2020) **21**. DOI: 10.3390/ijms21176356
13. Li Z, Yi CX, Katiraei S, Kooijman S, Zhou E, Chung CK. **Butyrate reduces appetite and activates brown adipose tissue via the gut-brain neural circuit**. *Gut* (2018) **67** 1269-79. DOI: 10.1136/gutjnl-2017-314050
14. Hayden RE, Kussaibati R, Cronin LM, Pratt G, Roberts C, Drayson MT. **Bezafibrate and medroxyprogesterone acetate target resting and CD40L-stimulated primary marginal zone lymphoma and show promise in indolent B-cell non-Hodgkin lymphomas**. *Leukemia Lymphoma* (2015) **56** 1079-87. DOI: 10.3109/10428194.2014.939962
15. Pirozzi C, Francisco V, Guida FD, Gomez R, Lago F, Pino J. **Butyrate Modulates Inflammation in Chondrocytes via GPR43 Receptor**. *Cell Physiol Biochem* (2018) **51** 228-43. DOI: 10.1159/000495203
16. Li M, van Esch B, Wagenaar G, Garssen J, Folkerts G, Henricks P. **Pro- and anti-inflammatory effects of short chain fatty acids on immune and endothelial cells**. *Eur J Pharmacol* (2018) **831** 52-9. DOI: 10.1016/j.ejphar.2018.05.003
17. Meli R, Mattace RG, Calignano A. **Role of innate immune response in non-alcoholic Fatty liver disease: metabolic complications and therapeutic tools**. *Front Immunol* (2014) **5** 177. DOI: 10.3389/fimmu.2014.00177
18. Luo QJ, Sun MX, Guo YW, Tan SW, Wu XY, Abassa KK. **Sodium butyrate protects against lipopolysaccharide-induced liver injury partially via the GPR43/ beta-arrestin-2/NF-kappaB network**. *Gastroenterol Rep* (2021) **9** 154-65. DOI: 10.1093/gastro/goaa085
19. Sun X, Luo S, Jiang C, Tang Y, Cao Z, Jia H. **Sodium butyrate reduces bovine mammary epithelial cell inflammatory responses induced by exogenous lipopolysaccharide, by inactivating NF-kappaB signaling**. *J Dairy Sci* (2020) **103** 8388-97. DOI: 10.3168/jds.2020-18189
20. Oh H, Visvalingam V, Wahli W. **The PPAR-microbiota-metabolic organ trilogy to fine-tune physiology**. *Faseb J* (2019) **33** 9706-30. DOI: 10.1096/fj.201802681RR
21. Aguilar EC, Da SJ, Navia-Pelaez JM, Leonel AJ, Lopes LG, Menezes-Garcia Z. **Sodium butyrate modulates adipocyte expansion, adipogenesis, and insulin receptor signaling by upregulation of PPAR-gamma in obese Apo E knockout mice**. *Nutrition* (2018) **47** 75-82. DOI: 10.1016/j.nut.2017.10.007
22. Yuan X, Wang L, Bhat OM, Lohner H, Li PL. **Differential effects of short chain fatty acids on endothelial Nlrp3 inflammasome activation and neointima formation: Antioxidant action of butyrate**. *Redox Biol* (2018) **16** 21-31. DOI: 10.1016/j.redox.2018.02.007
23. Huang W, Zhou J, Zhang G, Zhang Y, Wang H. **Decreased H3K9 acetylation level of LXRalpha mediated dexamethasone-induced placental cholesterol transport dysfunction**. *Bba-Mol Cell Biol L* (2019) **1864** 158524. DOI: 10.1016/j.bbalip.2019.158524
24. Schober A, Maleki SS, Nazari-Jahantigh M. **Regulatory Non-coding RNAs in Atherosclerosis**. *Handb Exp Pharmacol* (2022) **270** 463-92. DOI: 10.1007/164_2020_423
25. Zheng Z, Zhang S, Chen J, Zou M, Yang Y, Lu W. **The HDAC2/SP1/miR-205 feedback loop contributes to tubular epithelial cell extracellular matrix production in diabetic kidney disease**. *Clin Sci* (2022) **136** 223-38. DOI: 10.1042/CS20210470
26. Li R, Geng HH, Xiao J, Qin XT, Wang F, Xing JH. **miR-7a/b attenuates post-myocardial infarction remodeling and protects H9c2 cardiomyoblast against hypoxia-induced apoptosis involving Sp1 and PARP-1**. *Sci Rep-Uk* (2016) **6** 29082. DOI: 10.1038/srep29082
27. Yang H, Sun Y, Li Q, Jin F, Dai Y. **Diverse Epigenetic Regulations of Macrophages in Atherosclerosis**. *Front Cardiovasc Med* (2022) **9** 868788. DOI: 10.3389/fcvm.2022.868788
28. She ZG, Zheng W, Wei YS, Chen HZ, Wang AB, Li HL. **Human paraoxonase gene cluster transgenic overexpression represses atherogenesis and promotes atherosclerotic plaque stability in ApoE-null mice**. *Circ Res* (2009) **104** 1160-8. DOI: 10.1161/CIRCRESAHA.108.192229
29. Li Y, Yu Z, Liu Y, Wang T, Liu Y, Bai Z. **Dietary alpha-Linolenic Acid-Rich Flaxseed Oil Ameliorates High-Fat Diet-Induced Atherosclerosis via Gut Microbiota-Inflammation-Artery Axis in ApoE () Mice**. *Front Cardiovasc Med* (2022) **9** 830781. DOI: 10.3389/fcvm.2022.830781
30. Mehu M, Narasimhulu CA, Singla DK. **Inflammatory Cells in Atherosclerosis**. *Antioxidants-Basel* (2022) **11**. DOI: 10.3390/antiox11020233
31. Bayazid AB, Kim JG, Azam S, Jeong SA, Kim DH, Park CW. **Sodium butyrate ameliorates neurotoxicity and exerts anti-inflammatory effects in high fat diet-fed mice**. *Food Chem Toxicol* (2022) **159** 112743. DOI: 10.1016/j.fct.2021.112743
32. Koelwyn GJ, Corr EM, Erbay E, Moore KJ. **Regulation of macrophage immunometabolism in atherosclerosis**. *Nat Immunol* (2018) **19** 526-37. DOI: 10.1038/s41590-018-0113-3
33. Xiao Y, Guo Z, Li Z, Ling H, Song C. **Role and mechanism of action of butyrate in atherosclerotic diseases: a review**. *J Appl Microbiol* (2021) **131** 543-52. DOI: 10.1111/jam.14906
34. Cao RY, Zhang Y, Feng Z, Liu S, Liu Y, Zheng H. **The Effective Role of Natural Product Berberine in Modulating Oxidative Stress and Inflammation Related Atherosclerosis: Novel Insights Into the Gut-Heart Axis Evidenced by Genetic Sequencing Analysis**. *Front Pharmacol* (2021) **12** 764994. DOI: 10.3389/fphar.2021.764994
35. Yoo JY, Sniffen S, McGill PK, Pallaval VB, Chidipi B. **Gut Dysbiosis and Immune System in Atherosclerotic Cardiovascular Disease (ACVD)**. *Microorganisms* (2022) **10**. DOI: 10.3390/microorganisms10010108
36. Haghikia A, Zimmermann F, Schumann P, Jasina A, Roessler J, Schmidt D. **Propionate attenuates atherosclerosis by immune-dependent regulation of intestinal cholesterol metabolism**. *Eur Heart J* (2022) **43** 518-33. DOI: 10.1093/eurheartj/ehab644
37. Yan N, Wang L, Li Y, Wang T, Yang L, Yan R. **Metformin intervention ameliorates AS in ApoE-/- mice through restoring gut dysbiosis and anti-inflammation**. *Plos One* (2021) **16** e254321. DOI: 10.1371/journal.pone.0254321
38. Yin B, Tian-Chu H, Ling-Fen X. **Protection by microRNA-7a-5p Antagomir Against Intestinal Mucosal Injury Related to the JNK Pathway in TNBS-Induced Experimental Colitis**. *Turk J Gastroenterol* (2021) **32** 431-6. DOI: 10.5152/tjg.2021.20746
39. Chen Y, Xu C, Huang R, Song J, Li D, Xia M. **Butyrate from pectin fermentation inhibits intestinal cholesterol absorption and attenuates atherosclerosis in apolipoprotein E-deficient mice**. *J Nutr Biochem* (2018) **56** 175-82. DOI: 10.1016/j.jnutbio.2018.02.011
40. Aguilar EC, Leonel AJ, Teixeira LG, Silva AR, Silva JF, Pelaez JM. **Butyrate impairs atherogenesis by reducing plaque inflammation and vulnerability and decreasing NFkappaB activation**. *Nutr Metab Cardiovas* (2014) **24** 606-13. DOI: 10.1016/j.numecd.2014.01.002
41. Thobani A, Hassen L, Mehta LS, Agarwala A. **Management of Hypercholesterolemia in Pregnant Women with Atherosclerotic Cardiovascular Disease**. *Curr Atheroscler Rep* (2021) **23** 58. DOI: 10.1007/s11883-021-00957-w
42. Doddapattar P, Dhanesha N, Chorawala MR, Tinsman C, Jain M, Nayak MK. **Endothelial Cell-Derived Von Willebrand Factor, But Not Platelet-Derived, Promotes Atherosclerosis in Apolipoprotein E-Deficient Mice**. *Arterioscl Throm Vas* (2018) **38** 520-8. DOI: 10.1161/ATVBAHA.117.309918
43. Yang B, Qin Q, Xu L, Lv X, Liu Z, Song E. **Polychlorinated Biphenyl Quinone Promotes Atherosclerosis through Lipid Accumulation and Endoplasmic Reticulum Stress via CD36**. *Chem Res Toxicol* (2020) **33** 1497-507. DOI: 10.1021/acs.chemrestox.0c00123
44. Packard RR, Libby P. **Inflammation in atherosclerosis: from vascular biology to biomarker discovery and risk prediction**. *Clin Chem* (2008) **54** 24-38. DOI: 10.1373/clinchem.2007.097360
45. Blaut M.. **Gut microbiota and energy balance: role in obesity**. *P Nutr Soc* (2015) **74** 227-34. DOI: 10.1017/S0029665114001700
46. Bao T, He F, Zhang X, Zhu L, Wang Z, Lu H. **Inulin Exerts Beneficial Effects on Non-Alcoholic Fatty Liver Disease via Modulating gut Microbiome and Suppressing the Lipopolysaccharide-Toll-Like Receptor 4-Mpsi-Nuclear Factor-kappaB-Nod-Like Receptor Protein 3 Pathway via gut-Liver Axis in Mice**. *Front Pharmacol* (2020) **11** 558525. DOI: 10.3389/fphar.2020.558525
47. Liang X, Zhang Z, Lv Y, Tong L, Liu T, Yi H. **Reduction of intestinal trimethylamine by probiotics ameliorated lipid metabolic disorders associated with atherosclerosis**. *Nutrition* (2020) **79–80** 110941. DOI: 10.1016/j.nut.2020.110941
48. Zhang T, Ji X, Lu G, Zhang F. **The potential of Akkermansia muciniphila in inflammatory bowel disease**. *Appl Microbiol Biot* (2021) **105** 5785-94. DOI: 10.1007/s00253-021-11453-1
49. Ling X, Linglong P, Weixia D, Hong W. **Protective Effects of Bifidobacterium on Intestinal Barrier Function in LPS-Induced Enterocyte Barrier Injury of Caco-2 Monolayers and in a Rat NEC Model**. *Plos One* (2016) **11** e161635. DOI: 10.1371/journal.pone.0161635
50. Liang X, Zhang Z, Lv Y, Lu H, Liu T, Yi H. **Krill Oil Combined with Bifidobacterium animalis subsp. lactis F1-7 Alleviates the Atherosclerosis of ApoE() Mice**. *Foods* (2021) **10**. DOI: 10.3390/foods10102374
51. Hasani A, Ebrahimzadeh S, Hemmati F, Khabbaz A, Hasani A, Gholizadeh P. **The role of Akkermansia muciniphila in obesity, diabetes and atherosclerosis**. *J Med Microbiol* (2021) **70**. DOI: 10.1099/jmm.0.001435
52. Grander C, Adolph TE, Wieser V, Lowe P, Wrzosek L, Gyongyosi B. **Recovery of ethanol-induced Akkermansia muciniphila depletion ameliorates alcoholic liver disease**. *Gut* (2018) **67** 891-901. DOI: 10.1136/gutjnl-2016-313432
53. Yan N, Wang L, Li Y, Wang T, Yang L, Yan R. **Metformin intervention ameliorates AS in ApoE-/- mice through restoring gut dysbiosis and anti-inflammation**. *Plos One* (2021) **16** e254321. DOI: 10.1371/journal.pone.0254321
54. Liu Y, Song X, Zhou H, Zhou X, Xia Y, Dong X. **Gut Microbiome Associates With Lipid-Lowering Effect of Rosuvastatin in Vivo**. *Front Microbiol* (2018) **9** 530. DOI: 10.3389/fmicb.2018.00530
55. Zhou C, Gao X, Cao X, Tian G, Huang C, Guo L. **Gut Microbiota and Serum Metabolite Potential Interactions in Growing Layer Hens Exposed to High-Ambient Temperature**. *Front Nutr* (2022) **9** 877975. DOI: 10.3389/fnut.2022.877975
56. Zhu Y, Zhang JY, Wei YL, Hao JY, Lei YQ, Zhao WB. **The polyphenol-rich extract from chokeberry (Aronia melanocarpa L.) modulates gut microbiota and improves lipid metabolism in diet-induced obese rats**. *Nutr Metab* (2020) **17** 54. DOI: 10.1186/s12986-020-00473-9
57. Sugita K, Kabashima K. **Tight junctions in the development of asthma, chronic rhinosinusitis, atopic dermatitis, eosinophilic esophagitis, and inflammatory bowel diseases**. *J Leukocyte Biol* (2020) **107** 749-62. DOI: 10.1002/JLB.5MR0120-230R
58. Zhao J, Hu J, Ma X. **Sodium caprylate improves intestinal mucosal barrier function and antioxidant capacity by altering gut microbial metabolism**. *Food Funct* (2021) **12** 9750-62. DOI: 10.1039/d1fo01975a
59. Huuskonen J, Suuronen T, Nuutinen T, Kyrylenko S, Salminen A. **Regulation of microglial inflammatory response by sodium butyrate and short-chain fatty acids**. *Brit J Pharmacol* (2004) **141** 874-80. DOI: 10.1038/sj.bjp.0705682
60. Saemann MD, Bohmig GA, Osterreicher CH, Burtscher H, Parolini O, Diakos C. **Anti-inflammatory effects of sodium butyrate on human monocytes: potent inhibition of IL-12 and up-regulation of IL-10 production**. *Faseb J* (2000) **14** 2380-2. DOI: 10.1096/fj.00-0359fje
61. Hou Y, Moreau F, Chadee K. **PPARgamma is an E3 ligase that induces the degradation of NFkappaB/p65**. *Nat Commun* (2012) **3** 1300. DOI: 10.1038/ncomms2270
62. Feinberg MW, Moore KJ. **MicroRNA Regulation of Atherosclerosis**. *Circ Res* (2016) **118** 703-20. DOI: 10.1161/CIRCRESAHA.115.306300
63. Dai Y, Wang S, Chang S, Ren D, Shali S, Li C. **M2 macrophage-derived exosomes carry microRNA-148a to alleviate myocardial ischemia/reperfusion injury via inhibiting TXNIP and the TLR4/NF-kappaB/NLRP3 inflammasome signaling pathway**. *J Mol Cell Cardiol* (2020) **142** 65-79. DOI: 10.1016/j.yjmcc.2020.02.007
64. Hu J, Huang CX, Rao PP, Zhou JP, Wang X, Tang L. **Inhibition of microRNA-155 attenuates sympathetic neural remodeling following myocardial infarction via reducing M1 macrophage polarization and inflammatory responses in mice**. *Eur J Pharmacol* (2019) **851** 122-32. DOI: 10.1016/j.ejphar.2019.02.001
65. Liu F, Liu Y, Du Y, Li Y. **MiRNA-130a promotes inflammation to accelerate atherosclerosis via the regulation of proliferator-activated receptor gamma (PPARgamma) expression**. *Anatol J Cardiol* (2021) **25** 630-7. DOI: 10.5152/AnatolJCardiol.2021.56721
66. Klieser E, Urbas R, Swierczynski S, Stattner S, Primavesi F, Jager T. **HDAC-Linked "Proliferative" miRNA Expression Pattern in Pancreatic Neuroendocrine Tumors**. *Int J Mol Sci* (2018) **19**. DOI: 10.3390/ijms19092781
67. Cadavid IC, Da FG, Margis R. **HDAC inhibitor affects soybean miRNA482bd expression under salt and osmotic stress**. *J Plant Physiol* (2020) **253** 153261. DOI: 10.1016/j.jplph.2020.153261
68. Conte M, Dell’Aversana C, Sgueglia G, Carissimo A, Altucci L. **HDAC2-dependent miRNA signature in acute myeloid leukemia**. *Febs Lett* (2019) **593** 2574-84. DOI: 10.1002/1873-3468.13521
69. Li Y, Zhang K, Mao W. **Inhibition of miR34a prevents endothelial cell apoptosis by directly targeting HDAC1 in the setting of atherosclerosis**. *Mol Med Rep* (2018) **17** 4645-50. DOI: 10.3892/mmr.2018.8411
|
---
title: 'The associations of physical activity, sedentary time, and sleep with V˙O2max
in trained and untrained children and adolescents: A novel five-part compositional
analysis'
authors:
- Adam Runacres
- Kelly A. MacKintosh
- Sebastien Chastin
- Melitta A. McNarry
journal: PLOS ONE
year: 2023
pmcid: PMC9994740
doi: 10.1371/journal.pone.0275557
license: CC BY 4.0
---
# The associations of physical activity, sedentary time, and sleep with V˙O2max in trained and untrained children and adolescents: A novel five-part compositional analysis
## Abstract
The benefits of physical activity (PA) and the negative impacts of sedentary time (SED) on both short- and long-term health in youth are well established. However, uncertainty remains about how PA and SED jointly influence maximal oxygen uptake (V˙O2max). Therefore, the aim of this study was to determine the joint influence of PA and SED on V˙O2max using compositional analyses. 176 adolescents (84 girls, 13.8 ± 1.8 years) completed an incremental ramp test and supramaximal validation bout on a cycle ergometer, with PA and SED recorded for seven consecutive days on the right hip using a ActiGraph GT3X accelerometer. Time spent in Sleep, SED, light, moderate and vigorous PA was analysed using a compositional linear regression model. Compositions with 10 minutes more time in vigorous PA (> 27.5 mins⋅day-1) compared to the average 17.5 mins⋅day-1 were associated with a + $2.9\%$ - $11.1\%$ higher absolute and scaled V˙O2max whilst compositions with less (> -10 mins⋅day-1) VPA were associated with a reduced absolute and allometrically scaled V˙O2max (-$4.6\%$ - $24.4\%$). All associations were irrespective of sex, maturity, and training status. The proportion of time spent sedentary had little impact on absolute and scaled V˙O2max (0.01–$1.98\%$). These findings therefore highlight that intensity of PA may be of greater importance for increases in V˙O2max than reductions in SED and should be incorporated into future intervention designs.
## 1. Introduction
Poor maximal oxygen uptake (V˙O2max) has been associated with an increased risk of cardiovascular and metabolic disease, leading to an increased likelihood of premature mortality across the lifespan [1–4]. V˙O2max, defined as the highest rate of oxygen consumption despite further increases in work rate [5], is key to athletic performance, with youth athletes consistently reported to have a greater V˙O2max than their untrained counterparts [6–9]. However, the term V˙O2max in the paediatric literature is contentious as only 20–$40\%$ of children and adolescents display a plateau, and thus a supramaximal validation bout is necessary to validate a maximal effort [8, 9]. Consequently, the term peak V˙O2 will be used within this study where a supramaximal bout was not undertaken and V˙O2max used only when a scientifically rigorous supramaximal validation bout was employed. Whilst training is well-established to improve V˙O2max in youth athletes [6, 9–11], what remains less clear is the influence of habitual physical activity (PA) and sedentary time (SED) on V˙O2max. More specifically, some studies have reported a significant association between PA and peak V˙O2 [12–14], whereas others argue that children and adolescents rarely experience PA of a sufficient duration and intensity to significantly influence peak V˙O2 [11, 15].
Moderate-to-vigorous PA (MVPA) is perhaps the most widely used PA metric in children and adolescents [16–18] and remains the focus of governmental physical activity guidelines [19, 20]. Research in children has consistently shown that increased levels of MVPA are positively associated with peak V˙O2 [12–14], whilst excess SED is negatively correlated with peak V˙O2 [21, 22]. However, what remains unclear is the joint association between VPA and SED as some elite junior athletes may demonstrate similar, if not more, SED than their inactive, sedentary peers [23]. Therefore, whether VPA can offset the negative effects of SED requires urgent investigation, but this cannot be done with a continued reliance on correlational statistics or the use of linear regression modelling which assumes independence between variables [23, 24]. Indeed, traditional correlational statistics are inappropriate to account for the constrained and co-dependent nature of PA data, potentially creating spurious associations.
Compositional analysis allows all movement behaviours to be expressed as a proportion of a finite period, enabling the individual, and joint, effects of movement behaviours on outcome variables to be established [16–18, 25, 26]. Consequently, compositional analysis could provide novel insights into the concomitant influences of movement behaviours, intensity, and volume on V˙O2max. Indeed, Carson et al. [ 16] found that the overall movement behaviour composition explained ~$38\%$ of the variance in the peak V˙O2 of 4,169 Canadian children and adolescents (8–17 years). Despite this, when 10 minutes of time were allocated to, or removed from, MVPA, there was negligible effect on peak V˙O2, with predicted changes ranging from 0.03–$0.05\%$ [16]. This could be due, at least in part, to methodological considerations, including the estimation of peak V˙O2 from a field-based test which is likely to misrepresent true cardiorespiratory fitness [27], the pooling of data from boys and girls despite the well-established physiological differences [28], and the failure to account for maturity or training status. Furthermore, Carson et al [16] only explored the effect of changing MVPA compositions thereby combining MPA and VPA, which may potentially mask the importance of the intensity of physical activity for improving peak V˙O2 in youth. Indeed, training studies consistently show that significant improvements in absolute, and allometrically scaled, peak V˙O2 only occur when the intensity is sufficiently vigorous [10, 29]. Similarly, Gutin et al. [ 30] reported a stronger association between VPA and peak V˙O2 (r2 = 0.43, $p \leq 0.01$) than MPA (r2 = 0.30, $p \leq 0.01$) in adolescents, findings corroborated by both Dencker et al. [ 12] and Latt et al. [ 14] who reported that the amount of time spent in VPA explained 9.0–$15.8\%$ of the variance in peak V˙O2 in children and adolescents. However, none of these studies appropriately accounted for body mass differences, with Latt et al. [ 14] and Dencker et al. [ 13] utilising ratio scaled peak V˙O2, and Dencker et al. [ 12] using theoretical exponents to allometrically scale their peak V˙O2 data. By not fully accounting for body mass, heavier, more mature individuals may be penalised, creating spurious associations [15]. Hence, whether these results are a consequence of physiological pathways, or methodological inconsistencies, remains to be fully established.
Therefore, the aim of this study was to examine the independent, and interactive, effects of the five movement behaviours (SED, light intensity PA (LPA), MPA, VPA and Sleep) on V˙O2max in children and adolescents. The second aim was to explore the effect of baseline fitness, sex, and maturity on the predicted changes in V˙O2max elicited by changing PA compositions.
## 2. Methods
Ethics approval was granted by the institutional research ethics committee at Swansea University prior to the commencement of data collection and the study conformed to the Declaration of Helsinki. Before participants were accepted into this cross-sectional study, written informed parental consent and participant assent were obtained, along with all parents completing a pre-screening medical questionnaire on behalf of their child. Participants were excluded if they had known cardiovascular, metabolic, kidney, or any other disease that meant they would not have been able to complete the exercise protocol. The trained children and adolescents were all national level athletes who were part of a long-term athlete development (LTAD) program overseen by the national governing body (NGB) of their sport (Hockey, Football and Gymnastics). Untrained participants were recruited from local schools across South Wales and were not formally engaged in sport training outside of curricular physical education lessons. The final sample consisted of 237 participants, encompassing 108 trained (43 girls; age: 13.5 ± 2.1 years) and 129 untrained (51 girls; 13.8 ± 1.4 years) children and adolescents.
## 2.1 Experimental procedures
All participants were required to attend one session at which they initially had their stature and sitting height measured to the nearest 0.1 cm using a Holtain Stadiometer (Holtain, Crymych, Dyfed, UK) and their body mass measured to the nearest 0.1 kg using electronic scales (Seca 803, Seca, Chino, CA, USA). Maturity status was subsequently estimated using the equations of Mirwald et al. [ 31], with participants deemed pre-pubertal, pubertal, and post-pubertal if they were more than one year from, within one year of, or more than one year post peak height velocity (PHV), respectively.
V˙O2max was assessed using an incremental ramp test to volitional exhaustion on a cycle ergometer (Lode Excalibur Sport, Groningen, Netherlands) which started with a three-minute warm-up at 10 W before increasing by 20–25 W⋅min-1, depending on the participant’s age. All participants were instructed to maintain a cadence of 60–80 revolutions per minute (rpm) throughout the test, with volitional exhaustion defined as when participants could not maintain a cadence above 50 rpm. Inspired and expired air were measured on a breath-by-breath basis throughout the incremental ramp test using a Vyntus metabolic cart (VYAIRE medical Ltd, Mettawa, IL, USA). Following five minutes active and ten minutes passive rest, a supramaximal validation bout was performed [32]. Specifically, participants warmed up for a further three minutes at 10 W before a step-transition to $105\%$ of the peak power achieved during the incremental ramp test. Participants were instructed to maintain a cadence above 50 rpm for as long as possible, with gas exchange measured continuously on a breath-by-breath basis throughout the exercise bout.
Participant’s habitual physical activity was subsequently measured for seven consecutive days using a ActiGraph GT3X (ActiGraph, Pensacola, Florida, USA) worn on the right hip, sampling at 100 Hz. Children and adolescents also completed a seven-day log to detail periods when the monitor was removed, waking time and time going to bed, to minimise the misclassification of non-wear time as sedentary time or Sleep.
## 2.2 Data analyses
The raw breath-by-breath V˙O2 data from both the V˙O2max and supramaximal bout were averaged into 10-second bins, with the V˙O2max defined as the highest 10-second moving average during the ramp incremental or the supramaximal test. To aid comparisons between sex, maturity, and training sub-groups, V˙O2max was allometrically scaled (scaled V˙O2max) to account for body mass differences between participants [6, 32]. Evenson et al. [ 33] cut-points were utilised to determine the time spent in each PA intensity which have been shown to be the reliable for children and adolescents [34]. Sleep time and efficiency were calculated using the algorithms of Sadeh et al. [ 35]. Wear-time criteria was set as ≥ 8 hours on any three days. Using the Evenson et al. [ 33] cut-points, sleep algorithms, and wear-time, each day was expressed as a five-part movement composition (SED, LPA, MPA, VPA, Sleep) and linear predictive models were employed to predict changes in V˙O2max. The smallest worthwhile change (SWC) in V˙O2max (l⋅min-1) and scaled V˙O2max (ml⋅kg-b⋅min-1) was calculated for each sex, maturity and training sub-group using the formula 0.2*group SD [36]. The SWC was then subsequently presented as a percentage of the group mean to aid comparisons between all sub-groups.
All compositional analyses were conducted in R (http://cran.r-project.org) using the compositions package (version 1.40–2) and its dependencies [25]. Compositional geometric means were computed to indicate the proportion of time spent in each PA behaviour or Sleep each day, by expressing each behaviour, after normalisation, as a proportion of the total time [18, 25]. Variance matrices were calculated to provide an indication as to the dispersion and co-dependency of movement behaviours and were calculated by measuring the variance between pair-wise log ratios [25, 26]. Specifically, a ratio tending towards zero indicates high co-dependency, with the numbers further from zero indicating less co-dependency. Sequential linear regression models were created by rotating each of the five behaviours via isometric log ratio (ILR) transformations to examine the relative effect of all movement behaviours on the V˙O2max and scaled V˙O2max [18, 25]. The first coefficient and its p value were reported for each rotation to determine whether the individual movement behaviour was associated with the outcome variable relative to the other movement behaviours, and its relative significance. Additionally, the overall model significance (p value) and R2 value were reported to gain an insight into the variance explained by the overall movement composition. All movement behaviours were also sequentially mapped against each other, producing ternary heat maps displaying the predicted absolute and scaled V˙O2max for each sex, training, and maturity group. Finally, change matrices were conducted to predict the change in absolute and scaled V˙O2max by systematically reallocating 10 minutes from one movement behaviour to another [18, 25, 26]. All predictive changes were presented as a percentage change relative to the compositional mean, with significant changes identified as any change greater than the SWC (%).
## 2.3 Statistical analyses
All traditional statistical analyses were conducted in SPSS version 26 (IBM, Portsmouth, UK), with significance accepted as $p \leq 0.05.$ Between group differences in anthropometric characteristics and absolute and scaled V˙O2max were assessed using a MANOVA, with post-hoc tests with Bonferroni correction applied to identify the specific location of significant differences as appropriate.
## 3. Results
Of the original 237 participants, 61 were excluded for failing to meet the wear-time criteria, therefore 84 trained (40 girls) and 92 untrained (44 girls) children and adolescents were included in the final analyses. There were no significant differences in the anthropometrics of those included and excluded ($p \leq 0.05$). Post-pubertal adolescents were significantly older, taller, heavier, and more mature than the pubertal or pre-pubertal children ($p \leq 0.01$), with significant differences in the same parameters also evident between pubertal adolescents and pre-pubertal children ($p \leq 0.05$, Table 1). The trained children and adolescents were taller (F[1,175] = 12.7, $p \leq 0.01$) and had a higher V˙O2max (l⋅min-1) than their untrained counterparts (F[1, 175] = 15.3, $p \leq 0.01$), which persisted even after allometric scaling (F[1,175] = 18.7, $p \leq 0.01$). Overall, boys had a higher absolute and scaled V˙O2max than their female counterparts, irrespective of training or maturity status (F[1,175] = 19.7, $p \leq 0.01$). V˙O2max increased with maturity, irrespective of sex and training status (F[1,175] = 16.2, $p \leq 0.01$), but there was no significant difference between any maturity group for scaled V˙O2max. There were no significant training, sex, or maturity interactions for any anthropometric variable or V˙O2max, regardless of how it was expressed.
**Table 1**
| Training Group | Maturity | Sex | Age (years) | Stature (cm) | Body Mass (kg) | BMI (kg∙m-2) | Maturity Offset (years) | V˙O2max (l⋅min-1) | Scaled V˙O2max(ml⋅kg-b⋅min-1) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Trained (n = 84) | Pre-Pubertal (n = 34) | Boys (n = 20) | 11.8 ± 1.0 | 146.6 ± 7.5 | 38.5 ± 7.2 | 17.8 ± 2.2 | -2.27 ± 0.69 | 2.06 ± 0.37 * | 194.1 ± 22.2 * |
| Trained (n = 84) | Pre-Pubertal (n = 34) | Girls (n = 14) | 11.4 ± 1.5 | 151.0 ± 14.2 | 45.5 ± 13.7 | 19.4 ± 2.7 | -2.37 ± 1.16 | 1.89 ± 0.46 | 153.9 ± 23.7 |
| Trained (n = 84) | Pubertal (n = 30) | Boys (n = 14) | 14.1 ± 1.1 a | 167.9 ± 8.9 a | 53.3 ± 5.8 a | 18.9 ± 1.3 | +0.01 ± 0.43 a | 2.77 ± 0.43 * a | 185.7 ± 31.6 * |
| Trained (n = 84) | Pubertal (n = 30) | Girls (n = 16) | 14.4 ± 1.3 a | 164.6 ± 4.4 a | 56.4 ± 8.1 a | 20.7 ± 2.4 | +0.02 ± 0.57 a | 2.16 ± 0.27 a | 152.5 ± 15.5 |
| Trained (n = 84) | Post-Pubertal (n = 20) | Boys (n = 10) | 16.2 ± 1.4 a b | 178.9 ± 6.9 a b | 65.2 ± 6.1 a b | 20.4 ± 1.9 | +1.96 ± 0.57 a b | 3.24 ± 0.71 * a b | 205.5 ± 34.0 * |
| Trained (n = 84) | Post-Pubertal (n = 20) | Girls (n = 10) | 15.8 ± 1.0 a b | 165.8 ± 5.7 a b | 58.9 ± 7.9 a b | 21.5 ± 3.7 | +1.91 ± 0.32 a b | 2.30 ± 0.45 a b | 145.7 ± 31.2 |
| Untrained(n = 92) | Pre-Pubertal (n = 22) | Boys (n = 12) | 12.3 ± 1.7 | 151.5 ± 8.1 | 44.3 ± 10.2 # | 19.2 ± 3.1 # | -1.94 ± 0.94 | 1.94 ± 0.29 # * | 142.6 ± 34.7 # * |
| Untrained(n = 92) | Pre-Pubertal (n = 22) | Girls (n = 10) | 12.1 ± 0.7 | 150.0 ± 10.9 | 44.9 ± 9.7 # | 20.0 ± 1.4 # | -1.12 ± 0.12 | 1.35 ± 0.33 # | 123.9 ± 25.6 # |
| Untrained(n = 92) | Pubertal (n = 40) | Boys (n = 26) | 14.1 ± 0.9 a | 164.8 ± 8.2 a | 57.1 ± 11.2 # a | 20.9 ± 3.6 # | -0.04 ± 0.66 a | 2.31 ± 0.47 # * a | 159.5 ± 34.5 # * |
| Untrained(n = 92) | Pubertal (n = 40) | Girls (n = 14) | 13.1 ± 1.0 a | 155.8 ± 9.3 a | 49.4 ± 11.3 # a | 20.6 ± 3.4 # | +0.13 ± 0.38 a | 1.65 ± 20.8 # a | 130.9 ± 20.8 # |
| Untrained(n = 92) | Post-Pubertal (n = 30) | Boys (n = 10) | 15.3 ± 0.3 a b | 172.0 ± 5.9 a b | 70.4 ± 14.1 # a b | 23.7 ± 3.7 # | +1.66 ± 0.69 a b | 2.91 ± 0.62 # * a b | 166.1 ± 22.6 # * |
| Untrained(n = 92) | Post-Pubertal (n = 30) | Girls (n = 20) | 14.9 ± 0.7 a b | 162.3 ± 7.6 a b | 56.6 ± 10.2 # a b | 21.6 ± 3.0 # | +2.10 ± 0.62 a b | 1.86 ± 0.38 # a b | 143.8 ± 34.2 # |
## 3.1 Physical activity composition description
In the trained participants, the geometric means highlight that the largest portion of the day was spent in SED ($41.2\%$), followed by Sleep ($39.2\%$), with VPA only accounting for $1.6\%$ of the day (Table 2). Similarly, untrained children spent the longest period of the day in SED ($44.1\%$) and Sleep ($40.0\%$), with VPA making up just $1.3\%$ of the day. Trained athletes completed more LPA (F(1.175) = 38.1, $p \leq 0.01$) and VPA (F[1,175] = 18.6, $p \leq 0.01$), but spent significantly less time in Sleep (F[1,175] = 3.8, $$p \leq 0.05$$) compared to untrained participants, irrespective of sex or maturity. LPA and Sleep, and SED and LPA, demonstrated the smallest variation and therefore highest co-dependency, whereas VPA had the largest pair-wise log ratio variances compared to all other PA behaviours, indicating less co-dependency (Table 3). The ILR model revealed that the overall PA composition significantly predicted both V˙O2max and scaled V˙O2max (Table 4), explaining $48.7\%$ and $37.7\%$, respectively. However, when individual movements were considered in isolation, the only significant predictor of scaled V˙O2max was VPA (YVPA = 6.91, $p \leq 0.02$), with no significant individual associations evident for absolute V˙O2max (Table 4).
## 3.2 Impact of PA composition on V˙O2max
Compositions with 10 minutes difference in SED, LPA, MPA, or Sleep had a minimal effect on absolute or scaled V˙O2max in trained children and adolescents (Table 5), with all changes smaller than the SWC (S1 Table), irrespective of training, sex, or maturity status. However, when co-varying for sex, maturity, and training status, compositions with 10 minutes more time in VPA (> 27.5 mins⋅day-1) compared to the average 17.5 mins⋅day-1 were associated with a +$2.9\%$ - $11.1\%$ higher absolute and scaled V˙O2max and compositions with less than 10 mins⋅day-1 of VPA were associated with a reduced absolute and scaled V˙O2max (-$4.6\%$ - $24.4\%$). The proportion of SED, LPA, and Sleep had little impact on absolute and scaled V˙O2max (0.01–$1.98\%$). Consequently, VPA was the most influential PA behaviour for absolute, and scaled V˙O2max, irrespective of training status and sex, but the influence of PA behaviours was less clear in pubertal and post-pubertal adolescents (S1 and S2 Figs).
**Table 5**
| V˙O2max | V˙O2max.1 | V˙O2max.2 | V˙O2max.3 | V˙O2max.4 | V˙O2max.5 | Scaled V˙O2max | Scaled V˙O2max.1 | Scaled V˙O2max.2 | Scaled V˙O2max.3 | Scaled V˙O2max.4 | Scaled V˙O2max.5 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys |
| | SED | LPA | MPA | VPA | Sleep | | SED | LPA | MPA | VPA | Sleep |
| SED | - | 0.31 | -1.11 | -1.59 | 0.01 | SED | - | 0.11 | 1.78 | -4.58 * | -0.06 |
| LPA | -0.31 | - | -1.42 | -1.90 | -0.31 | LPA | -0.11 | - | 1.67 | -4.69 * | -0.16 |
| MPA | 0.87 | 1.12 | - | -0.72 | 0.87 | MPA | -1.39 | -1.27 | - | -5.97 * | -1.45 |
| VPA | 1.01 | 1.33 | -0.10 | - | 1.01 | VPA | 2.91 * | 3.02 * | 4.69 * | - | 2.85 * |
| Sleep | -0.01 | 0.32 | -1.11 | -1.59 | - | Sleep | 0.05 | 0.17 | 1.84 | -4.52 * | - |
| Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls |
| | SED | LPA | MPA | VPA | Sleep | | SED | LPA | MPA | VPA | Sleep |
| SED | - | 0.49 | -1.80 | -3.21 | 0.01 | SED | - | 0.15 | 2.49 | -7.96 * | -0.06 |
| LPA | -0.46 | - | -2.25 | -3.67 | 0.49 | LPA | -0.14 | - | 2.35 | -8.10 * | 0.12 |
| MPA | 1.35 | 1.84 | - | -1.86 | 1.35 | MPA | -1.88 | -1.72 | - | -9.83 * | -1.94 |
| VPA | 1.73 | 2.21 | -0.07 | - | 1.73 | VPA | 4.29 * | 4.45 * | 6.79 * | - | 4.23 * |
| Sleep | -0.01 | 0.49 | -1.80 | -3.21 | - | Sleep | 0.06 | 0.22 | 2.56 | -7.90 * | - |
| Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys |
| | SED | LPA | MPA | VPA | Sleep | | SED | LPA | MPA | VPA | Sleep |
| SED | - | 0.33 | -0.87 | -1.28 | 0.01 | SED | - | 0.15 | 1.70 | -4.47 * | -0.05 |
| LPA | -0.31 | - | -1.18 | -1.59 | -0.31 | LPA | -0.13 | - | 1.56 | -4.61 * | -0.19 |
| MPA | 0.69 | 1.02 | - | -0.59 | 0.69 | MPA | -1.34 | -1.19 | - | -5.81 * | -1.39 |
| VPA | 0.82 | 1.15 | -0.06 | - | 0.82 | VPA | 2.86 | 3.00 | 4.56 * | - | 2.80 |
| Sleep | -0.01 | 0.33 | -0.87 | -1.28 | - | Sleep | 0.05 | 0.20 | 1.75 | -4.41 * | - |
| Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls |
| | SED | LPA | MPA | VPA | Sleep | | SED | LPA | MPA | VPA | Sleep |
| SED | - | 0.45 | -1.79 | -7.60 * | 0.01 | SED | - | 0.19 | 3.24 * | -24.4 * | -0.07 |
| LPA | -0.43 | - | -1.34 | -8.03 * | -0.43 | LPA | -0.18 | - | 3.46 * | -24.6 * | -0.24 |
| MPA | 1.27 | 1.72 | - | -6.33 * | 1.27 | MPA | -2.28 * | -2.10 * | - | -26.7 * | -2.35 * |
| VPA | 1.98 | 2.44 | 0.19 | - | 1.98 | VPA | 6.38 * | 6.57 * | 9.62 * | - | 6.31 * |
| Sleep | -0.01 | 0.45 | -1.80 | -7.60 * | | Sleep | 0.07 | 0.25 | 3.30 * | -24.3 * | - |
| Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys |
| | SED | LPA | MPA | VPA | Sleep | | SED | LPA | MPA | VPA | Sleep |
| SED | - | 0.32 | -0.96 | -1.11 | 0.01 | SED | - | 0.16 | 2.03 | -4.21 * | -0.05 |
| LPA | -0.30 | - | -1.26 | -1.41 | -0.30 | LPA | -0.15 | - | 1.88 | -4.36 * | -0.20 |
| MPA | 0.71 | 1.03 | - | 0.40 | 0.72 | MPA | -1.51 | -1.35 | - | -5.72 * | -1.56 |
| VPA | 0.71 | 1.03 | -0.25 | - | 0.71 | VPA | 2.68 | 2.84 | 4.71 * | - | 2.63 |
| Sleep | -0.01 | 0.32 | -0.96 | -1.12 | - | Sleep | 0.05 | 0.21 | 2.08 | -4.16 * | - |
| Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls |
| | SED | LPA | MPA | VPA | Sleep | | SED | LPA | MPA | VPA | Sleep |
| SED | - | 0.41 | -1.25 | -1.89 | 0.01 | SED | - | 0.19 | 2.50 | -6.75 * | -0.06 |
| LPA | -0.38 | - | -1.88 | -2.27 | -0.38 | LPA | -0.18 | - | 2.32 | -6.93 * | 0.23 |
| MPA | 0.92 | 1.32 | - | -0.97 | 0.90 | MPA | -1.84 | -1.65 | - | -8.59 * | -1.90 |
| VPA | 1.05 | 1.46 | -2.27 | - | 1.05 | VPA | 3.77 | 3.96 | 6.27 * | - | 3.71 |
| Sleep | -0.01 | 0.40 | -0.97 | -1.89 | - | Sleep | 0.06 | 0.25 | 2.48 | -6.69 * | - |
Contrastingly, in pre-pubertal untrained children, compositions with 10 minutes less VPA (-5.2 mins∙day-1) compared to the average 15.2 mins∙day-1 were associated with a decrease in V˙O2max of between 3.2–$10.5\%$ (Table 6). Additionally, compositions with less LPA, but more MPA or VPA, were associated with an increase in V˙O2max between $5.2\%$ and $5.8\%$ in pre-pubertal untrained girls.
**Table 6**
| V˙O2max | V˙O2max.1 | V˙O2max.2 | V˙O2max.3 | V˙O2max.4 | V˙O2max.5 | Scaled V˙O2max | Scaled V˙O2max.1 | Scaled V˙O2max.2 | Scaled V˙O2max.3 | Scaled V˙O2max.4 | Scaled V˙O2max.5 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys | Pre-Pubertal Boys |
| | SED | LPA | MPA | VPA | Sleep | | SED | LPA | MPA | VPA | Sleep |
| SED | - | 0.63 | -1.34 | -3.17 * | 0.01 | SED | - | 0.23 | 2.11 | -8.93 * | -0.07 |
| LPA | -0.59 | - | -1.93 | -3.75 * | -0.59 | LPA | -0.22 | - | 1.90 | -9.14 * | -0.28 |
| MPA | 1.05 | 1.69 | - | -2.11 | 1.05 | MPA | -1.66 | -1.42 | - | -10.59 * | -1.72 |
| VPA | 1.62 | 2.25 | 0.27 | - | 1.62 | VPA | 4.58 | 4.81 | 6.69 * | - | 4.51 |
| Sleep | -0.01 | 0.63 | -1.34 | -3.17 | | Sleep | 0.07 | 0.30 | 2.18 | -9.15 * | - |
| Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls | Pre-Pubertal Girls |
| | SED | LPA | MPA | VPA | Sleep | | SED | LPA | MPA | VPA | Sleep |
| SED | - | 2.72 | -3.93 | -8.31 * | 0.01 | SED | - | 0.88 | 5.17 * | -19.43 * | -0.09 |
| LPA | -2.21 | - | -6.14 * | -10.52 * | -2.20 | LPA | -0.71 | - | 4.46 * | -20.13 * | -0.79 |
| MPA | 2.48 | 5.20 * | - | -5.84 * | 2.48 | MPA | -3.25 | -2.36 | - | -22.67 * | -3.33 |
| VPA | 2.86 | 5.58 * | -1.07 | - | 2.86 | VPA | 6.70 * | 7.57 * | 11.86 * | - | 6.61 * |
| Sleep | -0.01 | 2.72 | -3.94 | -8.31 * | | Sleep | 0.06 | 0.96 | 5.24 * | -19.34 * | - |
| Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys | Pubertal Boys |
| | SED | LPA | MPA | VPA | Sleep | | SED | LPA | MPA | VPA | Sleep |
| SED | - | 0.53 | -1.12 | -3.00 | 0.01 | SED | - | 0.27 | 2.22 | -10.64 * | -0.07 |
| LPA | -0.53 | - | -1.64 | -3.53 | -0.53 | LPA | -0.25 | - | 1.97 | -10.89 * | -0.32 |
| MPA | 0.87 | 1.44 | - | -2.13 | 0.87 | MPA | -1.72 | -1.45 | - | -12.36 * | -1.79 |
| VPA | 1.40 | 1.97 | 0.28 | - | 1.40 | VPA | 4.96 * | 5.23 * | 7.18 * | - | 4.89 * |
| Sleep | -0.01 | 0.57 | -1.12 | -3.01 | - | Sleep | 0.07 | 0.34 | 2.29 | -10.58 * | - |
| Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls | Pubertal Girls |
| | SED | LPA | MPA | VPA | Sleep | | SED | LPA | MPA | VPA | Sleep |
| SED | - | 0.77 | -1.43 | -3.85 | 0.01 | SED | - | 0.33 | 2.58 | -12.32 * | -0.08 |
| LPA | -0.71 | - | -2.15 | -4.57 | -0.71 | LPA | -0.30 | - | 2.28 | -12.63 * | -0.38 |
| MPA | 1.13 | 1.90 | - | -2.72 | 1.13 | MPA | -2.02 | -1.68 | - | -14.34 * | -2.10 |
| VPA | 1.84 | 2.62 | 2.72 | - | 1.84 | VPA | 5.90 * | 6.22 * | 4.90 * | - | 5.81 * |
| Sleep | -0.01 | 0.77 | -1.44 | -3.86 | | Sleep | 0.08 | 0.41 | 2.66 | -12.25 * | - |
| Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys | Post-Pubertal Boys |
| | SED | LPA | MPA | VPA | Sleep | | SED | LPA | MPA | VPA | Sleep |
| SED | - | 0.62 | -1.23 | -2.30 | 0.01 | SED | - | 0.32 | 2.67 | -8.89 * | -0.06 |
| LPA | -0.56 | - | -1.79 | -2.86 | -0.56 | LPA | -0.29 | - | 2.38 | -9.18 * | -0.35 |
| MPA | 0.89 | 1.51 | - | -1.41 | 0.89 | MPA | -1.93 | -1.60 | - | -10.82 * | -1.99 |
| VPA | 1.12 | 1.74 | -0.11 | - | 1.12 | VPA | 4.34 * | 4.66 * | 7.00 * | - | 4.27 * |
| Sleep | -0.01 | 0.62 | -1.23 | -2.30 | - | Sleep | 0.06 | 0.38 | 2.73 * | -8.83 * | - |
| Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls | Post-Pubertal Girls |
| | SED | LPA | MPA | VPA | Sleep | | SED | LPA | MPA | VPA | Sleep |
| SED | - | 0.44 | -1.54 | -3.55 | 0.01 | SED | - | 0.02 | 3.10 | -12.70 * | -0.08 |
| LPA | -0.42 | - | -1.96 | -3.96 | -0.41 | LPA | -0.19 | - | 2.91 | -12.89 * | -0.27 |
| MPA | 1.14 | 1.57 | - | -2.41 | 1.14 | MPA | -2.28 | -2.07 | - | -14.98 * | -2.36 |
| VPA | 1.59 | 2.02 | 1.59 | - | 1.59 | VPA | 5.70 * | 5.91 * | 5.68 * | - | 5.63 * |
| Sleep | -0.01 | 0.44 | -1.54 | -3.54 | - | Sleep | 0.08 | 0.28 | 3.17 | -12.62 * | - |
Scaled V˙O2max significantly decreased, irrespective of sex, maturity, or training status, when 10 minutes of VPA was reallocated to any other behaviour (Tables 5 and 6). Moreover, scaled V˙O2max tended to increase when time spent in other movement behaviours was reallocated to VPA. In isolation, both SED and Sleep were not significant predictors of absolute or scaled V˙O2max (Table 4) and displacing SED with any other behaviour had a negligible effect on V˙O2max (0.01–$1.98\%$; Tables 5 and 6) unless it was displaced with VPA where it was associated with an increase of 2.68–$6.38\%$ in scaled V˙O2max only. Similarly, compositions with 10 minutes less Sleep and 10 minutes more VPA were associated with a 2.85–$6.31\%$ greater scaled V˙O2max, irrespective of sex, training, and maturation. The effects of reallocating time to LPA, SED, or Sleep on V˙O2max were negligible, regardless of how V˙O2max was expressed and irrespective of sex, maturity, or training status.
## 4. Discussion
This is the first study to examine the inter-related effects of various movement behaviours (SED, LPA, MPA, VPA and Sleep), using a five-part compositional analysis, on absolute and scaled V˙O2max in trained and untrained children and adolescents. The main findings of the present study were that allocating time to, and removing time from, VPA significantly increased and decreased scaled V˙O2max, respectively, regardless of sex, training, or maturity status. Moreover, this study suggests that VPA is potentially 2.4–$4.7\%$ more potent in eliciting an improvement in VO2max over a 10-minute period in children and adolescents. These findings therefore highlight that intensity of PA may be of paramount importance in determining V˙O2max, especially in girls.
Engaging in 10 minutes more VPA, irrespective of which behaviour it displaces, significantly increases both absolute and scaled V˙O2max, regardless of training status. Moreover, of importance, untrained children were predicted to have a larger magnitude of change for the same 10-minute reallocation, in accord with the review of McNarry & Jones [8] which concluded that baseline fitness significantly impacts the magnitude of change experienced following a given stimulus. Furthermore, Mahon [37] reported that $52\%$ of the inter-individual variation in participant’s responses to a training stimulus can be explained by baseline peak V˙O2. The findings of the current study are, however, discordant with Carson et al. [ 16] who reported no significant differences when reallocating time to, or from, any movement behaviour. Such discrepancies may be explained by the use of a proxy measure of V˙O2max and not accounting for maturation or training status in the earlier study, which are critical when assessing cardiorespiratory fitness in children and adolescents [10, 11, 27].
The present study supports the notion that children and adolescents may require a vigorous stimulus to significantly improve absolute and scaled V˙O2max [8, 10, 12–14]. Whilst increasing levels of VPA is relatively time efficient, addressing a commonly cited barrier to physical activity [38], it is pertinent to note that the current findings suggest children and adolescents may need to increase their time spent in VPA by over $50\%$. Considering the limited success at increasing PA in many interventions to date [38, 39], and the small magnitude of increases in VPA reported even in those considered successful [40], the current findings highlight the need to drastically change our approach to PA promotion. These findings could be speculated to support the contention suggested by many authors that HIIT may represent an important public health intervention tool [29, 41].
Dencker et al. [ 12] reported weak, but significant, correlations between VPA and peak V˙O2 (r2 = 0.32) and scaled peak V˙O2 (r2 = 0.27). Furthermore, a recent review of the relationship between PA and peak V˙O2 in youth concluded that, despite decades of research, there was still no consensus [11]. These equivocal findings may be related to a reliance on techniques that fail to account for the inter-related and inherently constrained nature of PA behaviours, leading to spurious conclusions [18, 25, 26]. Moreover, the reliance on ratio scaled peak V˙O2 potentially creates erroneous associations [15, 42]. Of note, in the present study when V˙O2max was allometrically scaled by body mass, the overall PA composition explained ~$11\%$ less variance compared to absolute V˙O2max. This may be due, at least in part, to physically active children having a higher lean body mass (LBM) than their sedentary counterparts [43], indicating that differences in body composition may also be critical when determining the effect of re-allocating PA. Nevertheless, the PA composition still explained $37.7\%$ of the variance in allometrically scaled V˙O2max, demonstrating the powerful influence of habitual PA on aerobic fitness.
The finding that allocating time to MPA decreased scaled V˙O2max was surprising. These associations could be to the high amount of VPA undertaken by the trained group within this study and, consequently, if time is removed and replaced with MPA, it will have a negative impact on V˙O2max. Future research is required to explore the interaction of MPA and VPA in other athletic populations to confirm this hypothesis. Of note, the fixed time reallocation used within compositional analysis studies to date [18, 20], and in the present study, may over-estimate the magnitude of change in a given variable. More specifically, a 10-minute change in VPA constituted a ~$50\%$ increase in VPA within the current sample but the same 10-minute reallocation only represented a $1.9\%$ increase in SED time. Therefore, a greater insight into the independent, and interactive, effects of movement behaviours on V˙O2max may be gained by investigating the effects of the same percentage change in movement behaviours on V˙O2max. Nevertheless, evidence is emerging that the intensity of PA may be critical in improving both performance and health-related parameters in paediatric populations [18, 20, 25] and thus VPA should be encouraged, as opposed to MPA, to engender the greatest long-term health benefits.
Sedentary time in isolation was not a significant predictor of either absolute or scaled V˙O2max in children and adolescents within the current study, irrespective of sex, maturation, or training status. This is in direct contrast to the growing body of literature suggesting that excess sedentary time could lead to a decreased V˙O2max [21, 22] and suggests that instead of SED being the problem per se, it is potentially the PA behaviour it replaces that is influential. More specifically, significant increases in V˙O2max were only associated with compositions where the amount of time spent sedentary decreased and was replaced with VPA. However, whilst displacing SED with LPA and MPA were not associated with an increased V˙O2max, if these behavioural changes were introduced as part of a wider health initiative, they could still contribute to improving the health of the nation.
Future research should seek to implement targeted interventions informed by compositional analyses to ascertain the required duration needed to elicit the changes predicted. This is of particular importance as a plethora of research has investigated the influence of different training methodologies on both absolute and scaled V˙O2max, with their effectiveness being reviewed elsewhere [8, 10]. One major issue with many paediatric training studies to date is the lack of accounting for changes in habitual PA levels across the intervention period, and this could help explain the equivocal findings of some intervention types [10, 43].
Whilst there are numerous strengths associated with this study, such as the use of a novel five-part compositional analysis approach, allometrically scaling V˙O2max, and accounting for training, maturity and sex differences, there are limitations which must be acknowledged. Firstly, a relatively low wear-time criteria was set of any three days with at least eight hours of wear-time; a more stringent wear-time criteria could potentially influence the relationships established between PA metrics and V˙O2max. Nevertheless, this wear-time has been validated in a paediatric population [34] and was used to maximise participant inclusion within the study. Secondly, the cross-sectional study does not allow the duration over which the habitual changes need to be maintained to observe the associated changes to be elucidated. Thirdly, the small sample size compared to other studies of this type [18, 25] and the representativity of this population needs to be considered when interpreting the results of this study. Finally, the applicability of cycle derived V˙O2max to habitual PA levels is contentious, and therefore future research should endeavour to establish V˙O2max using treadmills to maximise specificity, and to establish whether these findings persist.
## 5. Conclusion
In conclusion, the proportion of time VPA is a significant predictor of scaled V˙O2max in children and adolescents, independent of training, sex, or maturity status and even when the proportion of time spent in other behaviours is considered. Moreover, reallocating time from VPA in pre-pubertal children predicts a reduced absolute V˙O2max, potentially highlighting the importance of promoting VPA in pre-pubertal children. Future research should seek to establish the duration of targeted PA interventions needed to elicit the significant changes predicted from compositional analyses and report the individual levels of MPA and VPA to ascertain the relative importance of VPA for current, and future, health in children and adolescents.
## References
1. Hallal P., Anderson l, Bull F, Guthold R., Haskell W, Ekelund U. **Global physical activity levels: surveillance progress. pitfalls, and prospects**. (2012.0) **380** 247-257
2. Lee I. M., Hseih C. C., Paffenbarger R. S.. **Exercise intensity and longevity in men. The Harvard Alumni Health Study**. (1995.0) **273** 1179-1184. PMID: 7707624
3. Paffenbarger R. S., Lee I. M.. **A natural history of athleticism, health and longevity. J**. *Sports Sci.* (1998.0) **16** S31-S45
4. Laukkanen J. A., Zaccardi F., Khan H., Kurl S., Jae S. Y., Rauramaa R.. **Long-term Change in Cardiorespiratory Fitness and All-Cause Mortality: A Population-Based Follow-up Study**. *Mayo Clin Proc* (2016.0) **91** 1183-8. DOI: 10.1016/j.mayocp.2016.05.014
5. Hill A., Lupton H.. **Muscular exercise, lactic acid, and the supply and utilsation of oxygen**. *QJM* (1923.0) **16** 135-171
6. Armstrong N.. **Commentary: Pediatric Aerobic Fitness and Trainability**. *Pediatr. Exerc. Sci* (2017.0) **29** 8-13. PMID: 28271801
7. McNarry M., Welshman J., Jones A.. **Influence of training and maturity status on the cardiopulmonary responses to ramp incremental cycle and upper body exercise in girls**. *J. Appl. Physiol* (2011.0) **110** 375-381. DOI: 10.1152/japplphysiol.00988.2010
8. McNarry M., Jones A.. **The influence of training status on the aerobic and anaerobic responses to exercise in children: A review**. *Eur J Sport Sci.* (2014.0) **14** 57-68. DOI: 10.1080/17461391.2011.643316
9. Carazo-Vargas P., Moncada-Jiménez J.. **A meta-analysis on the effects of exercise training on the VO2 max in children and adolescents**. *Retos.* (2015.0) **27** 184-187
10. Baquet G., Van Praagh E., Berthoin S.. **Endurance training and aerobic fitness in young people**. *Sports Med* (2003.0) **33** 1127-1143. DOI: 10.2165/00007256-200333150-00004
11. Armstrong N., Barker A., Armstong N., Mcmanus A.. *The Elite Young Athlete.* (2011.0)
12. Dencker M., Thorsson O., Karlsson M., Lindén C., Svensson J., Wollmer P., Andersen L.. **Daily physical activity and its relation to aerobic fitness in children aged 8–11 years. Eur**. *J. Appl. Physiol* (2006.0) **2006** 96
13. Dencker M., Thorsson O., Karlsson M., Lindén C., Eiberg S., Wollmer P., Andersen L.. **Gender differences and determinants of aerobic fitness in children aged 8–11 years. Eur**. *J. Appl. Physiol* (2007.0) 99
14. Latt E., Maestu J., Raask T., Rubin D., Purge P., Saar M., Utsal L., Jurimae J., Maasalu K., Jurimae T.. **Association of physical activity to cardiovascular fitness and fatness in 12–13 year old boys in different weight status. J**. *Public Health* (2013.0) **21** 231-239
15. Armstrong N., Welsman J.. **Youth cardiorespiratory fitness: evidence, myths and misconceptions**. *Bull World Health Organ* (2019.0). DOI: 10.2471/BLT.18.227546
16. Carson V., Tremblay M., Chaput J., Chastin S.. **Associations Between Sleep Duration, Sedentary Time, Physical Activity, and Health Indicators Among Canadian Children and Youth Using Compositional Analyses**. *Appl Physiol Nutr Metab.* (2016.0) **41**. DOI: 10.1139/apnm-2016-0026
17. Lynch B. A., Kaufman T. K., Rajjo T. I., Mohammed K., Kumar S.. **Accuracy of Accelerometers for Measuring Physical Activity and Levels of Sedentary Behaviour in Children: A Systematic Review**. *J Prim Care Community Health* (2019.0) **10** 1-8
18. Carson V., Tremblay M., Chaput J., Mcgregor D., Chastin S.. **Compositional analyses of the associations between sedentary time, different intensities of physical activity, and cardiometaolic biomarkers among children and youth from the United States**. *PLoS One.* (2019.0) **14**
19. CARE D. O. H. A. S.. **Chief Medical Officer**. *UK Chief Medical Officers’ Physical Activity Guidelines* (2019.0)
20. 20WHO
2020. WHO Guidelines on Physical Activity and Sedentary Behaviour.. *WHO Guidelines on Physical Activity and Sedentary Behaviour* (2020.0)
21. Leitzmann M. F., Jochem C., Schmid D.. **Sedentary Behaviour Epidemiology,**. *Springer International* (2018.0)
22. Ku P., Steptoe A., Liao Y., Hsueh M., Chen L.. **A cut-off of daily sedentary time and all cause mortality in adults: a meta-regression analysis involving more than one million participants**. *BMC Medicine.* **201** 1-9
23. Exel J, Mateus N, Travassos B, Gonçalves B, Gomes I, Leite N, Sampaio J. **Off-training levels of physical activity and sedentary behavior in young athletes: Preliminary results during a typical week**. *Sports.* (2018.0) **6** 141. DOI: 10.3390/sports6040141
24. Diaz K., Howard V., Hutto B., Colabianchi N., Vena J.. **Patterns of Sedentary Behavior and Mortality in U.S. Middle-Aged and Older Adults: A National Cohort Study**. *Ann. Intern. Med* (2017.0) **167**. DOI: 10.7326/M17-0212
25. Chastin S., Palarea-Albaladejo J., Dontje M., Skelton D.. **Combined Effects of Time Spent in Physical Activity, Sedentary Behaviors and Sleep on Obesity and Cardio-Metabolic Health Markers: A Novel Compositional Data Analysis Approach**. *PloS one.* (2015.0) 10. DOI: 10.1371/journal.pone.0139984
26. Dumuid D., Stanford T., Martin-Fernández J., Pedišić Ž., Maher C.. **Compositional data analysis for physical activity, sedentary time and Sleep research**. *Stat Methods Med Res.* (2018.0) **27**. DOI: 10.1177/0962280217710835
27. Armstrong N., Welsman J.. **Scientific Rigour in the Assessment and Interpretation of Youth Cardiopulmonary Fitness: A Response to the Paper ‘Normative Reference Values and International Comparisons for the 20-Metre Shuttle Run Test: Analysis of 69,960 Test Results among Chinese Children and Youth. J**. *Sports Sci. Med* (2020.0) **19** 627-629
28. Rogol A.. **Androgens and puberty**. *Mol Cell Endocrinol* (2002.0) **198** 25-9. DOI: 10.1016/s0303-7207(02)00365-9
29. Cao M., Quan M., Zhuang J.. **Effect of High-Intensity Interval Training versus Moderate-Intensity Continuous Training on Cardiorespiratory Fitness in Children and Adolescents: A Meta-Analysis**. *Int J Environ Res Public Health* (2019.0) **16** 1533. DOI: 10.3390/ijerph16091533
30. Gutin B., Yin Z., Humphries M., Barbeau P.. **Relations of moderate and vigorous physical activity to fitness and fatness in adolescents**. *AJCN* (2005.0) **81** 746-750. DOI: 10.1093/ajcn/81.4.746
31. Mirwald R. L., Baxter-Jones A. D. G., Bailey D. A., Beunen G. P.. **An assessment of maturity from anthropometric measurements**. *Med Sci Sports Exerc.* (2002.0) **34** 689-694. DOI: 10.1097/00005768-200204000-00020
32. Barker A. R., Williams C. A., Jones A. M., Armstrong N.. **Establishing maximal oxygen uptake in young people during a ramp cycle test to exhaustion**. *Br. J. Sports Med* (2011.0). DOI: 10.1136/bjsm.2009.063180
33. Evenson K. R., Catellier D. J., Gill K. I., Ondrak K. S., Mcmurray R. G.. **Calibration of two objective measures of physical activity for children. J**. *Sports Sci* (2008.0) **26** 1557-1565
34. Trost S.. **State of the Art Reviews: Measurement of Physical Activity in Children and Adolescents. Am. J**. *Lifestyle Med* (2016.0) **1** 299-314
35. Sadeh A., Sharkey K., Carskadon M.. **Activity-based Sleep-wake identification: an empirical test of methodological issues**. *Sleep* (1994.0) **17** 201-207. DOI: 10.1093/sleep/17.3.201
36. Hopkins W., Marshall S., Batterham A., Hanin J.. **Progressive statistics for studies in sports medicine and exercise science**. *Med Sci Sports Exerc* (2009.0) **41** 3-12. DOI: 10.1249/MSS.0b013e31818cb278
37. Mahon A., Armstrong N., Van Mechelen W.. *Paediatric Exercise Science and Medicine* (2008.0)
38. Mannocci A., D’egidio V., Backhaus I., Federici A., Sinopoli A.. **Are there effective interventions to increase physical activity in children and young people**. *An Umbrella Review. Int. J. Environ. Res. Public Health* (2020.0) **17** 3528. PMID: 32443505
39. Love R., Adams J., Van Sluijs E.. **Are school-based physical activity interventions effective and equitable? A meta-analysis of cluster randomized controlled trials with accelerometer-assessed activity**. *Obes. Rev* (2019.0) **20** 859-870. DOI: 10.1111/obr.12823
40. Goode A., Hall K., Batch B., Huffman K., Hastings S.. **The Impact of Interventions that Integrate Accelerometers on Physical Activity and Weight Loss: A Systematic Review**. *Ann. Behav. Med* (2017.0) **51** 79-93. DOI: 10.1007/s12160-016-9829-1
41. Runacres A., Mackintosh K.A., McNarry M.A.. **The effect of constant-intensity endurance training and high-intensity interval training on aerobic and anaerobic parameters in youth, J**. *Sports Sci.* (2019.0) **37** 2492-2498
42. Runacres A, Mackintosh K, Evans T, McNarry MA. **Effects of Sex, Training, and Maturity Status on the Cardiopulmonary and Muscle Deoxygenation Responses during Incremental Ramp Exercise**. *International Journal of Environmental Research and Public Health* (2022.0) **19** 7410. DOI: 10.3390/ijerph19127410
43. Bitar A., Vernet J., Coudert J., Vermorel M.. **Longitudinal changes in body composition, physical capacities and energy expenditure in boys and girls during the onset of puberty**. *Eur J Nutr* (2000.0) **39** 157-63. DOI: 10.1007/s003940070019
|
---
title: Green synthesized silver nanoparticles from eucalyptus leaves can enhance shelf
life of banana without penetrating in pulp
authors:
- Durr-e- Nayab
- Shamim Akhtar
journal: PLOS ONE
year: 2023
pmcid: PMC9994744
doi: 10.1371/journal.pone.0281675
license: CC BY 4.0
---
# Green synthesized silver nanoparticles from eucalyptus leaves can enhance shelf life of banana without penetrating in pulp
## Abstract
Bananas are exposed to serious post-harvest problems resulting in agricultural and economic losses across the world. The severity of problem is linked with the process of rapid ripening and pathogens attack. Such problems have led to economic losses as well as a lower yield of nutritionally rich bananas. The global demand to increase the life span of bananas and their protection from pathogens-borne diseases urged the use of antimicrobial edible coatings of nanoparticles. The present experiment has explored the innovative development of green synthesized nanoparticles from Eucalyptus leaf extract (ELE) to increase the shelf life of bananas up to 32 days from the day of collection. Statistically significant results were recorded ($$P \leq 0.05$$) by applying five different concentrations of silver nanoparticles (AgNPs) in ranges of 0.01–$0.05\%$. Various morphological and physiological parameters such as color, decay, firmness, weight loss, pulp to peel ratio, pH, titrable acidity (TA), phenolic contents, protein estimation, ethylene production, starch content and total soluble sugars were measured in *Cavendish banana* (Basrai). Bananas treated with $0.01\%$ AgNPs showed maximum control on its ripeness over morphological and physiological changes. The increase in shelf life was in order $0.01\%$>$0.02\%$>$0.03\%$>$0.04\%$>$0.05\%$> control. Further, AgNPs reduced the process of ripening by controlling ethylene production. The result has also proved the safety of banana consumption by simple removal of banana peel as penetration of AgNPs from the peel to the pulp was not detected. It is recommended to use $0.01\%$ AgNPs to enhance the shelf life of banana without effecting its nutritive value.
## Introduction
Banana (Musa spp.) is an important commercial fruit that has a great economic value due to its high consumption demand. It is a balanced diet source rich in various minerals, vitamins and carbohydrates with a little amount of protein, making it favorite global food [1]. Despite its huge demand, banana suffers from post-harvest losses, which is major concern of researchers [2]. Bananas are transported from their production areas to distant locations for marketing and consumption. If post-harvest bananas are not treated appropriately, they become susceptible to damage and degradation during transportation, marketing and storage. Bananas are physiologically sensitive to decay after harvest due to continuous change in metabolic processes such as transpiration or respiration rate [3]. Physical injuries and enzymatic actions by microorganism attack, or a combination of these factors can cause damage and degradation. Banana injuries and damage may result in moisture loss due to more surface evaporation [4] as well as microorganisms (fungi, bacteria) attack on injured fruits, causing substantially faster respiration rate than that of healthy bananas. Fruits with faster respiration and metabolic activity result in early storage decay or rots [5]. However, it suffers from many post-harvest nutritional losses which cause physiological and morphological changes such as color change, decay, weight loss, loss of starch, protein and phenolic contents [6,7]. For this reason, number of technologies are in practice to extend the shelf life of bananas by subjecting the controlled environmental conditions as low temperature storage, modify the atmospheric condition of storage and packing but these techniques are highly expensive [8].
Nanotechnology is an advanced field of research that has a vast application in medicine, pharmaceuticals, biochemistry, bio-sensing and biomaterial production by the synthesis of nano devices for its physiochemical properties to overcome the limitations of pre-existing technologies [9]. These nano sized particles improved the approach to produce the desirable consequences by the use of reliable, safe, cost effective and easy methodology. These nanomaterials are used almost in every field of life as in cosmetics, paints, environmental remediation, waste water management, electronic devices and the food industry [10]. Today it is a novel approach in food market to prevent post-harvest losses and increase the shelf life of food [11].
Various chemical, mechanical, physical and biological ways are used for synthesis of silver nanoparticles. These mechanisms are laborious, time consuming, costly and have environmental defects as they are the source of toxic byproducts [12]. So, the use of technically reliable, rapid, low cost and ecofriendly techniques are the current challenge for the synthesis of nanoparticles. Biological synthesis of silver nanoparticle by using enzymes and microbes is an environmentally safe technique, but the green chemistry has more advantage on it being easy, quick, safe, economically effective, environmentally non-hazardous and a single-step method for nanoparticles formation [13].
Green synthesis is more valuable platform for large scale nano sized particles synthesis as silver, gold and titanium by using different plant material such as plant extract, vegetables peels, fruits pulp, bark and fruit peels. These plant mediated synthesis of nanoparticles reduce the expensive, laborious and environment unfriendly mechanisms [14]. Green innovative approach of nanoparticles with below 100 nm dimensions has become the great interest of researchers and scientist to develop ecofriendly cost-effective and antimicrobial agent [15].
Silver is a safe, innocuous and antimicrobial agent which can be used to retard the growth of microbes. Control of ethylene production is another property of silver. Due to these applications, the synthesis of AgNPs have grabbed the attention of various researchers. Besides this, AgNPs have extensive role as drug delivery devices, catalysts, anti-microbial and bio-sensing agents. AgNPs produced from banana peel extract proved cost-effective solution for post-harvest management upto eight days by controlling ethylene production, determining starch content, measuring weight loss effect and acting as antimicrobial agent [7]. Similarly, Eucalyptus spp. is also a cheap source for AgNPs formation as edible coatings [16].
The Eucalyptus plant is cultivated in irrigated areas of Punjab and Sindh in Pakistan as well as other tropical countries and it is facing several allegations due to adverse effects on environmental and ecological conditions. Eucalyptus tree is found in large numbers in Pakistan, it is addressed as soil rendering water table and nutrients depletion country due to its high rate of evaporation and deep root system than water recharge as large amount of eucalyptus cultivation is causing a debate in various countries including South East Asia [17,18]. On the other hand, eucalyptus is accused for allelopathic effect by production of a large amount of toxic allelochemicals [19,20]. Due to its various negative effects, eucalyptus leaves can be an effective source to produce nanoparticles [21]. The great concern is to develop nanoparticles with dual effects being safe, reliable, and viable treatment on microbial activity and ripening process of bananas in an eco-friendly manner [22].
Present study comprehends the green synthesis of nanoparticles from eucalyptus leaves to manage post harvest losses in banana. The aim was extended to optimize different factors as silver nitrate and ELE to synthesize green route AgNPs for its better assembly and characterization by using various analytical techniques as UV-vis spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and Fourier transform infrared spectroscopy (FTIR) analysis. These techniques are helpful to describe the size, shape and functional features of nanoparticles [23]. Besides this, green synthesized AgNPs can be helpful in enhancing shelf life of banana in lower concentration without effecting its nutrition.
## Biosynthesis of AgNPs from eucalyptus leaves
Young Eucalyptus leaves from around 40 years old eucalyptus tree were collected from GT Road near Pindi Bypass, Gujranwala produce silver nanoparticles. Eucalyptus leaves were collected randomly, washed with tap water, and distilled water respectively. The small pieces of leaves were dried in shade to obtain the fine powder. Finally, the leaf powder was autoclaved under the pressure of 15Ib/sq inch and temperature of 121°C for 5 min [21]. Leaf powder of 5 g was boiled in 500 mL of distilled water (10 mg/mL) for 10 minutes and filtered by Whatman filter paper. The extract was stored at 4°C for further use. For silver nanoparticle synthesis, 2 mM silver nitrate solution was mixed with ELE (10 mg/mL) in different ratio. The change in color from yellow to dark brown represented the formation of AgNPs [21].
## Optimization of AgNPs
Different factors were studied to check their effect on the synthesis of nanoparticles. Briefly, 0.1 mL of the extract was poured in 0.9 mL of silver nitrate solution (1:9 ratio). Further, different concentrations of extract and silver nitrate solution were mixed until the final volume of 9:1 ratio was attained. After that the reaction mixture was heated at 80°C for 1 hour with continuous stirring and the synthesis of AgNPs was examined by recording UV–visible spectra (λ 300–600 nm) [24].
## Characterization of AgNPs
To analyze the physiochemical properties of synthesized AgNPs, different analytical techniques were used. For morphological study, synthesis of AgNPs was monitored by the change in color of the reaction mixture and for quantitative study, the subsequent reaction mixture was centrifuged at 44000 rpm for 15 minutes to attain pellet. Pellet was washed with distilled water for 2 to 3 times to remove the silver ions. After discarding the supernatant remaining pellet was dried in oven at 80°C to attain AgNPs for characterization [25].
## UV-VIS Spectrometry
It is a reliable analytical technique which was used to evaluate the synthesis and functional stability. For this process dried nanoparticles were re-suspended in distilled water by recording spectra between 300–700 nm at 0.1 nm resolution through UV-VIS spectrophotometer (UV-1800 SHIMADZU, Shimazdu, Japan) [26].
## Fourier transform infrared spectroscopy (FTIR)
The FTIR technique was used to study the stability and synthesis of nano-scale silver particle by monitoring the 10 mg dry sample of AgNPs at resolution power of 4 at range of 500–4000 cm−1 infrared through Fourier-transform infrared spectrophotometer (NICOLET iS5, Thermo Scientific, USA) [27].
## X-ray diffraction
It was a process to measure the purity and crystalline nature of powder form silver nanomaterial by determining the physiochemical property of crystal component. The diffraction pattern obtained from X ray diffractometer (JDX-3532, JEOL Japan) described the crystalline nature size of AgNPs at 2θ range of 0 to 100 by using the Debye–Scherrer’s equation [28].
## Scanning electron microscopy (SEM)
Morphology of AgNPs (Shape and size of powdered Ag nanoparticles) was measured by SEM (JSM-5910, JEOL Japan) [29].
## Transmission electron microscopy (TEM)
Internal structure (size, shape and organization) of AgNPs was measured by TEM (JEM-2100, JEOL Japan). For this purpose, liquid nanoparticle mixture was subjected on carbon coated copper grid and allowed to dry at room temperature. It is more valuable than SEM due to its high-resolution power and ability to determine analytical features [30].
## Energy dispersive x-ray analysis (EDX)
This analytical method was used to identify the desired elements. The spectra of peaks showed the true composition of sample by EDX with SEM (JSM-5910, INCA200 Oxford instruments, UK) [31].
## Nanoparticle’s coating applications
Cavendish bananas (Basrai) were collected from banana field Tando Jam, Sindh and each set had six bananas per treatment beside an uncoated (T0 = control) set. Banana is widely grown for research purpose by Pakistan Agriculture Research Council, Islamabad, and were available on request without any permission. The banana was dipped in different concentration of AgNps solution (T1 = $0.01\%$, T2 = $0.02\%$, T3 = $0.03\%$, T4 = $0.04\%$, T5 = $0.05\%$) for 5 minutes. Then an experimental set of banana was kept at 25°C. The data was recorded weekly to study the effect of silver nanoparticles.
## Ripening stages of peel color
Banana peel color was the most important morphological character to study the ripeness in banana at different stages. All varieties of bananas were studied weekly according to a standard color scale by using visual sense.1-Green, 2-Pale green, 3-Greenish yellow, 4-Yellow and less green, 5-Full Yellow, 6-Almost yellow, 7- Yellow with light brown flecked, 8-Yellow with full brown areas/Decay [32].
## Decay rate of banana
The decay rate of each banana variety was determined in percentage by using a standard decay scale after the interval of each week [32].
## Firmness (N)
Banana samples firmness was measured by using manual penetrometer (GY-3). Banana finger was placed on plain surface and pushed the penetrometer at its middle portion to take the perfect reading [8].
## Weight loss (%) measurement
The difference between initial and final weight was recorded in control and coated set of bananas to check the effect of AgNPs on total weight [22].
## Pulp to peel ratio (%)
In order to determine the effect of AgNPs on pulp to peel ratio, the weight of pulp was divided by peel weight [22].
## Moistening contents (%)
Moisture content of banana pulp from un-ripe to ripened stages were determined by placing 3-4mm thick slice in drying oven for 24 hours at 105°C. The moisture contents were calculated by using the following formula [33].
## Effect of pH
The banana pulp was homogenized by using a blender and then sieved in a beaker through muslin cloth to determine the pH value at different stages of banana. A digital pH meter was used by dipping the sensory electrode in beaker after calibration [33].
## Titrable acidity (%)
Titrable acidity (TA) of banana pulp was measured by direct titration method in which 10 mL pulp was taken in beaker and 2–3 drops of phenolphthalein ($1\%$ solution) were added to it. For titration, NaOH (0.1 N solution in burette) was added dropwise in beaker until the color of sample turned light pink. Titrable acidity was calculated by using the following formula [33].
## Measurement the starch content (%)
To study the starch conversion, anthrone test was conducted. For this reason, the 0.5g banana pulp was homogenized with 5ml $80\%$ ethanol in air tight beaker and incubated in water bath for 30 minutes at 80°C. Then it was centrifuged at 44000 rpm for 5 minutes. After that, 20 mL of distilled water and 6.5mL of perchloric acid was added in it. Then centrifuged at 4°C for 20 minutes and the supernatant was saved as extract. This process was repeated second time by adding 5mL distilled water and the supernatant was added in first extract by making the volume upto 100 mL with distilled water. Then 0.1 mL was taken and final volume was made upto 1mL by adding distilled water. Absorbance was measured at 620 nm by single beam spectrophotometer. Further, stock solution of glucose was prepared by adding 100 mg glucose in 100 mL distilled water and standard solution was prepared by diluting the 10 mL of stock solution by making final volume upto 100 mL by using distilled water. The standard curve was drawn by using different concentration of standard solutions (0.1, 0.4, 0.6, 0.8, 1 mL) in each test tube and by making the volume of 1 mL. Then, 4 mL of anthrone reagent was added to each test tube and heated them for 8 minutes. After cooling, 5 mL distilled water was mixed with it. To measure the starch content, the value found for glucose was multiplied by 0.9 [34].
## Total soluble sugar content (%)
To access TSS, 15 g banana pulp from each treatment was blended with 45 mL distilled water and few drops of extract were placed on refractometer prism after centrifugation at 11000 rpm for 5 minutes. Brix reading (sugar contents in aqueous medium) was calculated by calibration of refractometer with distilled water [35].
## Measurement of phenolic content (mg GAE/ 100g) of banana
Folin Ciocalteu (F.C.) colorimetric method was used to detect the phenolic content in banana. For this purpose, 0.5 mL banana extract was added in 0.5 mL F.C. reagent and it was homogenized manually for 20s. After 3 minutes, $7\%$ sodium carbonate solution of 2 mL was added in control and experimental test tubes and were placed in boiling water for 1 minute. After cooling the mixture, absorbance was measured by single beam spectrophotometer at 760 nm. The results were presented in mg of Gallic acid equivalents (mg/10 g). A calibration curve was plotted by using different concentrations of standard solution of Gallic acid (20–100 mg/L) [36].
## Protein estimation (%)
Protein content in bananas was estimated by adding 0.25 g pulp extract in 10 mL of potassium phosphate buffer (50 mM) by adjusting its pH 7.8 at room temperature. After that, all samples were centrifuged for 15 minutes and 2mL Bradford reagent was added in 0.1 mL aliquot of each sample. Absorbance of each sample was measured by spectrophotometer at 590 nm [37].
## Ethylene production (ppm)
To measure the ethylene concentration, all banana samples were weighed to record its initial weight and kept in the airtight plastic bag with rubber seal to prevent the gas leakage. Each bag contained 2–3 labelled banana samples. A three gas detector (F-950, Felix instruments, USA) with a gas sensor was used to measure the concentration of ethylene at different post-harvest stages of banana. Ethylene production was measured in each treatment of coated and controlled banana in triplicate [38].
## Statistical analysis
The experiment was carried out in a completely randomized design (CRD) in which each treatment was directed in three replications. In order to study the effect of each treatment, the data was subjected to ANOVA test at $5\%$ level of significance and these statistical tests were subjected by using the “Minitab 19” software (Originated at Pennsylvania State University, USA). Means were separated by the Duncan’s multiple range test. PCA (principal component analysis) was done to reduce multidimensional dataset.
## Synthesis and characterization of AgNPs
The formation of AgNPs from ELE indicated the visible change in color from colorless to dark brown. The UV-visible absorption spectra of AgNPs were recorded by mixing silver nitrate solution (2 mM) with eucalyptus leaf extract (Fig 1). AgNPs were characterized by using following analytical tools.
**Fig 1:** *Uv-vis spectrum of green synthesized AgNPs from ELE.*
## UV-visible analysis of AgNPs
UV-VIS absorption spectroscopy was used to evaluate the optical properties of AgNPs in which metal nanoparticles absorbed strong electromagnetic waves in the visible range and their formation showed the change in color from colorless to dark brown. The ELE and silver nitrate solution was mixed in different ratio for attaining the UV-visible absorption spectra of AgNPs. The results showed different peaks of surface plasmon resonance (SPR) in the range of 400–500 nm. The reaction mixture with 1:1 (v/v) ratio showed the highest absorbance peak of UV-VIS spectra at 419 nm while the mixture with 9:1 (v/v) did not show characteristic absorbance peak (Fig 2). The broad and lowest absorption peak at the lowest concentration of ELE was due to the formation of large anisotropic nanoparticles and low concentration of functional groups (alcohol, alkaloid and sugar) that were responsible for capping and stabilization of nanoparticles [39]. *In* general, absorbance peak intensity increased with an increase in plant extract and decreased with the decrease in plant extract. Moreover, absorbance peaks indicated a greater particle size at maximum wavelength and a smaller size at a shorter wavelength [27].
**Fig 2:** *Optimization of AgNPs with different ratio of silver nitrate and ELE solution.*
## FTIR analysis of AgNPs
Fourier transform infrared spectroscopy (FTIR) analysis of AgNPs synthesized by using ELE is shown in Fig 3. It was performed to find out the functional groups which have an efficient role in capping, reduction and stabilization of AgNPs. The FTIR spectrum observed at 3361 cm-1 correspond to N-H stretch containing alcohol or phenol and the band at 2915 cm1 corresponds to C-H (alkane) stretch. Similarly, the bands found at 1606 cm-1 associated with C = N, C = C and C = O stretching (Imine/alkene, conjugated alkene while 1441 cm-1 contains C-H (alkene) respectively. The bands seen at 1358 cm−1 attributes to C-H of the aldehyde group and peak at 1225 showed the C-O stretching of ester. Another peak at 1030 cm−1 and 833 cm−1 indicated that C-O, N-H and C-H functional group might also be bounded with AgNPs respectively. So the result revealed that these functional groups were responsible for the formation and stabilization of AgNPs as a capping agent due to the presence of soluble elements in ELE containing high percentage of C-N, C-C and O-H molecules [28,40]. Several reports have proposed that the phytochemicals present in ELE as stabilizing and capping agent play a crucial role in the reduction of silver ions [27,41,42].
**Fig 3:** *FTIR analysis of green synthesized AgNPs.*
## XRD analysis of AgNPs
X‐ray diffraction analysis is used to study the structure and crystal size of nanoparticles. XRD pattern of AgNPs showed that main peaks were 38.1°,44.2°,64.5° and 77.3° at 2 theta that can be corresponded to the [111], [200], [220] and [311] crystallographic planes (JPDS Card No. 04‐0783). In addition, two other unassigned weak peaks appeared at 27.8° and 32.1° at 2 theta that might be due to the presence of organic compounds in ELE (Fig 4). The average size of AgNPs was 10 nm as calculated by estimating (Full Width Half Maximum) FWHM of peak [111] through Debye–Scherrer’s equation [43]. The sharp peak in XRD pattern confirmed the existence of capping agent in leaf extract that stabilized the nanoparticles. Similarly, weaker peaks might be due to the presence of some biological macromolecules. In XRD analysis, height, width and position of peaks illustrated the size of AgNPs. The increase in peak showed the smaller size of nanoparticles. Similar results were reported by researcher who find the peak at 38.45°, 44.67°, 64° and 77° that were due to the presence of phytochemical compounds in extract [42]. Similarly, the results of AgNPs synthesis were reported where the highest peak were recorded at range of 38.0–38.9 [31,43–45].
**Fig 4:** *XRD analysis of green synthesized AgNPs.*
## SEM analysis of AgNPs
Scanning electron microscope is used to analyze the surface morphology, size and shape of nanoparticles at different magnification. The results depicted the spherical agglomerated particles with the range of 50–100 nm in a micrograph of AgNPs and the scale was used at range of 1–10 μm (Figs 5 and 6). The results of the present work are parallel with previous studies that reported the spherical AgNPs synthesis by pomegranate peel extract [29], seed extract of dates [46] and extract of cheese weed mallow [47].
**Fig 5:** *SEM image of green synthesized AgNPs with resolution power of X 10,000.* **Fig 6:** *SEM image of green synthesized AgNPs with resolution power of X 25,00.*
## TEM analysis of AgNPs
Transmission electron microscopy as imaging tool confirmed the microstructure, morphology and size distribution of AgNPs that were synthesized from ELE. The experiment revealed the spherical shape and ploy dispersal nature of these particles as well as its crystalline structure with size range of 5–20 nm (Figs 7 and 8). The close observation of AgNPs showed the shaded layer surrounding them and these foreign components come from leaf extract act as capping agent used to stabilize AgNPs [48]. Earlier investigations also reported the spheroidal morphological structure with stabilizing agents [30,49,50].
**Fig 7:** *TEM image of green synthesized AgNPs from ELE.* **Fig 8:** *TEM size distribution histogram of AgNPs.*
## EDX analysis of AgNPs
Energy-dispersive X-ray spectroscopy (EDX) was used to reveal the elemental composition of the AgNPs. The analysis reported the strong peak of silver at 3 keV with standard weight percent that was $70\%$ and weak signals of other organic compounds as carbon, oxygen and chlorine at range of 0–2.5 keV (Fig 9). The highest peak indicated the presence of silver element while the minor peak revealed the presence of other soluble elements in leaf extracts that act as stabilizing agents of AgNPs [51]. Previous research work stated the quantitative information of AgNPs solution which confirmed the $72\%$ silver component by using onion extract, $69\%$ silver by tomato extract and $77\%$ silver medicinal plant (Carduus crispus) extract [31,42].
**Fig 9:** *EDX analysis of green synthesized AgNPs.*
## Application of AgNPs on banana
Different concentrations of AgNPs were applied on banana samples to select the best concentration of nanoparticles involved in reducing the post-harvest losses in banana.
## Effect of AgNPs on banana color
Color is the most important criterion to determine the quality of fresh fruit. The surface color of banana, which changes from green to yellow as the fruit ripens, is one of the key elements to study the quality and market value of fruit [38]. All the samples of banana treated with different levels of AgNPs (T1 = 0.01, T2 = 0.02, T3 = 0.03, T4 = 0.04 and T5 = $0.05\%$) resulted the least change in banana color as compared to control banana samples. The untreated banana samples reached to fully ripened stage within 14 days of storage period and it may be due to degradation of green pigment chlorophyll that is replaced by new pigments like carotenoids, microbial growth and more ripening due to ethylene production [52] and the samples treated with AgNPs reduced the postharvest losses till 32 days at 25°C. Among all concentrations, bananas treated with $0.01\%$ (T1) of AgNPs showed the lowest score (3.00) of peel color with more greenish shade at 32 days of storage while untreated bananas got the highest score of 7.00 at 21 days and score 8.00 at 28 days of storage (Fig 10). The results are consistent with previous reports that AgNPs from citrus peel extract delayed the color development in different fruits and vegetables at $0.1\%$ concentration of neem-AgNPs that delayed the color of banana by minimizing the microbial attack [53]. Similarly, AgNPs from marigold flower and lemon peel extract improved the shelf life of berries upto 7 days [54] as these nanoparticles have the ability to reduce ethylene production and control microbial growth that inhibit the degradation of chlorophyll and formation of carotenoid in banana peel and retain the banana color fresh [55,56].
**Fig 10:** *Effect of AgNPs concentrations (T0 = control, T1 = 0.01%, T2 = 0.02%, T3 = 0.03%, T4 = 0.04%, T5 = 0.05%) on color of Cavendish banana (BASRAI) during 32 days of storage.*
## Effect of AgNPs on banana decay
Banana fruit decay was observed primarily after harvesting, followed by marketing, transportation and storage. The banana samples treated with different concentrations of AgNPs showed 0 decay during early storing stage. It was noted that the untreated bananas showed maximum decay at 14 days as compared to other treatments. Further, the treated samples showed maximum increase in shelf life with $0.01\%$ and $0.02\%$ AgNPs and got the value of 3.6 and 5.6 respectively for decay at 32 days of storage time. As compared to $0.01\%$ and $0.02\%$ of AgNPs, other three treatments as $0.03\%$, $0.04\%$ and $0.05\%$ concentration of AgNPs indicated maximum decay that was 7.6, 7.6 and 8.0 respectively at 32 days of storage. So, the results revealed that $0.01\%$ and $0.02\%$ were responsible to delay the process of decay in banana except control that showed full ripeness within 14 days. Within 21 days of storage, bananas became completely non edible with the maximum score of 8.0 (Fig 11) that may be due to the fast fruit ripening as a result of higher ethylene production and microbial attack that was the key factor to fruit deterioration. Further, fresh fruits continues to respire and transpire after harvesting that ultimately extend the decay rate in such fruits [57]. Generally, the results were in alignment with previous research work in which AgNPs attained from marigold flower and lemon peel extract increased the shelf life of berries for 7 days [54] and AgNPS with maize starch controlled the post-harvest decay in apricot for 8 days at 25°C and 24 days at 6°C [58]. Silver nanoparticle treatment maintained water loss and retarded the microbial growth in fruits by controlling the permeability of membranous tissues for minimization of decay process as well as inhibition of ethylene production showed the shelf life enhancement in different fruit cultivars [30,55].
**Fig 11:** *Effect of AgNPs concentrations (T0 = control, T1 = 0.01%, T2 = 0.02%, T3 = 0.03%, T4 = 0.04%, T5 = 0.05%) on decay of Cavendish banana (BASRAI) during 32 days of storage.*
## Effect of AgNPs on banana firmness (N)
The firmness of all banana samples was in range of 6.2–7.1 at 14 days of storage after application of AgNPs with different concentrations including untreated bananas. After 14 days of storage, bananas showed a decrease in firmness with control (3.3) while treated banana showed less decrease in firmness that was 6.2, 5.7, 5.5, 4.8 and 4.8 with $0.01\%$, $0.02\%$, $0.03\%$ $0.04\%$ and $0.05\%$ AgNPs respectively. In control, firmness loss was noted as 0.9 and it showed complete decay at day 21 and this might have contributed for the textural changes of banana due to change in amount of starch and pectic substances and polysaccharides in banana pulp [52]. The less increase in firmness as compared to control was observed 4.3, 4.1, 3.2, 2.4 and 2.1 with $0.01\%$, $0.02\%$, $0.03\%$, $0.04\%$ and $0.05\%$ of AgNPs respectively till 32 days of storage period (Fig 12). The results are in accordance with the study that used the guar gum based AgNPs with carboxymethyl cellulose to enhance the shelf life of mango for 14 days at 25°C after the spoilage of untreated mango over the total storage period of 28 days [59]. The loss of firmness during storage period was due to the change in banana texture but the coating of silver nano particles slowed down the metabolic and enzymatic activities by inhibiting the respiratory metabolism in the fruits that results a slower degradation of pulp tissues [59,60]. Consequently, bananas stored with AgNPs can retain their firmness by reducing the ethylene production, respiration rate, the speed of converting sugar and decay rate of banana [61].
**Fig 12:** *Effect of AgNPs concentrations (T0 = control, T1 = 0.01%, T2 = 0.02%, T3 = 0.03%, T4 = 0.04%, T5 = 0.05%) on firmness of Cavendish banana (BASRAI) during 32 days of storage.*
## Effect of AgNPs on weight loss (%) of banana
Weight loss is an important element to determine the quality of banana during prolonged storage period and to investigate the increase in the shelf life of banana. The results showed the weight loss percentage range of cavendish banana was 3.2–$4.3\%$ during storage time of 7 days that depends on the size of banana. During 14 days of storage, the weight loss percentage slightly increased in all banana samples at the range of 5.8–$6.6\%$ except control that was $9.3\%$. Similarly, the weight loss percentage was $13.9\%$ in control banana after 21 days, $21.5\%$ at 28 days and $33\%$ at 32 days of storage and they became non-edible. This could be linked to the reduced moisture retaining ability of un treated banana due to accelerated rate of transpiration and respiration from banana surface as well as deterioration in tissues of banana peel [62], while the weight loss percentage showed less increase as $10.8\%$, $12.8\%$, $12.8\%$, $11.6\%$ and $16.8\%$ with $0.01\%$, $0.02\%$, $0.03\%$, $0.04\%$ and $0.05\%$ AgNPs respectively at 32 days of storage (Fig 13). The results are similar to the previous work where AgNPs synthesized from tea extract increased the weight loss percentage with increase in storage period in cherry tomatoes for 15 days [63]. The increase in weight loss percentage with storage time was due to AgNPs coatings that act as semipermeable barriers which hinders the oxygen, carbon dioxide, water loss, respiration and other oxidation reactions and maintain the weight loss percentage in fruits by improving their post-harvest quality [6,58].
**Fig 13:** *Effect of AgNPs concentrations (T0 = control, T1 = 0.01%, T2 = 0.02%, T3 = 0.03%, T4 = 0.04%, T5 = 0.05%) on weight loss percentage of Cavendish banana (BASRAI) during 32 days of storage.*
## Effect of AgNPs on pulp to peel ratio (%) of banana
Pulp to peel ratio is a consistent index to study the post-harvest losses during ripening of banana, that also reveal the change in their moisture contents [64]. The effect of various AgNPs coatings on pulp to peel ratio of bananas showed the range of 1.2–$1.3\%$ with all treatments at the start of experiment and the treated banana with $0.01\%$, $0.02\%$ and $0.03\%$ had more effect on percentage of pulp to peel ratio as compared to $0.04\%$ and $0.05\%$. Basrai showed less increase in pulp to peel ratio percentage that was 1.4–$1.6\%$ at day 7 and 1.9–$2.4\%$ at day 32. As clearly observed in (Fig 14) the pulp to peel ratio of uncoated banana increased rapidly that was $2.4\%$ on day 14 and $3.4\%$ on day 32 with control. So, it was noted that when the peel weight was divided by the pulp weight in the current study, the pulp to peel ratio of bananas showed more upward tendency in untreated banana as compared to treated bananas. This might be due to water loss from peel to pulp and atmosphere as well as the increased amount of soluble sugar in pulp ultimately increased the pulp to peel ratio by transferring the osmotic pressure in the pulp more quickly [65]. Similarly, the pulp to peel ratio was $1.6\%$ with $0.01\%$, $0.02\%$, $0.03\%$, $0.04\%$ and $1.7\%$ with $0.05\%$ AgNPs on day 14. It was revealed that all banana samples with AgNPs treatments got less increase in pulp to peel ratio at 32 days of storage in which $0.03\%$ ($2.1\%$), $0.04\%$ ($2.1\%$) and $0.05\%$ ($2.5\%$) that was less adequate for post-harvest quality of banana as compared to $0.01\%$ ($1.8\%$) and $0.02\%$ ($1.9\%$) with more appropriate results (Fig 7A). The results are similar to the work in which $1.15\%$ and $1.25\%$ concentration of chitosan NPs increased the shelf life of banana upto 11 days with higher pulp to peel ratio in un treated banana as compared to treated banana samples [8]. So, the less increase in pulp to peel ratio was due to AgNPs coatings that reduce the respiration and other oxidation reactions and maintain the pulp to peel ratio in fruits by improving their post-harvest quality. The rapid conversion of starch into sugar in banana pulp during ripening process and the created osmotic gradient increases the weight of pulp while transpiration and respiration loss from peel again decrease the weight of peel [33]. AgNPs act as barrier and maintain the moisture evaporation and respiration of gases and transpiration rate from coated banana surface. Ultimately, treated banana fruits showed less increase in pulp to peel ratio than uncoated fruits [6,58].
**Fig 14:** *Effect of AgNPs concentrations (T0 = control, T1 = 0.01%, T2 = 0.02%, T3 = 0.03%, T4 = 0.04%, T5 = 0.05%) on pulp to peel ratio (%) of Cavendish banana (BASRAI) during 32 days of storage.*
## Effect of AgNPs on moisture content (%) of banana
The study of moisture content is another trend to check the post-harvest quality of banana. It was recorded that moisture content was in range of 77.6–82.0 including control banana at 7 days of storage, it gradually decreased and attained the value of $73.3\%$ in untreated banana. The value of moisture content of untreated banana reached to $56\%$ on day 21 and completely destroyed at day 28 with reduced moisture content. Rather than control, treated banana samples showed less increase in moisture content that was $66.3\%$, $63\%$, $60.6\%$, $60.6\%$ and $59.6\%$ with $0.01\%$, $0.02\%$ and $0.03\%$ $0.04\%$ and $0.05\%$ AgNPs respectively at the end of storage (32 days). This might be due to more respiration rate and evaporation in untreated banana than treated bananas [66]. The banana samples exhibited minimum decay and decrease in moisture content with $0.01\%$, $0.02\%$ and $0.03\%$ as compared to $0.04\%$ and $0.05\%$ of AgNPs (Fig 15). Similar trends were obtained by researchers who used $0.01\%$ AgNPs with Polyvinyl pyrolidone (PVP) solution and used these nanoparticle coatings in packaging of Rutab dates to enhance their shelf life for 30 days at 12°C in Italy [67] and coating of AgNPs to maintain the post-harvest quality in strawberry by increasing its shelf life for 16 days [68]. The researchers explained the role of AgNPs in reduction of respiration or evaporation that increased the shelf life of treated banana samples by maintaining the moisture level in fruits and vegetables [63,67].
**Fig 15:** *Effect of AgNPs concentrations (T0 = control, T1 = 0.01%, T2 = 0.02%, T3 = 0.03%, T4 = 0.04%, T5 = 0.05%) on moisture content (%) of Cavendish banana (BASRAI) during 32 days of storage.*
## Effect of AgNPs on pH of banana
Banana pulp pH was influenced by different treatments of AgNPs. Normally, it decreases at the start of the ripening stage and continuously increase until it attains a fully ripen stage [69]. Untreated banana showed pH values in range of 5.3–5.6 along with all banana samples treated with different concentrations of AgNPs at stage 0 to 7. It was noted that the control banana sample of Basrai started decay at pH 6.0 on day 21 and showed complete decay at pH 6.3 on day 28. The sharp increase in pH was due to change in metabolic process and less acidity that is directly proportional to respiration rate in fruit [70]. In comparison to control, all treated banana samples maintain pH till 32 days of storage with slight change. Treated banana depicted the less change in pH as 5.6 with $0.01\%$ and 5.5 with $0.02\%$ among all treatments at 32 days of storage (Fig 16). The results showed similarities with previous literature in which AgNPs maintain the pH value during the 30 days storage period of loquat at 4°C [70] and 10 days storage period of carrot at 10°C [71] while the sharp increase was seen in control samples and the fluctuations in results could be due to different climate conditions and type of fruit [70]. Generally, pH indicated the amount of organic acids in pulp of banana, which decreased at ripened stage of untreated banana due to use of these organic acids for respiration. However, bananas treated with AgNPs maintained the pH throughout the storage period. The acidity at un-ripen stage affects the taste of banana due to partial presence of oxalic acid which de-carboxylate at ripening stage by oxalate oxidase. So, control banana samples showed more change in pH as compared to treated banana samples. Similarly, different varieties of banana showed changes in pH depending on their ripeness and experimental conditions [33,69,70].
**Fig 16:** *Effect of AgNPs concentrations (T0 = control, T1 = 0.01%, T2 = 0.02%, T3 = 0.03%, T4 = 0.04%, T5 = 0.05%) on pH of Cavendish banana (BASRAI) during 32 days of storage.*
## Effect of AgNPs on titrable acidity (%) in banana
Change in titrable acidity is linked to decay of banana. The change in titratable acidity of banana pulp was studied by applying various concentration of AgNPs beside untreated bananas. A gradual increase in titrable acidity was detected in all treatments over the storage period of 21 days, where decline was noted after 21 days till 32 days of storage. Titrable acidity ranged from 0.32–$0.33\%$ in treated banana at day 0, that gradually increased with the increase of pH during 7 days of storage except untreated banana samples. It was observed that the untreated bananas showed decrease in titrable acidity making banana non-edible in 28 days of storage with maximum change in titrable acidity that was $0.16\%$ while banana treated with AgNPs maintained the post-harvest quality of banana till 32 days storage period. It could be linked to the rise in malic acid, citric acid, and oxalic acid with start of ripening. However, main cause of the decline in acidity of banana at maturity was conversion of acid into sugar content [72]. Among all, lower change in titarable acidity was recorded with $0.01\%$, $0.02\%$ and $0.03\%$ AgNPs which was $0.33\%$, while gradual decrease in titrable acidity was recorded with $0.04\%$ and $0.05\%$ AgNPs that was $0.32\%$ from day 0 to day 32 (Fig 17). Similarly in mango fruits coated with guar gum based AgNPs recorded less change in titrable acid values while untreated fruits had significantly more change at the end of the storage period [59]. In cherry tomatoes with Oolong tea-AgNps application showed stable acid contents at the end of 15 days storage as compared to chemically prepared AgNPs that showed only 3 days increase in shelf life [63]. The fluctuation in results was due to the application of silver nanoparticle coatings that is responsible to maintain acid contents in banana during storage [70]. Rapid increase in acid accumulation at immature stage raised the titrable acidity during ripening. Meanwhile the formation of sugar contents and physiological processes minimize the excessive increase of organic acids during maturity of banana [33,72,73].
**Fig 17:** *Effect of AgNPs concentrations (T0 = control, T1 = 0.01%, T2 = 0.02%, T3 = 0.03%, T4 = 0.04%, T5 = 0.05%) on titrable acidity (%) of Cavendish banana (BASRAI) during 32 days of storage.*
## Effect of AgNPs on TSS (Brix) in banana (%)
TSS is the total amount of optically active compounds in fruits and vegetables, and it is an important indicator of fruit maturity. The flavor in fruit is mostly due to its titratable acids and total soluble solids (sugars). Sugars such as glucose, sucrose, and fructose are the major components of total soluble solids in banana pulp. Breakdown of starch into sugars during ripening accelerate the sugar content and decrease the starch content [38]. The analysis of total soluble solids (TSS) showed significant difference between treated and untreated banana. The results depicted that there was a high increase ($12\%$) in TSS with untreated banana at day 14 and bananas were non-edible that reached to maximum at day 28 ($19\%$) and bananas were fully decayed at 32 days of storage period followed by less increase in those bananas which were treated with different concentration of AgNPs. Among all, the lowest TSS was from $0.01\%$, $0.02\%$ and $0.03\%$ AgNPs that was $9.0\%$, $10.6\%$ and $10.6\%$ respectively while other $0.04\%$ and $0.05\%$ AgNPs showed a similar pattern of TSS as $11.3\%$ and $13\%$ (Fig 18). The more increase of TSS in untreated bananas was due to the formation of more organic solutes by moisture loss through water evaporation and conversion of these organic solutes into sugar contents [74]. The study was in line with the previous results according to that silver nanoparticle from grapefruit were used for the postharvest management of cucumber upto 21 days of storage [75]. Similarly, AgNPs were also used to increase the shelf life of banana for 5 days by maintaining the TSS in treated banana instead of control banana [22]. This increase in sugar content could also be attributed to conversion of starch into soluble sugars during ripening stage in the presence of ripening enzymes [35,76]. So, the silver nano-coating may reduce the fruit respiration rate and slow down the consumption of acid content during the physiological and metabolic processes of fruit. As a result, the AgNPs may prolong the ripeness of banana after harvesting and increase its shelf life [58,68].
**Fig 18:** *Effect of AgNPs concentrations (T0 = control, T1 = 0.01%, T2 = 0.02%, T3 = 0.03%, T4 = 0.04%, T5 = 0.05%) on total soluble solids of Cavendish banana (BASRAI) during 32days of storage.*
## Effect of AgNPs on starch (%) in banana
A quantitative study for starch analysis showed the maximum value during the early stage of storage in all banana samples in the range of 27–29. During 14 days, starch content showed maximum decrease with untreated banana and slight decrease with treated banana and on day 28 all controlled banana became rotten with starch content of $0.84\%$. Other than untreated bananas, all of the treated banana samples showed less change in the starch content till the end of storage period (day 32) with $0.01\%$ AgNPs that was $4.44\%$. It was also noted that $0.02\%$, $0.03\%$, $0.04\%$ and $0.05\%$ showed more change in starch pattern as compared to $0.01\%$ as the value was 3.45, 3.45, 3.44 and 3.44 respectively (Fig 19). Shelf life of tomato was increased by using AgNPs from tea extract. Maximum decline of starch was noticed in untreated samples rather than AgNPs treated sample that increased shelf life of tomato for 15 days in maturation period [63]. Lower starch values were noted in untreated banana samples due to the formation of soluble sugar by the hydrolysis of starch and high rate of respiration and moisture evaporation [69], while AgNPs control respiration rate in peel by lowering oxygen level and increasing CO2 level which is an inhibitor of starch breakdown [58,77,78].
**Fig 19:** *Effect of AgNPs concentrations (T0 = control, T1 = 0.01%, T2 = 0.02%, T3 = 0.03%, T4 = 0.04%, T5 = 0.05%) on starch content of Cavendish banana (BASRAI) during 32days of storage.*
## Effect of AgNPs on phenolic content (mg GAE/ 100 g) in banana
Phenolic content involved in fruit shelf life act as stress defensive mechanism [79]. The study of phenolic content revealed a gradual increase in value from day 0 to day 21 and the slightly decrease till day 32 except un-coated banana. The banana without application of AgNPs started to become rotten showed a maximum decrease in phenolic content that was 33.3 mg GAE/ 100 g in day 21 and 26.7 mg GAE/ 100 g in day 32. All treated banana samples showed increase in phenolic content till the end of 21 days storage. In the start of banana ripening, the phenolic contents were recorded in the range of 26–29 mg GAE/ 100 g while at 21 days of storage, the phenolic content were $0.01\%$ (46.3 mg GAE/ 100 g), $0.02\%$ (43.2 mg GAE/ 100 g), $0.03\%$ (44.8 mg GAE/ 100 g), $0.04\%$ (43.6 mg GAE/ 100 g), and $0.05\%$ (43.5 mg GAE/ 100 g) while in end of storage, reduced decline was observed in phenolic contents with $0.01\%$ (42.5 mg GAE/ 100 g), $0.02\%$ (40.7 mg GAE/ 100 g), $0.03\%$ (40.5 mg GAE/ 100 g), $0.04\%$ (39.7 mg GAE/ 100 g), and $0.05\%$ AgNPs (40.1 mg GAE/ 100 g) as compared to control sample (Fig 20). The previous literature supported the effect of AgNPs from longknog peel extract on the post-harvest quality of longkong fruit and the significant lower decrease of phenolic content was noted with increase in storage for 9 days at 13°C [73]. The lowest phenolic contents in untreated banana could be due to high respiration and oxidation mechanism that ultimately reduce the concentration of phenolic content that increase the risk of microbial attack and reduce the anti-oxidant ability in banana by the activity of PPO enzymes. PAL is the key enzyme in biosynthesis of the phenolic compounds in the phenylpropanoid pathway. AgNPs induce the activity of PAL enzymes and showed increase initially [80] and then slight decline in phenolic content of treated banana as compared to control. Another enzyme PPO oxidized the phenolic content in untreated banana by using oxygen as co-substrate and the lower concentration of oxygen could be the reason to reduce phenolic content [70,73,81] while the AgNPs application on fruits enable them to maintain the phenolic content throughout the storage [38].
**Fig 20:** *Effect of various AgNPs concentration (T0 = control, T1 = 0.01%, T2 = 0.02%, T3 = 0.03%, T4 = 0.04%, T5 = 0.05%) on phenolic content of Cavendish banana (BASRAI) during 32 days of storage.*
## Effect of AgNPs on protein (%) in banana
The peak value in protein contents was noticed at the start of ripening (day 7) which in the range of 1.57–$1.59\%$. The control banana sample attained the highest decrease in protein value on day 21 ($1.36\%$), day 28 ($1.23\%$) and day 32 ($0.95\%$) of storage and completely decayed during 32 days. It could be due to proteolysis in which protein breakdown and converted into amino acid by proteolytic enzymes for the use in metabolic process and sugar formation as the change in protein content indicates the nutritional maturity in banana and physiochemical changes [82]. As compared to control banana, other banana sample showed gradual decrease in protein content from day 0 to day 32 with $0.01\%$, $0.02\%$, $0.03\%$,$0.04\%$ and $0.05\%$ AgNPs. The protein content value was $1.49\%$ ($0.01\%$), $1.41\%$ ($0.02\%$), $1.35\%$ ($0.03\%$), $1.33\%$ ($0.04\%$) and $1.33\%$ ($0.05\%$) at 32 days of storage. It was seen that banana samples showed minimum change in protein value by using $0.01\%$ AgNPs as compared to other treatments (Fig 21). In previous research, the less change in protein contents under post-harvest conditions of rice over the 10 days treatment of AgNPs [37], cut flower of gladiolus over the 12 days treatment of AgNPS [83] and banana over the 30 days treatment of *Aloe vera* with AgNPs coatings [38] were studied by using AgNPs application as compared to control. The ability of AgNPs as semi permeable coating reduced respiration rate and change the internal atmosphere of banana by lowering the activity of those enzymes that are responsible for degradation of protein [37,83,84].
**Fig 21:** *Effect of various AgNPs concentration (T0 = control, T1 = 0.01%, T2 = 0.02%, T3 = 0.03%, T4 = 0.04%, T5 = 0.05%) on protein content of Cavendish banana (BASRAI) during 32 days of storage.*
## Effect of AgNPs on ethylene production (ppm) in banana
Ethylene plays an important role in the ripening of banana. In addition to increase the process of fruit ripening, ethylene frequently causes over-ripening and even rotting, which shortens shelf life of fruits and vegetables. Maximum increase in ethylene production was recorded during ripening of uncoated banana sample as it was 36.5 ppm on day 0 which reached 77.5 ppm on day 14 with maximum decay and 1129.2 ppm on day 32. This could be due to high respiration rate and autocatalytic ethylene production that cause the physiological and metabolic changes by change in chloroplast structure that reduce the chlorophyll content and increase decay in uncoated bananas [85]. Rather than control banana sample, treated banana depicted the less increase in ethylene rate from day 0 to day 32. All treated banana sample showed ethylene rate in rang of 35 to 38 ppm at day 0 while its production rate was 68.5 ppm with $0.01\%$, 70.3 ppm with $0.02\%$, 87.6 ppm with $0.03\%$, 89.4 ppm with $0.04\%$ and 91.9 ppm with $0.05\%$ AgNPs concentrations (Fig 22). Different concentrations of edible coating with chitosan nanoparticles suppressed the ethylene production and increase the shelf life of banana till 30 days of storage [38]. Similarly, another study investigated the use of guar gum based AgNPs to accelerate the shelf life of mango for 28 days at 25°C by reducing the ethylene production [59]. So, the AgNPs as fruit coating treatments act as a semipermeable membrane that reduce the respiration rate and ethylene production by altering internal atmosphere. It delay metabolic activity and potentially reduce the ripening process which ultimately lead to the increase in fruit storage life [59].
**Fig 22:** *Effect of various AgNPs concentration (T0 = control, T1 = 0.01%, T2 = 0.02%, T3 = 0.03%, T4 = 0.04%, T5 = 0.05%) on ethylene production in Cavendish banana (BASRAI) during 32 days of storage.*
## Principle component analysis
Principal component analysis was done to provide better understanding of interaction within the results. The first two components showed a total variance of $91.7\%$. The first component, PC1 with a total variance of $78.8\%$ mainly consisted of firmness, pH, starch, titrable acidity (TA), moisture and protein content on its negative axis and color, decay, weight loss percentage, total soluble solid (TSS), pulp to peel ratio, ethylene rate and phenolic content on its positive axis. The second component, PC2 with $12.9\%$ of the variance made up the firmness, starch, moisture content, weight loss percentage, pulp to peel ratio and color were on positive axis while decay, pH, titrable acidity, total soluble solid (TSS), starch, phenolic and protein content were on negative axis (Fig 23). When the banana samples were accounted on the plane by the first two principal components PC1 and PC2, the different scoring positions were observed, depending on the treatments and storage week (Fig 24). It was noted that control banana samples appeared on the upper part of plane on positive axis of PC1 and PC2 with storage of 32 days which showed no increase in shelf life, while treated samples score appeared at the lower part of PC1 on negative axis from day 14 to 21 and on positive axis from day 28 to 32 which showed the increase in shelf life in order $0.01\%$ > $0.02\%$ > $0.03\%$ > $0.04\%$ > $0.05\%$. Silver NPs concentrations $0.04\%$ and $0.05\%$ showed score far away the positive axis of PC1 which represented the less increase in shelf life of banana as compared to other treatments. Among all treatments, $0.01\%$ and $0.02\%$ AgNPs showed the best scoring position that was near the positive axis of PC1 with less decay and maximum increase in shelf life. As the storage period progresses, the score of treated banana samples moves from negative axis to positive axis of PC1 and from positive axis to negative axis with PC2 as the shelf-life period showed a diagonal upward shift in the PC1–PC2 plane. The similar results were observed with three cultivars of apricot to enhance its shelf life [59,86].
**Fig 23:** *Banana samples scores with different parameters in the PCA plane with PC1: 78.8% of total variance and PC2: 12.9% of total variance.* **Fig 24:** *Banana samples scores with various AgNPs concentration (T0 = control, T1 = 0.01%, T2 = 0.02%, T3 = 0.03%, T4 = 0.04%, T5 = 0.05%) in the PCA plane with PC1: 78.8% of total variance and PC2: 12.9% of total variance.Symbols represent the storage days: ‘Day 0’ (●), Day 7 (►), Day 14 (■), Day 21 (♦), Day 28 (▲) and Day 32 (◄).*
## Shelf life
Post-harvest losses is a major concern in food industry. Longer shelf life is recommended for the bestselling, storage, preservation, packaging and transportation of fresh fruit [87]. The results illustrate the impact of green synthesized AgNPs ($0.01\%$, $0.02\%$, $0.03\%$, $0.04\%$ and $0.05\%$) from ELE on the shelf life of banana stored on 32 days of storage. The maximum banana shelf life was attained till 32 days by using $0.01\%$ and $0.02\%$ AgNPs followed by $0.03\%$ and $0.04\%$ for 28 days, whereas the shortest shelf life was found at 14 days with untreated bananas (Fig 25). These nanoparticles serve as barrier to retard the morphological and physio-chemical ripening process by controlling the microbiological activity that may contribute to reduce the post-harvest losses of banana by increasing its shelf life. According to the previous results the use of chemically synthesized chitosan nanoparticles with *Aloe vera* and moringa coatings extended the shelf life of banana upto 30 days [38].Furthermore, it was also found that the silver NPs has superior effects as edible coatings throughout the storage period of mango upto 14 days [59] and strawberry upto 15 days [4] by extending their shelf life. So, this is the first study that report the 32 days shelf life of banana by using the green synthesized AgNPs as compared to previously reported increase in shelf life with chemical synthesized nanoparticles. Moreover, AgNPs has long term preservation (up to 1 year) and can be used for log time [88,89].
**Fig 25:** *The effect of AgNPs concentrations (T0 = control, T1 = 0.01%, T2 = 0.02%, T3 = 0.03%, T4 = 0.04%, T5 = 0.05%) on the shelf life of Cavendish bananas (BASRAI) stored at 25°C from 0 to 32 days.*
## Detection of AgNPs in banana pulp
Energy-dispersive X-ray spectroscopy (EDX) was used to study the silver element inside the banana pulp. Carbon, oxygen, magnesium, calcium, phosphorus, potassium and chlorine were measured in samples at range of 0–4.0 keV (Fig 26). The analysis revealed that silver did not penetrate inside the banana pulp and banana treated with lower concentrations of AgNPs is safe to be used as food.
**Fig 26:** *EDX analysis to detect AgNPs penetration in banana pulp.*
## Practical implications
The study aimed at addressing the cheap and long lasting methods to enhance the shelf life of Banana. Negative allelopathic effects of Eucalyptus have been reported. AgNPs synthesized from ELE can give dual benefits by enhancing shelf life of Banana and reducing allelopathic effects of Eucalyptus, as AgNPs can be stored upto 1 year or more without effecting their efficacy.
## Conclusion
Shelf life of banana was increased upto 32 days at 25°C by using $0.01\%$ and $0.02\%$ of AgNPs prepared from ELE. This is also supported by the less change in color, decay, firmness, pulp to peel ratio, weight loss, moistening contents, titrable acidity, pH, total soluble solids, ethylene production, phenolic and protein content of bananas during storage period as compared to untreated banana. Moreover, this study demonstrated that AgNPs enhanced the post-harvest quality of banana by exploring the green nanotechnology through leaves of eucalyptus tree to balance its ecological threat as it is unfit in future for arid and semi-arid regions. These findings indicated that the green synthesized nanoparticles as non-hazardous coating on banana that offer a great potential for postharvest management of agricultural products by preventing the distribution, supply and storage problem of tropical commodities. This approach will ultimately lead toward agro-industrial and eco-environmental sustainability and it will also reduce the economic burden of country. The approach is being novel as AgNPs can be stored for a longer period and Ag did not penetrated in the pulp, hence safe to be used as food.
## References
1. Gemechu GE, Abewoy D, Jaleto K. **Post-harvest Loss Assessment of Banana (Musa spp.) at Jimma Town Market**. *J Plant Stud* (2021.0) **8** 1025-1032. DOI: 10.5539/jps.v10n1p23
2. Ahmad K, Afridi M, Khan NA, Sarwar A. **Quality Deterioration of Postharvest Fruits and Vegetables in Developing Country Pakistan: A Mini Overview**. *Asian J Agric Food Sci* (2021.0) **9** 83-90. DOI: 10.24203/ajafs.v9i2.6615
3. Kumar M, Singh R, Sharma I. **Integrated Management for Post-Harvest Diseases of Fruits and Vegetables**. *Bio Sci Res Bull* (2021.0) 37. DOI: 10.5958/2320-3161.2021.00005.5
4. Abu Salha B, Gedanken A. **Extending the Shelf Life of Strawberries by the Sonochemical Coating of their Surface with Nanoparticles of an Edible Anti-Bacterial Compound**. *Appl Nano* (2021.0) **2** 14-24. DOI: 10.3390/applnano2010002
5. Kumari P, Kumar S. *A study on post-harvest losses and constraints in banana cultivation in Vaishali district (Bihar)* (2018.0) **7** 93-95
6. Kale SK, Parishwad G V, Husainy ASN, Patil AS. **Emerging Agriculture Applications of Silver Nanoparticles**. *ES Food Agrofor* (2021.0) **3** 17-22. DOI: 10.30919/esfaf438
7. Jagana D R., Hegde Y, Lella R. **Green Nanoparticles—A Novel Approach for the Management of Banana Anthracnose Caused by**. *Int J Curr Microbiol Appl Sci* (2017.0) **6** 1749-1756. DOI: 10.20546/ijcmas.2017.610.211
8. Lustriane C, Dwivany FM, Suendo V, Reza M. **Effect of chitosan and chitosan-nanoparticles on post harvest quality of banana fruits**. *J Plant Biotechnol* (2018.0) **45** 36-44. DOI: 10.5010/JPB.2018.45.1.036
9. Raj S, Chand Mali S, Trivedi R. **Green synthesis and characterization of silver nanoparticles using**. *Biochem Biophys Res Commun* (2018.0) **503** 2814-2819. DOI: 10.1016/j.bbrc.2018.08.045
10. Baig N, Kammakakam I, Falath W, Kammakakam I. **Nanomaterials: A review of synthesis methods, properties, recent progress, and challenges**. *Mater Adv* (2021.0) **2** 1821-1871. DOI: 10.1039/d0ma00807a
11. Sahoo M, Vishwakarma S, Panigrahi C, Kumar J. **Nanotechnology: Current applications and future scope in food**. *Food Front* (2021.0) **2** 3-22. DOI: 10.1002/fft2.58
12. Ijaz M, Zafar M, Iqbal T. **Green synthesis of silver nanoparticles by using various extracts: a review**. *Inorg Nano-Metal Chem* (2020.0) **51** 1-12. DOI: 10.1080/24701556.2020.1808680
13. Bahrulolum H, Nooraei S, Javanshir N, Tarrahimofrad H, Mirbagheri VS, Easton AJ. **Green synthesis of metal nanoparticles using microorganisms and their application in the agrifood sector**. *J Nanobiotechnology* (2021.0) **19** 1-26. DOI: 10.1186/s12951-021-00834-3
14. Sila MJ, Nyambura MI, Abong’o DA, Mwaura FB, Iwuoha E. **Biosynthesis of Silver Nanoparticles from**. *Nano Hybrids Compos* (2019.0) **25** 32-45. DOI: 10.4028/www.scientific.net/nhc.25.32
15. Castillo-Henríquez L, Alfaro-Aguilar K, Ugalde-álvarez J, Vega-Fernández L, de Oca-Vásquez GM, Vega-Baudrit JR. **Green synthesis of gold and silver nanoparticles from plant extracts and their possible applications as antimicrobial agents in the agricultural area**. *Nanomaterials* (2020.0) **10** 1-24. DOI: 10.3390/nano10091763
16. Balamurugan M, Saravanan S. **Green Synthesis of Silver Nanoparticles by using i Leaf Extract**. *J Inst Eng Ser A* (2017.0) **98** 461-467. DOI: 10.1007/s40030-017-0236-9
17. Bayle GK. **Ecological and social impacts of eucalyptus tree plantation on the environment**. *J Biodivers Conserv Bioresour Manag* (2019.0) **5** 93-104. DOI: 10.3329/jbcbm.v5i1.42189
18. Nazli S, Siddiqui S, Rehman NU. **Assessing the Impact of Eucalyptus Plantation on Groundwater Availability in Pakistan**. *Int J Econ Environ Geol* (2020.0) **11** 59-64. DOI: 10.46660/ojs.v11i1.413
19. Amenu BT. **Review on Impact of Eucalyptus Plantation on the Soil**. *Int J Sci Res Civ Eng* (2017.0) **2** 37-43
20. Shi M, Xu J, Liu S, Xu Z. **Productivity-Based Land Suitability and Management Sensitivity Analysis: The Eucalyptus**. *Forests* (2022.0) **13** 1-18. DOI: 10.3390/f13020340
21. Ying S, Guan Z, Ofoegbu PC, Clubb P, Rico C, He F. **Green synthesis of nanoparticles: Current developments and limitations**. *Environ Technol Innov* (2022.0) **26** 1-20. DOI: 10.1016/j.eti.2022.102336
22. Nguyen TT, Huynh Nguyen TT, Tran Pham BT, Van Tran T, Bach LG, Bui Thi PQ. **Development of poly (vinyl alcohol)/agar/maltodextrin coating containing silver nanoparticles for banana (**. *Food Packag Shelf Life* (2021.0) **29** 1-11. DOI: 10.1016/j.fpsl.2021.100740
23. Samuggam S, Chinni S V, Mutusamy P, Gopinath SCB, Anbu P, Venugopal V. **Green synthesis and characterization of silver nanoparticles using spondias mombin extract and their antimicrobial activity against biofilm-producing bacteria**. *Molecules* (2021.0) **26** 1-13. DOI: 10.3390/molecules26092681
24. Ali M, Kim B, Belfield KD, Norman D, Brennan M, Ali GS. **Green synthesis and characterization of silver nanoparticles using**. *Mater Sci Eng C* (2016.0) **58** 359-365. DOI: 10.1016/j.msec.2015.08.045
25. Rautela A, Rani J, Debnath M. **Green synthesis of silver nanoparticles from Tectona grandis seeds extract: characterization and mechanism of antimicrobial action on different microorganisms**. *J Anal Sci Technol* (2019.0) **10** 1-10. DOI: 10.1186/s40543-018-0163-z
26. Shafiqa AR, Abdul Aziz A, Mehrdel B. **Nanoparticle Optical Properties: Size Dependence of a Single Gold Spherical Nanoparticle**. *J Phys Conf Ser* (2018.0) 1083. DOI: 10.1088/1742-6596/1083/1/012040
27. Jaast S, Grewal A. **Green synthesis of silver nanoparticles, characterization and evaluation of their photocatalytic dye degradation activity**. *Curr Res Green Sustain Chem* (2021.0) **4** 1-6. DOI: 10.1016/j.crgsc.2021.100195
28. Kumar D, Kumar G, Das R, Agrawal V. **Strong larvicidal potential of silver nanoparticles (AgNPs) synthesized using Holarrhena antidysenterica (L.) Wall. bark extract against malarial vector, Anopheles stephensi Liston**. *Process Saf Environ Prot* (2018.0) **116** 137-148. DOI: 10.1016/j.psep.2018.02.001
29. Saad PG, Castelino RD, Ravi V, Al-Amri IS, Khan SA. **Green synthesis of silver nanoparticles using Omani pomegranate peel extract and two polyphenolic natural products: characterization and comparison of their antioxidant, antibacterial, and cytotoxic activities**. *Beni-Suef Univ J Basic Appl Sci* (2021.0) **10** 1-10. DOI: 10.1186/s43088-021-00119-6
30. Santhoshkumar R, Hima Parvathy A, Soniya E V. **Phytosynthesis of silver nanoparticles from aqueous leaf extracts of**. *J Exp Nanosci* (2021.0) **16** 295-309. DOI: 10.1080/17458080.2021.1970140
31. Chand K, Cao D, Eldin Fouad D, Hussain Shah A, Qadeer Dayo A, Zhu K. **Green synthesis, characterization and photocatalytic application of silver nanoparticles synthesized by various plant extracts**. *Arab J Chem* (2020.0) **13** 8248-8261. DOI: 10.1016/j.arabjc.2020.01.009
32. Ringer T, Blanke M. **Non-invasive, real time in-situ techniques to determine the ripening stage of banana**. *J Food Meas Charact* (2021.0) **15** 4426-4437. DOI: 10.1007/s11694-021-01009-2
33. Ghimire R, Yadav PK, Khanal S, Shrestha AK, Devkota AR, Shrestha J. **Effect of different levels of gibberellic acid and kinetin on quality and self-life of banana (**. *Heliyon* (2021.0) **7** 1-8. DOI: 10.1016/j.heliyon.2021.e08019
34. Costa JF de O, Assunção IP, Lima GS de A, Muniz M de FS, Luz EDMN. **Carbohydrates,Phenolic Compounds and Antioxidant activity in pulp and peel of 15 Banana cultivsrs**. *Rev Bras Frutic* (2016.0) **38** 1-11. DOI: 10.1590/0100-29452016
35. Dwivany FM, Aprilyandi AN, Suendo V, Sukriandi N. **Carrageenan edible coating application prolongs Cavendish banana shelf life**. *Int J Food Sci* (2020.0) **2020** 1-11. DOI: 10.1155/2020/8861610
36. Mondher B.. **Comparative study of the chemical properties and composition of the**. *Int J Agric Sci Food Technol* (2020.0) **6** 030-036. DOI: 10.17352/2455-815x.000051
37. Sultana T, Javed B, Raja NI, Zia-Ur-Rehman M. **Silver nanoparticles elicited physiological, biochemical, and antioxidant modifications in rice plants to control**. *Green Process Synth* (2021.0) **10** 314-324. DOI: 10.1515/gps-2021-0034
38. Odetayo T, Sithole L, Shezi S, Nomngongo P, Tesfay S, Ngobese NZ. **Effect of nanoparticle-enriched coatings on the shelf life of Cavendish bananas**. *Sci Hortic (Amsterdam)* (2022.0) **304** 111312. DOI: 10.1016/j.scienta.2022.111312
39. Siakavella IK, Lamari F, Papoulis D, Orkoula M, Gkolfi P, Lykouras M. **Effect of plant extracts on the characteristics of silver nanoparticles for topical application**. *Pharmaceutics* (2020.0) **12** 1-17. DOI: 10.3390/pharmaceutics12121244
40. Huq MA. **Green synthesis of silver nanoparticles using**. *Int J Mol Sci* (2020.0) **21** 1-14. DOI: 10.3390/ijms21041510
41. Karthik C, Suresh S, Sneha Mirulalini G, Kavitha S. **A FTIR approach of green synthesized silver nanoparticles by**. *Inorg Nano-Metal Chem* (2020.0) **50** 606-612. DOI: 10.1080/24701556.2020.1723025
42. Urnukhsaikhan E, Bold BE, Gunbileg A, Sukhbaatar N, Mishig-Ochir T. **Antibacterial activity and characteristics of silver nanoparticles biosynthesized from Carduus crispus**. *Sci Rep* (2021.0) **11** 1-12. DOI: 10.1038/s41598-021-00520-2
43. Ssekatawa K, Byarugaba DK, Kato CD, Wampande EM, Ejobi F, Nakavuma JL. **Green Strategy–Based Synthesis of Silver Nanoparticles for Antibacterial Applications**. *Front Nanotechnol* (2021.0) **3** 1-16. DOI: 10.3389/fnano.2021.697303
44. Thiruvengadam V, Bansod A V. **Characterization of silver nanoparticles synthesized using chemical method and its antibacterial property**. *Biointerface Res Appl Chem* (2020.0) **10** 7257-7264. DOI: 10.33263/BRIAC106.72577264
45. Mahiuddin M, Saha P, Ochiai B. **Green synthesis and catalytic activity of silver nanoparticles based on**. *Nanomaterials* (2020.0) **10** 1-15. DOI: 10.3390/nano10091777
46. Ansari MA, Alzohairy MA. **One-pot facile green synthesis of silver nanoparticles using seed extract of**. *Evidence-based Complement Altern Med* (2018.0) **2018** 1-9. DOI: 10.1155/2018/1860280
47. Hublikar L V, Ganachari S V, Raghavendra N, Banapurmath NR, Patil VB, Yunus Khan TM. **Biogenesis of silver nanoparticles and its multifunctional anti-corrosion and anticancer studies**. *Coatings* (2021.0) **11** 1-19. DOI: 10.3390/coatings11101215
48. Talabani RF, Hamad SM, Barzinjy AA, Demir U. **Biosynthesis of silver nanoparticles and their applications in harvesting sunlight for solar thermal generation**. *Nanomaterials* (2021.0) **11** 1-19. DOI: 10.3390/nano11092421
49. Wang M, Li H, Li Y, Mo F, Li Z, Chai R. **Dispersibility and size control of silver nanoparticles with anti-algal potential based on coupling effects of polyvinylpyrrolidone and sodium tripolyphosphate**. *Nanomaterials* (2020.0) **10** 1-15. DOI: 10.3390/nano10061042
50. Prema P, Veeramanikandan V, Rameshkumar K, Gatasheh MK, Hatamleh AA, Balasubramani R. **Statistical optimization of silver nanoparticle synthesis by green tea extract and its efficacy on colorimetric detection of mercury from industrial waste water**. *Environ Res* (2022.0) **204** 1-14. DOI: 10.1016/j.envres.2021.111915
51. Bhatia D, Mittal A, Malik DK. **Antimicrobial potential and in vitro cytotoxicity study of polyvinyl pyrollidone-stabilised silver nanoparticles synthesised from**. *IET Nanobiotechnology* (2021.0) **15** 427-440. DOI: 10.1049/nbt2.12054
52. Opara UL, Al-Yahyai R, Al-Waili N, Said F Al, Al-Ani M, Manickavasagan A. **Postharvest Responses of “Malindi” Cavendish Banana to Various Storage Conditions**. *Int J Fruit Sci* (2013.0) **13** 373-388. DOI: 10.1080/15538362.2013.748378
53. Roberto SR, Youssef K, Hashim AF, Ippolito A. **Nanomaterials as alternative control means against postharvest diseases in fruit crops**. *Nanomaterials* (2019.0) **9** 1-20. DOI: 10.3390/nano9121752
54. Rodino S, Butu M, Butu A. **Application of biogenic silver nanoparticles for berries preservation**. *Dig J Nanomater Biostructures* (2019.0) **14** 601-606
55. Skutnik E, Łukaszewska A, Rabiza-Świder J. **Effects of Postharvest Treatments with Nanosilver on Senescence of Cut Lisianthus (**. *Agronomy* (2021.0) **11** 1-15. DOI: 10.3390/agronomy11020215
56. Naing AH, Win NM, Han JS, Lim KB, Kim CK. **Role of nano-silver and the bacterial strain**. *Front Plant Sci* (2017.0) **8** 1-12. DOI: 10.3389/fpls.2017.01590
57. Abiso E, Alemnew A, Eshetu S, Awoke M. **Effect of packaging materials and postharvest treatments on postharvest quality and shelf life of banana fruits (**. *Ann Food Sci Technol* (2018.0) **19** 292-299
58. Shahat M, Ibrahim M, Osheba A, Taha I. **Preparation and characterization of silver nanoparticles and their use for improving the quality of apricot fruits**. *Al-Azhar J Agric Res* (2020.0) **45** 33-43. DOI: 10.21608/ajar.2020.126625
59. Hmmam I, Zaid N, Mamdouh B, Abdallatif A, Abd-Elfattah M, Ali M. **Storage behavior of “seddik” mango fruit coated with CMC and guar gum-based silver nanoparticles**. *Horticulturae* (2021.0) **7** 1-21. DOI: 10.3390/horticulturae7030044
60. Ahmed Salem E, Abdel Salam Nawito M, Abd El-Raouf Ahmed AE-R. **Effect of Silver Nano-Particles on Gray Mold of Tomato Fruits**. *J Nanotechnol Res* (2019.0) **01** 108-118. DOI: 10.26502/jnr.2688-8521009
61. 61Toan N Van. Effects of low temperature on the storage time of Canvendish banana (Musa AAA) after harvest. Procedings 2nd Conf Food Sci Technol. 2013;2: 554–560.
62. Kaka AK. **Effect of hot water treatments and storage period on the quality attributes of banana (**. *Pure Appl Biol* (2018.0) **7** 363-371. DOI: 10.19045/bspab.2018.700195
63. Gao L, Li Q, Zhao Y, Wang H, Liu Y, Sun Y. **Silver nanoparticles biologically synthesised using tea leaf extracts and their use for extension of fruit shelf life**. *IET Nanobiotechnology* (2017.0) **11** 637-643. DOI: 10.1049/iet-nbt.2016.0207
64. Tourkey MN, Tarabih ME, El-Eryan EE. **Physiological Studies on the marketability of Williams Banana Fruits**. *Am J Plant Physiol* (2014.0) **9** 1-15. DOI: 10.3923/ajpp.2014.1.15
65. Hailu M, Seyoum Workneh T, Belew D. **Effect of packaging materials on shelf life and quality of banana cultivars (**. *J Food Sci Technol* (2014.0) **51** 2947-2963. DOI: 10.1007/s13197-012-0826-5
66. Chukwu O, Sunmonu MO, Egbujor EC. **Effects of storage conditions on nutritional compositions of banana**. *Qual Assur Saf Crop Foods* (2011.0) **3** 135-139. DOI: 10.1111/j.1757-837X.2011.00103.x
67. Zaied S.F.. **Silver Nanoparticles in Polymeric Matrices for Keeping Quality of Dates Packaging**. *J Microb Biochem Technol* (2021.0) **13** 1-9
68. Taha IM, Zaghlool A, Nasr A, Nagib A, El Azab IH, Mersal GAM. **Impact of starch coating embedded with silver nanoparticles on strawberry storage time**. *Polymers (Basel)* (2022.0) **14** 1-16. DOI: 10.3390/polym14071439
69. Li MC, Chou CF, Hsu SC, Lin JS. **Physicochemical characteristics and resistant starch of different varieties of banana from Taiwan**. *Int J Food Prop* (2020.0) **23** 1168-1175. DOI: 10.1080/10942912.2020.1788077
70. Ali M, Ahmed A, Shah SWA, Mehmood T, Abbasi KS. **Effect of silver nanoparticle coatings on physicochemical and nutraceutical properties of loquat during postharvest storage**. *J Food Process Preserv* (2020.0) **44** 1-8. DOI: 10.1111/jfpp.14808
71. Becaro AA, Puti FC, Panosso AR, Gern JC, Brandão HM, Correa DS. **Postharvest Quality of Fresh-Cut Carrots Packaged in Plastic Films Containing Silver Nanoparticles**. *Food Bioprocess Technol* (2016.0) **9** 637-649. DOI: 10.1007/s11947-015-1656-z
72. Muhammad A, Hussain I, Ali K, Zeshan M, Ali S, Soomro NA. **Physicochemical, yield potential and sensory evaluation of banana cultivars introduced in Pakistan**. *Pakistan J Agric Sci* (2020.0) **57** 1581-1589. DOI: 10.21162/PAKJAS/20.688
73. Lichanporn I, Techavuthiporn C, Wongs-Aree C. **Effect of silver particle-longkong peel extract coating on postharvest decay and browning in longkong fruit**. *Hortic J* (2020.0) **89** 328-336. DOI: 10.2503/hortj.UTD-119
74. Yasmin T, Islam MA, Quadir QF, Joyce DC, Bhandari B. **Ripening quality of banana cv. Amritasagor through application of different ripening agents**. *Arch Agric Environ Sci* (2021.0) **6** 35-41. DOI: 10.26832/24566632.2021.060105
75. Faghihi R, Larijani K, Abdossi V, Moradi P. **Evaluation of the effect of plastics bags containing silver nanocomposite of grapefruit ‘ s peel on cucumber postharvest nutritional value and their possible penetration in tissue**. *Food Heal* (2020.0) **3** 35-39
76. Kanchana K, Senerath A, Thiruchchelvan N, Kumara A. **Use of maturity traits to identify optimal harvestable maturity of banana**. *Open Agric* (2021.0) **6** 143-151. DOI: 10.1515/opag-2021-0015
77. Elatafi E, Fang J. **Effect of Silver Nitrate (AgNO3) and Nano-Silver (Ag-NPs) on Physiological Characteristics of Grapes and Quality during Storage Period**. *Horticulturae* (2022.0) 8. DOI: 10.3390/horticulturae8050419
78. de Almeida Teixeira GH, Santos LO, Cunha Júnior LC, Durigan JF. **Effect of carbon dioxide (CO2) and oxygen (O2) levels on quality of ‘Palmer’ mangoes under controlled atmosphere storage**. *J Food Sci Technol* (2018.0) **55** 145-156. DOI: 10.1007/s13197-017-2873-4
79. Das D, Mandal P. **Use of biogenic silver nanoparticles in enhancing shelf life of**. (2020.0) **10** 1-18. DOI: 10.1038/s41598-020-65953-7
80. Alqahtani YS, Bahafi A, Mirajkar KK, Basavaraju RR, Mitra S, Shailaja S. **In vitro antibacterial activity of green synthesized silver nanoparticles using**. *Antibiotics* (2022.0) **11** 1-20. DOI: 10.3390/antibiotics11111503
81. Mwelase S, Fawole O. **Effect of Chitosan-24-Epibrassinolide Composite Coating on the Quality Attributes of Late-Harvested Pomegranate Fruit under**. *Plants* (2022.0) **11** 1-24. DOI: 10.3390/plants11030351
82. Du L, Song J, Forney C, Palmer LC, Fillmore S, Zhang Z. **Proteome changes in banana fruit peel tissue in response to ethylene and high-temperature treatments**. *Hortic Res* (2016.0) **3** 1-12. DOI: 10.1038/hortres.2016.12
83. Maity TR, Samanta A, Saha B, Datta S. **Evaluation of Piper betle mediated silver nanoparticle in post-harvest physiology in relation to vase life of cut spike of Gladiolus**. *Bull Natl Res Cent* (2019.0) **43** 1-11. DOI: 10.1186/s42269-019-0051-8
84. Islam T, Afrin N, Parvin S, Dana NH, Rahman KS, Zzaman W. **The impact of chitosan and guava leaf extract as preservative to extend the shelf-life of fruits**. *Int Food Res J* (2018.0) **25** 2056-2062
85. Wei H, Seidi F, Zhang T, Jin Y, Xiao H. **Ethylene scavengers for the preservation of fruits and vegetables: A review**. *Food Chem* (2021.0) **337** 1-12. DOI: 10.1016/j.foodchem.2020.127750
86. Velardo-micharet B, Agudo-corbacho F, Ayuso-yuste MC. **Evolution of Some Fruit Quality Parameters during Development and Ripening of Three Apricot Cultivars and Effect of Harvest Maturity on Postharvest Maturation**. *Agriculture* (2021.0) **11** 1-21. DOI: 10.3390/agriculture11070639
87. Guo J, Fu H, Yang Z, Li J, Jiang Y, Jiang T. **Research on the physical characteristic parameters of banana bunches for the design and development of postharvesting machinery and equipment**. *Agric* (2021.0) 11. DOI: 10.3390/agriculture11040362
88. Murillo-Rabago EI, Vilchis-Nestor AR, Juarez-Moreno K, Garcia-Marin LE, Quester K, Castro-Longoria E. **Optimized synthesis of small and stable silver nanoparticles using intracellular and extracellular components of fungi: an alternative for bacterial inhibition**. *Antibiotics* (2022.0) **11** 1-19. DOI: 10.3390/antibiotics11060800
89. Velgosova O, Cizmarova E, Malek J, Kavulicova J. **Effect of storage conditions on long-term stability of Ag nanoparticles formed via green synthesis**. *Int J Miner Metall Mater* (2017.0) **24** 1177-1182. DOI: 10.1007/s12613-017-1508-0
|
---
title: Associations of 5-year changes in alcoholic beverage intake with 5-year changes
in waist circumference and BMI in the Coronary Artery Risk Development in Young
Adults (CARDIA) study
authors:
- J. Lauren Butler
- Penny Gordon-Larsen
- Lyn M. Steffen
- James M. Shikany
- David R. Jacobs
- Barry M. Popkin
- Jennifer M. Poti
journal: PLOS ONE
year: 2023
pmcid: PMC9994756
doi: 10.1371/journal.pone.0281722
license: CC BY 4.0
---
# Associations of 5-year changes in alcoholic beverage intake with 5-year changes in waist circumference and BMI in the Coronary Artery Risk Development in Young Adults (CARDIA) study
## Abstract
### Objective
This study aimed to shed light on contradictory associations of alcohol intake with waist circumference (WC) and body mass index (BMI) by examining 5-yr changes in alcohol intake in relation to 5-yr WC and BMI changes.
### Methods
This prospective study included 4,355 participants (1,974 men and 2,381 women) enrolled in the Coronary Artery Risk Development in Young Adults (CARDIA) study at baseline (1985–1986) and followed over 25 years (2010–2011). Longitudinal random effects linear regression models were used to test whether changes in drinking (defined categorically) as starting to drink, increasing, decreasing, stable drinking or stopping drinking (versus stable non-drinking) over a series of 5-yr periods were associated with corresponding 5-yr WC and BMI changes. Associations with 5-yr changes (defined categorically as starting, stable or stopping) in drinking level (i.e., light/moderate and excessive) and 5-yr changes (defined categorically as increasing, no change, or decreasing) by beverage type (i.e., beer, wine and liquor/mixed drinks) were also examined.
### Results
In men, compared to stable non-drinking, decreasing total alcohol intake was associated with lower 5-yr WC (β:-0.62 cm; $95\%$ CI: -1.09, -0.14 cm) and BMI gains (β:-0.20 kg/m2; $95\%$ CI: -0.30, -0.03 kg/m2) and stopping excessive drinking was associated with lower 5-yr WC gains (β:-0.77 cm; $95\%$ CI: -1.51, -0.03 cm). In women, compared to those with stable non-drinking habits, starting light/moderate drinking was associated with lower 5-yr WC (β: -0.78 cm; $95\%$ CI: -1.29, -0.26 cm) and BMI gains (β:-0.42 kg/m2; $95\%$ CI: -0.64, -0.20 kg/m2). Increasing wine intake was associated with a lower 5-yr BMI gain (β:-0.27 kg/m2; $95\%$ CI: -0.51, -0.03 kg/m2). Decreasing liquor/mixed drink (β:-0.33 kg/m2; $95\%$ CI: -0.56, -0.09 kg/m2) intake was associated with lower 5-yr WC (β:-0.88 cm; $95\%$ CI: -1.43, -0.34 cm) and BMI (β:-0.33 kg/m2; $95\%$ CI: -0.56, -0.09 kg/m2) gains.
### Conclusions
Associations of alcohol intake with obesity measures are complex. In women, wine and liquor/mixed drink intakes had contrasting associations with WC and BMI change. In men, decreasing weekly alcoholic beverage intake with an emphasis on stopping excessive consumption may be beneficial in managing WC and BMI gains.
## Introduction
An increasing trend in energy consumed from alcoholic beverages coupled with secular increases in waist circumference (WC) and body mass index (BMI) have been reported in the US over the past two decades [1–3]. The Dietary Guidelines for Americans, 2020–2025 (DGA) explicitly state that “alcoholic beverages are not a component of the United States Department of Agriculture (USDA) Dietary Patterns and that regular consumption of alcoholic beverages can make it challenging for adults to meet food group and nutrient needs while not consuming excess calories” [4]. However, the DGA do not mention associations of alcohol intake with WC or BMI. In fact, results of studies of alcohol intake with measures of weight status are inconsistent. Positive, null and negative associations of alcohol intake with WC, BMI, and changes in WC and BMI have been reported. Residual confounding, selection bias and variation in associations by drinking level and alcoholic beverage type have been cited as potential contributors to contradictory findings [5–8].
Residual confounding by unmeasured characteristics that differ within and across drinking categories may underlie inconsistent findings [5,9,10]. In the US, people who don’t drink have been reported to engage in less physical activity, consume more energy and belong to lower socioeconomic subgroups as compared to those that drink [8,11]. In one study of Northern Californians, those who reported heavy drinking included higher proportions of persons who preferred drinking beer and liquor as compared to the proportion of those who preferred wine [12]. In a 2021, Mendelian randomization analysis of 334,507 white British adults Zhou et. al. found that socioeconomic status significantly mediated the relationship between educational attainment and alcoholic beverage choice. Those with higher educational attainment may be more likely to be exposed to messages that red wine is associated with long-term health benefits. Leading to higher consumption of wine as compared to their lower socioeconomic status counterparts [13,14]. Among US adults, wine drinking has been associated with higher educational attainment and higher intakes of food and beverage groups supported by the DGA [4,8,12,15]. In a systematic review of associations of alcoholic beverage preference with dietary habits, Sluik et. al concluded that those in Western and Mediterranean countries who prefer to drink beer and liquor generally have less healthy dietary habits [15]. For example, in one US-based study, beer and liquor drinkers had higher adjusted mean total fat and lower adjusted mean dietary fiber intakes as compared to those who preferred to drink wine [16]. Despite associations with alcohol intake and measures of obesity, dietary intake and physical activity have been cited as key omitted confounders in epidemiologic studies of alcohol and obesity outcomes [5,17]. People may self-select into alcohol consumption behavior patterns based on socio-demographic characteristics and inherent individual traits [18,19]. If unaccounted for, residual confounding and self-selection may bias associations of alcohol intake with WC and BMI and contribute to inconsistencies in the alcohol and obesity literature [8,9,20].
In addition, variation in associations by drinking level and alcoholic beverage type may add to the mixed literature. Positive and null associations of excessive drinking and BMI gains have been reported [5,21,22]. There is evidence that stable heavy drinking, in men, and maintaining stable light or moderate drinking, in women, may underlie positive and negative associations of within-person changes in total alcohol intake with WC and BMI change in men and women, respectively [23,24]. With regard to alcoholic beverage type, a standard drink contains roughly 14 grams of alcohol. Yet, energy content, percent alcohol by volume (%ABV) and bioactive components vary considerably across beverage types. Unlike liquor, beer and wine contain bioactive compounds (e.g., polyphenols and resveratrol), tend to have a lower %ABV, and can have fewer calories than liquor served with a mixer [25–27]. Positive and negative associations of beer intake and changes in beer intake with weight and BMI gains have been found [17,28]. While wine intake has been negatively associated with weight gain, positive associations with liquor consumption have been reported for both sexes [25,29]. The results are limited and inconclusive regarding associations of within-person changes in alcohol consumption levels and alcoholic beverages by type (e.g., decreasing beer or wine intake) and changes in WC and BMI [5,17,24,25,29–37]. Furthermore, to our knowledge no study has examined within-person changes in drinking level in relation to changes in WC in men and women [5,8].
To address these gaps in the literature, we used time-varying data on alcoholic beverage intake, diet, physical activity and socio-demographic covariates over 25 years from the Coronary Artery Risk Development in Young Adults (CARDIA) study to determine whether changes in WC and BMI differ between people who drink and those that do not. Using within-person change analyses to control for time-invariant unobserved individual characteristics, we examined changes in drinking level and changes in intake by beverage type in relation to changes in WC and BMI. We hypothesized that starting to consume alcohol in excess over a 5-yr period within the 25-year study period would be positively associated with WC and BMI change. Additionally, we hypothesized that 5-yr increases in liquor/mixed drinks would be positively associated with 5-yr WC and BMI change [8]. Findings from our study could provide evidence to support clinicians in discussing the DGA guidance for alcohol intake as part of weight-related health care recommendations [4].
## Materials and methods
CARDIA is an ongoing, prospective study of the determinants and evolution of cardiometabolic risk starting in young adulthood. Participants for the baseline examination were randomly selected and recruited by telephone or door to door from census tracts in the four field centers: Minneapolis, MN and Chicago, IL, by telephone exchanges within the Birmingham, AL city limits, and from lists of the Kaiser-Permanente Health Plan membership in Berkeley, CA. Although the source populations and methods of recruitment varied slightly, all centers adhered to the following basic eligibility criteria: 18–30 years of age, Black or white race, and permanent residential address in the target areas. Individuals living with a long-term illness or disability that would prevent participation and pregnant women and those who were up to 3 months postpartum were excluded from recruitment [38,39]. A total of 5,115 young adults aged 18–30 years were enrolled at baseline in 1985–1986 (Exam Year 0) with balance according to race (Black and white), sex, education (≤high school and >high school), and age (18–24 and 25–30 years) from the population in each of the four metropolitan areas. Follow-up examinations occurred in 1987–1988 (Exam Year 2), 1990–1991 (Exam Year 5), 1992–1993 (Exam Year 7), 1995–1996 (Exam Year 10), 2000–2001 (Exam Year 15), and 2005–2006 (Exam Year 20) and 2010–2011 (Exam Year 25); retention at each exam year was $91\%$, $90\%$, $86\%$, $81\%$, $79\%$, $74\%$, $72\%$ and $72\%$, respectively. The CARDIA study methods are described in detail elsewhere [38,39]. Each study participant provided written informed consent, and data were collected under protocols approved by the Institutional Review Boards at each study center and at the University of North Carolina at Chapel Hill [8]. The current study included CARDIA exam years 0, 5, 10, 15, 20 and 25. We excluded participants with only one of the six exams used in this analysis. As has been done in previous studies, to minimize bias resulting from illness that may affect body weight, we excluded participants with hypertension, diabetes or cancer at exam year 0 [32,40]. We further restricted the analytic sample to those with data on diabetes, hypertension, or self-reported cancer diagnoses at each exam and those with waist circumference and BMI data at exam year 0. For individuals included in the primary analytic sample, observations were excluded at given exam years if participants were pregnant or breastfeeding or had implausible energy intakes (<600 kcal/d or >6000/d kcal for women and <800 kcal/d or >8000 kcal/d for men) at any exam or if they were missing exposure, outcome, or covariate data at a given exam year. We censored observations for participants with diabetes, hypertension, or self-reported cancer during follow-up at the year in which the disease was reported. To facilitate sensitivity analyses, a secondary analytic dataset was created in accordance with the primary analytic dataset exclusion criteria except for censoring on diabetes, hypertension, or self-reported cancer. The secondary dataset is described in the (S1 File) [8,32,40].
## Measures
Participant and interviewer-administered questionnaires were used to obtain sociodemographic, lifestyle, medical and behavioral data. Education, income, and marital status were assessed using the CARDIA Sociodemographic Questionnaire. At each exam year, participants were queried on the highest degree earned (“< HS”; “HS or equivalency”; “Associate Degree”; “Bachelor’s Degree”; “Master’s Degree”, “Doctoral Degree, Professional Degree (e.g. medical doctor, Doctor of Dental Surgery))” and the years of education completed (0 to 20+ years). When degree data was missing, the years of education variable was used to categorize participants: “< HS” (<12 years), “HS or equivalency” (12 years), “Associate, Bachelor’s, Graduate or Professional Degree” (>12 years). Income data was not collected at exam year 0 or 2. From exam year 5 to 10 participants were asked to report annual family income categorically as “< $5,000”; “$5,000 to $11,999”; “$12,000 to $15,999”; “$16,000 to $24,999”; “$25,000 to $34,999”; “$35,000 to $49,999”; “$50,000 to $74,999”; “≥ $75,000”. In exam year 15, the “≥ $75,000” category was disaggregated into “$75,000 to $99,999” and “≥$100,000”. In this study, the income data from exam year 5 was carried backward; the first and second value of income were therefore the same while the third, fourth, fifth and sixth values changed based on participants’ reports over time. At all exam years, participants were asked to identify as “Married”; “Divorced”; “Separated”; “Never Married” or “Other”.
Smoking status was determined from the CARDIA Tobacco Use Questionnaire. Respondents were asked: [1] “Have you ever used any tobacco product such as cigarettes, cigars, tobacco pipe, chewing tobacco, snuff or nicotine chewing gum?”; [ 2] “Have you ever smoked cigarettes regularly for at least three months? By “regularly” we mean at least 5 cigarettes per week almost every week”. Those that reported “no” to regular tobacco use were categorized as “people who never smoked”. Respondents who answered “yes” to smoking regularly (smoking at least five cigarettes per week, almost every week for at least 3 months) completed a follow-up tobacco use questionnaire and were asked “Do you still smoke cigarettes regularly?”. Those that answered “no” were coded as “people who formerly smoked" and those that answered “yes” were coded as “people who currently smoke”.
Physical activity was assessed using the CARDIA Physical Activity Questionnaire (PAQ), a validated and reliable assessment of physical activity [41]. In brief, the PAQ queries participants on performance of 13 different physical activities, the total number of months of performance per year and how many months the activity has been performed for at least a specific number of hours per week during the month. An intensity level is assigned to each activity according to the number of kilocalories expended in one minute of activity and patterned using methods of coding intensity based on the formulation developed by Reiff et al [42]. Summary scores are calculated based on energy expenditure in heavy intensity and moderate intensity activities. Because no interpretation as caloric expenditure per week is available the summary score is left unaltered in “Exercise Units” (EU) per week [41].
Dietary intake data for this study was derived from a validated interviewer-administered comprehensive diet history questionnaire administered at exam years 0, 7, and 20 [43,44]. Interviewers asked participants open-ended questions about dietary consumption during the past month within 100 food categories, referencing 1609 separate food items in years 0 and 7 and several thousand food items in year 20. Follow-up questions addressed serving size, frequency of consumption and common additions to foods. Diet history data used codes of the University of Minnesota Nutrition Coordinating Center (NCC) and foods were placed into 166 food groups using the food grouping system developed by the NCC. Then, trained personnel translated the pre-coded dietary items to estimate the individual nutrient intake using the Nutrition Data System for Research (NDSR, versions 10, 20, and 36 for year 0, year 7, and year 20, respectively) [45,46]. A study evaluating the reliability and comparative validity of the CARDIA diet history was conducted using two dietary histories to assess reliability and seven telephone-assessed 24-hour dietary recalls to assess comparative validity. Results of this study indicate that the CARDIA diet history is a reliable and valid dietary survey method for obtaining estimates of usual dietary intake [43,47]. For those years that dietary intake was not assessed, data from the previous year was carried forward. In the current study, to characterize total dietary composition, the percent contribution of each macronutrient (% carbohydrate, % protein and % fat) was calculated based on the percent contribution of each macronutrient to total energy intake. We also report total non-alcoholic energy, calculated by excluding the energy from alcoholic beverages. Diet quality was defined using the a-priori diet quality score previously developed and used as a valid predictor of clinical cardiovascular disease, myocardial infarction and diabetes [48–50]. This summary score of diet quality was constructed by classifying 46 food groups according to investigator ratings of hypothesized health effects. Twenty food groups were identified as beneficial, 13 as adverse, and 13 as neutral. Within the CARDIA dataset, this ‘a-priori’ diet quality score has been associated with lipid peroxidation and age, gender, race and education [51,52]. In this study, alcoholic beverages were excluded from the calculation of the diet quality score.
Pregnancy and breastfeeding status, self-reported cancer and hypertension data were obtained from the CARDIA Medical History Questionnaire at each exam year. Women who answered “yes” to the questions “Are you pregnant?” or “Are you breastfeeding?” were coded as pregnant or breastfeeding. Respondents were asked “*Has a* doctor or nurse ever told you that you have cancer?”. Those that answered “yes” were coded as having a history of self-reported cancer. Hypertension was defined as a ‘‘yes” response to the question ‘‘*Has a* doctor ever told you that you have hypertension?” or average measured systolic or diastolic blood pressure (SBP/DBP) exceeding ≥ 140/≥90 mm Hg. Resting seated blood pressure was measured 3 times and the average of the 2nd and 3rd readings was used to define hypertension. Diabetes was determined based on a combination of measured fasting glucose levels (≥7.0 mmol/L and ≥126 mg/dL) at exam years 0, 7, 10, 15, 20, or 25; self-report of oral hypoglycemic medications or insulin at exam years 0, 7, 10, 15, 20, or 25; a 2-h postload glucose ≥11.1 mmol/L (≥200 mg/dL) during a 75-g oral glucose tolerance test at exam years 10, 20, and 25; or an HbA1c ≥$6.5\%$ at exam years 20 and 25. Blood was drawn by venipuncture and processed at the central laboratory according to a standard protocol. Glucose was assayed at baseline using the hexokinase ultraviolet method by American Bio Science Laboratories (Van Nuys, CA) and at years 7, 10, 15, 20, and 25 using hexokinase coupled to glucose-6-phosphate dehydrogenase (Linco Research, St. Louis, MO). Glucose values at follow-up were recalibrated to year 0 glucose values. HbA1c was measured using the Tosoh G7 high-performance liquid chromatography method at years 20 and 25. For clinical measures, participants were asked to fast for at least 12 hours before each examination and to avoid smoking or engaging in heavy physical activity for at least 2 hours.
All data were collected by trained staff with a standardized protocol. All exam materials can be found on the public CARDIA website: https://www.cardia.dopm.uab.edu/. The analytic code for creating the smoking status, physical activity score, hypertension and diabetes variables used in this study is publicly available on the CARDIA website: https://www.cardia.dopm.uab.edu/study-information/derived-variables-from-cardia-data.
## Alcoholic beverage consumption
Alcoholic beverage consumption was assessed at each exam using the CARDIA Alcohol Use Questionnaire (AUQ) that queried participants on annual, monthly, weekly and daily alcoholic beverage intake. Alcoholic beverage consumption was defined based on the following questions: “Did you drink any alcoholic beverages in the past year?”; “ How many drinks of wine (5 oz glass) do you usually have per week?”; “ How many drinks of beer (12 oz glass) do you usually have per week?”; “ How many drinks per week do you usually have of hard liquor (1 $\frac{1}{2}$ oz)?” To our knowledge there is no validated questionnaire available to measure usual alcoholic beverage intake in epidemiologic studies. However, in exploratory analyses, we found that alcoholic beverage intake (milliliters of alcohol per day) estimated from the AUQ was strongly correlated with mean alcohol intake (grams per day) estimated from the validated CARDIA diet history at exam years 0 ($r = 0.77$; $p \leq .001$); year 7 ($r = 0.71$; $p \leq .001$); and year 20 ($r = 0.79$; $p \leq .001$).
To describe the distribution of socio-demographic and lifestyle characteristics of people who drank compared to those who didn’t drink at baseline, participants were categorized into sex-specific drinking categories using alcoholic beverage intake at exam year 0. Category definitions were based on the National Institute on Alcohol Abuse and Alcoholism (NIAAA) guidance on drinking levels [34,53–56]. Based on the sum of the usual intake of beer, wine, and liquor/mixed drinks per week (drinks/wk) as reported on the AUQ at exam year 0, men were classified as “non-drinker”, “light drinker” (<7 drinks/wk), “moderate drinker” (7 to 14 drinks/wk), or “excessive drinker” (>14 drinks/wk), and women were classified as “non-drinker”, “light drinker” (<4 drinks/wk), “moderate drinker” (4 to 7 drinks/wk), or “excessive drinker (> 7 drinks/wk) [8,53].
## Changes in total alcohol intake
Alcoholic beverage intake data were collected at all examinations. To capture 5-yr changes in alcohol intake, we chose to use alcohol intake data from the six examinations administered with 5-yr time intervals from one exam to the next (i.e., exam years 0, 5, 10, 15, 20 and 25). Participants were categorized by the 5-yr change in total drinks/wk from one exam year to the next, relative to drinking status at the previous exam, as follows: “Stable non-drinking” (0 drinks/wk at previous and current exam), “Start drinking” (change from 0 drinks/wk at previous exam to > 0 drinks/wk at current exam), “Increase drinking” (drinks/wk at previous exam < drinks/wk at current exam), “Stable drinking” (drinks/wk > 0 and drinks/wk at previous exam equal to drinks/wk at current exam), “Stop drinking” (change from >0 drinks/wk at previous exam to 0 drinks/wk at current exam), “Decrease drinking” (drinks/wk at previous exam > drinks/wk at current exam) [8].
## Changes in drinking level
To investigate associations of 5-yr changes in drinking level with 5-yr changes in WC and BMI in the 25 year study period, participants were categorized by the change in NIAAA-based drinking levels from one exam year to the next, relative to drinking status at the previous exam, as follows: “Stable non-drinking” (0 drinks/wk at previous and current exam), “Start light/moderate drinking” (non-drinker at previous exam and light (0> to < 4 drinks/wk in women; 0> to <7 drinks/wk in men) or moderate (4 to 7 drinks/wk in women; 7 to 14 drinks/wk in men) drinker at current exam), “Start excessive drinking” (non-, light or moderate drinker at previous exam and excessive (> 7 drinks/wk in women; >14 drinks/wk in men) drinker at the current exam), “Stable light/moderate drinking” (light or moderate drinker at previous exam and light or moderate drinker at current exam), “Stable excessive drinking” (excessive drinker at previous exam and excessive drinker at current exam), “Stop light/moderate drinking” (light or moderate drinker at previous exam and non-drinker at current exam), “Stop excessive drinking” (excessive drinker at previous exam and non-, light or moderate drinker at the current exam) [8].
## Changes in alcoholic beverage type
To examine associations of 5-yr changes in beer, wine and liquor/mixed drink intake with 5-yr changes in WC and BMI over the 25-year period, participants were categorized relative to drinking status at the previous exam year and according to weekly consumption of each beverage type as follows: “Stable non-drinking” (0 drinks/wk at previous and current exam), “Increase” (beer, wine or liquor/mixed drinks/wk at previous exam < beer, wine or liquor/mixed drinks/wk at current exam), “No change” (no change in beer, wine or liquor/mixed drinks/wk intake from previous to current exam), “Decrease” (beer, wine or liquor/mixed drinks/wk at previous exam > beer, wine or liquor/mixed drinks/wk at current exam) [8].
## Anthropometrics
At each exam, height, weight, and WC were measured in replicate in light clothing without shoes according to standardized protocol [2,57]. Height was measured to the nearest 0.2 cm via portable stadiometer (Seca Corporation, Chino, CA), and weight was measured to the nearest 0.1 kg via calibrated balance beam scale. WC was measured midway between the iliac crest and the lowest lateral portion of the rib cage (anteriorly at the point midway between the xiphoid process of the sternum and the umbilicus) using a Seca tape measure, and an average of 2 measures to the nearest 0.5 cm was used. BMI was calculated as weight in kilograms divided by height in meters squared (kg/m2) [8].
## Statistical analyses
All data analyses were conducted using Stata, version 14 (StataCorp, College Station, TX).
Initial unadjusted descriptive analyses tested whether demographic, socioeconomic, and behavioral characteristics and anthropometric outcomes at baseline in 1985–1986 among people who did not drink differed from those who did in each NIAAA-based drinking category. Chi square tests were used to determine differences in the distribution of categorical covariates and analysis of variance (ANOVA) was used to test means of continuous covariates.
Longitudinal random effects linear regression models with an exchangeable correlation structure, which account for correlation between repeated measures within individuals across time with a random intercept for each individual, were used to determine whether 5-yr changes in alcohol intake were associated with changes in WC and BMI during the same time period [8]. All models adjusted for several demographic, socioeconomic, and behavioral factors that were assessed at each examination. These confounders were chosen based on their well-established associations with alcohol intake, WC and BMI and to be comparable to other similar studies of alcohol intake and obesity measures [17,25,58,59]. The time invariant covariates were: baseline age (18–24 years or 25–30 years); baseline WC (when change in WC was the outcome) or baseline BMI (when change in BMI was the outcome); and race (Black or White). We adjusted for time-varying education (< = high school; (HS) diploma; >HS); income (≤$24,999; $25,000–$74,999; ≥$75,000); and smoking status (people who never smoked, people who formerly smoked or people who currently smoke) at the start of each 5-yr interval. Because < $10\%$ of participants within each drink change category experienced changes in income, education or smoking status, we did not adjust for changes in these covariates within each 5-yr period. Modeling income, education and smoking status as time-varying addressed confounding from variation in these covariates that occurred over the 25-year study period. We adjusted for time-varying changes in marital status (stable single, stable married or change in marital status); physical activity score (continuous); and diet quality score (continuous) within each 5-yr period.
An example of the linear models that regressed 5-yr WC or BMI change on 5-yr change in total alcoholic drinks/wk over the same time period (categorized with stable non-drinkers as the referent group) is shown below.
Variables that are calculated as a change over the 5-yr period are preceded by δ.
For this model, δYi(t,t+5) is the 5-yr change in WC or BMI of person i during time interval t to t + 5; a is the population-level intercept; δDi(t,t+5) is the 5-yr change in alcoholic beverage intake of person i during time interval t to t + 5, defined by categories start, increase, stable, stop or decrease; Xi represents time invariant covariates for person i (including baseline WC or BMI measure depending on the outcome modeled);
Cit represents one set of time varying covariates, specifically education, income and smoking status, for person i at time t, the beginning of the interval; δZi(t,t+5) represents changes in a separate set of time varying covariates, including marital status, physical activity score and diet quality score, of person i during time interval t to t + 5; εit is the usual random disturbance, estimating the within person variability in 5-year change in WC or BMI; and μi is an individual-specific disturbance term, estimating the between person variability in 5-year change in WC or BMI.
Separate models were used to test whether associations between 5-yr changes in NIAA-based drinking level and 5-yr changes in WC and BMI differed from that of stable non-drinkers.
Separate models were also used to test whether 5-yr changes in each type of alcoholic beverage in drinkers were associated with 5-yr changes in WC and BMI. Models for each beverage type were adjusted for time-varying continuous changes in intake of each other alcoholic beverage within each 5-yr interval.
Because the existing literature suggests that alcohol intake has differential associations with adiposity among men and women, all analyses were stratified by sex [8]. All results (in men and women) are compared with the 5-yr WC and BMI gains observed among those with stable non-drinking patterns. Results were considered significant at $p \leq 0.05.$
## Results
For this study 5,114 participants (observations = 24,132) were screened for eligibility. 692 participants were excluded due to disease diagnoses or missing WC or BMI data at baseline; missing disease data at any exam year or having only one wave of data. After excluding participants who did not meet initial study inclusion criteria 4,422 participants (observations = 22,199) remained. 6,750 observations were excluded due to censoring, pregnancy or breastfeeding, implausible energy intakes or missing exposure, outcome, or covariate data. 67 participants were excluded due to excluded observations at every exam year. In total 759 participants were excluded, and our primary analytic sample included 4,355 participants (observations = 15,499) (Fig 1). The secondary analytic dataset, that did not include censoring on diabetes, hypertension or self-reported cancer included $$n = 4$$,377 participants (observations = 18,671) (S1 File).
**Fig 1:** *STROBE flow diagram of study participant inclusion and exclusion criteria.a Participants (n). b Observations censored for participants with diabetes, hypertension or self-reported cancer during follow-up at the year in which the disease was reported. c 114 participants included in the final analytic sample had excluded observations at exam year 0.*
Excluded participants were more likely to be Black people, living with obesity, currently smoking and belong to the lowest education subgroup at exam year 0 as compared to those included in the primary analyses (S1 Table).
## Baseline characteristics
Men who do not drink had higher proportions of Black people and people who do not smoke than moderate and excessive drinking men (Table 1) and women (Table 2). The proportion of adults aged 25 to 30 years was lower among those who do not drink as compared to all drinking categories. $13.8\%$ of men and $9.9\%$ of women reported drinking excessively. In men, the proportion of those in the lowest education group was higher among people who don’t drink and those that drink excessively than people with light and moderate drinking frequencies. Men who drink excessively had significantly higher WC compared to people who do not drink. In women, the proportion of those who were married was higher among those who did not drink and drink only lightly as compared to those who drink excessively. Women who did not drink had significantly higher WC than those who did drink. People who didn’t drink had significantly lower total energy intake coupled with lower diet quality and physical activity as compared to all drinking levels among men and women.
## Waist circumference and body mass index gains
Within a 5-yr period, WC and BMI gains were observed across all change categories of drinking among men and women. The adjusted mean WC gain was 3.77 ± 0.18 cm and 3.78 ± 0.14 cm, among men and women with stable non-drinking, respectively. The adjusted mean BMI gain was 1.18 ± 0.06 kg/m2 and 1.49 ± 0.06 kg/m2 among men and women with stable non-drinking, respectively (S2 Table). To determine the absolute WC or BMI gain in each of the drinking change categories, the adjusted mean 5-yr WC or BMI change reported in S3 Table may be added to the adjusted mean 5-yr WC or BMI change among those who reported stable non-drinking over a 5-yr period as reported in S2 Table. The results of this computation for men and women according to 5-yr changes in total alcoholic beverage intake are reported in S2 Table.
Men who decreased their total alcoholic beverage intake over a 5-yr period experienced lower 5-yr WC (β:-0.62 cm; $95\%$ CI: -1.09, -0.14 cm (Fig 2A) and BMI gains (β:-0.20 kg/m2; $95\%$ CI: -0.36, -0.03 kg/m2) (Fig 2B).
**Fig 2:** *Adjusted associations of 5-yr changes in total alcoholic beverage intake with 5-yr changes in (A) Waist Circumference (WC) (cm) and (B) Body Mass Index (BMI) (kg/m2) for men and women in the CARDIA Study from 1985–1986 to 2010–2011.Data from men (N = 1,974) and women (N = 2,381) for 5-yr changes in (A) WC and (B) BMI from CARDIA exam yrs 5, 10, 15, 20 and 25. Values are β coefficients (95% CI) obtained from longitudinal random effects linear regression models adjusted for baseline age cohort membership, baseline WC, race and study center and time-varying income, education, smoking status and time-varying changes in marital status, physical activity and diet quality score. When 5-yr change in BMI was the outcome, models were adjusted for baseline BMI instead of baseline WC. Estimates compared to the referent 5-yr change among “stable non-drinking”. For stable non-drinking men, the adjusted mean 5-yr WC change was +3.77 ± 0.18 cm and the adjusted mean 5-yr BMI change +1.18 ± 0.06 kg/m2; for stable non-drinking women, the adjusted mean 5-yr WC change was +3.78 ± 0.14 cm and the adjusted mean 5-yr BMI change was +1.49 ± 0.06 kg/m2. P-values correspond to the 2-tailed p-values used in testing the null hypothesis that β is 0. β estimates having p-values <0.05 were considered statistically significant.*
When 5-yr changes in drinking level were examined, men who stopped excessive drinking had lower 5-yr WC gains (β:-0.77 cm; $95\%$ CI: -1.51, -0.03 cm) (Fig 3A).
**Fig 3:** *Adjusted associations of 5-yr changes in drinking level with 5-yr changes in (A) Waist Circumference (WC) (cm) and (B) Body Mass Index (BMI) (kg/m2) for men and women in the CARDIA Study from 1985–1986 to 2010–2011.Data from men (N = 1,974) and women (N = 2,381) for 5-yr changes in (A) WC and (B) BMI from CARDIA exam yrs 5, 10, 15, 20 and 25. Values are β coefficients (95% CI) obtained from longitudinal random effects linear regression models adjusted for baseline age cohort membership, baseline WC, race and study center and time-varying income, education, smoking status and time-varying changes in marital status, physical activity and diet quality score. When 5-yr change in BMI was the outcome, models were adjusted for baseline BMI instead of baseline WC. Estimates compared to the referent 5-yr change among “stable non-drinking”. For stable non-drinking men, the adjusted mean 5-yr WC change was +3.77 ± 0.18 cm and the adjusted mean 5-yr BMI change +1.18 ± 0.06 kg/m2; for stable non-drinking women, the adjusted mean 5-yr WC change was +3.78 ± 0.14 cm and the adjusted mean 5-yr BMI change was +1.49 ± 0.06 kg/m2. P-values correspond to the 2-tailed p-values used in testing the null hypothesis that β is 0. β estimates having p-values <0.05 were considered statistically significant.*
When changes in beverage type were examined, associations between 5-yr changes in beer, wine and liquor/mixed drink intakes with 5-yr WC and BMI gains were non-significant (S1 Fig) [8].
Women who started to drink over a 5-yr period experienced lower 5-yr WC (β:-1.12 cm; $95\%$ CI: -1.69, -0.56 cm) (Fig 2A) and BMI gains (β: -0.48 kg/m2; $95\%$ CI: -0.73, -0.23 kg/m2) (Fig 2B). Compared to stable non-drinking, lower 5-year WC and BMI gains were observed in women who decreased drinking over a 5-yr period (β: -0.55 cm; $95\%$ CI: -1.08, -0.02cm (Fig 2A) and β:-0.33 kg/m2; $95\%$ CI: -0.56, -0.10 kg/m2) (Fig 2B), respectively.
When changes in drinking level were examined, women who started light/moderate drinking over a 5-yr period had lower 5-yr WC (β: -0.78 cm; $95\%$ CI: -1.29, -0.26 cm) (Fig 3A) and BMI gains (β:-0.42 kg/m2; $95\%$ CI: -0.64, -0.20 kg/m2) (Fig 3B). When 5-yr changes in beverage type were examined, a 5-yr increase in wine intake was associated with a lower 5-yr WC gain albeit with confidence intervals including the null (β: -0.51 cm; $95\%$ CI: -1.05, 0.04 cm; $$p \leq 0.067$$) (Fig 4A). An increase in wine intake was also associated with lower 5-yr BMI gains (β:-0.27 kg/m2; $95\%$ CI: -0.51, -0.03 kg/m2) (Fig 4B). Lower 5-yr WC and BMI gains were observed in women drinkers with decreasing liquor/mixed drink intake over a 5-yr period (β:-0.88 cm; $95\%$ CI: -1.43, -0.34 cm (Fig 4A) and β:-0.33 kg/m2; $95\%$ CI: -0.56, -0.09 kg/m2 (Fig 4B), respectively). Beer intakes were not associated with 5-yr WC change among women (Fig 4A). Yet, a 5-yr increase in beer intake was associated with lower 5-yr BMI gains (β:-0.32 kg/m2; $95\%$ CI: -0.58, -0.06 kg/m2) (Fig 4B) [8].
**Fig 4:** *Adjusted associations of 5-yr changes in alcoholic beverage intake by type with 5-yr changes in (A) Waist Circumference (WC) (cm) and (B) Body Mass Index (BMI) (kg/m2) for women in the CARDIA Study from 1985–1986 to 2010–2011.Data from women (N = 2,381) for 5-yr changes in changes in (A) WC and (B) BMI from CARDIA exam yrs 5, 10, 15, 20 and 25. Values are β coefficients (95% CI) obtained from longitudinal random effects linear regression models adjusted for baseline age cohort membership, baseline WC, race and study center and time-varying income, education, smoking status and time-varying changes in marital status, physical activity, diet quality and intake of each other alcoholic beverage type. When 5-yr change in BMI was the outcome, models were adjusted for baseline BMI instead of baseline WC. Estimates compared to the referent 5-yr change among “stable non-drinking”. For stable non-drinking women, the adjusted mean 5-yr WC change was +3.78 ± 0.14 cm and the adjusted mean 5-yr BMI change was +1.49 ± 0.06 kg/m2. P-values correspond to the 2-tailed p-values used in testing the null hypothesis that β is 0. β estimates having p-values <0.05 were considered statistically significant.*
## Supplemental analyses
Results were similar for 5-yr changes in total alcohol intake, drinking level and alcoholic beverage type with 5-yr changes in WC and BMI when sensitivity analyses were conducted without censoring on diabetes, hypertension or self-reported cancer (S2–S5 Figs) [8]. Including those with diabetes, hypertension or self-reported cancer had an inconsistent impact on the magnitude and statistical significance of a few estimates. Among men, the association of a 5-yr decrease in drinking with 5-yr WC change was attenuated (β:-0.52 cm; $95\%$ CI:-0.94, -0.10 cm) (S3 Table). The association with a decrease in drinking with 5-yr BMI change in men (β: -0.14 kg/m2; $95\%$ CI:-0.35, 0.07 kg/m2) and women (β: -0.21 kg/m2; $95\%$ CI:-0.42, 0.00 kg/m2) was attenuated and non-significant in sensitivity analyses (S3 Table). Similarly, the association with stopping excessive drinking and WC change over a 5-yr period in men (β: -0.66 cm; $95\%$ CI:-1.33, 0.02 cm) was attenuated and non-significant in sensitivity analyses. In women, the association of starting light/moderate drinking and WC was strengthened (β: -0.80 cm; $95\%$ CI:-1.26 cm, -0.34 cm) and estimates of BMI change were identical (β: -0.42 kg/m2; $95\%$ CI:-0.63, -0.22 kg/m2) in sensitivity analyses (S3 Table).
## Discussion
We observed that associations of 5-yr changes in total alcohol intake with 5-yr WC and BMI change over the 25-year study period differed between men and women and across drinking subgroups in women. In men, we found that decreasing total intake and stopping excessive consumption, were associated with lower 5-yr WC gains. In women, starting to drink, specifically starting light/moderate consumption, and increasing wine intake were associated with lower 5-yr WC and BMI gains. In contrast, decreasing total intake and decreasing liquor/mixed drink intake were also associated with lower 5-yr WC and BMI gains in women. Associations of 5-yr changes in alcoholic beverage intake by type with 5-yr WC and BMI change were observed in women but not men [8].
This study is observational and, as with all observational studies, we cannot make causal inferences. The ability to infer causation would require that all important confounders were identified, measured accurately, and included correctly in the statistical models. Even then it is likely that a secondary data analysis will suffer from residual confounding due to measurement errors [60]. For example, dietary intake was assessed at only three time points in this study and residual confounding bias by diet is likely. The amount of tobacco smoked has been associated with alcohol intake and measures of obesity [61,62]. Thus, residual confounding due to the definition of tobacco use is likely. In studies of associations of alcohol with measures of weight status smoking is typically defined as we have done (i.e., never smoked, formerly smoked, currently smoke) or by including tobacco amount based on the number of cigarettes smoked per day among those who currently smoke [22,29,55,63–67]. Prospective studies that have categorized smoking using both definitions have reported findings similar to ours [24,29,63]. In one prospective study of British men heavy drinking was associated with an increased odds of weight gain over a 5-yr period in never smokers with weaker non-significant associations observed in those who formerly smoked and those who currently smoke [24]. Similarly, in our study positive non-significant relationships with WC and BMI change were observed among men who started to drink excessively over a 5-yr period. Thus, it is possible that residual confounding bias from smoking status in our study has biased results towards the null. However, additional research is needed to understand the magnitude and direction of residual confounding bias from dietary intake and smoking status on associations of changes in alcohol intake with changes in WC and BMI. Furthermore, drinkers were identified from self-reported, weekly alcoholic beverage consumption. Thus, misclassification of drinkers due to seasonal changes in alcohol consumption, inaccurate reporting and/or infrequent consumption is possible [68]. Differential misclassification bias such as this can result in bias towards or away from the null. While underreporting of an exposure is likely to result in bias away from the null, additional research is needed to confirm this assumption. Although we cannot rule out misclassification bias of the self-reported alcoholic beverage exposure, the CARDIA Alcohol Use Questionnaire captures usual weekly drinking behavior and is less likely to misclassify participants in comparison to a shorter-term alcohol intake assessment tool [8]. Even after adjustment for multiple time-varying lifestyle and socio-demographic characteristics and controlling for time-invariant unobserved individual characteristics, conflicting findings persisted across categories of 5-yr alcohol change in relation to 5-yr WC and BMI change, particularly in women. There is a strong body of evidence indicating that ethanol metabolism, bioavailability and a dose response of alcohol’s effect on body processes differs between men and women, even after adjustment for body weight. Women have higher body fat composition and lower body water content than men of the same body weights which has been linked to differential sex-specific ethanol metabolism [69,70]. As such, it is possible that conflicting findings in women may be attributed to residual confounding from time-varying factors that cannot be measured and are related to the physiology of ethanol metabolism [59]. Residual confounding from inherent characteristics such as metabolism can bias estimates towards or away from the null. Further, it has been hypothesized that light and moderate drinkers live healthier lifestyles due to inherent immeasurable individual characteristics that might lead to lower WC and BMI gains as compared to people who don’t drink [5,25,32]. For example, light and moderate drinkers may lead more active social lives and be less isolated compared to their non-drinking and excessive drinking counterparts. Further, light and moderate drinkers have eating patterns that vary from those that drink heavily [67,71]. Even with similar total daily energy intakes, drinking heavily has been associated with lower consumption of calories from food and non-alcoholic beverages as compared to light and moderate drinking [67]. To address this bias, we used discrete interval change analyses controlling for unmeasured time-invariant characteristics associated with alcohol intake, WC, BMI, physical activity, diet quality and marital status [72,73]. Further, those who start to drink or chose not to drink may have unmeasured underlying time-varying health conditions associated with changes in WC or BMI [74]. To address this possible bias, individuals with diabetes, hypertension and self-reported cancer at baseline were excluded and individuals who developed these diseases were censored during follow-up. There is evidence suggesting that restriction to healthy individuals, as was done in the main analyses, might induce selection bias [9,75]. Selection bias specific to restricting to healthy individuals may bias results away from the null and the findings may not be generalizable to the entire study population [76]. Our results indicate that the magnitude and direction of associations of 5-yr changes in alcohol intake with 5-yr WC and BMI changes were robust to the inclusion of those with chronic diseases for almost all drink change categories. Yet, additional research is necessary to understand how restriction based on health status impacts associations of changes in alcoholic beverage consumption with changes in obesity measures [77]. A strength of this study is the sensitivity analyses conducted without censoring on disease status which serves as a comparator to the main analyses. Notably, the current analytic method assumes that associations of 5-yr changes in alcohol intake with 5-yr changes in obesity measures are constant across time and does not account for cross-sectional effects. An added strength of this study is the use of six longitudinal assessments of alcoholic beverage intake and measured anthropometric data. Multiple measurements of exposure and outcome data increase the precision of estimates [8].
In men, decreasing total weekly alcoholic beverage intake over a 5-yr period was associated with lower 5-yr WC and BMI gains. There is a paucity of literature on associations of decreasing drinking with BMI and WC change. Yet, excessive drinking, more common in men, has been associated with weight and BMI gains [70,78]. Thus, it is conceivable that decreasing intake, including stopping excessive drinking, is associated with lower WC gains for some men as we found in this study [5,78,79]. Notably, we found that stopping excessive drinking was associated with WC but not BMI change for men. This contrast suggests that the use of varying obesity measures may yield differing results across studies and contribute to inconsistent findings in the alcohol-obesity literature. To that point, a previously published study of middle-aged British men found that among those who stopped heavy drinking over a 5-yr time period, 5-yr weight gain did not differ from people who do not drink [24]. Chronic excess alcohol intake has been shown to be associated with osteopenia, as well as decreased muscle and lean mass [8,59,80,81]. In comparison to participants in the previous study, those in the current study were older and may have experienced longer exposure times to excess alcohol intake in addition to age–related changes in body composition [82]. These physiological conditions may have differential impacts on associations of alcohol intake with weight status, overall body size (BMI), and WC. Specifically, an interaction of age and excessive drinking might lead to inverse associations among older adults and positive and/or null associations among younger adults. Future prospective research examining changes in excessive drinking levels, particularly excess intake modified by age, in relation to changes in obesity measures with consistent exposure definitions and standardized outcomes are needed to establish the evidence base [8,83–85].
Light and moderate drinking, more common in women, has been associated with the prevention of weight gain [5,78,79]. Starting to consume alcohol and starting light/moderate drinking, were associated with lower 5-yr WC and BMI gains for women in our study. Similarly, increasing alcohol intake up to moderate daily levels over an 8-yr period has been associated with lower 8-yr weight gain in US women [23]. We also found that in women who drink increasing wine intake over a 5-yr period was associated with lower BMI/WC gains as compared to stable non-drinking. In comparison, lower drinking level increases in wine intake (< 0.5 servings per day) have been associated with lower 4-yr weight gain; whereas higher drinking level increases (> 1 serving per day) have been associated with greater 4-yr weight gain among women in the US [29]. The polyphenolic compounds in red wine (i.e., resveratrol) may have beneficial effects on lipid metabolism which might lead to lower overall adiposity accumulation reflected in lower BMI/WC gains among those who drink [5,25,79,86–88]. Thus, light to moderate drinking patterns coupled with polyphenols may contribute to lower WC and BMI gains in women who start to drink as compared to stable non-drinking; however additional research is needed to substantiate this hypothesis [8].
In contrast to findings that starting to consume alcohol and increasing wine intake were associated with lower BMI/WC gains, we found that decreasing total alcohol intake, particularly decreasing liquor/mixed drink intake, was associated with lower 5-yr WC and BMI gains in women. Compared to those with no change in intake, decreasing liquor intake has been associated with lower 4-yr weight gain in women [29]. Our findings add to previous reports that liquor consumption may contribute to increases in obesity-related outcomes over time [25]. In a 2004 study of Danish men and women, compared to those who did not drink, women who drank ≥ 4 drinks/wk of beer or spirits had higher subsequent 6-year WC changes [89]. In a later study using follow-up data from the same cohort, spirit consumption was positively associated with 5-yr WC change in women [90]. A standard drink in the US is the equivalent to a twelve ounce can of beer that contains 150 calories ($5\%$ABV), 5 ounces of wine that contains 120 calories ($12\%$ ABV), 1.5 ounces of liquor that contains 100 calories ($40\%$ABV), a rum and cola with 190 calories ($40\%$ ABV) and a 4.5 oz piña colada cocktail that can contain as much as 245 calories [25,27,91]. Liquor with or without a mixer has a higher %ABV and can contain almost twice the calories of a drink of beer or wine. Additionally, alcohol itself is an energy-dense, nutritionally poor, substance with an energy content of 7.1 calories per gram. However, alcohol metabolism differs from other nutrients. For example, the thermogenic effect of alcohol is much greater than that of carbohydrates and fat. Making it difficult to draw direct conclusions about how alcohol itself impacts energy balance [25]. Yet, independent of the alcohol itself, the high calorie content of a mixed drink could contribute to weight and WC gain if consumed in excess to normal dietary intake. Furthermore, liquor drinking has been associated with excessive drinking in the US [12]. It could be the case that decreasing liquor/mixed drink intake subsequent to excessive levels of consumption might contribute to the management of WC and BMI gains in women who drink. Relationships of changes in alcoholic beverage type with changes in WC and BMI are complex. Future research should focus on elucidating interactions of drinking level and alcoholic beverage type in associations of changes in alcohol intake with WC and BMI change among women [8].
Similar to our study, reported differences in obesity-related outcomes between those who drink and those who do not are generally small [23,92–94]. While the magnitude of associations of changes in alcoholic beverage intake with changes in WC and BMI may be statistically small, effect sizes between 0.30 and 0.50 have been considered minimally clinically meaningful for some patient self-reported health outcomes [95,96]. Further, our estimates suggest that sustained reductions in alcohol intake could equate to lower WC and BMI gains with aging. For example, among men starting to drink was associated with a 3.92 cm 5-yr WC gain. Over a 15-year period (three 5-year intervals), starting to drink could lead to as much as a 58.8 cm gain compared to a 47.4 cm gain among men who decrease drinking. Consequently changes in alcohol intake could have large impacts on obesity measures at individual and population levels over time [97]. The results of our study could be considered with DGA guidance in the development of diet and alcohol-related public health messages specific to aging [98].
Furthermore, our findings have policy relevance. The DGA recommends persons who do not drink should not start drinking and that alcoholic beverages be consumed in moderate amounts of no more than 1 drink per day for women and no more than 2 drinks per day for men. The DGA specifically states that among adults who choose to drink, average energy intake from alcoholic beverages exceeds the remaining calorie limit that is available after food group recommendations are met [4]. Recent studies have highlighted increasing trends in calories consumed from alcoholic beverages and in drinking that exceeds moderate amounts in the US [3,74]. According to the DGA, among those who drink, alcoholic beverages contribute $9\%$ to total caloric intake. On the days that drinking occurs, alcoholic beverage consumption typically exceeds moderate amounts and regular consumption of alcoholic beverages likely leads to the consumption of excess calories [4]. Thus, reductions in calories from alcohol intake could have large impacts on obesity measures at individual and population levels [97]. However, public knowledge of the DGA drinking guidelines and the extent to which concerns related to excess energy intake might motivate changed drinking behavior is unknown. Future research aimed at understanding consumer knowledge of moderate drinking guidelines and the extent to which calorie-related concerns might motivate changed drinking behavior is needed.
## Conclusions
The magnitude of associations of alcohol change with WC and BMI changes was small but could be clinically meaningful for both sexes in our study. The current study is one of the first to examine changes in WC and BMI in relation to changes in alcoholic beverage consumption by drinking level and beverage type in a US-based cohort. These findings add to previous reports that light and moderate drinking, wine intake, and decreasing liquor/mixed drink intake are associated with WC and BMI change in women. In men decreasing total alcohol intake, with an emphasis on stopping excessive drinking may be may be beneficial in managing WC and BMI gains [8]. Public health nutrition interventions that emphasize reductions in alcohol intake as part of dietary changes are needed to inform weight-related health care recommendations and policy.
## References
1. Albrecht S, Gordon-Larsen P, Stern D, Popkin BM. **Is waist circumference per body mass index rising differentially across the United States, England, China and Mexico?**. *Eur J Clin Nutr* (2015.0) **69** 1306-12. DOI: 10.1038/ejcn.2015.71
2. Freedman DS, Ford ES. **Are the recent secular increases in the waist circumference of adults independent of changes in BMI?**. *Am J Clin Nutr* (2015.0) **101** 425-31. DOI: 10.3945/ajcn.114.094672
3. Butler L, Poti JM, Popkin BM. **Trends in energy intake from alcoholic beverages among US adults by sociodemographic characteristics, 1989–2012**. *J Acad Nutr Diet* (2016.0) **116** 1087-100. e6. DOI: 10.1016/j.jand.2016.03.008
4. 4Dietary Guidelines for Americans, 2020–2025: US Department of Agriculture and US Department of Health and Human Services; December 2020 [cited 2022]. DietaryGuidelines.gov.
5. Traversy G, Chaput JP. **Alcohol Consumption and Obesity: An Update**. *Curr Obes Rep* (2015.0) **4** 122-30. DOI: 10.1007/s13679-014-0129-4
6. Poppitt SD. **Beverage Consumption: Are Alcoholic and Sugary Drinks Tipping the Balance towards Overweight and Obesity?**. *Nutrients* (2015.0) **7** 6700-18. DOI: 10.3390/nu7085304
7. Ferrieres J.. **The French paradox: lessons for other countries**. *Heart* (2004.0) **90** 107-11. DOI: 10.1136/heart.90.1.107
8. 8Butler JL. Understanding associations of alcoholic beverage consumption with weight status [dissertation]. Chapel Hill (NC): The University of North Carolina at Chapel Hill; 2016.
9. Stockwell T, Chikritzhs T. **Commentary: Another serious challenge to the hypothesis that moderate drinking is good for health?**. *Int J Epidemiol* (2013.0) **42** 1792-4. DOI: 10.1093/ije/dyt217
10. Naimi TS, Brown DW, Brewer RD, Giles WH, Mensah G, Serdula MK. **Cardiovascular risk factors and confounders among nondrinking and moderate-drinking U.S. adults**. *Am J Prev Med* (2005.0) **28** 369-73. DOI: 10.1016/j.amepre.2005.01.011
11. Hansen K, Remington P. **The effect of alcohol consumption on body weight among Wisconsin adults**. *Wis Med J* (1992.0) **91** 348-50. PMID: 1509774
12. Klatsky AL, Armstrong MA, Kipp H. **Correlates of alcoholic beverage preference: traits of persons who choose wine, liquor or beer**. *Br J Addict* (1990.0) **85** 1279-89. DOI: 10.1111/j.1360-0443.1990.tb01604.x
13. Zhou T, Sun D, Li X, Ma H, Heianza Y, Qi L. **Educational attainment and drinking behaviors: Mendelian randomization study in UK Biobank**. *Mol Psychiatry* (2021.0) **26** 4355-66. DOI: 10.1038/s41380-019-0596-9
14. Golan R, Gepner Y, Shai I. **Wine and health–new evidence**. *Eur J Clin Nutr* (2019.0) **72** 55-9. DOI: 10.1038/s41430-018-0309-5
15. Sluik D, van Lee L, Geelen A, Feskens EJ. **Alcoholic beverage preference and diet in a representative Dutch population: the Dutch national food consumption survey 2007–2010**. *Eur J Clin Nutr* (2014.0) **68** 287-94. DOI: 10.1038/ejcn.2013.279
16. McCann SE, Sempos C, Freudenheim JL, Muti P, Russell M, Nochajski TH. **Alcoholic beverage preference and characteristics of drinkers and nondrinkers in western New York (United States)**. *Nutr Metab Cardiovasc Dis* (2003.0) **13** 2-11. DOI: 10.1016/s0939-4753(03)80162-x
17. Bendsen NT, Christensen R, Bartels EM, Kok FJ, Sierksma A, Raben A. **Is beer consumption related to measures of abdominal and general obesity? A systematic review and meta‐analysis**. *Nutr Rev* (2013.0) **71** 67-87. DOI: 10.1111/j.1753-4887.2012.00548.x
18. Naimi TS, Stockwell T, Zhao J, Xuan Z, Dangardt F, Saitz R. **Selection biases in observational studies affect associations between ‘moderate’alcohol consumption and mortality**. *Addiction* (2016.0) **112** 207-14. DOI: 10.1111/add.13451
19. Delker E, Brown Q, Hasin DS. **Alcohol Consumption in Demographic Subpopulations: An Epidemiologic Overview**. *Alcohol Res* (2016.0) **38** 7-15. PMID: 27159807
20. Srivastava PR. **Recreational drug consumption. An economic perspective**. *Dev Health Econ Public Policy* (2013.0) **11** 1-176. DOI: 10.1007/978-3-319-02405-9_1
21. Lee K.. **Gender-specific relationships between alcohol drinking patterns and metabolic syndrome: the Korea National Health and Nutrition Examination Survey 2008**. *Public Health Nutr* (2012.0) **15** 1917-24. DOI: 10.1017/S136898001100365X
22. Breslow RA, Smothers BA. **Drinking patterns and body mass index in never smokers: National Health Interview Survey, 1997–2001**. *Am J Epidemiol* (2005.0) **161** 368-76. DOI: 10.1093/aje/kwi061
23. Wannamethee SG, Field AE, Colditz GA, Rimm EB. **Alcohol intake and 8-year weight gain in women: a prospective study**. *Obes Res* (2004.0) **12** 1386-96. DOI: 10.1038/oby.2004.175
24. Wannamethee SG, Shaper AG. **Alcohol, body weight, and weight gain in middle-aged men**. *Am J Clin Nutr* (2003.0) **77** 1312-7. DOI: 10.1093/ajcn/77.5.1312
25. Sayon-Orea C, Martinez-Gonzalez MA, Bes-Rastrollo M. **Alcohol consumption and body weight: a systematic review**. *Nutr Rev* (2011.0) **69** 419-31. DOI: 10.1111/j.1753-4887.2011.00403.x
26. Nielsen SJ, Kit BK, Fakhouri T, Ogden CL. **Calories consumed from alcoholic beverages by US adults, 2007–2010**. *NCHS Data Brief No. 110. NCHS Data Brief* (2012.0) **110**. PMID: 23384768
27. 27What is a standard drink?: National Institute on Alcohol Abuse and Alcoholism; 2022 [cited 2022]. https://www.niaaa.nih.gov/alcohols-effects-health/overview-alcohol-consumption/what-standard-drink.
28. Kahn HS, Tatham LM, Rodriguez C, Calle EE, Thun MJ, Heath CW. **Stable behaviors associated with adults’ 10-year change in body mass index and likelihood of gain at the waist**. *Am J Public Health* (1997.0) **87** 747-54. DOI: 10.2105/ajph.87.5.747
29. Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. **Changes in diet and lifestyle and long-term weight gain in women and men**. *N Engl J Med* (2011.0) **364** 2392-404. DOI: 10.1056/NEJMoa1014296
30. Lukasiewicz E, Mennen LI, Bertrais S, Arnault N, Preziosi P, Galan P. **Alcohol intake in relation to body mass index and waist-to-hip ratio: the importance of type of alcoholic beverage**. *Public Health Nutr* (2005.0) **8** 315-20. DOI: 10.1079/phn2004680
31. Sakurai Y, Umeda T, Shinchi K, Honjo S, Wakabayashi K, Todoroki I. **Relation of total and beverage-specific alcohol intake to body mass index and waist-to-hip ratio: a study of self-defense officials in Japan**. *Eur J Epidemiol* (1997.0) **13** 893-8. DOI: 10.1023/a:1007416322031
32. Sayon-Orea C, Bes-Rastrollo M, Nunez-Cordoba JM, Basterra-Gortari FJ, Beunza JJ, Martinez-Gonzalez MA. **Type of alcoholic beverage and incidence of overweight/obesity in a Mediterranean cohort: the SUN project**. *Nutrition* (2011.0) **27** 802-8. DOI: 10.1016/j.nut.2010.08.023
33. Brandhagen M, Forslund HB, Lissner L, Winkvist A, Lindroos AK, Carlsson LM. **Alcohol and macronutrient intake patterns are related to general and central adiposity**. *Eur J Clin Nutr* (2012.0) **66** 305-13. DOI: 10.1038/ejcn.2011.189
34. Vadstrup ES, Petersen L, Sorensen TI, Gronbaek M. **Waist circumference in relation to history of amount and type of alcohol: results from the Copenhagen City Heart Study**. *Int J Obes Relat Metab Disord* (2003.0) **27** 238-46. DOI: 10.1038/sj.ijo.802203
35. Wannamethee SG, Shaper AG, Whincup PH. **Alcohol and adiposity: effects of quantity and type of drink and time relation with meals**. *Int J Obes (Lond)* (2005.0) **29** 1436-44. DOI: 10.1038/sj.ijo.0803034
36. MacInnis RJ, Hodge AM, Dixon HG, Peeters A, Johnson LE, English DR. **Predictors of increased body weight and waist circumference for middle-aged adults**. *Public Health Nutr* (2014.0) **17** 1087-97. DOI: 10.1017/S1368980013001031
37. Poli A, Marangoni F, Avogaro A, Barba G, Bellentani S, Bucci M. **Moderate alcohol use and health: a consensus document**. *Nutr Metab Cardiovasc Dis* (2013.0) **23** 487-504. DOI: 10.1016/j.numecd.2013.02.007
38. Friedman GD, Cutter GR, Donahue RP, Hughes GH, Hulley SB, Jacobs DR. **CARDIA: study design, recruitment, and some characteristics of the examined subjects**. *J Clin Epidemiol* (1988.0) **41** 1105-16. DOI: 10.1016/0895-4356(88)90080-7
39. Hughes GH, Cutter G, Donahue R, Friedman GD, Hulley S, Hunkeler E. **Recruitment in the Coronary Artery Disease Risk Development in Young Adults (Cardia) Study**. *Control Clin Trials* (1987.0) **8** 68S-73S. DOI: 10.1016/0197-2456(87)90008-0
40. Barrio-Lopez MT, Bes-Rastrollo M, Sayon-Orea C, Garcia-Lopez M, Fernandez-Montero A, Gea A. **Different types of alcoholic beverages and incidence of metabolic syndrome and its components in a Mediterranean cohort**. *Clin Nutr* (2013.0) **32** 797-804. DOI: 10.1016/j.clnu.2012.12.002
41. Jacobs DR, Hahn LP, Haskell WL, Pirie P, Sidney S. **Validity and Reliability of Short Physical Activity History: Cardia and the Minnesota Heart Health Program**. *J Cardiopulm Rehabil* (1989.0) **9** 448-59. DOI: 10.1097/00008483-198911000-00003
42. Reiff GG, Montoye HJ, Remington RD, Napier JA, Metzner HL, Epstein FH. **Assessment of physical activity by questionnaire and interview**. *J Sports Med Phys Fitness* (1967.0) **7** 135-42. PMID: 6062485
43. Liu K, Slattery M, Jacobs D, Cutter G, McDonald A, Van Horn L. **A study of the reliability and comparative validity of the cardia dietary history**. *Ethn Dis* (1994.0) **4** 15-27. PMID: 7742729
44. McDonald A, Van Horn L, Slattery M, Hilner J, Bragg C, Caan B. **The CARDIA dietary history: development, implementation, and evaluation**. *J Am Diet Assoc* (1991.0) **91** 1104-12. PMID: 1918764
45. Hirahatake KM, Jacobs DR, Shikany JM, Jiang L, Wong ND, Odegaard AO. **Cumulative average dietary pattern scores in young adulthood and risk of incident type 2 diabetes: the CARDIA study**. *Diabetologia* (2019.0) **62** 2233-44. DOI: 10.1007/s00125-019-04989-5
46. Qin B, Xun P, Jacobs DR, Zhu N, Daviglus ML, Reis JP. **Intake of niacin, folate, vitamin B-6, and vitamin B-12 through young adulthood and cognitive function in midlife: the Coronary Artery Risk Development in Young Adults (CARDIA) study**. *Am J Clin Nutr* (2017.0) **106** 1032-40. DOI: 10.3945/ajcn.117.157834
47. Van Horn L, Slattery M, Hilner J, Bragg C, Jacobs D, Gernhofer N. **The CARDIA dietary history: development, implementation, and evaluation**. *Journal of the American Dietetic Association* (1991.0) **91** 1104-12. PMID: 1918764
48. Nettleton JA, Schulze MB, Jiang R, Jenny NS, Burke GL, Jacobs DR. **A priori-defined dietary patterns and markers of cardiovascular disease risk in the Multi-Ethnic Study of Atherosclerosis (MESA)**. *Am J Clin Nutr* (2008.0) **88** 185-94. DOI: 10.1093/ajcn/88.1.185
49. Jacobs DR, Sluik D, Rokling-Andersen MH, Anderssen SA, Drevon CA. **Association of 1-y changes in diet pattern with cardiovascular disease risk factors and adipokines: results from the 1-y randomized Oslo Diet and Exercise Study**. *Am J Clin Nutr* (2009.0) **89** 509-17. DOI: 10.3945/ajcn.2008.26371
50. Lockheart MS, Steffen LM, Rebnord HM, Fimreite RL, Ringstad J, Thelle DS. **Dietary patterns, food groups and myocardial infarction: a case-control study**. *Br J Nutr* (2007.0) **98** 380-7. DOI: 10.1017/S0007114507701654
51. Meyer KA, Sijtsma FP, Nettleton JA, Steffen LM, Van Horn L, Shikany JM. **Dietary patterns are associated with plasma F2-isoprostanes in an observational cohort study of adults**. *Free Radic Biol Med* (2013.0) **57** 201-9. DOI: 10.1016/j.freeradbiomed.2012.08.574
52. Sijtsma FP, Meyer KA, Steffen LM, Shikany JM, Van Horn L, Harnack L. **Longitudinal trends in diet and effects of sex, race, and education on dietary quality score change: the Coronary Artery Risk Development in Young Adults study**. *Am J Clin Nutr* (2012.0) **95** 580-6. DOI: 10.3945/ajcn.111.020719
53. Halanych JH, Safford MM, Kertesz SG, Pletcher MJ, Kim YI, Person SD. **Alcohol consumption in young adults and incident hypertension: 20-year follow-up from the Coronary Artery Risk Development in Young Adults Study**. *Am J Epidemiol* (2010.0) **171** 532-9. DOI: 10.1093/aje/kwp417
54. 54Drinking Levels Defined: National Institute on Alcohol Abuse and Alcoholism; 2016 [cited 2022]. https://www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/moderate-binge-drinking.
55. Wang L, Lee IM, Manson JE, Buring JE, Sesso HD. **Alcohol consumption, weight gain, and risk of becoming overweight in middle-aged and older women**. *Arch Intern Med* (2010.0) **170** 453-61. DOI: 10.1001/archinternmed.2009.527
56. Schütze M, Schulz M, Steffen A, Bergmann M, Kroke A, Lissner L. **Beer consumption and the ‘beer belly’: scientific basis or common belief?**. *Eur J Clin Nutr* (2009.0) **63** 1143-9. DOI: 10.1038/ejcn.2009.39
57. Duffey KJ, Steffen L, Van Horn L, Jacobs D, Popkin B. **Dietary patterns matter: Diet beverages and cardio-metabolic risks in the longitudinal CARDIA Study**. *Am J Clin Nutr* (2011.0) **95** 909-15. DOI: 10.3945/ajcn.111.026682
58. Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. **Changes in diet and lifestyle and long-term weight gain in women and men**. *N Engl J Med* (2011.0) **364** 2392-404. DOI: 10.1056/NEJMoa1014296
59. Suter PM, Tremblay A. **Is alcohol consumption a risk factor for weight gain and obesity?**. *Crit Rev Clin Lab Sci* (2005.0) **42** 197-227. DOI: 10.1080/10408360590913542
60. Hammerton G, Munafò MR. **Causal inference with observational data: the need for triangulation of evidence**. *Psychol Med* (2021.0) **51** 563-78. DOI: 10.1017/S0033291720005127
61. Chiolero A, Wietlisbach V, Ruffieux C, Paccaud F, Cornuz J. **Clustering of risk behaviors with cigarette consumption: a population-based survey**. *Prev Med* (2006.0) **42** 348-53. DOI: 10.1016/j.ypmed.2006.01.011
62. Clair C, Chiolero A, Faeh D, Cornuz J, Marques-Vidal P, Paccaud F. **Dose-dependent positive association between cigarette smoking, abdominal obesity and body fat: cross-sectional data from a population-based survey**. *BMC public health* (2011.0) **11** 1-10. DOI: 10.1186/1471-2458-11-23
63. Liu S, Serdula MK, Williamson DF, Mokdad AH, Byers T. **A prospective study of alcohol intake and change in body weight among US adults**. *Am J Epidemiol* (1994.0) **140** 912-20. DOI: 10.1093/oxfordjournals.aje.a117179
64. Williamson DF, Forman MR, Binkin NJ, Gentry EM, Remington PL, Trowbridge FL. **Alcohol and body weight in United States adults**. *Am J Public Health* (1987.0) **77** 1324-30. DOI: 10.2105/ajph.77.10.1324
65. O’Donovan G, Inan-Eroglu E, Stamatakis E, Hamer M. **Alcohol drinking in one’s thirties and forties is associated with body mass index in men, but not in women: A longitudinal analysis of the 1970 British Cohort Study**. *Prev Med* (2021.0) **153** 106811. DOI: 10.1016/j.ypmed.2021.106811
66. Koh-Banerjee P, Chu N-F, Spiegelman D, Rosner B, Colditz G, Willett W. **Prospective study of the association of changes in dietary intake, physical activity, alcohol consumption, and smoking with 9-y gain in waist circumference among 16 587 US men**. *Am J Clin Nutr* (2003.0) **78** 719-27. DOI: 10.1093/ajcn/78.4.719
67. Butler L, Popkin BM, Poti JM. **Associations of alcoholic beverage consumption with dietary intake, waist circumference, and body mass index in US adults: National Health and Nutrition Examination Survey 2003–2012**. *J Acad Nutr Diet* (2018.0) **118** 409-20. e3. DOI: 10.1016/j.jand.2017.09.030
68. Stockwell T, Donath S, Cooper‐Stanbury M, Chikritzhs T, Catalano P, Mateo C. **Under‐reporting of alcohol consumption in household surveys: a comparison of quantity–frequency, graduated–frequency and recent recall**. *Addiction* (2004.0) **99** 1024-33. DOI: 10.1111/j.1360-0443.2004.00815.x
69. Mumenthaler MS, Taylor JL, O’Hara R, Yesavage JA. **Gender differences in moderate drinking effects**. *Alcohol Res Health* (1999.0) **23** 55-64. PMID: 10890798
70. Holmila M, Raitasalo K. **Gender differences in drinking: why do they still exist?**. *Addiction* (2005.0) **100** 1763-9. DOI: 10.1111/j.1360-0443.2005.01249.x
71. Kwok A, Dordevic AL, Paton G, Page MJ, Truby H. **Effect of alcohol consumption on food energy intake: A systematic review and meta-analysis**. *Br J Nutr* (2019.0) **121** 481-95. DOI: 10.1017/S0007114518003677
72. Powell LM, Nguyen BT. **Fast-food and full-service restaurant consumption among children and adolescents: effect on energy, beverage, and nutrient intake**. *JAMA Pediatr* (2013.0) **167** 14-20. DOI: 10.1001/jamapediatrics.2013.417
73. Smith JD, Hou T, Hu FB, Rimm EB, Spiegelman D, Willett WC. **A Comparison of Different Methods for Evaluating Diet, Physical Activity, and Long-Term Weight Gain in 3 Prospective Cohort Studies**. *J Nutr* (2015.0) **145** 2527-34. DOI: 10.3945/jn.115.214171
74. Kerr WC, Mulia N, Zemore SE. **US trends in light, moderate, and heavy drinking episodes from 2000 to 2010**. *Alcohol Clin Exp Res* (2014.0) **38** 2496-501. DOI: 10.1111/acer.12521
75. Marschner IC, Simes RJ, Keech A. **Biases in the identification of risk factor thresholds and J-curves**. *Am J Epidemiol* (2007.0) **166** 824-31. DOI: 10.1093/aje/kwm145
76. Bergmann MM, Rehm J, Klipstein-Grobusch K, Boeing H, Schütze M, Drogan D. **The association of pattern of lifetime alcohol use and cause of death in the European Prospective Investigation into Cancer and Nutrition (EPIC) study**. *Int J Epidemiol* (2013.0) **42** 1772-90. DOI: 10.1093/ije/dyt154
77. Keller DL. **Benefits of Moderate Alcohol Consumption Not Disproven**. *Am J Med* (2016.0) **129** e149. DOI: 10.1016/j.amjmed.2016.02.035
78. Wilsnack RW, Vogeltanz ND, Wilsnack SC, Harris TR, Ahlstrom S, Bondy S. **Gender differences in alcohol consumption and adverse drinking consequences: cross-cultural patterns**. *Addiction* (2000.0) **95** 251-65. DOI: 10.1046/j.1360-0443.2000.95225112.x
79. Zhou Y, Zheng J, Li S, Zhou T, Zhang P, Li HB. **Alcoholic Beverage Consumption and Chronic Diseases**. *Int J Environ Res Public Health* (2016.0) **13** 522. DOI: 10.3390/ijerph13060522
80. Santolaria F, González-Reimers E, Pérez-Manzano JL, Milena A, Gómez-Rodríguez MaA, González-Díaz A. **Osteopenia assessed by body composition analysis is related to malnutrition in alcoholic patients**. *Alcohol* (2000.0) **22** 147-57. DOI: 10.1016/s0741-8329(00)00115-4
81. Yeomans MR. **Alcohol, appetite and energy balance: is alcohol intake a risk factor for obesity?**. *Physiol Behav* (2010.0) **100** 82-9. DOI: 10.1016/j.physbeh.2010.01.012
82. Rom O, Kaisari S, Aizenbud D, Reznick AZ. **Lifestyle and sarcopenia-etiology, prevention, and treatment**. *Rambam Maimonides Med J* (2012.0) **3** e0024. DOI: 10.5041/RMMJ.10091
83. Breslow RA, Mukamal KJ. **Measuring the burden—current and future research trends: results from the NIAAA Expert Panel on Alcohol and Chronic Disease Epidemiology**. *Alcohol Res* (2013.0) **35** 250-9. PMID: 24881334
84. Dawson DA. **Methodological issues in measuring alcohol use**. *Alcohol Res Health* (2003.0) **27** 18-29. PMID: 15301397
85. Stevens J, McClain JE, Truesdale KP. **Selection of measures in epidemiologic studies of the consequences of obesity**. *Int J Obes (Lond)* (2008.0) **32** S60-6. DOI: 10.1038/ijo.2008.88
86. Yajima H, Noguchi T, Ikeshima E, Shiraki M, Kanaya T, Tsuboyama-Kasaoka N. **Prevention of diet-induced obesity by dietary isomerized hop extract containing isohumulones, in rodents**. *Int J Obes (Lond)* (2005.0) **29** 991-7. DOI: 10.1038/sj.ijo.0802965
87. Obara K, Mizutani M, Hitomi Y, Yajima H, Kondo K. **Isohumulones, the bitter component of beer, improve hyperglycemia and decrease body fat in Japanese subjects with prediabetes**. *Clin Nutr* (2009.0) **28** 278-84. DOI: 10.1016/j.clnu.2009.03.012
88. Shimura M, Hasumi A, Minato T, Hosono M, Miura Y, Mizutani S. **Isohumulones modulate blood lipid status through the activation of PPARα**. *Biochim Biophys Acta* (2005.0) **1736** 51-60. DOI: 10.1016/j.bbalip.2005.06.008
89. Halkjær J, Sørensen TI, Tjønneland A, Togo P, Holst C, Heitmann BL. **Food and drinking patterns as predictors of 6-year BMI-adjusted changes in waist circumference**. *Br J Nutr* (2004.0) **92** 735-48. DOI: 10.1079/bjn20041246
90. Halkjær J, Tjønneland A, Thomsen BL, Overvad K, Sørensen TI. **Intake of macronutrients as predictors of 5-y changes in waist circumference**. *Am J Clin Nutr* (2006.0) **84** 789-97. DOI: 10.1093/ajcn/84.4.789
91. Nielsen SJ, Kit BK, Fakhouri T, Ogden CL. **Calories consumed from alcoholic beverages by US adults, 2007–2010**. *NCHS Data Brief No. 110. NCHS Data Brief* (2012.0) **110** 1-8. PMID: 23384768
92. Duncan BB, Chambless L, Schmidt MI, Folsom AR, Szklo M, Crouse JR. **Association of the waist-to-hip ratio is different with wine than with beer or hard liquor consumption**. *Am J Epidemiol* (1995.0) **142** 1034-8. DOI: 10.1093/oxfordjournals.aje.a117556
93. French MT, Norton EC, Fang H, Maclean JC. **Alcohol consumption and body weight**. *Health Econ* (2010.0) **19** 814-32. DOI: 10.1002/hec.1521
94. Koppes LL, Twisk J, Van Mechelen W, Snel J, Kemper HC. **Cross-sectional and longitudinal relationships between alcohol consumption and lipids, blood pressure and body weight indices**. *J Stud Alcohol* (2005.0) **66** 713-21. DOI: 10.15288/jsa.2005.66.713
95. Angst F, Aeschlimann A, Angst J. **The minimal clinically important difference raised the significance of outcome effects above the statistical level, with methodological implications for future studies**. *J Clin Epidemiol* (2017.0) **82** 128-36. DOI: 10.1016/j.jclinepi.2016.11.016
96. Copay AG, Subach BR, Glassman SD, Polly DW, Schuler TC. **Understanding the minimum clinically important difference: a review of concepts and methods**. *The Spine Journal* (2007.0) **7** 541-6. DOI: 10.1016/j.spinee.2007.01.008
97. Rose G.. **Sick individuals and sick populations**. *Int J Epidemiol* (2001.0) **30** 427-32. DOI: 10.1093/ije/30.3.427
98. Partridge L, Deelen J, Slagboom PE. **Facing up to the global challenges of ageing**. *Nature* (2018.0) **561** 45-56. DOI: 10.1038/s41586-018-0457-8
|
---
title: A Conformational Change in the N Terminus of SLC38A9 Signals mTORC1 Activation
authors:
- Hsiang-Ting Lei
- Xuelang Mu
- Johan Hattne
- Tamir Gonen
journal: 'Structure (London, England : 1993)'
year: 2020
pmcid: PMC9994763
doi: 10.1016/j.str.2020.11.014
license: CC BY 4.0
---
# A Conformational Change in the N Terminus of SLC38A9 Signals mTORC1 Activation
## SUMMARY
mTORC1 is a central hub that integrates environmental cues, such as cellular stresses and nutrient availability to modulate metabolism and cellular responses. Recently, SLC38A9, a lysosomal amino acid transporter, emerged as a sensor for luminal arginine and as an activator of mTORC1. The amino acid-mediated activation of mTORC1 is regulated by the N-terminal domain of SLC38A9. Here, we determined the crystal structure of zebrafish SLC38A9 (drSLC38A9) and found the N-terminal fragment inserted deep within the transporter, bound in the substrate-binding pocket where normally arginine would bind. This represents a significant conformational change of the N-terminal domain (N-plug) when compared with our recent arginine-bound structure of drSLC38A9. We propose a ball-and-chain model for mTORC1 activation, where N-plug insertion and Rag GTPase binding with SLC38A9 is regulated by luminal arginine levels. This work provides important insights into nutrient sensing by SLC38A9 to activate the mTORC1 pathways in response to dietary amino acids.
## Graphical Abstract
## In Brief
SLC38A9 is an amino acid sensor in mTORC1 signaling through a process that is largely unknown. In this study, Lei et al. describe the structure of SLC38A9 with its N terminus plugged and propose a ball-and-chain mechanism of mTORC1 activation and arginine-enhanced transport by SLC38A9.
## INTRODUCTION
The mechanistic target of rapamycin complex 1 (mTORC1) protein kinase acts as a central signaling hub to control cell growth and balance the products from anabolism and catabolism (Ben-Sahra and Manning, 2017; Saxton and Sabatini, 2017; Shimobayashi and Hall, 2014). Not surprisingly, this pathway is dysregulated in many diseases (Laplante and Sabatini, 2012; Zoncu et al., 2011b). Activation of mTORC1 is mediated by a variety of environmental cues, such as nutrient availability, cellular stresses, and energy levels (Dibble and Manning, 2013; Sengupta et al., 2010). Specifically, certain amino acids signal to mTORC1 through two Ras-related guanosine triphosphatases (GTPases) (Kim et al., 2008; Sancak et al., 2008). When amino acids are abundant, the heterodimeric Rag GTPases adopt an active state and promote the recruitment of mTORC1 to the lysosomal surface (Sancak et al., 2010), which is now recognized as a key subcellular organelle involved in mTORC1 regulation (Zoncu et al., 2011a). Several essential amino acids in the lysosomal lumen, including arginine, leucine, and glutamine have been identified as effective activators of mTORC1 (Goberdhan et al., 2016; Jewell et al., 2013; Shimobayashi and Hall, 2016; Wolfson and Sabatini, 2017). However, the molecular basis of the amino acids-sensing mechanism has remained, by and large, elusive. Recently, SLC38A9, a low-affinity arginine transporter on lysosome vesicles, was identified as a direct sensor of luminal arginine levels for the mTORC1 pathway (Jung et al., 2015; Rebsamen et al., 2015; Wang et al., 2015). SLC38A9 also mediates the efflux of essential amino acids from lysosomes, such as leucine, in an arginine-regulated manner (Wyant et al., 2017), to drive cell growth by modulating cytosolic sensors (Saxton et al., 2016; Wolfson et al., 2016). Moreover, SLC38A9 senses the presence of luminal cholesterol and activates mTORC1 independently of its arginine transport function (Castellano et al., 2017).
SLC38A9 is a transceptor. Studies showed that two parts of SLC38A9, its N-terminal domain and its transmembrane (TM) bundle, are responsible for two distinct functions. The bulk of SLC38A9 are 11 α helices that pack against one another forming a TM bundle that transports amino acids and functions as an amino acid transporter (Lei et al., 2018). The N terminus of SLC38A9, on the other hand, was previously shown to interact directly with the Rag-Regulator complex to activate mTORC1 (Wang et al., 2015). Collectively, these results suggest that SLC38A9 is a “transceptor,” which is a membrane protein that embodies the functions of both a transporter and a receptor (van den Berg et al., 2016; Hundal and Taylor, 2009; Lei et al., 2018; Popova et al., 2010; Van Zeebroeck et al., 2009). Signaling, however, may or may not involve substrate transport.
We recently solved the crystal structure of N-terminally truncated SLC38A9 from Danio rerio (ΔN-drSLC38A9) with arginine bound (Lei et al., 2018). The substrate arginine was observed deep in the transporter at a binding pocket consisting of residues from TM1a, TM3, and TM8 of SLC38A9. Because the N-terminally truncated form of SLC38A9 was used, the initial study focused solely on the transporter function of SLC38A9 and the resulting structures could not inform on the signaling function of SLC38A9. Here, we report a crystal structure of drSLC38A9 with its N terminus but without the substrate arginine. Surprisingly, we found that a section of the N terminus formed a β hairpin that lodged itself deep within the transporter occupying the arginine binding site. These results suggest that, in the presence of high luminal arginine levels, the N-terminal domain could be displaced from the binding pocket by arginine and freed to interact with the Rag GTPase to activate mTORC1. We propose a ball-and-chain model to describe this mechanism of amino acid sensation and signaling by SLC38A9.
## RESULTS
We used the antibody fragment 11D3 to facilitate the crystallization of drSLC38A9 in the absence of substrate. Well-ordered crystals were diffracted to ~3.4Å with high completeness and acceptable refinement statistics (Table 1). Each asymmetric unit contained two copies of the drSLC38A9-Fab complex, arranged in a propeller-like head-to-head fashion (Figure S1). As with the recently determined structure (Lei et al., 2018), the TM domain of drSLC38A9 was captured in the cytosol-open state and was folded into the same inverted topology repeats made up of TMs 1–5 and TMs 6–10, with TM11 wrapping around the transceptor (Figures 1A and 1B). The two structures shared an overall similar fold with a root-mean-square deviation of 0.8 Å. However, instead of an arginine molecule bound, this time an unexpected electron density was observed, which extended along the solvent-accessible tunnel leading from the substrate-binding site to the cytosolic side of drSLC38A9 (Figure S2). The density was of sufficient quality to allow an unambiguous assignment of the drSLC38A9 N-terminal section from Asp75 to Leu90 (Figure S3). This fragment formed a folded domain, resembling a β hairpin, filling the entire path from the cytosolic side of SLC38A9 to the substrate-binding site (Figure 1C).
The binding of the N-terminal domain (referred to as the “N-plug” from this point on) inside drSLC38A9 does not appear to be a crystallization artifact. No crystal contacts exist near the N-plug, and the crystal has $40\%$ solvent content and has not undergone dehydration, suggesting that the N-plug motif is not artificially displacing water within the crystal. Electrostatic potential analysis indicated that the transport pathway in drSLC38A9 is generally positively charged, while the N-plug is largely negatively charged (Figure S4), suggesting that the interaction is electrostatically driven.
We captured drSLC38A9 in a physiologically relevant state that we term the “N-plug inserted state.” TMs 1, 5, 6, and 8 of drSLC38A9 form a V-shaped cavity into which the N-plug inserts and is stabilized by several bonds (Figure 2). At the tapered tip on the N-plug, Ser80 and His81 are bound to the main-chain carbonyl oxygens of Thr117, Met118, and Met119 in the unwound region of TM1 (Figure 2A). Thr117, Met118, and M119 are residues known to be important for arginine uptake in humans and in drSLC38A9 (Lei et al., 2018; Wyant et al., 2017). His81 further stabilizes the tip region of the N-plug through a hydrogen bond between its imidazole side chain and Thr121 (Figure 2A). Likewise, the main-chain carbonyl oxygen of Ile 84 is bound to Cys363 on TM6 (Figure 2B). At this juncture, the N-plug is jammed in between the two essential TMs 1 and 6, where it would probably prevent the TM domain from transitioning to an alternate state for transport. At the N terminus of the N-plug, the flanking residues are anchored against TM5 through a hydrogen bond formed between the main-chain carbonyl oxygens of Val77 and the side-chain hydroxyl group of Thr303 (Figure 2C). At the C terminus of the N-plug, the Tyr-Ser pairs involving Tyr87, Tyr448 and Ser88, Ser297 also stabilize the interaction by hydrogen bonds (Figure 2D). All residues that participate in the inter-domain interactions are conserved across species as indicated in the sequence alignments (Figure S3), suggesting that this interaction is evolutionarily conserved and likely plays an important functional role. The β hairpin-like structure of the N-plug is also self-stabilized by several hydrogen bonds between Ser80 and Glu82, His76, and Tyr85, which fasten the two ends of the N-plug together (Figure 2E). Structural modeling by PEP-FOLD (Shen et al., 2014; Thevenet et al., 2012) indicated that the β hairpin motif would be converted to an α-helical fragment should these residues be changed to alanine (Figure S4).
Functional assays in reconstituted liposomes indicated that the N-plug plays an important role in modulating arginine uptake. An overlay of the N-plug bound structure and the arginine-bound structure of drSLC38A9 indicated that the same set of backbone atoms are used for binding the N-plug and the arginine molecule (Figure 3A). This superposition suggests that, in the presence of arginine, the N-terminal plug may not occupy the binding site, but that in the absence of arginine it would be free to insert and bind.
Several drSLC38A9 variants were generated to test the influence of the N-plug on arginine uptake, single site mutants (V77W, H81W, Y87F) and a triple site mutant (V77W + H81W + Y87F). These mutations were chosen to interfere with the binding of the N-plug in the arginine binding site of drSLC38A9. While the construct studied structurally here and the wild-type drSLC38A9 transport arginine with similar rates, all mutant SLC38A9 displayed significantly higher arginine transport efficiency (Figure 3B). These results suggest that the N-plug plays an inhibitory role to downregulate the transport of arginine by drSLC38A9. Consistent with this postulate, the triple site mutant has a 2-fold decrease in Km for arginine without a significant change in Vmax (Figure 3C). These results indicate that the insertion of the N-plug into the arginine binding pocket of drSLC38A9 is physiologically relevant and not simply a crystallization artifact. It remains to be discovered why such a mechanism is required to modulate the arginine transport by this transporter.
SLC38A9 has higher affinity toward leucine than arginine, although the transport of leucine is largely facilitated by the presence of arginine (Wyant et al., 2017). Uptake studies performed here with drSLC38A9 corroborate the previous findings using the human protein (Figure 3D). Leucine uptake was significantly higher in the presence of supplemented arginine than without. Is it possible, therefore, that in the presence of arginine the N-terminal plug could play an important role in facilitating leucine transport?
To examine whether the N-terminal plug plays an important role in facilitating leucine transport, we used two drSLC38A9 variants, one without an N-plug (drSLC38A97−549) and the other with five-point mutations on the N-plug (P79A, S80A, H81A, E82A, and Y85A). From the results of leucine uptake by drSLC38A9, the arginine-enhanced transport of leucine is reflected as increased uptake of [3H]leucine when the buffer was supplemented with arginine. This characteristic of arginine-enhanced leucine transport was lost when the N-plug was eliminated, or its structure altered by mutation. Only the drSLC38A9 with an intact N-terminal plug in its native β hairpin structure showed the characteristic of enhanced leucine uptake in the presence of supplemented arginine (Figure 3D).
It is known that the N-terminal domain of SLC38A9 can bind to, and activate, the Rag GTPase complex (Wang et al., 2015). Moreover, it was shown that the N-terminal fragment of human SLC38A9 (hSLC38A9) was sufficient and required to bind the Ragulator-Rag GTPase complex (Wang et al., 2015). The binding of Rag GTPases and the human SLC38A9 involves the 85PDH87 motif (Rebsamen et al., 2015), Pro 85 and Pro 90 (Wang et al., 2015), corresponding to a conserved region on the N-plug in drSLC38A9 (Figure S3). To probe the N-plug interaction with the Rag GTPases in drSLC38A9, we co-purified the zebrafish Rag GTPase complex (drRagA and drRagC) with two N-terminal fragments of drSLC38A9 by size-exclusion chromatography. The first fragment (residues 1–96) contained the entire N terminus (called drSLC38A9-N.1), while in the second fragment (residues 1–70) the N-plug was deleted (called drSLC38A9-N.2). Fractions from size-exclusion chromatography were collected and analyzed by SDS-PAGE (Figure S5). Contrary to fragment drSLC38A9-N.1, which maintains the N-terminal domain in its entirety, the N-plug-deleted construct, drSLC38A9-N.2, did not associate with the Rag GTPase complex (Figure S5). These results clearly demonstrated that the interaction between the zebrafish SLC38A9 N terminus and the zebrafish Rag GTPase recapitulate the experiments reported previously using human proteins (Rebsamen et al., 2015; Wang et al., 2015): the same region of the N-plug of drSLC38A9 is essential for binding with the Rag GTPase complex.
## DISCUSSION
In considering our recently determined structure of SLC38A9 with arginine bound, and the current structure without arginine but with the N-plug inserted into the arginine binding site, we have now revealed that SLC38A9 has at least two distinct conformations of the N terminus. The first is when the N-plug is bound snugly in the arginine binding site (in the absence of arginine, low luminal arginine state) and the second is where the N-terminal plug is released and the substrate-binding site is occupied by arginine (in the presence of arginine, high luminal arginine state). The vestibule into which the N-terminal plug inserts measures ~20Å in diameter. A recently determined crystal structure of Rag GTPase-regulator (De Araujo et al., 2017; Su et al., 2017; Yonehara et al., 2017) indicated that the GTPase-regulator is far too large to fit inside the vestibule of SLC38A9 suggesting that the N-plug must exit the transceptor for binding the Rag GTPase. Together, these data suggest a mechanism by which SLC38A9 can act as a receptor to signal the activation of Rag GTPase and therefore of mTORC1 in the presence of arginine.
Thus, we propose a ball-and-chain model (Figure 4). At low luminal arginine concentrations, two conformational states could be at an equilibrium where the N-terminal plug is inserted or released from the arginine binding site of SLC38A9 at equal rates. When the equilibrium shifts to the high luminal arginine state, an arginine molecule will occupy the binding site of SLC38A9 for transport and the N-terminal plug would spend more time in the released state as long as arginine occupies the binding site. As a result, the N-terminal plug becomes available for binding to the Rag GTPase complex, which in turn could activate mTORC1. Moreover, the release of the N-terminal plug from the helical bundle of SLC38A9 will also facilitate the efflux of other essential amino acids, which simultaneously increases the cytosolic concentration of amino acids and synergistically activates mTORC1 through other cytosolic sensors.
While this study provides evidence on the function of SLC38A9 as a transporter and sensor for amino acids, it remains unclear how the N-terminal domain associates with the Rag GTPase complex at the lysosomal surface. Recently, a cryoelectron microscopic structure of the RagA/C-Ragulator in complex with the N-terminal peptide of SLC38A9 demonstrated that the human SLC38A9 is structurally incorporated in the GTPase for its nucleotide exchange activity (Fromm et al., 2020). This study agrees with our proposed ball-and-chain model of SLC38A9 for amino acid-mediated signaling and mTORC1 activation. Likewise, it is still not known what the open-to-lumen conformation of the transporter looks like and we are only now beginning to understand the dynamics of the N-plug insertion and release and its effect on arginine and leucine transport. For example, is it sufficient to simply bind arginine for signaling to occur through the N-plug or is transport of arginine required? Is it possible that additional arginine binding sites exist on SLC38A9 other than the substrate binding site describe here? Future studies must delve into these important open questions but with the above proposed ball-and-chain model for signaling, new biochemical assays can be designed and tested.
## Lead Contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Tamir Gonen ([email protected]).
## Materials Availability
There are restrictions to the availability of monoclonal antibody 11D3 due to the lack of an external centralized repository for its distribution and our need to maintain the stock. We are glad to share monoclonal antibody 11D3 with reasonable compensation by requestor for its processing and shipping.
## Data and Code Availability
The atomic coordinates of drSLC38A9 with N-plug has been deposited into the Protein Data Bank (PDB: 7KGV).
## Cell Line
Spodoptera frugiperda Sf-9.
## Culture Conditions for In Vitro Systems
Cells were grown in ESF 921 Insect Cell Culture Medium in flasks at 27°C.
## Protein Expression and Purification
The protein used in this study is drSLC38A971−549 referred to as drSLC38A9 in this manuscript. *The* gene of wild-type SLC38A9 (NP_001073468.1) from Danio rerio and its mutants were synthesized and then sub-cloned into a pFastbac1 vector containing an octa-histidine tag with a thrombin-cleavage site at the N-terminus. drSLC38A9 protein and its variants were overexpressed in *Spodoptera frugiperda* Sf-9 insect cells following the protocol of Bac-to-Bac Baculovirus Expression System (Invitrogen). Cells were harvested at 60 hours after infection and homogenized in the low salt buffer containing 20 mM Tris pH 8.0, 150 mM NaCl supplemented with cOmplete Protease Inhibitor Cocktail (Roche). The lysate was collected and ultra-centrifuged at 130,000×g for 1 hour. Pelleted membrane was then resuspended and washed with the high salt buffer containing 1.0 M NaCl and 20 mM Tris (8.0) followed by ultracentrifugation. The pellets were resuspended in the low salt buffer, frozen in liquid nitrogen and stored in −80°C until further use.
To purify drSLC38A9 protein and its variants, membrane fraction was thawed and solubilized with $2\%$ n-dodecyl-b-D-maltopyranoside (DDM, Anatrace) in 20 mM Tris pH 8.0, 500 mM NaCl, $5\%$ glycerol, and $0.2\%$ Cholesteryl Hemisuccinate Tris Salt (CHS, Anatrace) for 4 hours at 4°C. Following another ultra-centrifugation at 130,000×g for 1 hour, the supernatant was loaded onto TALON Metal Affinity Resin (Clontech) and incubated at 4°C overnight. The resins were washed by 5× column volumes of 50 mM imidazole, 20 mM Tris pH 8, 500 mM NaCl, $0.1\%$ DDM before equilibration in 20 mM Tris pH 8.0, 500 mM NaCl, $0.4\%$ decyl-b-D-maltoside (DM) and $0.02\%$ DDM. The N-terminal octa-histidine tag was removed by in-column thrombin digestion overnight at enzyme:protein molar ratio of 1:1000. The cleaved drSLC38A9 proteins collected in flow-through were then flash-frozen in liquid nitrogen and stored in −80°C until use.
## Fab Fragments Production
Fab fragments were produced at Monoclonal Antibody Core of Vaccine and Gene Therapy Institute, OHSU. Mouse IgG monoclonal antibodies against drSLC38A9 were raised by standard protocol (Harlow and Lane, 1988) using purified protein in the buffer containing 20 mM Tris pH 8.0, 150 mM NaCl, $0.02\%$ DDM, $0.002\%$ CHS as antigen. Western blot and native-to-denature ELISA assays (Lim et al., 2011) were performed to assess the binding affinity and specificity of the antibodies generated from hybridoma cell lines. Several monoclonal antibodies showing high binding affinity and specificity to conformational epitope were then selected and purified from the hybridoma supernatants. Fab fragments were generated by Papain (Thermo Fisher Scientific) digestion and purified by Protein A affinity chromatography (GE Healthcare) in 20 mM Sodium phosphates pH 8.0, 150 mM NaCl.
## Purification of drSLC38A9-Fab Complexes for Crystallization
Purified drSLC38A9 proteins was mixed with excess Fab fragments at a molar ratio of 1:2 for 2 hours, and the mixture was subjected to gel filtration (Superdex 200 Increase $\frac{10}{300}$ GL, GE Healthcare) in the buffer containing 20 mM Tris-HCl pH 8.0, 150 mM NaCl and $0.2\%$ DM. The peak fractions containing appropriate drSLC38A9-Fab complexes were then pooled and concentrated to 5 mg/mL for crystallization.
## Crystallization
Crystallization was carried out by hanging-drop vapor diffusion at 4°C. Initial hits of drSLC38A9 were identified in multiple conditions containing PEG 400. However, these crystals gave anisotropic diffraction to ~6 Å. Well-diffracting crystals were only obtained when drSLC38A9 was co-crystallized as a complex with Fab fragment prepared from hybridoma cell line 11D3 (IgG2a, kappa) at 5 mg/mL mixed 1:1 with drop solution containing $30\%$ PEG 400, 100 mM ADA pH 6.0 and 350 mM Li2SO4.
## Data Collection and Structure Determination
Before data collection, crystals were soaked in a cryoprotectant buffer containing $30\%$ PEG 400 in the same crystallizing solution for 1 min, and rapidly frozen in liquid nitrogen. All diffraction data for drSLC38A9-Fab complex were collected at 100K using synchrotron radiation at the Advanced Photon Source (NE-CAT 24-ID-C and 24-ID-E). Diffraction data indexing, integration and scaling were performed with the online RAPD system and the CCP4 suite (Winn et al., 2011). Data collection statistics, phasing and refinement are given in Table 1. Molecular replacement using Phaser (McCoy et al., 2007) was able to place two copies of Fab fragment (PDB: 1F8T) in native datasets. Helices of drSLC38A9 were manually placed in the density-modified map and extended within Coot (Emsley et al., 2010) according to the reference model of ΔN-drSLC38A9-Fab complex (PDB: 6C08). Subsequent cycles of density modifications, model building and refinement were carried out in Phenix (Adams et al., 2010; Zwart et al., 2008) and Coot until structure completion (Figure S2). The Ramachandran analyses of final structures were performed using Molprobity (Chen et al., 2010). The model has been deposited into the PDB (PDB: 7KGV).
## Proteoliposome Reconstitution and Arginine Uptake Assay
The full-length drSLC38A9, three single-mutants (V77W; H81W; Y87F), and triple-mutant (V77W, H81W, and Y87F) proteins were expressed and purified as described above. Chloroform-dissolved chicken egg phosphatidylcholine (egg-PC, Avanti Polar Lipids) was evaporated using dry nitrogen to yield a lipid film in a small glass vial and further dried under vacuum overnight. The lipids were hydrated in inside buffer (20 mM Mes pH 5.0, 90 mM KCl, 10 mM NaCl) at 25 mg/mL by vortex for 3 minutes and then aged in room temperature for 1 hour. Liposomes were clarified by 5 rounds of freezing and thawing in liquid nitrogen and extruded through a 100 nm membrane with 21 passes (Milipore). The liposomes were pre-incubated with $1\%$ n-octyl-β-D-glucoside (β-OG) and 1 mM DDT for 1 hour at 4°C before protein reconstitution. Purified full-length drSLC38A9 and variants were incorporated at a 1:80 (w/w) ratio into destabilized liposomes for 1 hour in the 4°C rotator. Glycerol-supplemented protein buffer was used in lieu of drSLC38A9 protein in liposome-only control groups. The detergents were removed by incubation overnight with 200 mg per reaction Bio-Beads, and the proteoliposomes were further incubated with 40 mg per reaction fresh Bio-Beads for an additional hour. The proteoliposomes and liposome-only controls were collected using ultracentrifuge at 100,000×g for 30 minutes at 4°C and then resuspended in outside buffer (20 mM Tris pH 7.4, 100 mM NaCl) to final lipid concentration of 32 μg/μL.
Transport reactions were initiated by adding 0.5 μM L-[3H]-arginine (American Radiolabeled Chemicals, Inc) to 50 μL of proteoliposomes. Assays of Liposome-only controls were carried out in parallel to experimental groups as negative controls. All buffers were chilled and assays were performed at room temperature. For time-course uptake assay, at various time points, proteoliposomes were filtered, washed by 5mL of ice-cold wash buffer (outside buffer with 10 mM unlabeled L-arginine), and collected on 0.22 μm nitrocellulose membranes (Millipore) which had been pre-wet by washing buffer. After washing, each filter was dried by vacuum for exactly 1 minute and transferred into a glass vial with 10 mL scintillation fluid for counting. Measurements at 5 minutes of the arginine uptake were used to establish the transport comparisons between various constructs of drSLC38A9, normalized to that of the full-length wildtype drSLC38A9. Non-specific adsorptions of L-[3H]-arginine by liposomes-only controls were subtracted from experimental measurements.
The measurements of Km and kcat were performed in the presence of unlabeled L-arginine at the indicated concentrations supplemented with outside buffer, together with the same concentration of L-[3H]-arginine at 0.5 μM. All outside buffers (with different concentrations of unlabeled arginine) were adjusted to pH 7.4. The uptake of L-[3H]-arginine was stopped at 5 minutes when the transport activity still remained linear. The experiment was repeated more than three times with similar results and a representative one is shown.
## Proteoliposome Reconstitution and Leucine Uptake Assay
The full-length drSLC38A9 and two variants, N-terminal deletion (truncate N-terminus from Met 1 to Val 96) and 5A (P79A, S80A, H81A, E82A, and Y85A) mutant protein, were expressed and purified as described above. Liposomes were prepared using a 3:1 ratio of E. coli total lipid extract (Avanti Polar Lipids) to chicken egg phosphatidylcholine (egg-PC, Avanti Polar Lipids) at 20 mg/mL in assay buffer (20mM MES pH 5.0, 150mM NaCl and 1mM DTT). An extruder with pore size of 0.4 μm was used to obtain unilamellar vesicles. Triton X-100 was then added to the extruded liposomes at 10:1 (w:w) lipid:detergent ratio. Purified wild-type drSLC38A9 and variants were reconstituted at a 1:200 (w/w) ratio in destabilized liposomes and excess detergent was removed by SM2 Bio-Beads (Bio-Rad) at 4°C overnight. Next day, proteoliposomes were collected, aliquoted and frozen at −80°C for storage until needed.
Transport reactions were initiated by adding [3H]-labeled amino acids (American Radiolabeled Chemicals) to 50 μL of 10-fold diluted proteoliposomes (total of 0.5 μg protein) to final concentration of 0.5 μM at room temperature. As controls, non-specific uptake was assessed by using protein-free liposomes under identical conditions in parallel to experimental groups. At various time points, reactions were stopped by quenching the samples with 5 mL assay buffer followed by rapid filtration through 0.22μm membrane filter (GSWP02500, MilliporeSigma) to remove excess radioligands. The filter was then washed three times with 5 mL assay buffer, suspended in 10 mL of scintillation fluid and quantified by scintillation counting. A time course profile indicates that the retained radio-ligands reached saturation after 10 min. Measurements at various time points of the uptake were plotted to establish the transport comparisons between various constructs of drSLC38A9. All experiment and control groups were repeated two to three times.
## Co-purification of Zebrafish Rag GTPase Complex with N-Terminal Fragment of drSLC38A9
The synthesized cDNA encoding RagA (UniProtKB - Q7ZUI2) and RagC (UniProtKB - F1Q665) from Danio rerio were cloned into pFastBac Dual vector. The Rag GTPase complex were overexpressed in *Spodoptera frugiperda* Sf-9 insect cells, which was harvested at 48 hours post-infection. Cell pellets were resuspended in lysis buffer containing 20 mM Tris pH 8.0, 150 mM NaCl. 30 homogenizing cycles were then carried out to break cells on ice, followed by a centrifugation at 130,000×g for 30 mins. The supernatant was incubated with Ni-NTA Agarose (QIGEN) for 2 hours at 4°C. The resins were then washed with 5× column volumes of wash buffer containing 50mM Imidazole, 20 mM Tris pH 8.0, 150 mM NaCl. The protein was eluted by elution buffer containing 300 mM imidazole, 20 mM Tris pH 8.0, 150 mM NaCl, and then applied to gel filtration (Superdex 200 Increase $\frac{10}{300}$ GL, GE Healthcare) in 20 mM Tris pH 8.0, 150 mM NaCl. The peak fractions were collected for further analysis.
To enhance solubility and stability, the N-terminal fragments of drSLC38A9 were fused with GB1 domain-tag (Cheng and Patel, 2004). drSLC38A9-N.1 is from Met 1 to Val 96, and drSLC38A9-N.2 is from Met 1 to Leu 70. The fusion proteins were overexpressed in E. coli BL21 (DE3) at 16°C for overnight with 0.2 mM isopropyl-β-D-thiogalactopyranoside (IPTG) as inducer. Then, the cells were harvested, homogenized in a lysis buffer containing 20mM Tris pH 8.0 and 150mM NaCl, and disrupted using a Microfluidizer (Microfluidics Corporation) with 3 passes at 15,000 p.s.i., followed by a centrifugation for 30 mins to remove cell debris. The supernatant was then loaded onto Ni-NTA Agarose and purified as above.
The purified Rag GTPase complex was mixed with excess GB1-drSLC38A9-N.x fragment at a molar ration of 1:2 for 1 hour, and the mixture was then subjected to gel filtration (Superdex 200 Increase $\frac{10}{300}$ GL, GE Healthcare) in the buffer containing 20 mM Tris pH 8.0, 150 mM NaCl. SDS-PAGE and Coomassie blue staining was used to analyze the size exclusion chromatography elution profile.
All figures in this paper were prepared with PyMOL v1.8.6.0 (Schrodinger LLC, 2015). Figure S3 was prepared using the program Clustal Omega (Sievers et al., 2011) for alignments and ESPript 3.0 (Robert and Gouet, 2014) for styling.
## QUANTIFICATION AND STATISTICAL ANALYSIS
All statistical analyses were performed in Microsoft Excel and GraphPad Prism.
The statistical details of arginine and leucine uptake assay can be found in main text and figure legends. The significance was determined by unpaired t-test in Figure 3B.
## References
1. Adams PD, Afonine PV, Bunkóczi G, Chen VB, Davis IW, Echols N, Headd JJ, Hung LW, Kapral GJ, Grosse-Kunstleve RW. **PHENIX: a comprehensive Python-based system for macromolecular structure solution**. *Acta Crystallogr. D Biol. Crystallogr* (2010) **66** 213-221. PMID: 20124702
2. De Araujo MEGG, Naschberger A, Fürnrohr BG, Stasyk T, Dunzendorfer-Matt T, Lechner S, Welti S, Kremser L, Shivalingaiah G, Offterdinger M. **Crystal structure of the human lysosomal mTORC1 scaffold complex and its impact on signaling**. *Science* (2017) **358** 377-381. PMID: 28935770
3. Ben-Sahra I, Manning BD. **mTORC1 signaling and the metabolic control of cell growth**. *Curr. Opin. Cell Biol* (2017) **45** 72-82. PMID: 28411448
4. van den Berg B, Chembath A, Jefferies D, Basle A, Khalid S, Rutherford JC. **Structural basis for Mep2 ammonium transceptor activation by phosphorylation**. *Nat. Commun* (2016) **7** 11337. PMID: 27088325
5. Castellano BM, Thelen AM, Moldavski O, Feltes M, Van Der Welle REN, Mydock-McGrane L, Jiang X, van Eijkeren RJ, Davis OB, Louie SM. **Lysosomal cholesterol activates mTORC1 via an SLC38A9-Niemann-Pick C1 signaling complex**. *Science* (2017) **355** 1306-1311. PMID: 28336668
6. Chen VB, Arendall WB, Headd JJ, Keedy DA, Immormino RM, Kapral GJ, Murray LW, Richardson JS, Richardson DC. **MolProbity: all-atom structure validation for macromolecular crystallography**. *Acta Crystallogr. D Biol. Crystallogr* (2010) **66** 12-21. PMID: 20057044
7. Cheng Y, Patel DJ. **An efficient system for small protein expression and refolding**. *Biochem. Biophysical Res. Commun* (2004) **317** 401-405
8. Dibble CC, Manning BD. **Signal integration by mTORC1 coordinates nutrient input with biosynthetic output**. *Nat. Cell Biol* (2013) **15** 555-564. PMID: 23728461
9. Emsley P, Lohkamp B, Scott WG, Cowtan K. **“Features and development of Coot**. *Acta Crystallogr. Biol. Crystallogr* (2010) **66** 486-501
10. Fromm SA, Lawrence RE, Hurley JH. **Structural mechanism for amino acid-dependent Rag GTPase nucleotide state switching by SLC38A9**. *Nat. Struct. Mol. Biol* (2020) **27** 1017-1023. DOI: 10.1038/s41594-020-0490-9
11. Goberdhan DCI, Wilson C, Harris AL. **Amino acid sensing by mTORC1: intracellular transporters mark the spot**. *Cell Metab* (2016) **23** 580-589. PMID: 27076075
12. Harlow E, Lane D. *Antibodies: A Laboratory Manual* (1988)
13. Hundal HS, Taylor PM. **Amino acid transceptors: gate keepers of nutrient exchange and regulators of nutrient signaling**. *Am. J. Physiol. Endocrinol. Metab* (2009) **296** E603-E613. PMID: 19158318
14. Jewell JL, Russell RC, Guan K-L. **Amino acid signalling upstream of mTOR**. *Nat. Rev. Mol. Cell Biol* (2013) **14** 133-139. PMID: 23361334
15. Jung J, Genau HM, Behrends C. **Amino acid-dependent mTORC1 regulation by the lysosomal membrane protein SLC38A9**. *Mol. Cell. Biol* (2015) **35** 2479-2494. PMID: 25963655
16. Kim E, Goraksha-Hicks P, Li L, Neufeld TP, Guan K-L. **Regulation of TORC1 by Rag GTPases in nutrient response**. *Nat. Cell Biol* (2008) **10** 935-945. PMID: 18604198
17. Laplante M, Sabatini DM. **mTOR signaling in growth control and disease**. *Cell* (2012) **149** 274-293. PMID: 22500797
18. Lei H-T, Ma J, Sanchez Martinez S, Gonen T. **Crystal structure of arginine-bound lysosomal transporter SLC38A9 in the cytosol-open state**. *Nat. Struct. Mol. Biol* (2018) **25** 522-527. PMID: 29872228
19. Lim HH, Fang Y, Williams C. **High-efficiency screening of monoclonal antibodies for membrane protein crystallography**. *PLoS One* (2011) **6** 1-6
20. McCoy AJ, Grosse-Kunstleve RW, Adams PD, Winn MD, Storoni LC, Read RJ. **Phaser crystallographic software**. *J. Appl. Crystallogr* (2007) **40** 658-674. PMID: 19461840
21. Popova Y, Thayumanavan P, Lonati E, Agrochao M, Thevelein JM. **Transport and signaling through the phosphate-binding site of the yeast Pho84 phosphate transceptor**. *Proc. Natl. Acad. Sci. U S A* (2010) **107** 2890-2895. PMID: 20133652
22. Rebsamen M, Pochini L, Stasyk T, de Araújo MEG, Galluccio M, Kandasamy RK, Snijder B, Fauster A, Rudashevskaya EL, Bruckner M. **SLC38A9 is a component of the lysosomal amino acid sensing machinery that controls mTORC1**. *Nature* (2015) **519** 477-481. PMID: 25561175
23. Robert X, Gouet P. **Deciphering key features in protein structures with the new ENDscript server”**. *Nucleic Acids Res* (2014) **42** W320-W324. PMID: 24753421
24. Sancak Y, Bar-Peled L, Zoncu R, Markhard AL, Nada S, Sabatini DM. **Ragulator-Rag complex targets mTORC1 to the lysosomal surface and is necessary for its activation by amino acids**. *Cell* (2010) **141** 290-303. PMID: 20381137
25. Sancak Y, Peterson TR, Shaul YD, Lindquist RA, Thoreen CC, Bar-Peled L, Sabatini DM. **The rag GTPases bind raptor and mediate amino acid signaling to mTORC1**. *Science* (2008) **320** 1496-1501. PMID: 18497260
26. Saxton RA, Knockenhauer KE, Wolfson RL, Chantranupong L, Pacold ME, Wang T, Schwartz TU, Sabatini DM. **Structural basis for leucine sensing by the Sestrin2-mTORC1 pathway**. *Science* (2016) **351** 53-58. PMID: 26586190
27. Saxton RA, Sabatini DM. **mTOR signaling in growth, metabolism, and disease**. *Cell* (2017) **168** 960-976. PMID: 28283069
28. Schrodinger LLC (2015). The PyMOL Molecular Graphics System (New York: Schrodinger LLC), Version 1.8.. *The PyMOL Molecular Graphics System* (2015)
29. Sengupta S, Peterson TR, Sabatini DM. **Regulation of the mTOR complex 1 pathway by nutrients, growth factors, and stress**. *Mol. Cell* (2010) **40** 310-322. PMID: 20965424
30. Shen Y, Maupetit J, Derreumaux P, Tufféry P. **Improved PEP-FOLD approach for peptide and miniprotein structure prediction**. *J. Chem. Theor. Comput* (2014) **10** 4745-4758
31. Shimobayashi M, Hall MN. **Making new contacts: the mTOR network in metabolism and signalling crosstalk**. *Nat. Rev. Mol. Cell Biol* (2014) **15** 155-162. PMID: 24556838
32. Shimobayashi M, Hall MN. **Multiple amino acid sensing inputs to mTORC1**. *Cell Res* (2016) **26** 7-20. PMID: 26658722
33. Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H, Remmert M, Söding J. **Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega**. *Mol. Syst. Biol* (2011) **7** 539. PMID: 21988835
34. Su M-Y, Morris KL, Kim DJ, Fu Y, Lawrence R, Stjepanovic G, Zoncu R, Hurley JH. **Hybrid structure of the Raga/C-ragulator mTORC1 activation complex**. *Mol. Cell* (2017) **68** 835-846.e3. PMID: 29107538
35. Thevenet P, Shen Y, Maupetit J, Guyon F, Derreumaux P, Tuffery P. **PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides**. *Nucleic Acids Res* (2012) **40** W288-W293. PMID: 22581768
36. Wang S, Tsun Z, Wolfson RL, Shen K, Wyant GA, Plovanich ME, Yuan ED, Jones TD, Chantranupong L, Comb W. **Lysosomal amino acid transporter SLC38A9 signals arginine sufficiency to mTORC1**. *Science* (2015) **347** 188-194. PMID: 25567906
37. Weiss MS. **Global indicators of X-ray data quality**. *J. Appl. Crystallogr* (2001) **34** 130-135
38. Winn MD, Ballard CC, Cowtan KD, Dodson EJ, Emsley P, Evans PR, Keegan RM, Krissinel EB, Leslie AG, McCoy A. **Overview of the**. *Acta Crystallogr. D Biol. Crystallogr* (2011) **67** 235-242. PMID: 21460441
39. Wolfson RL, Chantranupong L, Saxton RA, Shen K, Scaria SM, Cantor JR, Sabatini DM. **Sestrin2 is a leucine sensor for the mTORC1 pathway**. *Science* (2016) **351** 43-48. PMID: 26449471
40. Wolfson RL, Sabatini DM. **The dawn of the age of amino acid sensors for the mTORC1 pathway**. *Cell Metab* (2017) **26** 301-309. PMID: 28768171
41. Wyant GA, Abu-Remaileh M, Wolfson RL, Chen WW, Freinkman E, Danai LV, Vander Heiden MG, Sabatini DM. **mTORC1 activator SLC38A9 is required to efflux essential amino acids from lysosomes and use protein as a nutrient**. *Cell* (2017) **171** 642-654.e12
42. Yonehara R, Nada S, Nakai T, Nakai M, Kitamura A, Ogawa A, Nakatsumi H, Nakayama KI, Li S, Standley DM. **Structural basis for the assembly of the Ragulator-Rag GTPase complex**. *Nat. Commun* (2017) **8** 1625
43. Van Zeebroeck G, Bonini BM, Versele M, Thevelein JM. **Transport and signaling via the amino acid binding site of the yeast Gap1 amino acid transceptor**. *Nat. Chem. Biol* (2009) **5** 45-52. PMID: 19060912
44. Zoncu R, Bar-Peled L, Efeyan A, Wang S, Sancak Y, Sabatini DM. **mTORC1 senses lysosomal amino acids through an inside-out mechanism that requires the vacuolar H**. *Science* (2011a) **334** 678-683. PMID: 22053050
45. Zoncu R, Efeyan A, Sabatini DM. **mTOR: from growth signal integration to cancer, diabetes and ageing**. *Nat. Rev. Mol. Cell Biol* (2011b) **12** 21-35
46. Zwart PH, Afonine PV, Grosse-Kunstleve RW, Hung L-W, Ioerger TR, McCoy AJ, McKee E, Moriarty NW, Read RJ, Sacchettini JC. **Automated structure solution with the PHENIX suite. Methods**. *Mol. Biol* (2008) **426** 419-435
|
---
title: 'Patient characteristics associated with the acceptability of teleconsultation:
a retrospective study of osteoporotic patients post-COVID-19'
authors:
- Benedetta Pongiglione
- Flaminia Carrone
- Alessandra Angelucci
- Gherardo Mazziotti
- Amelia Compagni
journal: BMC Health Services Research
year: 2023
pmcid: PMC9994774
doi: 10.1186/s12913-023-09224-x
license: CC BY 4.0
---
# Patient characteristics associated with the acceptability of teleconsultation: a retrospective study of osteoporotic patients post-COVID-19
## Abstract
### Background
Due to the COVID-19 pandemic, teleconsultations (TCs) have become common practice for many chronic conditions, including osteoporosis. While satisfaction with TCs among patients increases in times of emergency, we have little knowledge of whether the acceptability of TCs persists once in-person visits return to being a feasible and safe option. In this study, we assess the acceptability of TCs across five dimensions for osteoporosis care among patients who started or continued with TCs after the COVID-19 pandemic had waned. We then explore the patient characteristics associated with these perceptions.
### Methods
Between January and April 2022, 80 osteoporotic patients treated at the Humanitas Hospital in Milan, Italy, were recruited to answer an online questionnaire about the acceptability of TCs for their care. The acceptability of TCs was measured using a modified version of the Service User Technology Acceptability Questionnaire (SUTAQ), which identifies five domains of acceptability: perceived benefits, satisfaction, substitution, privacy and discomfort, and care personnel concerns. Multivariable ordinary least squares (OLS) linear regression analysis was performed to assess which patient characteristics in terms of demographics, socio-economic conditions, digital skills, social support, clinical characteristics and pattern of TC use were correlated with the five domains of acceptability measured through the SUTAQ.
### Results
The degree of acceptability of TCs was overall good across the 80 respondents and the five domains. Some heterogeneity in perceptions emerged with respect to TCs substituting for in-person visits, negatively impacting continuity of care and reducing the length of consultations. For the most part, acceptability was not affected by patient characteristics with a few exceptions related to treatment time and familiarity with the TC service modality (i.e., length of osteoporosis treatment and number of TCs experienced by the patient).
### Conclusions
TCs appear to be an acceptable option for osteoporosis care in the aftermath of the COVID-19 pandemic. This study suggests that other characteristics besides age, digital skills and social support, which are traditionally relevant to TC acceptability, should be taken into account in order to better target this care delivery modality.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12913-023-09224-x.
## Background
The COVID-19 pandemic has caused profound disruptions globally to the delivery of healthcare services. Notably, it has affected the management of many chronic conditions as resources have been diverted to urgent care and as people have been less inclined to or prevented from attending healthcare facilities for fear of contagion [1, 2]. Since the early outbreak of COVID-19 in 2020, to guarantee continuity in the management of chronic conditions, many healthcare providers worldwide switched to remote consultations (teleconsultations [TCs]), in which the interaction between specialist or general practitioner and patients is mediated by some form of information technology-based platform [2, 3]. In Italy, one of the first and worst hit countries by the pandemic [4], telehealth services became forcibly widespread during the first outbreak, when the country adopted a strict lockdown policy. Previously, telehealth services had been rather uncommon and mostly treated with some suspicion by patients and healthcare professionals [5, 6].
In 2021, once the pandemic decreased in severity and people started to get vaccinated, the TC modality was maintained to limit the risk of crowding healthcare facilities [2] and is currently still in place in many countries for a select set of health conditions. Now that the pandemic is less pressing, it is of paramount importance to understand how patients continue to perceive the TC modality of delivering care, especially in comparison to in-person visits. There is still a limited understanding of which patient characteristics are associated with higher perceived benefits and the acceptability of TCs and therefore, which patients are more likely to continue with TCs even in times of non-emergency.
## Determinants of patients’ perceptions of telehealth and TCs
Perceptions of telehealth services before the COVID-19 pandemic were traditionally shaped by determinants such as age and digital literacy [6, 7]. For instance, a survey related to telehealth conducted in 15 European countries at the beginning of 2000 showed that interest in and acceptability of telehealth was significantly lower among older adults [6] and people with a lower education level [7]. In recent years, however, studies have shown much more heterogeneous results and an increase in satisfaction with telehealth, even among the elderly [8–10]. In a recent survey, European citizens ages 75 and over indicated their perception of telehealth as both making their lives easier and more difficult [9]. The ease of use of digital technology, convenience due to reduced travel time, increased access to healthcare professionals and even perception of improved health outcomes have all contributed to a high level of satisfaction with telehealth [8–10]. In these studies, though, some barriers were still highlighted, including patients’ inability to understand the technological side of telehealth, the need for social support when using new digital devices [9] and interactional challenges during video consultation (e.g., disruption to conversational flow and difficulty conducting examinations) [11].
With the COVID-19 pandemic, there has been an overall increase in patients’ degree of satisfaction with telehealth [12–15]. Nguyen et al. [ 14], for instance, found that during the COVID-19 outbreak, patients with diverse health conditions consistently reported 95–$100\%$ satisfaction rates with TCs in comparison to in-person visits. Similarly, studies in which TCs were targeted at patients with chronic cardiovascular [12] or rheumatologic [15] problems showed that during the early COVID-19 outbreak, TCs were greatly appreciated, and the majority of patients felt that without them, they would have stopped receiving pharmacological therapy or their health would have gotten worse. Most patients also indicated a willingness to continue with TCs in non-emergency situations [12].
## Telehealth in osteoporosis care
Osteoporosis is a common chronic condition worldwide that is linked to bone fragility; long-term pharmacological treatment is required for the prevention of further bone loss, deterioration of skeletal micro-architecture and disabling bone fractures [15, 16]. Given that osteoporotic patients have no real physical symptoms of the progression of the disease, one of the major issues in the treatment of osteoporosis is the low level of compliance with pharmacological therapy [16].
Consistent with studies of other chronic conditions, a review examining different models of telehealth for osteoporosis before the COVID-19 pandemic found limited evidence of the acceptability of these service modalities among osteoporotic patients and unclear evidence that telehealth services could improve drug therapy adherence [17]. More recently, higher acceptability of TCs among osteoporotic patients has been recorded. For instance, a study of 69 osteoporotic patients in Toronto found that participants were comfortable with TCs and perceived receiving a comparable quality of care to in-person visits [18]. They perceived the benefits of TCs in terms of convenience of timely care close to home, reduced burden of travel and costs and an enhanced sense of confidence in their osteoporosis specialist [18]. Patients also indicated the presence of some critical issues with TCs, including difficulties with sharing tests, conducting investigations through TCs and coordinating care with other healthcare professionals [18].
During the COVID-19 pandemic, osteoporotic patients experienced an increase in the use of TCs for their care [19] accompanied by an increase in the degree of acceptability of this service modality [17, 20]. A study based in the UK examined the perception of a virtual service for fracture risk assessment and fracture prevention advice during the COVID-19 pandemic [21]. The study showed that $90\%$ of the 60 respondents rated their overall experience with the service as very good or excellent. Almost all of the respondents indicated that they would recommend the service to others and would continue the service after the end of the pandemic.
The previous findings mostly reported patients’ perceptions of TCs in times of emergency, while evidence of the acceptability of TCs after the emergency has waned is scant. This paper employs a modified version of the Service User Technology Acceptability Questionnaire (SUTAQ) [22–24] to assess how 80 Italian patients perceived the acceptability of TCs for their osteoporosis care post-COVID-19 across five different dimensions. We consider both patients that experienced their first TC during the early COVID-19 outbreak when in-person visits were unfeasible as well as patients who started with TCs later on when the pandemic was less pressing and in-person visits were again a feasible alternative. We then explore the patient characteristics correlated with such perceptions.
## Recruitment strategy, sample and data sources
This was a monocentric, retrospective study of patients at the Metabolic Bone Diseases and Osteoporosis Section of the IRCCS Humanitas Research Hospital in Rozzano, Milan, Italy. The inclusion criteria of the study were patients affected by osteoporosis who 1) were being treated with bone-active drugs, 2) had followed up by the time of recruitment (November 2021), and 3) had experienced at least one TC session. We retrospectively selected 102 subjects who met the inclusion criteria starting from those who had a follow-up visit in November 2021 and proceeding backward until June 2020. Patients were contacted via phone by the osteoporosis specialists of the Humanitas Hospital. Eighty accepted the invitation to participate in the study, three had died since their previous follow-up, 10 refused to participate and nine did not reply.
Of the 80 respondents, $38.7\%$ had started TC visits between June 2020 and March 2021 (i.e., the emergency phase). This time period corresponded to the months immediately after the first COVID-19 outbreak (March–June 2020) and during the following two major pandemic outbreaks (i.e., September–December 2020 and January–March 2021) [25]. During this time, in-person visits were either not allowed or perceived as risky for fear of infection. The remaining $61.3\%$ of the sample started with TC visits between April and November 2021 (i.e., the post-emergency phase), when in-person visits had become feasible again, the emergency had waned and patients (who were mostly vaccinated [25]) were less afraid of being infected while attending a healthcare facility. Of the 80 patients, $12.5\%$ had explicitly asked to stop TC visits and return to in-person visits.
The TC service did not change during the study period. It consisted of a computer-based TC using a Google cloud platform (Humanitas Televisita Sicura Platform) developed by the Humanitas Hospital. Patients could connect with their osteoporosis specialists via video (through Google Meet). These specialists had been previously trained to use the TC platform by an internal board of technicians and experts in communication. The TC platform also allowed patients to share clinical data (e.g., the results of biochemical exams, previous clinical visits and imaging) with high safety standards for data protection. TCs were carried out interchangeably by two specialists (F.C. and G.M.), and osteoporotic patients received a TC every 6–7 months.
## Clinical data, questionnaire and analyses
For each recruited patient, demographics (e.g., age and gender), clinical characteristics (e.g., years since diagnosis of osteoporosis) and pattern of TC use (e.g., number of TCs before enrolment in the study) were retrieved from their clinical records. Additional patient characteristics were collected through a questionnaire. The questionnaire was administered to study participants via the Qualtrics online platform (Qualtrics, Provo, UT, USA; https://www.qualtrics.com). The informed consent form was first illustrated to the patients on the phone at the time of their recruitment by the *Humanitas osteoporosis* specialists and then sent via email (together with the link to the online questionnaire). Patients were instructed to carefully read the informed consent and to confirm they agreed with its contents by responding affirmatively to a statement contained in the first page of the online questionnaire. To guarantee the candidness of answers, the informed consent form explained to the patients that their answers would not be visible to their osteoporosis specialists.
The questionnaire included three parts. Part A asked questions about the socio-economic conditions of respondents, their digital skills and the presence of social support while using the TC platform. Table 1 reports descriptive statistics about these questions and the clinical characteristics of the sample of 80 patients. Additional file 1, instead, reports the overall baseline characteristics for the two groups (i.e., first TC in emergency phase and first TC in post-emergency phase) and shows that there was no significant difference between them. Table 1Descriptive statistics of patient sample and their characteristics Variable % or mean (sd) Demographics Gender (female)85.0Age < 505.0 50–6432.5 65–7430.0 75 + 32.5 Socio-economic conditions *Employment status* Retired61.3 Employed32.5 Unemployed6.3 Current job (or previous if retired or unemployed)a Business person, manager/academic, researcher, teacher22.1 White collar/tradesman42.9 Blue collar16.9 Housewife18.2 Education Low level37.5 Middle level30.0 High level32.5 Clinical characteristics Charlson Comorbidity Index7.3 (3.0) *No previous* history of bone fracture21.3 Bone fractures during observation period (i.e., TC)7.5 Years since diagnosis of osteoporosis10.8 (5.6) Total length of pharmacological treatment for osteoporosis (years)b 8.2 (4.7) Anti-osteoporosis pharmacological therapy Oral bisphosphonate7.5 Denosumab53.8 Teriparatide32.5 Zoledronic acid6.3 Pattern of TC use Date of first TC Emergency phase (June 2020-March2021)38.7 Post-emergency phase (April 2021-November 2021)61.3 Switch to TC from in-presence visits51.3 N. TCs before enrolment in the study2.2 [1] In-person visits during TC35.0 In-person visits during TC due to patient’s request12.5 Use of TC for other chronic conditions10.0 Digital skills and social support Social support in operating TC platform55.0 IT skills Excellent/good42.5 Mediocre35.0 Very poor22.5 Legend: sd standard deviation, TC teleconsultation a $$n = 77$$ b $$n = 79$$ Part B of the questionnaire asked patients questions on the acceptability of the TC service for their osteoporosis care, while Part C collected qualitative suggestions for improvement of the TC service. For Part B, we adapted the Service User Technology Acceptability Questionnaire (SUTAQ, which has been previously validated in the literature [22–24] and translated into Italian [22]. Among the available questionnaires to evaluate telemedicine services, the SUTAQ is the third most used tool in the literature [26] and, unlike to the first two (i.e., Telehealth Usability Questionnaire, Telemedicine Satisfaction Questionnaire), is specifically designed to gather patients’ opinions about the acceptability of telemedicine and not about the usability of the technological platform associated with telemedicine or about patients’ overall satisfaction [26]. Thus, we considered the SUTAQ the most suitable instrument for our purposes. We obtained the translated and validated version of the SUTAQ [22] and adapted it to our purposes. Table 2 lists the modified SUTAQ questions and corresponding five domains of perceived benefits, satisfaction, substitution, privacy and discomfort, and care personnel concerns identified by Hirani et al. [ 23]. Answers used a 6-point Likert scale as previously done in the literature. We also added two new item questions (see NEW in Table 2) to the domains of perceived benefits and care personnel concerns by means of a qualitative interpretation of the domains. Table 2Modified SUTAQ questions and corresponding domainQuestionDomainThe teleconsultation service has made me more actively involved in my healthPerceived benefitsThe teleconsultation service allows the specialists who are treating me to better monitor me and my osteoporosisPerceived benefitsThe use of the teleconsultation service can and should be recommended to people in a similar situation to minePerceived benefitsThe teleconsultation service can certainly be a good addition to my regular health carePerceived benefitsThe use of the teleconsultation service has helped me to correctly follow the drug therapy prescribed for my osteoporosis. ( NEW)Perceived benefitsThe teleconsultation service I received saved me time in that I did have to visit my osteoporosis specialist less oftenPerceived benefitsThe teleconsultation service I received increased my access health services for the treatment of osteoporosisPerceived benefitsThe teleconsultation service for osteoporosis treatment I received has helped me to improve my health statusPerceived benefitsUsing the teleconsultation service has made it easier to get in touch with my specialistPerceived benefitsThe teleconsultation service has been explained to me sufficientlySatisfactionThe teleconsultation service can be trusted to work appropriatelySatisfactionOverall, I am satisfied with the teleconsultation service I received for the treatment of my osteoporosisSatisfactionThe use of the teleconsultation service can be a replacement for the usual way of consulting in personSubstitutionUsing the teleconsultation service is not as suitable as regular face to face consultation with the person treating meSubstitutionUsing the teleconsultation service has allowed me to be less concerned about my health statusSubstitutionThe teleconsultation service I received interfered with my everyday routinePrivacy and discomfortThe teleconsultation service I received has invaded my privacyPrivacy and discomfortUsing the teleconsultation service made me feel uncomfortable, e.g. physically and/or psychologicallyPrivacy and discomfortI am worried about the confidentiality of the private information being exchanged through the teleconsultation servicePrivacy and discomfortI am not convinced of the level of expertise of the specialists who monitor my health status through the teleconsultation serviceCare personnel concernsThe teleconsultation service interferes with the continuity of care I am receiving (e.g., I do not see the same specialist each time)Care personnel concernsThe use of the teleconsultation service has reduced the time that the osteoporosis specialist dedicates to me. ( NEW)Care personnel concerns Legend: Added items are listed as (NEW) To evaluate the fit of the original five-domain structure of the SUTAQ with our data, we applied confirmatory factor analysis (CFA). As shown in Additional File 2, factor loadings were above the 0.4 threshold commonly used in the literature with the exception of two items (invasion of privacy and interference with routine). This indicates that the five dimensions of acceptability proposed by the SUTAQ were largely found in our data as well. The CFA also confirmed our qualitative attribution of the two new items added to the SUTAQ domains of perceived benefits and care personnel concerns.
For each of the five domains, we calculated the mean values of the answers. The mean difference between domains was assessed using the t-test. We then applied multivariable ordinary least squares (OLS) linear regressions to assess the correlation between each domain of TC acceptability and patients’ characteristics.
Fifty-seven patients responded to the open question at the end of the questionnaire asking for aspects to improve the TC service. Of these, almost half ($$n = 26$$) of the respondents had no suggestion for improvement to report. For the remaining, we analysed the text of their answers inductively by identifying common themes across answers.
## Osteoporotic patients’ degree of acceptability of TCs
Our analysis showed that patients overall accepted and appreciated the TC modality for their osteoporosis care, even after the COVID-19 emergency when in-person visits were a feasible alternative. Figure 1 illustrates the average scores of patients’ answers across the five domains of TC acceptability. For the domains of perceived benefits, satisfaction and substitution, a higher average value indicates higher acceptability, while for the domains of privacy and discomfort and care personnel concerns, a higher average value indicates lower acceptability. The first three domains displayed average scores between 4 and 5.5 (out of 6): 4.8 for perceived benefits, 5.2 for satisfaction and 4.9 for substitution. The other two domains had average scores between 1.5 and 3 (out of 6): 1.8 for privacy and discomfort and 2.7 for care personnel concerns. Being within these ranges indicated a good level of acceptability of TC among respondents. Fig. 1Average scores for the five domains of the modified SUTAQ. Legend: Average scores per domain are presented with respect to the overall average Figure 2 reports the most and least skewed distributions of answers for select item questions, while the table in the Additional File 3 reports the mean, median and standard deviation for each questionnaire item. As evident from Fig. 2, some items displayed rather skewed distributions, indicating a homogeneously positive perception of TCs for those items. For instance, approximately $90\%$ of respondents agreed or strongly agreed that TCs were time convenient (Fig. 2A) and did not invade their privacy (Fig. 2B). To a lesser extent, the question item we added to the SUTAQ exploring the impact of TCs on compliance with osteoporosis drug therapy showed an overall positive perception, with $62.5\%$ of respondents agreeing or strongly agreeing that TCs have a positive impact on drug compliance (Fig. 2C).Fig. 2Exemplary skewed (left column) and distributed (right column) replies to items for each domain Items that showed a higher variability across respondents were related to the perception of TCs as a replacement for in-person visits (Fig. 2D), that TCs could interfere with continuity of care (Fig. 2E) or that TCs reduce the time dedicated by the specialist to the patient (Fig. 2F). In this last case, for instance, over $50\%$ of respondents at least somewhat agreed that TCs had reduced the length of the consultation with the specialist.
The heterogeneity of the perception of these items was also confirmed by the qualitative suggestions provided by respondents to the questionnaire’s open question. Respondents mainly commented on the poor substitutability of in-person visits with TCs, as demonstrated by the following quote:*In* general, I am satisfied with TC. It makes you save time if it is about looking at exams or renewing drug therapies, but it must not completely substitute for in-person visits with the specialist for a matter of trust between doctor and patient. ( Patient #31; emphasis added) Other respondents indicated that not being able to see the same specialist during TC negatively impacted the continuity of their care and their overall perception of TCs. One patient commented:In principle, I am fine with the idea of TC but, through the visits, there is the need to get to know better the specialist and create a trust relationship. If one [the patient] does not know who will be on the other side and maybe his competence… then it is not great. ( Patient #19)
## Patient characteristics and acceptability of TCs for osteoporosis care
Table 3 reports the results of the OLS regression model expressing the correlation between each SUTAQ domain and patient characteristics. Table 4 synthesizes the most interesting results obtained through this analysis. Several characteristics that the literature previously found significant, such as age, the presence of social support with operating the TC platform and the level of digital skill, did not correlate with any dimension of the acceptability of TCs in this study. For context, $62.5\%$ of our respondents were over 65 years old, $45\%$ of them did not have any support to operate the TC platform and $57.5\%$ of the sample perceived themselves as having mediocre or very poor digital skills. Table 3Multivariable OLS linear regressions for each SUTAQ domain Perceived benefits Satisfaction Substitution Care personnel concerns Privacy and discomfort ß ($95\%$ CI)ß ($95\%$ CI)ß ($95\%$ CI)ß ($95\%$ CI)ß ($95\%$ CI) Demographics Gender Female1.001.001.001.001.00 Male0.18 (-0.56—0.91)0.21 (-0.35—0.76)0.6 (-0.23—1.43)-0.41 (-1.09—0.26) -0.50* (-1.03—0.04) Age in years < 500.09 (-1.53—1.72)-0.01 (-1.24—1.23)-0.02 (-1.86—1.83)0.1 (-1.39—1.60)-0.46 (-1.65—0.73) 50–640.24 (-0.65—1.12)0.03 (-0.64—0.70)0.35 (-0.65—1.35)-0.15 (-0.96—0.66)-0.39 (-1.03—0.26) 65–741.001.001.001.001.00 75 + -0.2 (-1.03—0.62)-0.09 (-0.71—0.54)-0.21 (-1.15—0.72)-0.27 (-1.02—0.49)0.01 (-0.60—0.61) Socio-economic conditions *Employment status* Employed1.001.001.001.001.00 Retired0.22 (-0.64—1.09)0.08 (-0.58—0.74)0.17 (-0.81—1.15)-0.15 (-0.94—0.65) -0.54* (-1.17—0.10) Unemployed0.52 (-0.68—1.73)0.47 (-0.45—1.38)0.48 (-0.88—1.85) -0.99* (-2.10—0.12)-0.27 (-1.16—0.61) Education Low1.001.001.001.001.00 Intermediate0.35 (-0.37—1.08)0.26 (-0.29—0.81)0.22 (-0.60—1.05)-0.25 (-0.92—0.42)-0.15 (-0.69—0.38) High0.44 (-0.34—1.22)0.38 (-0.22—0.97)0.17 (-0.71—1.05)-0.29 (-1.00—0.43)-0.15 (-0.72—0.42) Clinical characteristics Charlson comorbidity index0.03 (-0.08—0.14)-0.02 (-0.11—0.06)0.02 (-0.11—0.15)0.01 (-0.10—0.11)0.03 (-0.05—0.11) History of bone fractures Never had1.001.001.001.001.00 Had fractures0.1 (-0.63—0.83)0.41 (-0.15—0.96)-0.02 (-0.84—0.81)0.19 (-0.48—0.86)-0.11 (-0.65—0.42) Fractures during observation period No1.001.001.001.001.00 Yes0.11 (-0.61—0.82)0.22 (-0.32—0.76)0.51 (-0.30—1.32)-0.45 (-1.11—0.20)-0.18 (-0.71—0.34) Years since osteoporosis diagnosis0.04 (-0.01—0.09)0.02 (-0.02—0.06)0.04 (-0.01—0.10)-0.02 (-0.06—0.03)0 (-0.04—0.04) Total length of pharmacological treatment for osteoporosis (years)-0.05 (-0.12—0.01) -0.04* (-0.10—0.01) -0.09** (-0.17—-0.01) 0.06* (-0.00—0.12)0.02 (-0.03—0.07) Anti-osteoporosis treatment Teriparatide1.001.001.001.001.00 Denosumab-0.24 (-0.90—0.42)-0.38 (-0.88—0.12)-0.4 (-1.15—0.35)0.37 (-0.24—0.97)-0.01 (-0.50—0.47) Oral bisphosphonate-0.22 (-1.22—0.77)0.07 (-0.69—0.83)0.43 (-0.70—1.56)0.11 (-0.81—1.02)-0.16 (-0.89—0.58) Zolendronic acid-0.08 (-1.34—1.17)0.18 (-0.78—1.13)0.04 (-1.39—1.46)0.16 (-0.99—1.32)0.15 (-0.77—1.07) Pattern of TC use Date of first TC June 2020-March 20211.001.001.001.001.00 April-November 20210.11 (-0.53—0.76)0.41 (-0.08—0.90)0.36 (-0.37—1.09)-0.17 (-0.76—0.42)-0.01 (-0.48—0.46) Switch to TC from in-person visits No1.001.001.001.001.00 Yes-0.07 (-0.66—0.52)-0.13 (-0.58—0.32)0.06 (-0.60—0.73)0.05 (-0.49—0.59)-0.06 (-0.49—0.37) N. TCs before enrolment in study0.12 (-0.17—0.40) 0.22** (0.00—0.43)0.08 (-0.24—0.40)-0.06 (-0.32—0.20)-0.12 (-0.33—0.08) Face to face visits during TC No1.001.001.001.001.00 Yes-0.04 (-0.69—0.62)-0.27 (-0.77—0.23)-0.41 (-1.16—0.33)0.14 (-0.46—0.75)0.34 (-0.14—0.83) Face to face visits during TC due patient’s request No1.001.001.001.001.00 Yes0.07 (-0.88—1.02)0.48 (-0.25—1.21)0.14 (-0.94—1.22)-0.05 (-0.93—0.82)-0.21 (-0.91—0.49) Use of TC for other chronic conditions No1.001.001.001.001.00 Yes-0.37 (-1.25—0.50)-0.53 (-1.20—0.13)-0.19 (-1.18—0.81)0.17 (-0.63—0.98)0.27 (-0.38—0.91) Digital skills and social support Social support in operating TC platform No1.001.001.001.001.00 Yes-0.02 (-0.73—0.69)-0.22 (-0.76—0.32)-0.27 (-1.08—0.53)0.26 (-0.39—0.91)-0.22 (-0.74—0.30) Digital skills Excellent/good1.001.001.001.001.00 Mediocre0.19 (-0.54—0.93)0.33 (-0.23—0.88)0.17 (-0.66—1.00)0.04 (-0.63—0.71)0.3 (-0.23—0.84) Very poor0.39 (-0.85—1.63)0.69 (-0.25—1.63)0.49 (-0.91—1.90)-0.4 (-1.54—0.74)-0.01 (-0.92—0.90) Legend: Significant results in bold; *** $p \leq 0.01$, ** $p \leq 0.05$, * $p \leq 0.1$Table 4Selection of most significant correlations (or lack thereof) between patient characteristics and TC acceptability Legend: no significant correlation significant negative correlation significant positive correlation We found some significant correlations elsewhere, although often the significance was weak. In particular, men were less concerned about issues of privacy and discomfort linked to TCs in comparison to women (ß = -0.50, $$p \leq 0.07$$). Considering that women are the most affected by osteoporosis, perhaps more caution should be used when dealing with osteoporotic women than men in TC. Retired patients were also less concerned in comparison to working individuals about the issues of privacy and discomfort (ß = -0.54, $$p \leq 0.095$$), and unemployed patients seemed less concerned with the quality of the relationship with the specialist than employed patients (ß = -0.99, $$p \leq 0.078$$).
The most interesting associations, though, referred to how suffering from a long-term condition such as osteoporosis affected the acceptability of TCs. Patients who had been treated for osteoporosis longer were the least satisfied with TCs (ß = -0.04, $$p \leq 0.086$$) and the least convinced of the substitutive capacity of TCs for in-person visits (ß = -0.09, $$p \leq 0.020$$). In addition, patients who had been treated for osteoporosis for longer were more concerned about the relationship with the specialist (ß = 0.06, $$p \leq 0.070$$). At the same time, patients who had experienced more TCs had greater satisfaction with TCs (ß = 0.22, $$p \leq 0.047$$), suggesting that the more patients become familiar with this service modality, the more acceptable it can become. We cannot completely exclude selection bias such that those satisfied with TC were more likely to continue with successive TCs. However, we controlled for patients requesting to stop TCs and go back to in-person visits ($12.5\%$ of study participants) and found no significant association between this dummy variable and the five acceptability domains.
Notably, the date of the first TC (emergency phase versus post-emergency phase) did not impact the acceptability of TCs across any of the five domains. This result was valid for all five acceptability domains and both the multivariable (Table 3) and univariable (i.e., mean difference; Additional File 4) analyses.
## Discussion
This study shows an overall good level of acceptability of TCs among patients with osteoporosis independently of whether they started TCs during the COVID-19 emergency phase or later on (when the emergency had waned and in-person visits returned as a feasible alternative). The findings indicate that for chronic conditions requiring regular follow-ups, TCs might be a valid care delivery modality even in non-emergency situations.
Despite this overall positive perception of TCs, some heterogeneity among patients was evident, especially with respect to the perception of the capacity of TCs to substitute completely for the kind of doctor–patient relationship that in-person visits engender. Specifically, several patients openly indicated concerns about TC negatively affecting the trust relationship with the specialist by reducing the continuity of care or consultation length.
Patient characteristics could only partially explain this heterogeneity in the acceptability of TCs. This might be due to unobservable individual characteristics, such as personality or attitudes, that we did not include in our model. For instance, Baudier et al. [ 27] showed that self-efficacy and personal innovativeness were relevant explanatory variables of patients’ intention to use telehealth services. Notably, in our study, old age, poor digital skills and a lack of social support with using the TC platform were not correlated with a lower acceptability of TCs. This confirms the trend observed in the literature of the increased acceptability of TCs for these kinds of patients even before the COVID-19 pandemic [8–10]. The pandemic may have just accelerated this process and, as such, further attenuated the relevance of these characteristics on the perception of TCs.
This study points to other characteristics that might be relevant to the acceptability of TCs, in particular, how long a person has received treatment for a chronic condition such as osteoporosis. Patients who have been treated for a longer amount of time may display more concern with TCs in terms of overall satisfaction, the possibility of TCs substituting for in-person visits and ensuring a high-quality relationship with the specialist. This alerts healthcare professionals to the fact that over time, people suffering from a chronic condition might feel fatigued and need a close relationship with their specialist in order to continue with the care of their chronic condition. In this, TCs might be perceived as less effective or satisfying in comparison to in-person visits.
The paper is a first attempt to measure the acceptability of TCs in the aftermath of the COVID-19 pandemic and is not exempt from limitations. The retrospective design and the small size of the study group could mean that the answers are not highly representative of osteoporotic patients. Moreover, some conditioning descending from patients being recruited to the study by their own osteoporosis specialists may have biased patients’ answers about the acceptability of TCs in a positive direction. We tried to attenuate this bias by explicitly informing patients that their specialist would not have access to their answers and including this information in the informed consent form that recruited patients had to sign.
## Conclusions
Our study provides useful insights into the acceptability of TCs for chronic conditions post-COVID-19. The significance of our findings lie in showing how TCs have become largely acceptable to categories of chronic patients who in the past were sceptical about this service modality, mainly for technological reasons. However, the study indicates that when these concerns are overcome, others might arise with respect to the quality of the doctor–patient relationship afforded by TCs. This suggests that as TCs and telemedicine in general become more widely adopted by healthcare systems, it is important to strengthen doctors’ and patients’ communication capacities in addition to their digital skills. In addition, it will be necessary to continue monitoring chronic patients’ perceptions of TCs in future years to understand how persistent this acceptability actually is and what affects it the most.
## Supplementary Information
Additional file 1. Descriptive statistics of respondents by date of first TC and their comparability. Additional file 2. Confirmatory factor analysis showing loading factors for questions in the modified SUTAQ.Additional file 3. Modified SUTAQ descriptors. Additional file 4. Mean difference in SUTAQ acceptability domains between patients who experienced TC for the first time during and after the Covid-19 emergency phases.
## References
1. Monaghesh E, Hajizadeh A. **The role of telehealth during COVID-19 outbreak: a systematic review based on current evidence**. *BMC Public Health* (2020.0) **20** 1-9. DOI: 10.1186/s12889-020-09301-4
2. Garfan S, Alamoodi AH, Zaidan BB, Al-Zobbi M, Hamid RA, Alwan JK. **Telehealth utilization during the Covid-19 pandemic: a systematic review**. *Comput Biology Med* (2021.0) **138** 104878. DOI: 10.1016/j.compbiomed.2021.104878
3. De Simone S, Franco M, Servillo G, Vargas M. **Implementations and strategies of telehealth during COVID-19 outbreak: a systematic review**. *BMC Health Serv Res* (2022.0) **22** 1-10. DOI: 10.1186/s12913-022-08235-4
4. Bosa I, Castelli A, Castelli M, Ciani O, Compagni A, Galizzi MM. **Response to COVID-19: was Italy (un)prepared?**. *Health Econ Policy Law* (2022.0) **17** 1-10. DOI: 10.1017/S1744133121000141
5. Petracca F, Ciani O, Cucciniello M, Tarricone R. **Harnessing digital health technologies during and after the COVID-19 pandemic: context matters**. *J Med Internet Res* (2020.0) **22** e21815. DOI: 10.2196/21815
6. Stroetmann VN, Hüsing T, Kubitschke L, Stroetmann KA. **The attitudes, expectations and needs of elderly people in relation to e-health applications: results from a European survey**. *J Telemed Telecare* (2002.0) **8** 82-84. DOI: 10.1177/1357633X020080S238
7. Anthony DL, Campos-Castillo C, Lim PS. **Who isn’t using patient portals and why? Evidence and implications from a national sample of US adults**. *Health Aff* (2018.0) **37** 1948-1954. DOI: 10.1377/hlthaff.2018.05117
8. Kruse CS, Krowski N, Rodriguez B, Tran L, Vela J, Brooks M. **Telehealth and patient satisfaction: a systematic review and narrative analysis**. *BMJ Open* (2017.0) **7** e016242. DOI: 10.1136/bmjopen-2017-016242
9. Raja M, Bjerkan J, Kymre IG, Galvin KT, Uhrenfeldt L. **Telehealth and digital developments in society that persons 75 years and older in European countries have been part of: a scoping review**. *BMC Health Serv Res* (2021.0) **21** 1-15. DOI: 10.1186/s12913-021-07154-0
10. Robinson MD, Branham AR, Locklear A, Robertson S, Gridley T. **Measuring satisfaction and usability of FaceTime for virtual visits in patients with uncontrolled diabetes**. *Telemed JE Health* (2016.0) **22** 138-143. DOI: 10.1089/tmj.2014.0238
11. Shaw SE, Seuren LM, Wherton J, Cameron D, Vijayaraghavan S, Morris J. **Video consultations between patients and clinicians in diabetes, cancer, and heart failure services: linguistic ethnographic study of video-mediated interaction**. *J Med Internet Res* (2020.0) **22** e18378. DOI: 10.2196/18378
12. Li H, Chan YC, Huang J, Cheng SW. **Pilot study using telemedicine video consultation for vascular patients’ care during the COVID-19 period**. *Ann Vasc Surg* (2020.0) **68** 76-82. DOI: 10.1016/j.avsg.2020.06.023
13. Holtz BE. **Patients’ perceptions of telemedicine visits before and after the coronavirus disease 2019 pandemic**. *Telemed J E Health* (2021.0) **27** 107-112. DOI: 10.1089/tmj.2020.0168
14. Nguyen M, Waller M, Pandya A, Portnoy J. **A review of patient and provider satisfaction with telemedicine**. *Curr Allergy Asthma Rep* (2020.0) **20** 1-7. DOI: 10.1007/s11882-020-00969-7
15. Shenoy P, Ahmed S, Paul A, Skaria TG, Joby J, Alias B. **Switching to teleconsultation for rheumatology in the wake of the COVID-19 pandemic: feasibility and patient response in India**. *Clin Rheumatol* (2020.0) **39** 2757-2762. DOI: 10.1007/s10067-020-05200-6
16. Clynes MA, Harvey NC, Curtis EM, Fuggle NR, Dennison EM, Cooper C. **The epidemiology of osteoporosis**. *Br Med Bull* (2020.0) **133** 105-117. DOI: 10.1093/bmb/ldaa005
17. Keshishian A, Boytsov N, Burge R, Krohn K, Lombard L, Zhang X. **Examining the effect of medication adherence on risk of subsequent fracture among women with a fragility fracture in the US medicare population**. *J Manag Care Spec Pharm* (2017.0) **23** 1178-1190. DOI: 10.18553/jmcp.2017.17054
18. Paskins Z, Crawford-Manning F, Bullock L, Jinks C. **Identifying and managing osteoporosis before and after COVID-19: rise of the remote consultation?**. *Osteoporosis Int* (2020.0) **31** 1629-1632. DOI: 10.1007/s00198-020-05465-2
19. Fuggle NR, Singer A, Gill C, Patel A, Medeiros A, Mlotek AS. **How has COVID-19 affected the treatment of osteoporosis?**. *An IOF-NOF-ESCEO global survey Osteoporosis Int* (2021.0) **32** 611-617. DOI: 10.1007/s00198-020-05793-3
20. Palcu P, Munce S, Jaglal SB, Allin S, Chishtie JA, Silverstein A. **Understanding patient experiences and challenges to osteoporosis care delivered virtually by telemedicine: a mixed methods study**. *Osteoporosis Int* (2020.0) **31** 351-361. DOI: 10.1007/s00198-019-05182-5
21. English S, Coyle L, Bradley S, Wilton W, Cordner J, Dempster R. **Virtual fracture liaison clinics in the COVID era: an initiative to maintain fracture prevention services during the pandemic associated with positive patient experience**. *Osteoporosis Int* (2021.0) **32** 1221-1226. DOI: 10.1007/s00198-021-05882-x
22. Dario C, Luisotto E, Dal Pozzo E, Mancin S, Aletras V, Newman S. **Assessment of patients’ perception of telemedicine services using the service user technology acceptability questionnaire**. *Int J Integrated Care* (2016.0) **16** 13. DOI: 10.5334/ijic.2219
23. Hirani SP, Rixon L, Beynon M, Cartwright M, Cleanthous S, Selva A. **Quantifying beliefs regarding telehealth: development of the whole systems demonstrator service user technology acceptability questionnaire**. *J Telemed Telecare* (2017.0) **23** 460-469. DOI: 10.1177/1357633X16649531
24. Torbjørnsen A, Småstuen MC, Jenum AK, Årsand E, Ribu L. **The Service User Technology Acceptability Questionnaire: psychometric evaluation of the Norwegian version**. *JMIR Hum Factors* (2018.0) **5** e10255. DOI: 10.2196/10255
25. 25.Istituto Superiore di Sanità (ISS). Dati della Sorveglianza integrata COVID-19 in Italia. https://www.epicentro.iss.it/coronavirus/sars-cov-2-dashboard. Accessed 26 July 2022.
26. Hajesmaeel-Gohari S, Bahaadinbeigy K. **The most used questionnaires for evaluating telemedicine services**. *BMC Med Inform Decis Mak* (2021.0) **21** 1-11. DOI: 10.1186/s12911-021-01407-y
27. Baudier P, Kondrateva G, Ammi C, Chang V, Schiavone F. **Patients’ perceptions of teleconsultation during COVID-19: a cross-national study**. *Technol Forecasting Soc Change* (2021.0) **163** 120510. DOI: 10.1016/j.techfore.2020.120510
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---
title: 'Associations of body mass index with severe outcomes of COVID-19 among critically
ill elderly patients: A prospective study'
authors:
- Zahra Gholi
- Zahra Vahdat Shariatpanahi
- Davood Yadegarynia
- Hassan Eini-Zinab
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC9994813
doi: 10.3389/fnut.2023.993292
license: CC BY 4.0
---
# Associations of body mass index with severe outcomes of COVID-19 among critically ill elderly patients: A prospective study
## Abstract
### Background and Aim
Few studies assessed the associations of overweight and obesity with severe outcomes of coronavirus disease 2019 (COVID-19) among elderly patients. This study was conducted to assess overweight and obesity in relation to risk of mortality, delirium, invasive mechanical ventilation (IMV) requirement during treatment, re-hospitalization, prolonged hospitalization, and ICU admission among elderly patients with COVID-19.
### Methods
This was a single-center prospective study that was done on 310 elderly patients with COVID-19 hospitalized in the intensive care unit (ICU). We collected data on demographic characteristics, laboratory parameters, nutritional status, blood pressure, comorbidities, medications, and types of mechanical ventilation at baseline. Patients were followed up during ICU admission and until 45 days after the first visit, and data on delirium incidence, mortality, need for a form of mechanical ventilation, discharge day from ICU and hospital, and re-hospitalization were recorded for each patient.
### Results
During the follow-up period, we recorded 190 deaths, 217 cases of delirium, and 35 patients who required IMV during treatment. After controlling for potential confounders, a significant association was found between obesity and delirium such that obese patients with COVID-19 had a $62\%$ higher risk of delirium compared with normal-weight patients (HR: 1.62, $95\%$ CI: 1.02–2.57). This association was not observed for overweight. In terms of other outcomes including ICU/45-day mortality, IMV therapy during treatment, re-hospitalization, prolonged hospitalization, and ICU admission, we found no significant association with overweight and obesity either before or after controlling for potential confounders.
### Conclusion
We found that obesity may be a risk factor for delirium among critically ill elderly patients with COVID-19.
## Introduction
Coronavirus disease 2019 (COVID-19) has been a pandemic disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. It has become a major threat to global public health during the last 2 years [1]. COVID-19 is associated with a high variation in disease severity [2]. Young patients usually experience mild-to-moderate symptoms, while elderly patients and those with comorbidities including diabetes [3, 4], cardiovascular diseases (CVDs), cancer, and pulmonary disease are at an increased risk of severe symptoms such as acute respiratory distress syndrome (ARDS) and even death due to COVID-19 pneumonia [2, 5]. Therefore, detecting the factors involved in the high severity of disease among the elderly is urgently required.
To date, it has been shown that older age, smoking, taking immunosuppressive drugs such as glucocorticoids and benzodiazepine, and having comorbidities including diabetes, pulmonary diseases, and CVDs are associated with high severity of COVID-19 (6–8). Also, protein–energy malnutrition (PEM) and micronutrient deficiencies such as vitamin D deficiency are associated with a weakening immune system and therefore adversely affect COVID-19 outcomes. Moreover, elevated levels of inflammatory biomarkers may increase the risk of mortality and other severe outcomes in these patients. Recently, great attention has been paid to obesity (9–11). Some studies have shown that obese patients with COVID-19 are at a higher risk of requiring admission to the intensive care unit (ICU) and invasive mechanical ventilation [12, 13]. Also, in ICU patients with or without COVID-19, overweight and obesity are associated with an increased risk of ARDS [14]. By contrast, several studies indicated that overweight and obesity have a protective effect against mortality among critically ill patients in the ICU [15, 16]. Therefore, a potential bidirectional relationship may exist between obesity and COVID-19. Studies have shown an increased SARS-CoV-2 susceptibility in individuals with overweight/obesity, more so in those with coexisting diabetes, as well as an increase in body mass index (BMI) following predominant mild and asymptomatic SARS-CoV-2 infection [17, 18]. It has been suggested that a higher metabolic reserve in patients with obesity and differences in pulmonary mechanics and immunological aspects between patients with obesity and normal-weight patients are involved in the protective effect [19]. The different effects of obesity on ICU patients are known as the “obesity paradox” [20].
This obesity paradox might be present in critically ill patients with COVID-19, and it is not clear how obesity affects the risk of mortality in these patients. However, the HOPE COVID-19-Registry showed no evidence of the obesity paradox and revealed that increasing BMI was not related to the mortality risk in patients with COVID-19 [21]. Some studies have shown a significant association between obesity and mortality due to COVID-19 [22], while others revealed no significant association [23] or even an inverse association [24, 25]. In a meta-analysis of 22 studies from seven countries, Zhang et al. [ 26] reported that obesity is associated with a more severe COVID-19 course but may not be associated with increased mortality. In another meta-analysis, Ho et al. [ 27] concluded that obesity increased the risk of severe complications, mortality, and infection among patients with COVID-19. In addition, the influence of obesity on other outcomes of critically ill patients with COVID-19 such as delirium and duration of ICU stay has not been studied. Delirium is the most common form of acute brain dysfunction affecting approximately $80\%$ of ICU patients [28]. Overall, given the aforementioned points, this study was conducted to assess the associations between obesity and severe outcomes of COVID-19 among critically ill patients.
## Study design and participants
This was a single-center prospective study that was conducted in the Khatam hospital, which was a government-designated referral hospital for patients with COVID-19. The location of this hospital was such that patients with COVID-19 from different socioeconomic levels could be admitted to it. This study was conducted from August 2021 to January 2022. We recruited critically ill older (≥65 years) patients with COVID-19 who were hospitalized in the intensive care unit (ICU). SARS-COV-2 infection was diagnosed by reverse transcriptase polymerase chain reaction (RT-PCR) test and also chest CT scan lesions. Based on the classification of the Guidance for Coronavirus Disease 2019 (6th edition), published by the National Health Commission of China [29], we defined critically ill patients with COVID-19 according to the following criteria: [1] respiratory failure requiring a form of mechanical ventilation; [2] septic shock; and [3] having at least one organ failure necessitating monitoring and treatment in the intensive care unit (ICU). Other inclusion criteria were willingness to participate in the study and having an age of ≥65 years. We did not include patients with COVID-19 if [1] they were admitted to the ICU for the second time; [2] they had severe comorbidities including any brain damage and pre-existing end-stage liver disease, end-stage renal disease, and cancer; and [3] they had a history of pre-existing neurodegenerative disorders, mental illness, dementia, and cognitive disorders. Data from these disorders were obtained by evaluating medical records in the hospital. In addition, patients with COVID-19 who died or were discharged within the first 48 h of hospitalization were excluded because of the avoidance of bias in collecting information on complications and reviewing the effectiveness of treatments prescribed in the ICU. In total, 392 elderly patients with COVID-19 were included. We collected data on demographic characteristics, laboratory parameters, nutritional status, blood pressure, comorbidities, medications used for controlling the infection, and types of mechanical ventilation at baseline. Patients were followed up during the ICU admission and also until 45 days after the first visit to the ICU. During the follow-up period, we recorded data on delirium incidence, mortality, need for a form of mechanical ventilation, discharge day from the ICU and hospital, and re-hospitalization for each patient.
## Ethics statement
We took written informed consent from each participant. If a patient was not conscious, the consent was taken from his/her first-degree relatives. Patients were reassured that data collected from medical records would be used for the current study in accordance with privacy laws. The study was approved by the Ethics Committee of Shahid Beheshti University of Medical Sciences, Tehran, Iran (IR.SBMU.NNFTRI.REC.1400.071). We conducted this study based on the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.
The studies involving human participants were reviewed and approved by Ethics Committee of Shahid Beheshti University of Medical Sciences, Tehran, Iran. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
## Sample size calculation
We calculated the required sample size using Power Analysis Software (PAS). By considering the type 1 error of $5\%$, study power of $80\%$, estimated hazard ratio (HR) of 1.2 for mortality, and mortality rate of $60\%$ among critically ill patients with COVID-19, we needed a sample size of 272 elderly patients with COVID-19. However, we recruited 392 patients in the current study to increase study power and consider the probable drop-out.
## Baseline assessment
During the first 24 h of ICU admission, data on demographic characteristics, laboratory parameters, nutritional status, blood pressure, comorbidities, medications used for controlling the infection, and types of mechanical ventilation were collected.
## Demographic and clinical characteristics
We collected data on age (year), sex (male/female), weight (kg), height (m), marital status (single/married/divorced), having health insurance (yes/no), education (university educated/under-university educated), smoking (non-smokers/ex-smokers/current smokers), systolic and diastolic blood pressure (mmHg), and alcohol consumption (yes/no) by evaluating the hospital’s electronic medical records or questionnaires and also by a direct interview with patients if needed. BMI was determined as weight in kilograms divided by height in meters squared. To collect data on weight, we used data from the medical records of patients. However, these records lacked data on height. Due to the inability of patients to move, the height was estimated by the length of the forearm’s ulna bone. Based on this technique, the patient’s arm was positioned on the shoulder by being bent across the chest to the opposite side. Then, we measured the distance between the conspicuous wrist bone and the elbow bone using a tape measure. The following formula, designed for critically ill patients, was used to estimate height based on gender and ulna bone length: Height (cm) = 153.492 – [7.97 × sex (male = 1, female = 2) + (0.974 × Ulna length (cm)] [30].
In addition, by reviewing the medical records at baseline, we obtained data on comorbidities (yes/no) including pulmonary diseases (i.e., acute pulmonary edema, asthma, bronchitis, chronic obstructive pulmonary disease, pleural effusion, pneumonia, pulmonary mass, pulmonary edema, respiratory tract infection, and sleep apnea syndrome), hyperlipidemia (total cholesterol levels of ≥4.7 mmol/L, triglyceride levels of ≥2.3 mmol/L, or LDL-C levels of ≥4.1 mmol/L), diabetes (2-h plasma glucose ≥200 mg/dL, HbA1c ≥ $6.5\%$, and fasting plasma glucose ≥126 mg/dL), hypertension (SBP ≥ 140 and DBP ≥ 90), CVDs (i.e., heart failure, left ventricular systolic dysfunction, right heart failure, dysrhythmia, ischemic heart disease, inflammatory heart disease or pericardium, non-ischemic cardiomyopathy, cardiogenic shock, cardiac arrest, and thrombotic disorders), chronic renal failure and liver disease (any type, based on data from medical records), incidence of organ failure from the time of entering in ICU, and ear and eye problems (any type, based on data from medical records). Organ failure was considered the failure of at least one organ to perform typical bodily tasks. This failure comprised at least one of the following: cardiovascular illness, lung failure, acute liver dysfunction, acute renal damage, a wide range of hematological abnormalities, and neurological diseases, as determined by a specialist. During the ICU admission, the incidence of acute kidney injury (AKI), caused by COVID-19 or medications (i.e., Remdesivir), was recorded by reviewing the medical records. AKI was defined as a rise in serum creatinine by 0.3 mg/dL (26.5 μmol/L) or more within 48 h [31]. If data from medical records were incomplete for the diagnosis of mentioned diseases, we asked some questions to patients or their relatives to complete the aforementioned information.
The treatment protocols including medications and types of mechanical ventilation [invasive and non-invasive mechanical ventilation (IMV and NIMV), high-flow nasal cannula, and face mask] used for controlling COVID-19 and its symptoms were also recorded. We recorded the drugs that were currently used by patients. By using data on demographic (age), clinical (body temperature, mean arterial pressure, blood pH, heart rate, respiratory rate, oxygen partial pressure, and *Glasgow coma* scale), and laboratory variables (sodium, potassium, creatinine, hematocrit, and white blood cells), we calculated acute physiology and chronic health examination II (APACHE II) score for each patient. APACHE II scores range between zero and 71, with higher scores indicating a more severe condition. Details on the calculation of APACHE II were published elsewhere [32].
## Laboratory parameters
On the first day of ICU admission, patients’ medical records were assessed to obtain data on fasting blood sugar (FBS, mg/dL), serum levels of inflammatory biomarkers [C-reactive protein (CRP, mg/L) and interleukin-6 (IL-6, pg./mL)], albumin (g/dL), creatinine (mg/dL), urea (mg/dL), bilirubin (mg/dL), and 25-hydroxy vitamin D3 [25(OH)D3, ng/mL]. Serum levels of electrolytes including magnesium (mEq/L), phosphorous (mg/dL), calcium (mg/dL), sodium (mEq/L), and potassium (mEq/L) were also assessed. We also collected data on hematological factors including white blood cells (neutrophil and lymphocyte, 103/μL), hematocrit (%), and platelet (103/μL).
## Follow-up
The incidence of delirium and the need for a form of mechanical ventilation (yes/no), particularly invasive ventilation, were recorded during the ICU admission. Delirium was diagnosed based on the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) [33]. Accordingly, delirium has four features: [1] acute onset of changes or fluctuations in the course of mental status, [2] inattention, [3] disorganized thinking, and [4] an altered level of consciousness (other than alert). Patients were delirious if they had features 1 and 2 plus either feature 3 or 4. In the present study, delirium was evaluated every day using the CAM-ICU by an experienced ICU physician. To facilitate the assessment of acute onset or fluctuation of mental status changes, patients were followed up daily with the *Glasgow coma* scale. In addition to delirium, we recorded the occurrence of mortality (yes/no) during the ICU admission. After the ICU discharge, patients were admitted to the other wards of the hospital. Therefore, we followed patients in the hospital until they were discharged. Furthermore, the length of ICU and hospital stays was recorded for each patient. After the hospital discharge, we had phone contact with patients or their relatives every week, until 45 days after the baseline, to record probable death and re-hospitalization. ICU admission ≥7 days was considered a prolonged stay in ICU and hospitalization ≥14 days was a prolonged stay in the hospital.
## Statistical analysis
We first categorized elderly patients with COVID-19 based on BMI [normal-weight (BMI < 25), overweight (25 ≤ BMI < 30), and obesity (BMI ≥ 30)], according to the recommended classification by the World Health Organization [34, 35]. Then, we compared continuous variables across categories of BMI using one-way ANOVA if the distribution of those variables was normal. For the non-normally distributed continuous variables, we used the Kruskal–Wallis test for comparison. To assess the distribution of categorical variables across categories of BMI, we used the Chi-square test. In order to analyze the associations of BMI categories with mortality, delirium, and IMV therapy during treatment, we used univariable and multivariable Cox proportional hazards models. In the time-to-event analysis, follow-up time was considered as the day that outcome occurred or the day that the patient was followed up. To assess the associations of BMI categories with prolonged stay in ICU (≥7 days) or hospital (≥14 days) and odds of re-hospitalization after discharge, we used univariable and multivariable binary logistic regression. In the adjusted models, we controlled for age, gender, taking benzodiazepine during ICU admission, and vitamin D and IL-6 levels. To identify potential confounders, we calculated the magnitude of confounding for each variable as the percent difference between the crude and adjusted measures of association (Supplementary Tables S1, S2). The following formula was used for the relative risk estimates: Magnitude of confounding (%) = RRcrude−RRadjustedRRadjusted×100.
If the value was ≥$10\%$ for a variable, that variable was considered a confounding variable. By this approach, we found that age, benzodiazepine intake, and IL-6 levels (only adjusted for death during ICU admission) were confounders for the associations of BMI with delirium, IMV therapy, and COVID-19 mortality. Also, for the associations of BMI with re-hospitalization and prolonged hospital/ICU stays, we considered age, gender, benzodiazepine intake, and vitamin D levels as confounders. In all analyses, normal-weight patients with COVID-19 were considered as a reference group. All statistical analyses were done using the SPSS software version 18 (SPSS, Inc. Chicago, IL, USA). $p \leq 0.05$ was considered significant.
## Results
Of the 392 critically ill elderly patients with Covid-19 admitted to the intensive care unit, 48 patients did not meet the inclusion criteria as shown in Figure 1. Out of 344 patients who met the inclusion criteria, 19 patients died within the first 48 hours of admission to the ICU and 7 patients were discharged from the intensive care unit within the first 48 hours of admission. During the follow-up period of the patients in the ICU, 8 patients were discharged from the ICU with personal consent to continue the treatment process at home. Therefore, the data of 310 patients were included in the final analysis. All patients had received antiviral and antibiotic drugs. Antiviral drugs included remdesivir, favipiravir, tocilizumab, and lopinavir/ritonavir, which were available in Iranian hospitals. During the 45-day follow-up, 190 ($61.3\%$) COVID-19 deaths were recorded among the baseline 310 patients. In addition, during the ICU admission, 217 ($70.0\%$) cases of delirium and 53 ($17.1\%$) patients who required IMV therapy from the beginning of treatment were found in the ICU. Also, during the 45-day follow-up, 65 ($21.0\%$) patients were hospitalized for the second time.
**Figure 1:** *Study flow diagram.*
The baseline characteristics of patients across categories of BMI are shown in Tables 1, 2. Patients with obesity had lower ages and were more likely to consume vasopressors compared to those with normal weight. In terms of other variables including government health insurance, laboratory parameters, comorbidities, drug use, duration of hospital and ICU stays, and types of ventilation, we found no significant difference.
Multivariable-adjusted HRs and $95\%$ confidence intervals (CIs) of delirium, COVID-19 mortality, and IMV therapy during treatment across categories of baseline BMI among critically ill elderly patients with COVID-19 are indicated in Table 3. We found no significant association between overweight/obesity and delirium among elderly patients with COVID-19 (overweight; HR: 1.06, $95\%$ CI: 0.77–1.46, obesity; HR: 1.27, $95\%$ CI: 0.81–1.99). However, after controlling for potential confounders including age and benzodiazepine intake, a significant association was seen for obesity; such that obese patients with COVID-19 had a $62\%$ higher risk of delirium compared with normal-weight patients (HR: 1.62, $95\%$ CI: 1.02–2.57). Such an association was not seen for overweight. Before and after taking potential confounders into account, no significant association was found between BMI categories (overweight and obesity) and risk of IMV requirement during treatment. Regarding the risk of mortality during ICU admission and 45 days after the baseline, we found no significant association with overweight or obesity either before or after controlling for potential confounders.
**Table 3**
| Unnamed: 0 | Normal | Overweight | Obesity |
| --- | --- | --- | --- |
| Delirium | Delirium | Delirium | Delirium |
| Cases | 54 | 133 | 30 |
| Unadjusted | 1.00 | 1.06 (0.77–1.46) | 1.27 (0.81–1.99) |
| Adjusted modelb | 1.00 | 1.20 (0.86–1.67) | 1.62 (1.02–2.57) |
| IMV therapy during treatmenta | IMV therapy during treatmenta | IMV therapy during treatmenta | IMV therapy during treatmenta |
| Cases | 20 | 30 | 3 |
| Unadjusted | 1.00 | 0.66 (0.37–1.17) | 0.30 (0.90–1.02) |
| Adjusted modelb | 1.00 | 0.95 (0.52–1.74) | 0.56 (0.16–1.92) |
| Death during 45 days | Death during 45 days | Death during 45 days | Death during 45 days |
| Cases | 52 | 117 | 21 |
| Unadjusted | 1.00 | 0.90 (0.65–1.25) | 0.73 (0.44–1.21) |
| Adjusted modelb | 1.00 | 1.16 (0.82–1.64) | 1.10 (0.65–1.86) |
| Death during ICU admission | Death during ICU admission | Death during ICU admission | Death during ICU admission |
| Cases | 42 | 76 | 14 |
| Unadjusted | 1.00 | 0.79 (0.54–1.16) | 0.67 (0.37–1.23) |
| Adjusted modelb | 1.00 | 0.97 (0.65–1.45) | 0.96 (0.52–1.79) |
Multivariable-adjusted odds ratios (ORs) and $95\%$ CIs for re-hospitalization and prolonged stay in ICU and hospital across categories of BMI are presented in Table 4. Overweight and obesity were not significantly associated with re-hospitalization and prolonged stay in ICU and hospital. These associations remained non-significant after taking potential confounders into account [re-hospitalization (overweight; OR: 0.98, $95\%$ CI: 0.48–1.99, obesity; OR: 1.12, $95\%$ CI: 0.41–3.06), prolonged stay in ICU (overweight; OR: 1.01, $95\%$ CI: 0.56–1.81, obesity; OR: 1.12, $95\%$ CI: 0.49–2.56), and prolonged stay in ICU and hospital (overweight; OR: 0.99, $95\%$ CI: 0.55–1.78, obesity; OR: 1.57, $95\%$ CI: 0.67–3.64)].
**Table 4**
| Unnamed: 0 | Normal | Overweight | Obesity |
| --- | --- | --- | --- |
| Hospital stay ≥ 14 days | Hospital stay ≥ 14 days | Hospital stay ≥ 14 days | Hospital stay ≥ 14 days |
| Cases | 38 | 97 | 26 |
| Unadjusted | 1.00 | 1.11 (0.65–1.88) | 2.00 (0.91–4.39) |
| Adjusted modela | 1.00 | 0.99 (0.55–1.78) | 1.57 (0.67–3.64) |
| ICU stay≥7 days | ICU stay≥7 days | ICU stay≥7 days | ICU stay≥7 days |
| Cases | 50 | 118 | 25 |
| Unadjusted | 1.00 | 0.93 (0.54–1.61) | 0.96 (0.44–2.12) |
| Adjusted modelb | 1.00 | 1.01 (0.56–1.81) | 1.12 (0.49–2.56) |
| Re-hospitalization | Re-hospitalization | Re-hospitalization | Re-hospitalization |
| Cases | 19 | 38 | 8 |
| Unadjusted | 1.00 | 0.78 (0.41–1.46) | 0.79 (0.31–2.00) |
| Adjusted modelc | 1.00 | 0.98 (0.48–1.99) | 1.12 (0.41–3.06) |
## Discussion
Since obesity and overweight are similar in nature, we discussed both in the same manner, particularly when the findings of both conditions were similar. In the current study, we found that elderly patients with COVID-19 with obesity had an increased risk of delirium than those with normal weight. This association was not seen for overweight. In terms of other outcomes including ICU and 45-day mortality, IMV therapy during treatment, prolonged stay in ICU and hospital, and odds of re-hospitalization, we observed no significant association with overweight and obesity.
Our study represented the overall mortality rate of $42.3\%$ among elderly patients with COVID-19 admitted to the ICU. Compared with the rate obtained from previous studies [36, 37], it seems to be high. In a meta-analysis, Qian et al. [ 38] indicated a prevalence of $32\%$ for COVID-19 mortality among critically ill patients. The higher prevalence of mortality in the current study might be due to the age range of participants who were 65 years and older. It has been shown that older age is the main risk factor for COVID-19 mortality (39–41). It should be noted that the prevalence of $32\%$ in the study of Qian et al. was obtained by assessing different age groups.
Delirium is an acute disturbance of consciousness that is associated with mental disorders such as sleep disorders, changes in cognitive functions, anxiety, fear, and irritability [42, 43]. Delirious patients in ICU have an increased risk of mortality, longer ICU hospitalizations, extended periods of mechanical ventilation, and long-term cognitive and functional deficits. Known risk factors for developing delirium include aging, baseline cognitive impairment, comorbidities (particularly respiratory disease), frailty, sepsis, prolonged mechanical ventilation, and major surgery (44–46). However, few studies investigated the link between obesity and delirium in critically ill patients. In the current study, we found that elderly obese patients with COVID-19 had a higher risk of delirium compared with normal-weight patients. Such an association was not seen for overweight. In a study on 9,189 adults, Anand et al. [ 47] reported that obesity was associated with a reduced cognitive score indicating lower cognitive function. In contrast, Lachmann et al. [ 48] reported that diabetes, but not obesity or hypertension, was associated with an increased risk of postoperative cognitive dysfunction in older people. In a retrospective cohort study, a high BMI was independently associated with a lower frequency of acute delirium in ICU patients with septic shock [49]. Discrepant findings might be explained by the different ages and the different medications of study participants in previous studies. For instance, the administration of analgesic medications, some anticholinergic drugs, and benzodiazepine infusions for mechanical ventilation is associated with a higher risk of delirium. It should be noted that medications among the patients who participated in the current study were not different across categories of BMI. In addition, different cognitive and physical reserves of participants are other reasons for the observed discrepancy among previous studies on the link between obesity and cognitive dysfunction.
The mechanism involved in the association between obesity and delirium in ICU patients with COVID-19 infection is unclear. Recent studies have shown that obese patients with COVID-19 have severe symptoms and more need for IMV compared with normal-weight patients [12, 50]. In addition, obesity causes mechanical disturbances because abdominal thrusts increase inter-abdominal pressure, which makes it difficult for the lungs to breathe [51]. In addition, hospitalized patients should be lying down, particularly in a supine position. This position in patients with obesity makes breathing more difficult [51]. Therefore, patients with obesity commonly develop hypoventilation and sleep apnea syndromes with hypoxic and hypercapnic ventilatory responsiveness [52]. This condition disrupts the levels of oxygen and CO2 in the blood, induces cerebral oxygen desaturation, and can cause delirium in patients with obesity [53]. It should be noted that in the current study, patients with obesity ($7.5\%$) used IMV less frequently than normal-weight patients ($25.3\%$) and this may increase the rate of cerebral oxygen desaturation and might be a reason for the increased odds of delirium among patients with obesity. Another proposed mechanism is the effect of obesity on cognitive function. In a review article, Miller et al. [ 54] concluded that obesity-induced inflammation (particularly elevated circulating IL-12 and IL-6) was associated with disruption to cognitive function mediated by brain regions such as the hippocampus, amygdala, and reward-processing centers.
In the current study, we found no significant association between BMI categories and IMV requirement among elderly patients with COVID-19 hospitalized in ICU. In line with our findings, Rovirosa et al. [ 55] showed that patients with obesity and overweight, according to the WHO classification, had no significant association with requiring intubation and IMV in patients with COVID-19. In a study in the US, Kompaniyets et al. [ 50] reported that overweight and obesity were risk factors for IMV in patients with COVID-19. The study by Kim et al. [ 56] showed that overweight and all classes of obesity were associated with increased odds of IMV. That study showed that the use of IMV in patients who are overweight and with obesity may be affected by clinical bias toward early intervention based on proven pulmonary complications in patients with obesity. This finding is limited in generalizability due to the differences between the patient populations. Limitations on clinical information include the severity of dyspnea, resuscitation and/or intubation status, or the reason for clinical decision-making to explain which patients were intubated. The results of the CORONADO study, with a large population and good phenotypes of COVID-19 individuals with diabetes admitted to the hospital ward and ICU, showed that the relationship between IMV and BMI appeared with overweight [57]. In a case–control study, Ferreira et al. [ 58] reported that the need for IMV was higher among patients with COVID-19 if they were obese. Different medications, different cutoff points used for the definition of obesity, and different quality of previous studies are probable reasons for the observed discrepancy. In addition, limited facilities in hospitals might be another reason. On the other hand, a limited number of hospital beds with ventilators and not using them for qualified patients may affect the risk estimates obtained from the current and previous studies. However, it must be kept in mind that the hospital where we recruited patients with COVID-19 for the current study had 98 ICU beds and all of them had ventilators for IMV therapy. Therefore, there was no limitation for IMV therapy for patients admitted to ICU. However, because of the low number of ICU beds in that hospital, IMV therapy may not be done for some qualified patients admitted to other wards of the hospital.
Regarding COVID-19 mortality and prolonged hospital stay, no significant association was seen between overweight and obesity in the current study. In agreement with our findings, Pouwels et al. [ 59] reported that obesity was not related to 28-day mortality and duration of ICU and hospital stay among critically ill patients with COVID-19 infection. In contrast, Kompaniyets et al. [ 50] indicated that higher BMI in patients with COVID-19 was associated with an increased risk of mortality, hospitalization, and ICU admission. Another study revealed that obesity was an independent risk and prognostic factor for the disease severity and the requirement for advanced medical care in patients with COVID-19 [60]. The discrepant findings on obesity and COVID-19 mortality might be due to the obesity paradox. Al-Salameh et al. [ 61] study showed that the relative risk of transfer to ICU and occurrence of some outcomes, including intubation for mechanical ventilation, ARDS, and acute renal injury, were high in the overweight group, but without the risk of mortality, which indicates the “survival paradox of obesity”. Previous studies on ICU patients have shown a J-shaped association between BMI and mortality, with overweight and moderate obesity being protective compared with a normal BMI or more severe obesity [62]. This is in line with our findings, in which a non-significant inverse association was seen between overweight and ICU mortality among patients with COVID-19. Despite this protective effect regarding mortality, it has been shown that obesity among ICU patients increases the risk of infection and respiratory and cardiovascular complications [62]. These complications are associated with an increased risk of mortality among ICU patients [63]. In addition, using different cutoff points for the definition of overweight and obesity might be involved in the discrepant findings. In terms of different findings on the link between overweight/obesity and prolonged hospitalization, we may justify different treatment protocols and different hospital admission capacities in different countries. Because of a high incidence of COVID-19 infection and limited hospital beds, patients may be discharged prematurely. Therefore, the lack of significant association between overweight/obesity and prolonged hospitalization should be considered with caution. Further studies are needed to substantiate these findings.
This study had some strengths. To the best of our knowledge, this was the first study that examined the link between obesity and the risk of delirium among critically ill elderly patients with COVID-19. The prospective design of our study and controlling for potential confounders were other strengths. Our present study was subjected to some limitations. First, the sample size of this study did not allow us to perform subgroup analyses based on gender and other important variables. In addition, because of the low sample size, we had a lower number of patients in the normal-weight and obese groups compared with the overweight group. Second, the number of patients in the obese group was too small which may reduce the robustness of the analysis result. Third, although we extracted data on height and weight values from medical chart records, some values might have been self-reported, which may lead to measurement bias. Fourth, even though potential confounders had been adjusted in the analysis, our results might be still affected by residual confounders such as lifestyle information and therapeutic protocols used for controlling COVID-19 infection. In addition, the low number of nurses and physicians in the hospital may affect the quality of health services and consequently the risk estimates obtained in the current study. Fifth, we excluded patients with cancer, end-stage liver disease, and end-stage kidney disease. These patients usually have obesity and severe outcomes of COVID-19. Therefore, this exclusion may attenuate the risk estimates calculated for the association between obesity and clinical outcomes of COVID-19. This may also explain the non-significant association between obesity and COVID-19 mortality in the current study.
In conclusion, we found that elderly patients with COVID-19 with obesity have an increased risk of delirium compared with normal-weight patients. However, overweight was not significantly associated with the risk of delirium. Also, overweight and obesity were not significantly associated with other outcomes of elderly patients with COVID-19 such as IMV requirement, ICU/45-day mortality, and prolonged hospitalization. Further studies with higher sample sizes and considering a wide range of confounders are needed to confirm our findings.
## What is already known on this subject?
Previous studies presented inconsistent results on the association between obesity and COVID-19 mortality. Few studies have been done on elderly patients. Also, the influence of obesity on other outcomes of critically ill patients with COVID-19 such as delirium and duration of ICU stay has not been studied.
## What this study adds?
We found that obese elderly patients with COVID-19 have an increased risk of delirium compared with normal-weight patients. Regarding other outcomes including IMV requirement, death, prolonged hospitalization, and ICU admission, we found no significant association with overweight or obesity among elderly patients with COVID-19.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Author contributions
ZGh, DY, HEZ, and ZVSh designed the research project. ZGh and ZVSh conducted the research; ZGh analyzed data; ZGh and ZVSh wrote the paper; ZGh and ZVSh had primary responsibility for final content. All authors read and approved the final manuscript.
## Funding
This study was funded by the Shahid Beheshti University of Medical Sciences, Tehran, Iran. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.993292/full#supplementary-material
## References
1. Ciotti M, Ciccozzi M, Pieri M, Bernardini S. **The COVID-19 pandemic: viral variants and vaccine efficacy**. *Crit Rev Clin Lab Sci* (2022) **59** 66-75. DOI: 10.1080/10408363.2021.1979462
2. Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX. **Clinical characteristics of coronavirus disease 2019 in China**. *N Engl J Med* (2020) **382** 1708-20. DOI: 10.1056/NEJMoa2002032
3. Bradley SA, Banach M, Alvarado N, Smokovski I, Bhaskar SMM. **Prevalence and impact of diabetes in hospitalized COVID-19 patients: a systematic review and meta-analysis**. *J Diabetes* (2022) **14** 144-57. DOI: 10.1111/1753-0407.13243
4. de Almeida-Pititto B, Dualib PM, Zajdenverg L, Dantas JR, de Souza FD, Rodacki M. **Severity and mortality of COVID 19 in patients with diabetes, hypertension and cardiovascular disease: a meta-analysis**. *Diabetol Metab Syndr* (2020) **12** 1-12. DOI: 10.1186/s13098-020-00586-4
5. Wu F, Zhou Y, Wang Z, Xie M, Shi Z, Tang Z. **Clinical characteristics of COVID-19 infection in chronic obstructive pulmonary disease: a multicenter, retrospective, observational study**. *J Thorac Dis* (2020) **12** 1811-23. DOI: 10.21037/jtd-20-1914
6. Zhou Y, Yang Q, Chi J, Dong B, Lv W, Shen L. **Comorbidities and the risk of severe or fatal outcomes associated with coronavirus disease 2019: a systematic review and meta-analysis**. *Int J Infect Dis* (2020) **99** 47-56. DOI: 10.1016/j.ijid.2020.07.029
7. Schoot TS, Kerckhoffs APM, Hilbrands LB, van Marum RJ. **Immunosuppressive drugs and COVID-19: a review**. *Front Pharmacol* (2020) **11** 1333. DOI: 10.3389/fphar.2020.01333
8. Patanavanich R, Glantz SA. **Smoking is associated with COVID-19 progression: a meta-analysis**. *Nicotine Tob Res* (2020) **22** 1653-6. DOI: 10.1093/ntr/ntaa082
9. Miri A, Nasiri M, Zonoori S, Yarahmad F, Dabbagh-Moghadam A, Askari G. **The association between obesity and migraine in a population of Iranian adults: a case-control study**. *Diabetes Metab Syndr* (2018) **12** 733-6. DOI: 10.1016/j.dsx.2018.04.020
10. Asbaghi O, Sadeghian M, Rahmani S, Mardani M, Khodadost M, Maleki V. **The effect of green coffee extract supplementation on anthropometric measures in adults: a comprehensive systematic review and dose-response meta-analysis of randomized clinical trials**. *Complement Ther Med* (2020) **51** 102424. DOI: 10.1016/j.ctim.2020.102424
11. Mansouri M, Hasani-Ranjbar S, Yaghubi H, Rahmani J, Tabrizi YM, Keshtkar A. **Breakfast consumption pattern and its association with overweight and obesity among university students: a population-based study**. *Eat Weight Disord* (2020) **25** 379-87. DOI: 10.1007/s40519-018-0609-8
12. Simonnet A, Chetboun M, Poissy J, Raverdy V, Noulette J, Duhamel A. **High prevalence of obesity in severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) requiring invasive mechanical ventilation**. *Obesity (Silver Spring)* (2020) **28** 1195-9. DOI: 10.1002/oby.22831
13. Földi M, Farkas N, Kiss S, Zádori N, Váncsa S, Szakó L. **Obesity is a risk factor for developing critical condition in COVID-19 patients: a systematic review and meta-analysis**. *Obes Rev* (2020) **21** e13095. DOI: 10.1111/obr.13095
14. Nakeshbandi M, Maini R, Daniel P, Rosengarten S, Parmar P, Wilson C. **The impact of obesity on COVID-19 complications: a retrospective cohort study**. *Int J Obes* (2020) **44** 1832-7. DOI: 10.1038/s41366-020-0648-x
15. Pickkers P, de Keizer N, Dusseljee J, Weerheijm D, van der Hoeven JG, Peek N. **Body mass index is associated with hospital mortality in critically ill patients: an observational cohort study**. *Crit Care Med* (2013) **41** 1878-83. DOI: 10.1097/CCM.0b013e31828a2aa1
16. Hutagalung R, Marques J, Kobylka K, Zeidan M, Kabisch B, Brunkhorst F. **The obesity paradox in surgical intensive care unit patients**. *Intensive Care Med* (2011) **37** 1793-9. DOI: 10.1007/s00134-011-2321-2
17. Goyal A, Gupta Y, Kalaivani M, Praveen PA, Ambekar S, Tandon N. **SARS-CoV-2 Seroprevalence in individuals with type 1 and type 2 diabetes compared with controls**. *Endocr Pract* (2022) **28** 191-8. DOI: 10.1016/j.eprac.2021.12.009
18. Goyal A, Gupta Y, Kalaivani M, Bhatla N, Tandon N. **Impact of SARS-CoV-2 on progression of glycemic and cardiometabolic variables and changes in insulin indices: a longitudinal study**. *Diabetes Ther* (2021) **12** 3011-23. DOI: 10.1007/s13300-021-01158-z
19. de Jong A, Wrigge H, Hedenstierna G, Gattinoni L, Chiumello D, Frat JP. **How to ventilate obese patients in the ICU**. *Intensive Care Med* (2020) **46** 2423-35. DOI: 10.1007/s00134-020-06286-x
20. Zhi G, Xin W, Ying W, Guohong X, Shuying L. **"obesity paradox" in acute respiratory distress syndrome: a systematic review and meta-analysis**. *PLoS One* (2016) **11** e0163677. DOI: 10.1371/journal.pone.0163677
21. Abumayyaleh M, Núñez Gil IJ, el-Battrawy I, Estrada V, Becerra-Muñoz VM, Aparisi A. **Does there exist an obesity paradox in COVID-19? Insights of the international HOPE-COVID-19-registry**. *Obes Res Clin Pract* (2021) **15** 275-80. DOI: 10.1016/j.orcp.2021.02.008
22. Foulkes AS, Selvaggi C, Shinnick D, Lumish H, Kim E, Cao T. **Understanding the link between obesity and severe COVID-19 outcomes: causal mediation by systemic inflammatory response**. *J Clin Endocrinol Metab* (2022) **107** e698-707. DOI: 10.1210/clinem/dgab629
23. Biscarini S, Colaneri M, Ludovisi S, Seminari E, Pieri TC, Valsecchi P. **The obesity paradox: analysis from the SMAtteo COvid-19 REgistry (SMACORE) cohort**. *Nutr Metab Cardiovasc Dis* (2020) **30** 1920-5. DOI: 10.1016/j.numecd.2020.07.047
24. Kooistra EJ, Brinkman S, van der Voort PHJ, de Keizer NF, Dongelmans DA, Kox M. **Body mass index and mortality in coronavirus disease 2019 and other diseases: a cohort study in 35,506 ICU patients**. *Crit Care Med* (2022) **50** e1-e10. DOI: 10.1097/ccm.0000000000005216
25. Gupta S, Hayek SS, Wang W, Chan L, Mathews KS, Melamed ML. **Factors associated with death in critically ill patients with coronavirus disease 2019 in the US**. *JAMA Intern Med* (2020) **180** 1436-47. DOI: 10.1001/jamainternmed.2020.3596
26. Zhang X, Lewis AM, Moley JR, Brestoff JR. **A systematic review and meta-analysis of obesity and COVID-19 outcomes**. *Sci Rep* (2021) **11** 7193-11. DOI: 10.1038/s41598-021-86694-1
27. Ho JS, Fernando DI, Chan MY, Sia CH. **Obesity in COVID-19: a systematic review and meta-analysis**. *Ann Acad Med Singap* (2021) **49** 996-1008. DOI: 10.47102/annals-acadmedsg.2020299
28. Luetz A, Grunow JJ, Mörgeli R, Rosenthal M, Weber-Carstens S, Weiss B. **Innovative ICU solutions to prevent and reduce delirium and Post-intensive care unit syndrome**. *Semin Respir Crit Care Med* (2019) **40** 673-86. DOI: 10.1055/s-0039-1698404
29. Xu Y, Chen Y, Tang X. **Guidelines for the diagnosis and treatment of coronavirus disease 2019 (COVID-19) in China**. *Glob Health Med* (2020) **2** 66-72. DOI: 10.35772/ghm.2020.01015
30. Tarnowski MS, Rabito EI, Fernandes D, Rosa M, Oliveira ML, Hirakata VN. **Height prediction from ulna length of critically ill patients**. *Nutr Clin Pract* (2018) **33** 887-92. DOI: 10.1177/0884533617716432
31. Khwaja A. **KDIGO clinical practice guidelines for acute kidney injury**. *Nephron Clin Pract* (2012) **120** c179-84. DOI: 10.1159/000339789
32. KNAUS WA, DRAPER EA, WAGNER DP, ZIMMERMAN JE. **APACHE II: a severity of disease classification system**. *Crit Care Med* (1985) **13** 818-29. DOI: 10.1097/00003246-198510000-00009
33. Gusmao-Flores D, Salluh JI, Chalhub R, Quarantini LC. **The confusion assessment method for the intensive care unit (CAM-ICU) and intensive care delirium screening checklist (ICDSC) for the diagnosis of delirium: a systematic review and meta-analysis of clinical studies**. *Crit Care* (2012) **16** R115. DOI: 10.1186/cc11407
34. 34.WHO Organization. Obesity: Preventing and Managing the Global Epidemic. Geneva: WHO (2000).. *Obesity: Preventing and Managing the Global Epidemic* (2000)
35. Anjom-Shoae J, Keshteli AH, Sadeghi O, Pouraram H, Afshar H, Esmaillzadeh A. **Association between dietary insulin index and load with obesity in adults**. *Eur J Nutr* (2020) **59** 1563-75. DOI: 10.1007/s00394-019-02012-6
36. Baud D, Qi X, Nielsen-Saines K, Musso D, Pomar L, Favre G. **Real estimates of mortality following COVID-19 infection**. *Lancet Infect Dis* (2020) **20** 773. DOI: 10.1016/S1473-3099(20)30195-X
37. Acharya D, Lee K, Lee DS, Lee YS, Moon SS. **Mortality rate and predictors of mortality in hospitalized COVID-19 patients with diabetes**. *Healthcare* (2020) **8** 338. DOI: 10.3390/healthcare8030338
38. Qian Z, Lu S, Luo X, Chen Y, Liu L. **Mortality and clinical interventions in critically ill patient with coronavirus disease 2019: a systematic review and meta-analysis**. *Front Med* (2021) **8** 8. DOI: 10.3389/fmed.2021.635560
39. Li G, Liu Y, Jing X, Wang Y, Miao M, Tao L. **Mortality risk of COVID-19 in elderly males with comorbidities: a multi-country study**. *Aging (Albany NY)* (2021) **13** 27-60. DOI: 10.18632/aging.202456
40. Trecarichi EM, Mazzitelli M, Serapide F, Pelle MC, Tassone B, Arrighi E. **Clinical characteristics and predictors of mortality associated with COVID-19 in elderly patients from a long-term care facility**. *Sci Rep* (2020) **10** 20834-7. DOI: 10.1038/s41598-020-77641-7
41. Covino M, de Matteis G, Polla DAD, Santoro M, Burzo ML, Torelli E. **Predictors of in-hospital mortality AND death RISK STRATIFICATION among COVID-19 PATIENTS aged≥ 80 YEARs OLD**. *Arch Gerontol Geriatr* (2021) **95** 104383. DOI: 10.1016/j.archger.2021.104383
42. Carvalho JP, de Almeida AR, Gusmao-Flores D. **Delirium rating scales in critically ill patients: a systematic literature review**. *Rev Bras Ter Intensiva* (2013) **25** 148-54. DOI: 10.5935/0103-507x.20130026
43. 43.American Psychiatric Association D, Association AP. Diagnostic and statistical manual of mental disorders: DSM-5. Washington, DC: American psychiatric association (2013) doi: 10.1176/appi.books.9780890425596.. *Diagnostic and statistical manual of mental disorders: DSM-5* (2013). DOI: 10.1176/appi.books.9780890425596
44. van Rompaey B, Schuurmans MJ, Shortridge-Baggett LM, Truijen S, Bossaert L. **Risk factors for intensive care delirium: a systematic review**. *Intensive Crit Care Nurs* (2008) **24** 98-107. DOI: 10.1016/j.iccn.2007.08.005
45. Jung P, Pereira MA, Hiebert B, Song X, Rockwood K, Tangri N. **The impact of frailty on postoperative delirium in cardiac surgery patients**. *J Thorac Cardiovasc Surg* (2015) **149** 869-875.e1-2. DOI: 10.1016/j.jtcvs.2014.10.118
46. Vasilevskis EE, Han JH, Hughes CG, Ely EW. **Epidemiology and risk factors for delirium across hospital settings**. *Best Pract Res Clin Anaesthesiol* (2012) **26** 277-87. DOI: 10.1016/j.bpa.2012.07.003
47. Anand SS, Friedrich MG, Lee DS, Awadalla P, Després JP, Desai D. **Evaluation of adiposity and cognitive function in adults**. *JAMA Netw Open* (2022) **5** e2146324. DOI: 10.1001/jamanetworkopen.2021.46324
48. Lachmann G, Feinkohl I, Borchers F, Ottens TH, Nathoe HM, Sauer AM. **Diabetes, but not hypertension and obesity, is associated with postoperative cognitive dysfunction**. *Dement Geriatr Cogn Disord* (2018) **46** 193-206. DOI: 10.1159/000492962
49. Wurzinger B, Dünser MW, Wohlmuth C, Deutinger MC, Ulmer H, Torgersen C. **The association between body-mass index and patient outcome in septic shock: a retrospective cohort study**. *Wien Klin Wochenschr* (2010) **122** 31-6. DOI: 10.1007/s00508-009-1241-4
50. Kompaniyets L, Goodman AB, Belay B, Freedman DS, Sucosky MS, Lange SJ. **Body mass index and risk for COVID-19-related hospitalization, intensive care unit admission, invasive mechanical ventilation, and death - United States, march-December 2020**. *MMWR Morb Mortal Wkly Rep* (2021) **70** 355-61. DOI: 10.15585/mmwr.mm7010e4
51. Yamane T, Date T, Tokuda M, Aramaki Y, Inada K, Matsuo S. **Hypoxemia in inferior pulmonary veins in supine position is dependent on obesity**. *Am J Respir Crit Care Med* (2008) **178** 295-9. DOI: 10.1164/rccm.200801-113OC
52. Parameswaran K, Todd DC, Soth M. **Altered respiratory physiology in obesity**. *Can Respir J* (2006) **13** 203-10. DOI: 10.1155/2006/834786
53. He KQ, Wang S, Zhang W, Liu Q, Chai XQ. **What is the impact of perioperative cerebral oxygen desaturation on postoperative delirium in old population: a systemic review and meta-analysis**. *Aging Clin Exp Res* (2022) **34** 1761-70. DOI: 10.1007/s40520-022-02128-6
54. Miller AA, Spencer SJ. **Obesity and neuroinflammation: a pathway to cognitive impairment**. *Brain Behav Immun* (2014) **42** 10-21. DOI: 10.1016/j.bbi.2014.04.001
55. Coss-Rovirosa MF, Aguilar-Soto M, Cuenca D, Velez-Pintado M, Camiro-Zuñiga A, Ferreira-Hermosillo A. **Are overweight and obesity risk factors for invasive mechanical ventilation in severe coronavirus disease 2019 pneumonia?**. *Arch Endocrinol Metab* (2021) **65** 462-7. DOI: 10.20945/2359-3997000000350
56. Kim TS, Roslin M, Wang JJ, Kane J, Hirsch JS, Kim EJ. **BMI as a risk factor for clinical outcomes in patients hospitalized with COVID-19 in New York**. *Obesity* (2021) **29** 279-84. DOI: 10.1002/oby.23076
57. Smati S, Tramunt B, Wargny M, Caussy C, Gaborit B, Vatier C. **Relationship between obesity and severe COVID-19 outcomes in patients with type 2 diabetes: results from the CORONADO study**. *Diabetes Obes Metab* (2021) **23** 391-403. DOI: 10.1111/dom.14228
58. Ferreira AI, Sarmento MH, Cotter J. **Predictors of clinical outcomes of hospitalized patients with Covid-19: focusing on pre-existing liver disease**. *Intern Emerg Med* (2022) **17** 2209-17. DOI: 10.1007/s11739-022-03044-3
59. Pouwels S, Ramnarain D, Aupers E, Rutjes-Weurding L, van Oers J. **Obesity may not be associated with 28-day mortality, duration of invasive mechanical ventilation and length of intensive care unit and hospital stay in critically ill patients with severe acute respiratory syndrome Coronavirus-2: a retrospective cohort study**. *Medicina (Kaunas)* (2021) **57**. DOI: 10.3390/medicina57070674
60. Tamara A, Tahapary DL. **Obesity as a predictor for a poor prognosis of COVID-19: a systematic review**. *Diabetes Metab Syndr* (2020) **14** 655-9. DOI: 10.1016/j.dsx.2020.05.020
61. al-Salameh A, Lanoix J-P, Bennis Y, Andrejak C, Brochot E, Deschasse G. **The association between body mass index class and coronavirus disease 2019 outcomes**. *Int J Obes* (2021) **45** 700-5. DOI: 10.1038/s41366-020-00721-1
62. Schetz M, de Jong A, Deane AM, Druml W, Hemelaar P, Pelosi P. **Obesity in the critically ill: a narrative review**. *Intensive Care Med* (2019) **45** 757-69. DOI: 10.1007/s00134-019-05594-1
63. Dres M, Austin PC, Pham T, Aegerter P, Guidet B, Demoule A. **Acute respiratory distress syndrome cases volume and ICU mortality in medical patients**. *Crit Care Med* (2018) **46** e33-40. DOI: 10.1097/ccm.0000000000002816
|
---
title: Ectopic expression of SARS-CoV-2 S and ORF-9B proteins alters metabolic profiles
and impairs contractile function in cardiomyocytes
authors:
- Peng Zhang
- Yu Liu
- Chunfeng Li
- Lindsay D. Stine
- Pei-Hui Wang
- Matthew W. Turnbull
- Haodi Wu
- Qing Liu
journal: Frontiers in Cell and Developmental Biology
year: 2023
pmcid: PMC9994814
doi: 10.3389/fcell.2023.1110271
license: CC BY 4.0
---
# Ectopic expression of SARS-CoV-2 S and ORF-9B proteins alters metabolic profiles and impairs contractile function in cardiomyocytes
## Abstract
Coronavirus disease 2019 (COVID-19) is associated with adverse impacts in the cardiovascular system, but the mechanisms driving this response remain unclear. In this study, we conducted “pseudoviral infection” of SARS-CoV-2 subunits to evaluate their toxic effects in cardiomyocytes (CMs), that were derived from human induced pluripotent stem cells (hiPSCs). We found that the ectopic expression of S and ORF-9B subunits significantly impaired the contractile function and altered the metabolic profiles in human cardiomyocytes. Further mechanistic study has shown that the mitochondrial oxidative phosphorylation (OXPHOS), membrane potential, and ATP production were significantly decreased two days after the overexpression of S and ORF-9B subunits, while S subunits induced higher level of reactive oxygen species (ROS). Two weeks after overexpression, glycolysis was elevated in the ORF-9B group. Based on the transcriptomic analysis, both S and ORF-9B subunits dysregulated signaling pathways associated with metabolism and cardiomyopathy, including upregulated genes involved in HIF-signaling and downregulated genes involved in cholesterol biosynthetic processes. The ORF-9B subunit also enhanced glycolysis in the CMs. Our results collectively provide an insight into the molecular mechanisms underlying SARS-CoV-2 subunits-induced metabolic alterations and cardiac dysfunctions in the hearts of COVID-19 patients.
## Introduction
Coronavirus disease 2019 (COVID-19) is a potentially fatal respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). There have been 635 million confirmed cases of COVID-19 globally as of 23 November 2022, including 6.6 million deaths reported the World Health Organization (WHO). COVID-19 related morbidity and mortality also exert a devastating impact on global public health and socio-economic development. The SARS-CoV-2 is a single-stranded RNA virus athat shows great and fast mutation potential (Hoffmann et al., 2020; Azevedo et al., 2021). Viral infection occurs through the binding the surface spike protein (i.e., S protein) with angiotensin-converting enzyme 2 (ACE2), which acts as the primary receptor for the virus. ACE2 is highly expressed in the lungs and also present in large amounts in the heart, causing cardiovascular complications via binding with the S protein of the virus (Hoffmann et al., 2020; Redondo et al., 2021; Farooq et al., 2022). However, the exact mechanisms underlying the cardiovascular complications induced by individual SARS-CoV-2 subunits remain largely unknown.
The symptoms of COVID-19 vary from asymptomatic, mild disease, to acute respiratory distress syndrome (ARDS). While SARS-CoV-2 infection causes mild upper respiratory disease or even asymptomatic symptoms in the majority of patients, others develop ARDS, which can be fatal (Clerkin et al., 2020; Wu and McGoogan, 2020). It is understood that the cause of the severe COVID-19 symptoms are partially due to the cytokine dysregulation and hyperinflammation in patients, and is triggered by impaired interferon responses (Arunachalam et al., 2020; Hadjadj et al., 2020). Moreover, pre-existing cardiovascular diseases and risk factors were highly correlated well with the severity of COVID-19. Although the respiratory system is the major target of SARS-CoV-2, infection also can damage other organs. The hospitalized patients with COVID-19 showed elevated cardiac troponin I levels, which indicates the cardiac damages after infection (Shi et al., 2020; Zhou et al., 2020), and patients with cardiovascular disease showed higher mortality (Ruan et al., 2020). This leads to the aggravation of chronic underlying cardiac pathologies as well as acute-onset of new cardiovascular complications, indicating that myocardial injury can be present in some hospitalized patients with SARS-CoV-2 infection (Clerkin et al., 2020; Wu and McGoogan, 2020).
Cardiomyocytes (CMs) derived from human stem cells provide a great model for mechanistic and toxicologic studies in the cardiovascular system. In our previous studies, we established a human stem cell-based and omics-centric approach, in which transcriptomes and epigenomes were interrogated to identify the transcriptional regulatory mechanisms subserving early CM differentiation and drug responses in the differentiated cells (Liu et al., 2017; Zhao et al., 2017). Human stem cells-derived CMs have been applied to evaluation of toxicities from cancer drugs (Sharma et al., 2015; Wang et al., 2019). More recently, multiple investigators have reported the transcriptomic and functional remodeling induced by SARS-CoV-2 infection in iPSC-derived CM models, and this improved our understanding of COVID-19 related cardiac risks by exploiting human-originated cellular models (Sharma et al., 2020; Yang et al., 2020; Yang et al., 2021). However, although it is known that SARS-CoV-2 overexpresses nucleocapsid proteins in host cells after transfection, it remains unclear as to the specific role of each one of them in inducing the toxicity and functional failure in the heart (Bailey et al., 2021). In the present study, we generated “pseudoviral infection” of SARS-CoV-2 subunits in the CMs derived from human induced pluripotent stem cells (hiPSCs), and evaluated cardiac functions and metabolic profiles. Genome-wide transcriptomics was implemented to understand mechanisms underlying SARS-CoV-2 subunits-induced adverse impacts on CMs.
## Lentiviral plasmids construct for SARS-CoV-2 subunits
The sequences of SARS-CoV-2 (Wuhan-Hu-1 strain, GenBank: NC_045512.2) was used as template to synthesize each structural gene (General Bio, China). 11 viral genes and constructed into the lentivirus vector separately. *These* genes included: *Structural* genes (spike [S], membrane [M], Envelope [E], and Nucleocapsid [N], and open reading frames (ORF)-3, ORF-6, ORF-7A, ORF-7B, ORF-8, ORF-9A, ORF-9B (Zhang and Holmes, 2020). The viral genes were firstly cloned into pcDNA6B-Flag vector as described previously (Zhang et al., 2021), and then they were subcloned into a lentivirus plasmid pCDH-CMV-MCS-EF1α-copGFP (System Biosciences, #CD511B-1, Palo Alto, CA, United States) with standard molecular cloning methods. The vector only contains GFP-reporter was used as a control. Among them, the DNA sequences of S and ORF-3 were codon optimized to ensure a high expression level in human cells.
## Cell culture and cardiomyocyte differentiation
The hiPSC line was adopted in this study after obtaining them from the Stanford Cardiovascular Institute (SCVI) Biobank, Stanford University. It was generated thorough reprograming of peripheral blood mononuclear cells (PBMCs) from an anonymous healthy individual with Sendai virus. The pluripotent cell lines were grown in Matrigel (Corning, CA)-coated 12-well plates in Essential 8™ Medium (Thermo Fisher Scientific, MA) at 37°C in $5\%$ CO2 in compressed air and high humidity. Cardiomyocyte differentiation was conducted using a monolayer differentiation chemically defined method (Burridge et al., 2014). Briefly, iPSCs were kept in culture until $80\%$–$90\%$ confluence. For the differentiation, iPSCs were treated with 6 µM CHIR99021 in RPMI + B27 (minus insulin) for 2 days, fresh RPMI + B27 (minus insulin) for 1 day, followed by 5 µM IWR-1 treatment for 2 days, and then fresh RPMI + B27 (minus insulin) for another 2 days. Afterward, the cells will be supplied with fresh RPMI + B27 every other day. Beating cardiomyocytes will normally appear after 9–10 days, and the cells can be further treated with glucose free RPMI + B27 for 2–3 rounds.
## Lentivirus preparation and transfection of cardiomyocytes
HEK293T (ATTC, Cat# CRL-3216) cells were kept in 6-well plates with Dulbecco’s Modified Eagle Medium (DMEM, Gibco) supplemented with $10\%$ fetal bovine serum. Packaging plasmids (pVSVg and psPAX2), pCDH containing SARS-CoV-2 subunits, Opti-MEM (Thermo Fisher Scientific), and X-tremeGENE 9 DNA transfection reagent (Sigma-Aldrich) were combined to transfect HEK293T cells according to the manufacturer’s instructions. Medium supernatants containing virus particles were filtered through a 0.45-μM filter and further concentrated using a Lenti-x concentrator (Takara Bio) according to the manufacturer’s protocol. 2 μg/ml of polybrene was used for transfection of differentiated cardiomyocytes, and puromycin was used to select the transduced cells. Since the plasmids can express GFPs, successful transfection and expression of each unit were determined by evaluating the GFP with a Nikon Ti2-E fluorescence microscope (Supplementary Figure S1).
## Metabolic profiling by seahorse experiments
We used XF Cell Mito Stress Test and XF Glycolytic Rate Assay kit to measure the oxygen consumption rate (OCR) for the mitochondrial respiratory activity and proton efflux rate (PER) for the glycolytic levels in the cardiomyocytes, by using a Seahorse XFe96 Extracellular Flux Analyzer (Agilent, CA). Cells [45,000] were plated into an Xfe96 cell culture microplate (Agilent) containing RPMI/B27 supplemented with $10\%$ FBS and 10 μM ROCK inhibitor. After 48 h to allow recovery, we conducted the metabolic profiling using the XFe96 Seahorse analyzer with two kits according to the manufacture’s manual. Briefly, 1 day prior to the experiment, the Xfe96 sensor cartridges were hydrated in XF calibrator solution and incubated overnight at 37°C in a non-CO2 incubator. 1 hour prior to the experiment, the cells were incubated at 37°C (non-CO2) in 200 μl of Seahorse assay medium, containing XF base medium supplemented 1 mM pyruvate, 2 mM glutamine, and 10 mM glucose (pH 7.4). OCR was measured with sequential injections of 2 μM oligomycin, 2 μM FCCP and each 0.5 μM of rotenone/antimycin A. PER was measured with sequential injections of 0.5 μM of rotenone/antimycin A and 50 mM of 2-deoxy-D-glucose (2-DG). Data were normalized by fluorescence of cell viability using PrestoBlue reagent (Thermo Fisher).
## RNA-isolation
Total RNA was extracted from the same number of cells among each group using QIAzol lysis reagent (Qiagen), and RNA was then subjected to Dnase I digestion and purified using a miRNeasy Mini Kit (Qiagen) according to the manufacturer’s instructions. RNA integrity was assessed with a NanoDrop, and only samples with a ratio of $\frac{260}{280}$ between 2.0—2.1 were subsequently used for ribosome depletion.
## RNA-sequencing and analysis
The library preparation and RNA-sequencing were performed by Novogene Corporation Inc., (Sacramento, CA). The RNA-seq libraries were constructed using NEBNext UltraTM II RNA Library Prep Kit for Illumina and were sequenced by Novaseq 6,000 PE150 system. Raw reads of FASTQ format were firstly processed through fastp, and clean data (clean reads) were obtained by removing reads containing adapter and poly-N sequences and reads with low quality from raw data. All the downstream analyses were based on the clean data with high quality. The raw RNA-seq raw data were trimmed to remove the adapter sequences (GATCGGAAGAGCACACGTCTGAACTCCAGTCACGGTCTACTATCTCGTATGCCGTCTTCTGCTTG and AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGG -TGGTCGCCGTATCATT) with command-line tool cutadapt (1.8.1). Then the trimmed files were aligned with Tophat (version 2.0.9) to GRCh37/hg19 Homo sapiens reference genome. The human gene symbols and their raw counts were calculated using the HTSeq (version 0.6.1p1) package in Python with the hg19 Homo sapiens gtf file. *Differential* gene-expression analysis was performed using the edgeR package in R, and the normalization was performed using a trimmed mean of M-values (TMM) method across all samples. The Gene Ontology (GO) enrichment analysis was performed using on-line tools DAVID (version 6.8) (https://david.ncifcrf.gov/summary.jsp) and the Gene Ontology Resource (http://geneontology.org). The Gene Ontology (GO) enrichment analysis of differentially expressed genes was performed using DAVID (https://david.ncifcrf.gov).
## Western blot analysis
The cells were harvested in RIPA lysis buffer (EMD Millipore, CA) contain one tablet of Pierce™ protease and phosphatase inhibitor (Thermo Fisher Scientific), and the proteins were purified using a Branson Digital Sonifier homogenizer (Branson Ultrasonics, CT). 20 μg of protein from each sample was separated on NuPAGE 4–$12\%$ Bis-Tris protein gels (Thermo Fisher Scientific) and transferred to nitrocellulose membranes (Thermo Fisher Scientific). The protein-bound membranes were blocked with $5\%$ of blotting-grade blocker (Bio-Rad) in PBST for 1 h at room temperature and incubated with a primary antibody (1:1,000 dilution) in $5\%$ of blotting-grade blocker in PBST overnight at 4°C. After washing with PBST buffer, the membranes were incubated with horseradish peroxidase (HRP)-conjugated-secondary antibody for 1 h at room temperature. The membranes were developed with SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Fisher Scientific) and exposed on a ChemiDoc Touch imaging system (Bio-Rad) for imaging. The primary antibody used in this study was total OXPHOS human WB antibody cocktail (Abcam, ab110411). The secondary antibodies was HRP-conjugated-goat anti-mouse IgG (SouthernBiotech, 1030-05).
## Functional analysis of iPSC-derived cardiomyocyte
For contractile function analysis, CMs were seeded on 96 well plates at a density of 100 k cells per well. Cells will start to beat after 2–3 days of recovery, and were further matured until day 30 after differentiation before functional measurement. The contractile function of beating cardiomyocytes was analyzed using a Sony SI18000 cell motion imaging system. Briefly, high-resolution and high-frame rate contractile videos (1,024*1,024 at 75fps) were recorded, and the key contractility parameters, such as contractile velocity (μm/s), relaxation velocity (μm/s), percentile of beating area, and contraction/relaxation durations (s) were calculated according to the pixel displacement between video frames. To better evaluate the effect of COVID-19 protein overexpression in the CMs, the treatment duration was set to 1 week, and we measured the contractile function both before and after lentivirus infection.
## Mitochondrial DNA dynamics analysis
DNA from cardiomyocytes was isolated using AllPrep DNA/RNA Mini kit (Qiagen), and the human mitochondrial to nuclear DNA ratio kit (Takara) was used to determine mitochondrial DNA content. Two separate primer pairs were used to generate nuclear-mitochondrial DNA content ratios. SLCO2B1 and SERPINA1 were used as nuclear genes, while ND1 and ND5 were used as mitochondrial genes. *Two* genes for both nuclear and mitochondrial DNA were used as an average to prevent outliers. Average of ratio between mitochondrial genes and nuclear genes was used to determine the mitochondrial DNA content of each sample.
## High content imaging
ATP production, mitochondrial membrane potential, and gross mitochondrial size of differentiated cardiomyocytes were evaluated using an imaging-based multi-parametric analysis strategy (i.e., high content imaging). Differentiating cardiomyocytes were re-plated into optical 96-well plates, and cells were labeled with the following fluorescent dyes: BioTracker ATP-red live cell dye (Sigma,SCT045), tetramethylrhodamine, methyl ester (Thermo Fisher Scientific, I34361), and CellROX Orange Reagent (Thermo Fisher Scientific, C10443). The fluorescence intensities or areas were quantified using a Cytation one cell imaging multi-Mode reader with Gen5 Image Prime software (BioTek).
## Statistical analysis
We conducted statistical analysis using GraphPad Prism 8.4 (GraphPad Software, Inc., San Diego, CA). Non-parametric t-test was used to compare data between two groups and one-way or two-way ANOVA followed by Tukey’s test were used to compare data of multiple groups wherever appropriate. Data are reported as means ± standard error of the mean (SEM).
## Inducing expression of SARS-CoV-2 subunits altered cardiac functions in cardiomyocytes
Based on the genomic structural of SARS-CoV-2 (Wuhan-1 strain), we evaluated the role of the 11 SARS-CoV-2 proteins in human cardiomyocytes. These proteins were transfected into the differentiated CMs on day 30. 2 days (day 32) and 2 weeks (day 45) after transfection the CMs were used for the analyses (Figure 1). The contractile function of the monolayer CMs were then calculated using a traction force microscopy recording platform (Figure 2A). Our results showed that the over-expression of two of the SARS-CoV-2 proteins (ORF-9B and the spike protein S) were significantly related to the reduced contractile function in CMs, out of all the SARS-CoV-2 proteins tested (Figures 2B, C). Specifically, overexpression of ORF-9B and S induced significantly reduce in the beating area (%BA) and the contractile velocity, while no changes in the beating rate was detected. The functional changes result from ORF-9B and S protein overexpression were consistent in both CMs on day 32 and day 45, indicating the potential roles of them in the regulation of contractile function. The overexpression of other SARS-CoV-2 proteins generally induced a varied result: although some of them induced slightly functional changes at certain timepoints and to certain parameters, yet their impact on the contractile function was still not conclusive (Supplementary Table S1). Thus, we herein focused specifically on ORF-9B and S and their regulatory roles in the subsequent studies.
**FIGURE 1:** *Experimental design. Left, the genomic organization of SARS-CoV-2 (up) and the map of pCDH-CMV-CoV (down). Right, PBMCs from a health donor were collected and reprogrammed into iPSCs, and then were differentiated into CMs. The CMs were transduced with lentivirus carrying SARS-CoV-2 genes on day 30. Cells we collected for functional, metabolic, and toxicogenomic analyses 2 days (day 32) or 2 weeks (day 45) after transfection.* **FIGURE 2:** *Screening of the impact of SARS-CoV-2 subunits in CMs with contractility assay. (A) Phase contrast image of monolayer CMs plated for contractility assay. (B) Typical trace of the average displacement velocity generated by frame-to-frame displacement analysis. (C) Averaged motion peaks showing the start, peak, and end of contraction and relaxation, allowing detailed analysis of contractile and relaxation velocity and duration. (D–F) Functional measurement of the percentage of beating area (D), beating rate (E) and contractile velocity (F) in CMs before (control) and after (virus) over-expression of COVID proteins by lentiviral vectors. (G–I) Functional measurement of the percentage of beating area (G), beating rate (H) and contractile velocity (I) in CMs on day 30 before (control) and after (virus) overexpression of COVID proteins by lentivirus vectors. N > 8 wells of CMs from at least two independent experiments in each group. * and **: p < 0.05 and p < 0.01 vs. non-virus controls from the same group of COVID-19 experiment in two way-ANNOVA followed by Sidak’s multiple comparison.*
## Expression of SARS-CoV-2 subunits inhibited mitochondrial OXPHOS but enhanced glycolysis
Differentiated CMs principally use mitochondrial OXPHOS to support their large ATP demands; and we therefore examined mitochondrial OXPHOS by measuring oxygen consumption rate (OCR) using a Seahorse XFe96 Extracellular Flux Analyzer. We observed that short-term (2 days) induction of S and ORF-9B subunits caused diminutions in mitochondrial respiratory activity, including basal OCR, maximal and spare respiration, spare respiratory capacity, and ATP production (Figures 3A, B; Supplementary Figure S2), demonstrating an acute impairment on mitochondrial OXPHOS from S and ORF-9B proteins. Intriguingly, no significant differences in mitochondrial OXPHOS were observed between control and ORF-9B group after two weeks, except for higher maximal and spare respiration in S group (Figures 3C, D; Supplementary Figure S3); this suggests that CMs may change their metabolic profiles to adapted to infections after longer-term exposure to S and ORF-9B proteins. We then evaluated the glycolytic levels by measuring proton efflux rate (PER), and we found that both S and ORF-9B proteins caused slight reductions in basal glycolytic levels (Figures 3E, F, Supplementary Figure S4); however, inducing ORF-9B expression for two weeks evaluated basal and compensatory glycolysis (Figures 3G, H; Supplementary Figure S5). Short-term exposure to S or ORF-9B subunits was associated with attenuated ATP production and mitochondrial membrane potential (Figures 3I, J), and S protein was found to induce higher levels of reactive oxygen species (ROS) (Figure 3K). In addition, no significant alternations in mitochondrial DNA contents (mtDNA) or mitochondrial complexes were noted (Supplementary Figure S6). Collectively, these results suggested that SARS-CoV-2 subunits (i.e., S and ORF-9B) altered metabolic profiles without changing mitochondrial biogenesis.
**FIGURE 3:** *Evaluation of the effects of SARS-CoV-2 subunits on mitochondrial functions and glycolysis. (A, B) Evaluation of mitochondrial oxygen consumption rate (OCR) in CMs on day 32 after overexpression of S and ORF-9B subunits for two days. (C, D) Evaluation of mitochondrial oxygen consumption rate (OCR) in CMs on day 46 after overexpression of the S and ORF-9B subunits for two weeks. (E, F) Evaluation of glycolysis by measuring proton efflux rate (PER) in CMs on day 32 after overexpression of the S and ORF-9B subunits for 2 days. (G, H) Evaluation of glycolysis by measuring PER in CMs on day 45 after overexpression of S and ORF-9B subunits for 2 weeks. The data were normalized to cell numbers. (H–K) Mitochondrial ATP production (I), mitochondrial membrane potential (J), and reactive oxygen species (ROS) level in CMs were measured with a BioTek Cytation 1. Data are reported as means ± standard error of the mean (SEM). p< 0.05: **, p < 0.01.*
## Transcriptomic analysis reveals a metabolic remodeling mechanism in CMs by inducing ORF-9B protein expression
To elucidate the changes in gene expression during CM differentiation due to exposure to S and ORF_9B proteins, we performed genome-wide transcriptomic analysis of differentiated CMs using RNA-sequencing (RNA-seq) experiments. Based on the differential gene lists (FDR<0.05; Supplementary Tables S1, S2), we found that 1998 and 2,177 genes were dysregulated by S protein and 9B protein, respectively; and that over $50\%$ were shared in common (Figure 4A). The enriched Gene Ontology (GO) terms of the commonly dysregulated genes were associated with “cellular response to hypoxia,” “cholesterol biosynthetic process,” “sarcomere structure,” etc., ( Figure 4B), suggesting that both proteins dysregulated cardiac functions and metabolic process in CMs. The upregulated genes between S and ORF_9B groups showed a high similarity, including ankyrin repeat domain 1 (ANKRD1), actin alpha cardiac muscle 1 (ACTC1), connective tissue growth factor (CTGF), natriuretic peptide B (NPPB), and sorbin and SH3 domain-containing protein 2 (SORBS2) (Figures 4C–F). Cytochrome P450 family 26 subfamily A member 1 (CYP26A1) and fibrinogen beta chain (FGB) also revealed the highest fold changes in the S group and ORF-9B group, respectively (Figures 4C–F). However, downregulated genes showed slight differences between the two groups. For instance, serum amyloid A2 (SAA2) and WNT family member 6 (WNT6) were the two downregulated genes with the highest fold changes in the S group, compared to structural maintenance of chromosomes 1B (SMC1B) in the ORF-9B group. Moreover, dysregulated genes in both S and ORF-9B groups shared similar significantly enriched (FDR<0.05) GO terms: the down-regulated genes were associated with “DNA replication” and “cholesterol biosynthetic process” (Figures 4D–G); and the up-regulated genes are associated with “sarcomere organization,” “cardiac muscle cell development,” and “actin cytoskeleton organization” (Figures 4E–H).
**FIGURE 4:** *Transcriptomic analysis of CMs after overexpression of SARS-CoV-2 subunits. (A) The Venn diagram exhibits the statistically differential genes in CMs after overexpression ORF-9B or S (FDR < 0.05) day day32. (B) The heatmap exhibits the expression of overlapping differentially expressed genes in the ORF-9B and S group, compared to that in control. Each group had biological replicates. The representative enriched GO terms (FDR<0.05) are shown on the right side. (C) The scatter plot shows dysregulated genes in the S group compared to that in control (FDR < 0.05 with log[FC] larger than 1). Red, upregulated genes; blue, down-regulated genes. (D, E) The top enriched GO terms (FDR < 0.05) of the downregulated genes (D) and the upregulated genes (E) in the S group. (F) The scatter plot shows dysregulated genes in the ORF-9B group compared to that in control (FDR < 0.05 with log[FC] larger than 1). Red, upregulated genes; blue, down-regulated genes. (G, H) The top enriched GO terms (FDR < 0.05) of the downregulated genes (G) and upregulated genes (H) in the ORF-9B group. (I) The Venn diagram exhibits the statistically differential genes in CMs after overexpression ORF-9B or S (FDR < 0.05) day day45. (J–K) Left, the scatter plot shows dysregulated genes in the S group (J) and ORF-9B group (K) compared to that in control; right, the top enriched GO terms (FDR < 0.05) of the dysregulated genes of each group.*
After 2 weeks (i.e., on day 45), fewer dysregulated genes (FDR<0.05) in both the S and ORF-9B groups were uncovered in the CMs relative to day 32, and they were associated with “cardiac muscle tissue morphogenesis” and “heart development” (Figures 4I–K). Heat shock protein (Hsp70) family member 1A (HSP1A1) was the downregulated genes with the highest fold-change between the two groups; and crystallin alpha A (CRYAA) and crystallin beta B1 (CRYBB1) were the upregulated genes with the highest fold-changes only found in the ORF-9B group (Figures 4J–K).
Based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, we demonstrated that, “HIF-1 signaling pathway” was the most significantly upregulated pathway in both the S and ORF-9B groups. The upregulated genes in both groups are known to be involved in both cardiac diseases (e.g., “dilated cardiomyopathy,” “hypertrophic cardiomyopathy,” and “arrhythmogenic right ventricular cardiomyopathy”) and metabolic process (e.g., “FoxO signaling pathway,” “insulin resistance”); and the downregulated genes are involved in “steroid biosynthesis,” “fatty acid metabolism,” and “calcium signaling pathway” (Figures 5A–C; Supplementary Figure 7A). This demonstrated that the two SARS-CoV-2 subunits disrupted both cardiac metabolic processes and calcium handling properties in CMs. Most importantly, we also observed that upregulated genes in the ORF-9B group were involved in “glycolysis/gluconeogenesis” pathway, strongly supporting our glycolytic analysis in Figure 3 that shows elevated glycolytic levels in the CMs after induction of ORF-9B. In addition, the dysregulated genes in the S group were involved in “coronavirus disease,” suggesting a complex interaction among various signaling pathways during between coronavirus infection (Figures 5C, D).
**FIGURE 5:** *Pathway analysis of the differential genes in CMs after overexpression of SARS-CoV-2 subunits. (A) KEGG analysis of dysregulated genes in CMs of ORF-9B group. Y-axis shows statistically enriched (FDR < 0.05) KEGG pathways ranked by FDR. The red dots represent upregulated genes, and the blue dots represent downregulated genes. The size of the dot represents number sizes of dysregulated genes. (B, C) Interactive KEGG pathways (FDR < 0.05) from dysregulated genes from the ORF-9B group (B) and S groups (C). (D) The KEGG map illustrates the dysregulated genes from S group that are in volved in the “coronavirus disease” pathway. Red, upregulated genes; blue, downregulated genes.*
## Discussion
In this study, we evaluated the effects of the subunits of coronavirus SARS-CoV-2 on CMs derived from human iPSCs. The S and ORF-9B subunits caused cardiac dysfunctions and metabolic alterations after long-term infection. Based on our toxicogenomic analysis, we ascertained that both S and ORF-9B subunits dysregulated several pathways involved in metabolism and cardiomyopathy, and ORF-9B subunit also enhanced the glycolysis, leading to metabolic remodeling in the infected CMs.
Cardiometabolic management is critical to maintaining normal cardiac function and health, and requires high energy-production (i.e., ATP) demands. Mature CMs mainly use mitochondrial OXPHOS to generate ATPs, while metabolic reprograming (or shifting) between glycolysis and mitochondrial OXPHOS is notable in cardiomyopathy (e.g., heart failure) patients (Lai et al., 2014; Rosano and Vitale, 2018). In addition, the relationship between the immune system and metabolism is highly linked to chronic metabolic diseases, such as diabetes and cardiovascular disease. Thus, disruption of metabolic homeostasis typically causes systemic inflammatory responses (Hotamisligil, 2017; Zmora et al., 2017), and this phenomenon may then explain why patients with diabetes and cardiovascular disease showed higher mortality from SARS-CoV-2 infection, presenting serve inflammatory syndromes (such as cytokines storm) (Drucker, 2021; Zhou et al., 2021). Some studies also reported that SARS-CoV-2 infection caused metabolism reprograming of various nutrients, such as glucose, fatty acid, cholesterol, and glutamine (Thomas et al., 2020; Caterino et al., 2021; Krishnan et al., 2021).
A study by Zhu et al. ( Zhu et al., 2022) showed that expressing SARS-CoV-2 Nsp6 in Drosophila heart, leading to interaction with the host MGA/MAX complex (MGA, PCGF6, and TFDP1), ultimately causing metabplic swift to glycolysis. In addition, SARS-CoV-2 can manipulate mitochondrial functions of the host cells, probably by releasing ORFs proteins (such as ORF-9b) that can be localized into the host mitochondria (Singh et al., 2020). In our study, both ORF-9B and S reduced mitochondrial OXPHOS levels and ORF-9B elevated glycolysis in CMs, demonstrating a smiliar metabolic reprogramming results from SARS-CoV-2 subunits in our human stem-cell-based system. In addition, S protein augmented the mitochondrial ROS levels (Figure 3K), and both ORF-9B and S proteins upregulated the HIF-1 signaling pathway (Figure 5A; Supplementary Figure 7B) and genes involved in “cellular response to hypoxia” (Figure 4H), suggesting a potential pathway: SARS-CoV-2 subunits induce hypoxia so as activate HIF-1 signaling, and then elevate glycolysis (Codo et al., 2020). This mechanism is observed in monocytes after SARS-CoV-2 infection, and is accompanied by increased ROS levels in mitochondria, which then activates HIF-1 signaling and glycolysis and eventually leads to cytokine storms (Codo et al., 2020). Our study suggests a similar mechanism in CMs from SARS-CoV-2 infection. In addition, cholesterol homeostasis is key to viral infection, and decreased HDL cholesterol levels and higher triglycerides have been demonstrated with SARS-CoV-2 infection (Hu et al., 2020; Wei et al., 2020; Masana et al., 2021). It is likely that SARS-CoV-2 infection induces the activation of sterol-regulatory element-binding protein 2 (SREBP-2), leading to disrupted cholesterol biosynthesis (Dai et al., 2022) or liver damages and lowered lipid metabolism (Kočar et al., 2021). In our study, both ORF-9B and S downregulated genes that are involved in “cholesterol biosynthetic process” suggesting alternations in lipid metabolism in the heart subsequent to infection.
Some of the significantly dysregulated genes from our study showed similarity to the previous clinical reports. For instance, higher levels of fibrinogen and CTGF were found in COVID-19 patients, and they serve as indictors for coagulation, fibrinolysis, and lung injury (Long et al., 2021; Sur et al., 2021; Laloglu and Alay, 2022), We discerned that these genes were also upregulated in CMs after infection, suggesting a similar pathologic progression in the CMs. In addition to these non-cardiac genes, SARS-CoV-2 subunits caused aberrant expression of cardiac genes in this study. From enriched GO terms of the upregulated genes in Figure 4, we hypothesize that ORF-9B and S subunits can alter the transcriptional regulation in cardiac gene programs. Additional mechanisms underlying transcriptional dysregulation need be investigated using other technologies, such as ATAC-seq, which we previously conducted in other toxicologic research (Liu et al., 2018). In addition to these findings, in this study we applied an in-vitro system to understanding of infectious disease in vivo. This stem-cell-based system provides huge advances in our studies on cardiovascular system of the COVID-19 patients, while it also has some limitations, such as immaturity of the differentiated cardiomyocytes compared to that mature heart in vivo (Lundy et al., 2013; Wu et al., 2015); and a lack of spatial and cellular heterogeneity in the monolayer model compared to that of heart. Thus, the mouse or 3D-cardiac organoids can offer an important complementary solution in the future studies.
## Data availability statement
The RNA-seq data generated for this work have been deposited in the NCBI Gene Expression Omnibus, and they are accessible numbers are GSE202869.
## Author contributions
CL, HW, and QL conceived and designed the experiments and performed most of experiments and data analysis. YL provided assistance for bioinformatics. PZ and LS provided significant assistance. P-HW provided valuable insights and helpful assistance. CL, HW, and QL wrote the manuscript with input from all authors.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcell.2023.1110271/full#supplementary-material
## References
1. Arunachalam P., Wimmers F., Mok C., Perera R., Scott M., Hagan T.. **Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans**. *Science* (2020) **369** 1210-1220. DOI: 10.1126/science.abc6261
2. Azevedo R., Botelho B., Hollanda J., Ferreira L., Junqueira de Andrade L., Oei S.. **Covid-19 and the cardiovascular system: a comprehensive review**. *J. Hum. Hypertens.* (2021) **35** 4-11. DOI: 10.1038/s41371-020-0387-4
3. Bailey A., Dmytrenko O., Greenberg L., Bredemeyer A., Ma P., Liu J.. **SARS-CoV-2 infects human engineered heart tissues and models COVID-19 myocarditis**. *JACC Basic Transl. Sci.* (2021) **6** 331-345. DOI: 10.1016/j.jacbts.2021.01.002
4. Burridge P., Matsa E., Shukla P., Lin Z., Churko J., Ebert A.. **Chemically defined generation of human cardiomyocytes**. *Nat. Methods* (2014) **11** 855-860. DOI: 10.1038/nmeth.2999
5. Caterino M., Gelzo M., Sol S., Fedele R., Annunziata A., Calabrese C.. **Dysregulation of lipid metabolism and pathological inflammation in patients with COVID-19**. *Sci. Rep.* (2021) **11** 2941. DOI: 10.1038/s41598-021-82426-7
6. Clerkin K., Fried J., Raikhelkar J., Sayer G., Griffin J., Masoumi A.. **COVID-19 and cardiovascular disease**. *Circulation* (2020) **141** 1648-1655. DOI: 10.1161/CIRCULATIONAHA.120.046941
7. Codo A., Davanzo G., Monteiro L., de Souza G., Muraro S., Virgilio-da-Silva J.. **Elevated glucose levels favor SARS-CoV-2 infection and monocyte response through a HIF-1α/Glycolysis-Dependent Axis**. *Cell Metab.* (2020) **32** 498-499. DOI: 10.1016/j.cmet.2020.07.015
8. Dai J., Wang H., Liao Y., Tan L., Sun Y., Song C.. **Coronavirus infection and cholesterol metabolism**. *Front. Immunol.* (2022) **13** 791267. DOI: 10.3389/fimmu.2022.791267
9. Drucker D.. **Diabetes, obesity, metabolism, and SARS-CoV-2 infection: the end of the beginning**. *Cell Metab.* (2021) **33** 479-498. DOI: 10.1016/j.cmet.2021.01.016
10. Farooq M., Khan A., Ahmad B., Kim M., Choi S.. **Therapeutic targeting of innate immune receptors against SARS-CoV-2 infection**. *Front. Pharmacol.* (2022) **13** 915565. DOI: 10.3389/fphar.2022.915565
11. Hadjadj J., Yatim N., Barnabei L., Corneau A., Boussier J., Smith N.. **Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients**. *Science* (2020) **369** 718-724. DOI: 10.1126/science.abc6027
12. Hoffmann M., Kleine-Weber H., S S., Krüger N., Herrler T., Erichsen S.. **SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor**. *Cell* (2020) **181** 271-280. DOI: 10.1016/j.cell.2020.02.052
13. Hotamisligil G.. **Inflammation, metaflammation and immunometabolic disorders**. *Nature* (2017) **542** 177-185. DOI: 10.1038/nature21363
14. Hu X., Chen D., Wu L., He G., Ye W.. **Low serum cholesterol level among patients with COVID-19 infection in wenzhou, China**. *Lancet* (2020). DOI: 10.2139/ssrn.3544826
15. Kočar E., Režen T., Rozman D.. **Cholesterol, lipoproteins, and COVID-19: Basic concepts and clinical applications**. *Biochim. Biophys. Acta Mol. Cell Biol. Lipids* (2021) **1866** 158849. DOI: 10.1016/j.bbalip.2020.158849
16. Krishnan S., Nordqvist H., Ambikan A., Gupta S., Sperk M., Svensson-Akusjärvi S.. **Metabolic perturbation associated with COVID-19 disease severity and SARS-CoV-2 replication**. *Mol. Cell Proteomics* (2021) **20** 100159. DOI: 10.1016/j.mcpro.2021.100159
17. Lai L., Leone T., Keller M., Martin O., Broman A., Nigro J.. **Energy metabolic reprogramming in the hypertrophied and early stage failing heart: a multisystems approach**. *Circ. Heart Fail* (2014) **7** 1022-1031. DOI: 10.1161/CIRCHEARTFAILURE.114.001469
18. Laloglu E., Alay H.. **Role of transforming growth factor-beta 1 and connective tissue growth factor levels in coronavirus disease-2019-related lung injury: a prospective, observational, cohort study**. *Rev. Soc. Bras. Med. Trop.* (2022) **55** e06152021. DOI: 10.1590/0037-8682-0615-2021
19. Liu Q., Jiang C., Xu J., Zhao M., Van Bortle K., Cheng X.. **Genome-wide temporal profiling of transcriptome and open chromatin of early cardiomyocyte differentiation derived from hiPSCs and hESCs**. *Circ. Res.* (2017) **121** 376-391. DOI: 10.1161/CIRCRESAHA.116.310456
20. Liu Q., Van Bortle K., Zhang Y., Zhao M., Zhang J., Geller B.. **Disruption of mesoderm formation during cardiac differentiation due to developmental exposure to 13-cis-retinoic acid**. *Sci. Rep.* (2018) **8** 12960. DOI: 10.1038/s41598-018-31192-0
21. Long W., Yang J., Li Z., Li J., Chen S., Chen D.. **Abnormal fibrinogen level as a prognostic indicator in coronavirus disease patients: A retrospective cohort study**. *Front. Med. (Lausanne)* (2021) **8** 687220. DOI: 10.3389/fmed.2021.687220
22. Lundy S., Zhu W., Regnier M., Laflamme M.. **Structural and functional maturation of cardiomyocytes derived from human pluripotent stem cells**. *Stem Cells Dev.* (2013) **22** 1991-2002. DOI: 10.1089/scd.2012.0490
23. Masana L., Correig E., Ibarretxe D., Anoro E., Arroyo A., Jericó C.. **Low HDL and high triglycerides predict COVID-19 severity**. *Sci. Rep.* (2021) **11** 7217. DOI: 10.1038/s41598-021-86747-5
24. Redondo N., Zaldívar-López S., Garrido J., Montoya M.. **SARS-CoV-2 accessory proteins in viral pathogenesis: Knowns and unknowns**. *Front. Immunol.* (2021) **12** 708264. DOI: 10.3389/fimmu.2021.708264
25. Rosano G., Vitale C.. **Metabolic modulation of cardiac metabolism in heart failure**. *Card. Fail Rev.* (2018) **4** 99-103. DOI: 10.15420/cfr.2018.18.2
26. Ruan Q., Yang K., Wang W., Jiang L., Song J.. **Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China**. *Intensive Care Med.* (2020) **46** 846-848. DOI: 10.1007/s00134-020-05991-x
27. Sharma A., Li G., Rajarajan K., Hamaguchi R., Burridge P., Wu S.. **Derivation of highly purified cardiomyocytes from human induced pluripotent stem cells using small molecule-modulated differentiation and subsequent glucose starvation**. *J. Vis. Exp.* (2015) **97** 52628. DOI: 10.3791/52628
28. Sharma A., Garcia G. J., Wang Y., Plummer J., Morizono K., Arumugaswami V.. **Human iPSC-derived cardiomyocytes are susceptible to SARS-CoV-2 infection**. *Cell Rep. Med.* (2020) **1** 100052. DOI: 10.1016/j.xcrm.2020.100052
29. Shi S., Qin M., Shen B., Cai Y., Liu T., Yang F.. **Association of cardiac injury with mortality in hospitalized patients with COVID-19 in wuhan, China**. *JAMA Cardiol.* (2020) **5** 802-810. DOI: 10.1001/jamacardio.2020.0950
30. Singh K., Chaubey G., Chen J., Suravajhala P.. **Decoding SARS-CoV-2 hijacking of host mitochondria in COVID-19 pathogenesis**. *Am. J. Physiol. Cell Physiol.* (2020) **319** C258-C267. DOI: 10.1152/ajpcell.00224.2020
31. Sur S., Khatun M., Steele R., Isbell T., Ray R., Ray R.. **Exosomes from COVID-19 patients carry tenascin-C and fibrinogen-β in triggering inflammatory signals in cells of distant organ**. *Int. J. Mol. Sci.* (2021) **22** 3184. DOI: 10.3390/ijms22063184
32. Thomas T., Stefanoni D., Reisz J., Nemkov T., Bertolone L., Francis R.. **COVID-19 infection alters kynurenine and fatty acid metabolism, correlating with IL-6 levels and renal status**. *JCI Insight* (2020) **5** e140327. DOI: 10.1172/jci.insight.140327
33. Wang H., Sheehan R., Palmer A., Everley R., Boswell S., Ron-Harel N.. **Adaptation of human iPSC-derived cardiomyocytes to tyrosine kinase inhibitors reduces acute cardiotoxicity via metabolic reprogramming**. *Cell Syst.* (2019) **8** 412-426. DOI: 10.1016/j.cels.2019.03.009
34. Wei X., Zeng W., Su J., Wan H., Yu X., Cao X.. **Hypolipidemia is associated with the severity of COVID-19**. *J. Clin. Lipidol.* (2020) **14** 297-304. DOI: 10.1016/j.jacl.2020.04.008
35. Wu Z., McGoogan J.. **Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72 314 cases from the Chinese center for disease control and prevention**. *JAMA* (2020) **323** 1239-1242. DOI: 10.1001/jama.2020.2648
36. Wu H., Lee J., Vincent L., Wang Q., Gu M., Lan F.. **Epigenetic regulation of phosphodiesterases 2A and 3A underlies compromised β-adrenergic signaling in an iPSC model of dilated cardiomyopathy**. *Cell Stem Cell* (2015) **17** 89-100. DOI: 10.1016/j.stem.2015.04.020
37. Yang L., Han Y., Nilsson-Payant B., Gupta V., Wang P., Duan X.. **A human pluripotent stem cell-based platform to study SARS-CoV-2 tropism and model virus infection in human cells and organoids**. *Cell Stem Cell* (2020) **27** 125-136. DOI: 10.1016/j.stem.2020.06.015
38. Yang L., Han Y., Jaffré F., Nilsson-Payant B., Bram Y., Wang P.. **An immuno-cardiac model for macrophage-mediated inflammation in COVID-19 hearts**. *Circ. Res.* (2021) **129** 33-46. DOI: 10.1161/CIRCRESAHA.121.319060
39. Zhang Y., Holmes E.. **A genomic perspective on the origin and emergence of SARS-CoV-2**. *Cell* (2020) **181** 223-227. DOI: 10.1016/j.cell.2020.03.035
40. Zhang J., Cruz-Cosme R., Zhuang M., Liu D., Liu Y., Teng S.. **A systemic and molecular study of subcellular localization of SARS-CoV-2 proteins**. *Signal Transduct. Target Ther.* (2021) **5** 269. DOI: 10.1038/s41392-020-00372-8
41. Zhao M.-T., Chen H., Liu Q., Shao N., Sayed N., Liu C.. **Molecular and functional resemblance of terminally differentiated cells derived from isogenic human iPSCs and somatic cell nuclear transfer derived ESCs**. *Proc. Natl. Acad. Sci.* (2017) **114** E11111-E11120. DOI: 10.1073/pnas.1708991114
42. Zhou F., Yu T., Du R., Fan G., Liu Y., Liu Z.. **Clinical course and risk factors for mortality of adult inpatients with COVID-19 in wuhan, China: a retrospective cohort study**. *Lancet* (2020) **395** 1054-1062. DOI: 10.1016/S0140-6736(20)30566-3
43. Zhou Y., Chi J., Lv W., Wang Y.. **Obesity and diabetes as high-risk factors for severe coronavirus disease 2019 (Covid-19)**. *Diabetes Metab. Res. Rev.* (2021) **37** e3377. DOI: 10.1002/dmrr.3377
44. Zhu J., Wang G., Huang X., Lee H., Lee J., Yang P.. **SARS-CoV-2 Nsp6 damages Drosophila heart and mouse cardiomyocytes through MGA/MAX complex-mediated increased glycolysis**. *Commun. Biol.* (2022) **5** 1039. DOI: 10.1038/s42003-022-03986-6
45. Zmora N., Bashiardes S., Levy M., Elinav E.. **The role of the immune system in metabolic health and disease**. *Cell Metab.* (2017) **25** 506-521. DOI: 10.1016/j.cmet.2017.02.006
|
---
title: A comparison of perinatal outcomes following fresh blastocyst or cleavage stage
embryo transfer in singletons and twins and between singleton siblings
authors:
- Edwin-Amalraj Raja
- Siladitya Bhattacharya
- Abha Maheshwari
- David J McLernon
journal: Human Reproduction Open
year: 2023
pmcid: PMC9995092
doi: 10.1093/hropen/hoad003
license: CC BY 4.0
---
# A comparison of perinatal outcomes following fresh blastocyst or cleavage stage embryo transfer in singletons and twins and between singleton siblings
## Abstract
### STUDY QUESTION
Are perinatal outcomes following fresh blastocyst versus fresh cleavage stage embryo transfer (ET) different in singletons, twins, and between singleton siblings?
### SUMMARY ANSWER
Singleton babies conceived following fresh blastocyst, versus cleavage stage, ET are less likely to be small for gestational age (SGA) or to have a congenital anomaly (a result confirmed by comparing singleton siblings), while singletons born following fresh blastocyst ET were at a higher risk of being large for gestational age (LGA) than their sibling born following fresh cleavage stage ET.
### WHAT IS KNOWN ALREADY
Blastocyst stage transfer is now the preferred strategy in most IVF units. Previous studies have suggested that babies conceived through blastocyst transfer are at increased risk of preterm birth and LGA.
### STUDY DESIGN, SIZE, DURATION
A national population-based retrospective cohort study was performed using linked Human Fertilisation and Embryology Authority (HFEA) data on 130 516 IVF and ICSI livebirths occurring from 103 062 women between 2000 and 2017.
### PARTICIPANTS/MATERIALS, SETTING, METHODS
We included women who had at least one singleton livebirth resulting from IVF/ICSI fresh embryo treatment, using their own eggs and partner’s sperm. A linked HFEA dataset was analysed using a multilevel framework, which accommodated repeated IVF cycles resulting in livebirths in the same woman. A population-averaged robust Poisson model was used for binary outcomes and a multinomial logistic regression model was used for categorical outcomes. Unadjusted and adjusted risk ratios (aRRs) ($95\%$ CI) were calculated.
### MAIN RESULTS AND THE ROLE OF CHANCE
There were 130 516 livebirths in 103 062 women, including 86 630 singletons, 43 886 twin births, and 5384 pairs of singleton siblings. In comparison with fresh cleavage stage ET, fresh blastocyst stage transfer in singletons was associated with a lower risk of low birthweight (aRR = 0.92; $95\%$ CI 0.86, 0.99), lower risk of being SGA (0.83; 0.78, 0.89), and lower risk of congenital anomaly (0.79; 0.71, 0.89). This analysis did not show an increase in risk associated with preterm birth (1.00; 0.94, 1.06), high birthweight (0.99; 0.93, 1.06), LGA (0.99; 0.93, 1.05), and the chance of healthy singleton baby (1.00; 1.00, 1.02). Twins resulting from fresh blastocyst stage ET were at slightly higher risk of preterm birth (1.05; 1.02, 1.10) compared with twins conceived following fresh cleavage stage ET. There was insufficient evidence for an association with the other perinatal outcomes. Singleton siblings born following fresh blastocyst stage ET were at a higher risk of being LGA (1.57; 1.01, 2.46) and at lower risk of having a congenital anomaly (0.52; 0.28, 0.97) compared to their singleton siblings born following cleavage stage ET. There was some evidence of excess risk of preterm birth (1.42; 0.97, 2.23) associated with blastocyst stage transfer. However, we could not confirm an association between blastocyst stage ET and low birthweight (1.35; 0.81, 2.27), high birthweight (1.19; 0.80, 1.77), and the chance of being a healthy baby (0.97; 0.86, 1.09).
### LIMITATIONS, REASONS FOR CAUTION
This was an observational study where we were unable to adjust for some key confounders, such as maternal smoking status and BMI, which may change from one pregnancy to another and are not recorded in the HFEA dataset.
### WIDER IMPLICATIONS OF THE FINDINGS
In the largest study of its kind, our analysis of singleton siblings, corrected for unmeasured, non-time varying maternal factors, confirms the previously reported association between blastocyst transfer and LGA babies, and shows a reduced risk of congenital anomaly following blastocyst transfer. Our sibling analysis did not confirm a decreased risk of low birthweight following blastocyst transfer. Overall, absolute risks are low and there is insufficient evidence to challenge the practice of extended culture of embryos.
### STUDY FUNDING/COMPETING INTEREST(S)
This project is financed by an NHS Grampian Endowment Research Grant, project number $\frac{17}{052.}$ One of the authors, S.B., was the Editor in Chief of HROpen until 31 December 2022 and would have been in that role when the paper was first submitted. As an invited speaker, S.B. has received travel expenses, accommodation and honoraria from Merck, Organon, and Ferring. A.M. has received travel expenses, accommodation, and honoraria from Merck Serono, Cook Medical, Pharmasure, Gedeon Richter, and Ferring. D.J.M. is currently a HROpen Associate Editor.
### TRIAL REGISTRATION NUMBER
N/A.
## Introduction
Since the first report of successful blastocyst stage embryo transfer (ET) in 1985 (Cohen et al., 1985), this practice has been widely adopted in many countries (Farquhar et al., 2010; Dar et al., 2014; Kissin et al., 2015; HFEA, 2016; Holden et al., 2018; Marconi et al., 2019; Spangmose et al., 2020). In comparison to the more traditional practice of cleavage stage transfer at 2–3 days after fertilization, extended culture of embryos to the blastocyst stage, which offers potential advantages in terms of embryo selection and better endometrial–embryo synchrony, has been shown to result in improved live birth rates per transfer (Papanikolaou et al., 2006; Wang and Sun, 2014; Glujoversusky et al., 2016). This has encouraged clinics to adopt a policy of single embryo (blastocyst) transfer without compromising live birth rates, and data from national registries suggest that blastocyst transfers are now the preferred option in most IVF and ICSI treatment cycles (Banker et al., 2021; SART, 2021; HFEA, 2021a).
Although perinatal outcomes following blastocyst transfer are mostly reassuring, some observational studies have suggested an increased risk of preterm birth (Kalra et al., 2012; Dar et al., 2013; Chambers et al., 2015; Martins et al., 2016; Wang et al., 2017; Alviggi et al., 2018), low birthweight (Maheshwari et al., 2013), small for gestational age (SGA) babies (Kalra et al., 2012; Maheshwari et al., 2013; Chambers et al., 2015; Martins et al., 2016; Wang et al., 2017; Alviggi et al., 2018), and congenital anomalies (Källén et al., 2010).
It is unclear whether these risks are due to the laboratory processes associated with extended culture per se or inherent differences in maternal characteristics, as blastocyst transfer is usually undertaken in women who have a number of good quality embryos and tend to have a better IVF prognosis. Most published studies have reported outcomes for singleton pregnancies based on cycle-level analyses of registry data (Sunkara et al., 2014; Marconi et al., 2019) and have been unable to adjust for multiple cycles within a single woman, or report on outcomes in multiples. Absence of linked registry data has meant that most published studies have been unable to adjust for the clustering of IVF cycles within women or disaggregate the impact of maternal factors from those caused by extended culture by comparing perinatal outcomes in siblings conceived following ET at cleavage or blastocyst stage (Romundstad et al., 2008; Kalra et al., 2012).
In this study, we used linked UK IVF data collected by the Human Fertilisation Embryology Authority (HFEA) to compare perinatal outcomes within singletons and within twins conceived following blastocyst versus cleavage stage ET. This dataset contains cycle identifiers within each woman, which allowed us to identify different treatment cycles within each woman. We were therefore able to compare perinatal outcomes between singleton sibling pairs where one child was conceived from a blastocyst, while the other was conceived following a cleavage stage ET.
## Database
The HFEA has been the statutory regulator of assisted conception treatment tasked with collecting data on licenced IVF treatment cycles performed in the UK since 1991 (HFEA, 1990, 2021b). We analysed a version of the HFEA database which links all IVF cycles to individual women. Ethical approvals were obtained to utilize the HFEA dataset from the North of Scotland Research Ethics Committee (Ref: 19-YH-0041), the Confidentiality Advisory Group, and the HFEA Register Research Panel. The data were extracted by HFEA and transferred securely to the Data Management Team, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen.
## Study population
We included women who had at least one singleton live birth resulting from a fresh embryo transferred following IVF (including ICSI) treatment in the UK between 2000 and 2017. Babies born following frozen-thawed ET were excluded from the study since day of ET was not available for frozen cycles in the HFEA dataset. As blastocyst stage transfers were infrequent before 2000, we restricted our sample to women treated between 2000 and 2017. We included live born infants whose gestational age was 22 weeks or more, with a minimum birthweight of 500 g. We excluded still births, births in women under 18 years or over 50 years of age, and those involving oocyte donation, embryo donation, preimplantation genetic testing, or surrogacy. Cycles where more than three embryos were transferred were excluded as many of these resulted in triplet and quadruplet births. Births resulting from ETs on Day 6 were excluded as these only involved frozen embryos.
## Exposure
The exposed cohort comprised women who had a live birth resulting from fresh ET on Day 5 of culture (blastocyst stage). The unexposed cohort comprised women who had a live birth following fresh cleavage stage ET on Day 2 or Day 3 of culture.
## Outcomes
The main outcome measures were gestational age at birth, birthweight, congenital anomaly, and ‘healthy baby’, defined as a baby born at or after 37 weeks of gestation, weighing between 2500 and 4000 g with no evidence of any congenital malformations in each of the singletons and each infant in twins (Wang et al., 2010; Marconi et al., 2019). Gestational age was grouped into three categories: very preterm birth (<32 completed weeks of gestation), preterm birth (<37 completed weeks of gestation including very preterm), and full-term birth (≥37 completed weeks of gestation used as reference). Birthweight at delivery was grouped into three categories: low birthweight (<2500 g), normal birthweight (2500–3999 g used as reference), and high birthweight (≥4000 g). In singletons, birthweight was also categorized into SGA, appropriate for gestational age (AGA), and large for gestational age (LGA) using UK-based centile charts of birthweight for gestational age stratified by infant sex and maternal parity (Bonellie et al., 2008). SGA babies were babies whose birthweights were below the 10th percentile for babies of the same gestational age, and LGA babies were those whose birthweights were above the 90th percentile for babies of the same gestational age. AGA babies were those within the 10th to 90th percentile range and used as reference. A small proportion of infants ($$n = 64$$) born at 22, 23, or 44 weeks of gestation and missing baby gender ($$n = 1583$$) were excluded from this particular analysis as the birthweight reference table did not contain birthweights for these gestational ages. Twins could not be categorized as SGA or LGA because the twin population-based reference chart of birthweight for gestational age is stratified by infant gender and chorionicity (Briffa et al., 2021). Unfortunately, the HFEA dataset does not contain a variable which would allow us to identify twins who are monochorionic or dichorionic.
## Statistical analysis
Descriptive statistics were calculated for each of the women’s characteristics, split by live birth as a result of either a cleavage stage or blastocyst ET.
## Singleton live birth
The unit of analysis here was a singleton live birth episode resulting from transfer of a fresh blastocyst or cleavage stage ET. As some women had two or more singleton live birth episodes arising from several ETs within the study period, all analyses were conducted under a multilevel framework, which accommodated repeated cycles resulting in livebirths within the same women. In order to account for the dependency between cycles resulting in live birth within women, a population-averaged model using generalized estimating equations was used to explore associations between the exposure groups (blastocyst versus cleavage stage ET) and perinatal outcomes (Hardin and Hilbe, 2003) and to estimate $95\%$ CI using robust standard errors that allowed for correlation within women (McCullagh and Nelder, 1989). We specified an exchangeable correlation structure, which assumes that the risk of a perinatal adverse event was the same for any live birth within a woman. For the outcomes of preterm birth (preterm birth versus full-term birth), congenital anomaly (yes versus no), and healthy baby status (yes versus no), a robust Poisson regression model was used. For the two birthweight outcome variables (i.e. birthweight coded as low, normal or high, and birthweight adjusted for gestational age coded as SGA, AGA, or LGA), a multinomial logistic regression model was employed since each of these variables had three categories (Chamberlain, 1980; Pforr, 2014). The association between treatment strategy (blastocyst or cleavage stage ET) and very preterm birth (versus full-term birth) was estimated using multinomial logistic regression (where we also included 32–37 weeks gestation as a nuisance outcome category). Crude risk ratios (RRs), adjusted RRs (aRRs), and $95\%$ CI were calculated. The following factors were considered as confounders: maternal age (years), cause of infertility (i.e. tubal disease, ovulatory disorder, male factor, unexplained), previous pregnancy status (yes/no), treatment type or type of insemination (IVF versus ICSI), number of eggs collected, and year of treatment. The covariates considered for adjustment differed for each of the outcomes and are listed in the footnote under each table. Since ET stage could influence birthweight through its effect on gestational age, gestational age can be considered to be a mediator on the causal pathway from cleavage or blastocyst stage ET to birthweight. Therefore, it was excluded to avoid bias since its inclusion does not allow us to estimate the total direct effect of the stage of ET on birthweight (Wilcox et al., 2011). In the same way, the number of embryos transferred was considered as a mediator and was excluded from multivariable analyses. Further, congenital anomalies or the underlying cause of congenital anomalies have been linked with iatrogenic preterm birth owing to early induction of labour (Brown, 2009). In this case, gestational age would be considered a collider rather than a confounder as both ET stage and congenital anomaly can affect gestational age through independent routes. Therefore, gestational age was also excluded from this analysis.
## Twin livebirths
The first set of live born twins was considered for each woman. A very small number of women had a second set of twins, so these were excluded from the analysis for pragmatic reasons. All analyses were conducted under a multilevel framework, which accommodated for twins within the same woman (Carlin et al., 2005; Chambers et al., 2015). For the binary outcomes of preterm birth, congenital anomaly and healthy baby, a Poisson model was used while for the categorical outcomes, term of birth (very preterm birth, preterm birth and full-term birth) and birthweight, a multinomial logistic regression model was employed.
## Singleton siblings
We compared singleton sibling pairs in which one sibling resulted from a fresh blastocyst-stage ET, while the other was born following a fresh cleavage-stage ET. A fixed effect (conditional) Poisson regression model for paired data was used to compare binary perinatal outcomes (preterm birth, congenital anomaly, and healthy baby) between singleton siblings from the same woman (Carlin et al., 2005). This conditional approach allowed us to measure the RR of a perinatal outcome for a change in ET stage (blastocyst versus cleavage ET), whilst keeping the uterine environment (i.e. the mother’s cycle invariant characteristics) fixed (Neuhaus, 2006). A fixed effect multinomial logistic regression analysis for paired data was used to compare categorical birthweight outcomes (low birthweight versus normal birthweight, high birthweight versus normal birthweight, SGA versus AGA, and LGA versus AGA) between Sibling 1 and Sibling 2 (Pforr, 2014). Therefore, since some of the maternal factors, measured and unmeasured, remained constant between siblings, any observed association between ET strategy and perinatal outcome was related to the transfer strategy (Henningsen et al., 2011; Seggers et al., 2016). The model was adjusted for characteristics that vary from one cycle to another and differed between Siblings 1 and 2, such as maternal age, order of birth as a proxy for parity, treatment type (IVF versus ICSI), number of eggs collected, and year of treatment.
To determine whether the association between treatment strategy and perinatal outcome differed over time, as a secondary analysis, we included an interaction term between treatment strategy (blastocyst ET versus cleavage stage ET) and two time periods (2000–2008 and 2009–2017). We did this for the singleton and twin analysis.
All analyses were carried out using Stata version 17 MP (StataCorp, College Station, TX, USA). A P-value <0.05 was considered to be statistically significant.
## Results
A total of 130 516 livebirths were included in the analyses (Fig. 1). This included 86 630 singleton livebirths (from 81 119 women), 43 886 twin births, and 5384 pairs of singleton siblings.
**Figure 1.:** *Flow chart of cohort exclusions in a study of perinatal outcomes. GIFT: gamete intrafallopian transfer; SUZI: subzonal insemination; ET: embryo transfer.*
## Singleton livebirths
Among singletons, 28 814 livebirths resulted from blastocyst stage transfer and 57 816 from cleavage stage transfer. There were 32 817 ($56.7\%$) Day 2 ETs out of 57 816 cleavage stage ETs. Maternal and treatment characteristics are shown in Table I. On average, women in the blastocyst group were younger, had a higher oocyte yield and were more likely to have had a single ET.
**Table I**
| MATERNAL/COUPLE CHARACTERISTICS | Live births following blastocyst stage ET (N = 28 814) n (%) | Live births following cleavage stage ET (N = 57 816) n (%) | P-value |
| --- | --- | --- | --- |
| Maternal age at treatment (years), mean (SD) | 33.4 (4.1) | 33.9 (4.1) | <0.001 |
| Tubal disease | 4311 (14.9) | 10 954 (18.9) | <0.001 |
| Ovulatory disorder | 4544 (15.8) | 7081 (12.2) | <0.001 |
| Male factor | 11 719 (40.7) | 25 908 (44.8) | <0.001 |
| Endometriosis | 2007 (6.9) | 3917 (6.8) | 0.310 |
| Unexplained | 8712 (30.2) | 15 990 (27.7) | <0.001 |
| Duration of infertility (years), median (IQR) | 4 (3, 7) | 4 (3, 7) | <0.001 |
| Missing | 26 118 | 25 184 | <0.001 |
| Previous live birth | 2588 (8.9) | 5190 (8.9) | <0.001 |
| Type of fertilization | | | <0.001 |
| IVF | 12 981 (45.1) | 27 422 (47.4) | |
| ICSI | 15 833 (54.9) | 30 394 (52.6) | |
| Number of eggs collected | 12 (9, 16) | 9 (6, 13) | <0.001 |
| Number of embryos transferred | | | <0.001 |
| 1 | 18 841 (65.4) | 7372 (12.8) | <0.001 |
| 2 | 9670 (33.6) | 46 477 (80.4) | <0.001 |
| 3 | 303 (1.1) | 3967 (6.8) | <0.001 |
| Elective single ET | | | <0.001 |
| Yes | 15 722 (54.6) | 3132 (5.4) | <0.001 |
| No | 13 092 (45.4) | 54 684 (94.6) | <0.001 |
After adjusting for confounders, on average there was a $8\%$ decreased risk of low birthweight among singletons born following blastocyst transfer versus those born following cleavage stage transfer ($8.1\%$ versus $9.0\%$; aRR 0.92; $95\%$ CI 0.86, 0.99) (Table II); however, absolute risks were low in both groups. Blastocyst stage ET was associated with lower risk of being SGA in comparison to cleavage stage ET ($8.0\%$ versus $10.4\%$; aRR 0.83; $95\%$ CI 0.78, 0.89). There was no statistically significant difference in the risk of very preterm birth or preterm birth, high birthweight, LGA, or healthy baby between the two groups. A total of 422 babies born with gestational age below 22 weeks and birthweight below 500 g were excluded as they were born outside the definition of ‘perinatal’, and 33 of these ($7.8\%$) had a congenital anomaly. Since there may be a possibility of bias because of exclusion, a sensitivity analysis was carried out in which these babies were included in the analysis. We found that the results were consistent with our original findings.
**Table II**
| Perinatal outcomes | Live births following blastocyst stage ET (N = 28 814) n (%) | Live births following cleavage stage ET (N = 57 816) n (%) | Live births following blastocyst versus cleavage crude RR (95% CI) | Live births following blastocyst versus cleavage adjusted RR@ (95% CI) |
| --- | --- | --- | --- | --- |
| Gestational age at birth | | | | |
| Very preterm birth (vs full-term birth) | 451 (1.6) | 1000 (1.7) | 0.91 (0.81, 1.01) | 0.89 (0.77, 1.03) |
| Preterm birth (vs full-term birth) | 2644 (9.2) | 5232 (9.1) | 1.01 (0.97, 1.06) | 1.00 (0.94, 1.06) |
| Birthweight categories | | | | |
| Low birthweight (vs normal birthweight) | 2325 (8.1) | 5220 (9.0) | 0.88 (0.83, 0.92) | 0.92 (0.86, 0.99) |
| High birthweight (vs normal birthweight) | 2255 (7.8) | 4915 (8.5) | 0.90 (0.86, 0.95) | 0.99 (0.93, 1.06) |
| Birthweight adjusted for gestational age | (n = 28 270) | (n = 56 777) | | |
| Small for gestational age (vs appropriate for gestational age) | 2249 (8.0) | 5897 (10.4) | 0.74 (0.71, 0.78) | 0.83 (0.78, 0.89) |
| Large for gestational age (vs appropriate for gestational age) | 3070 (10.9) | 6158 (10.9) | 0.97 (0.93, 1.02) | 0.99 (0.93, 1.05) |
| Congenital anomaly | 542 (1.9) | 2398 (4.2) | 0.46 (0.42, 0.50) | 0.79 (0.71, 0.89) |
| Healthy baby | 22 744 (78.9) | 43 981 (76.1) | 1.04 (1.03, 1.05) | 1.00 (1.00, 1.02) |
In the secondary analysis, we did not find any change in the association between stage of transfer and all perinatal outcomes between 2000–2008 and 2009–2017 in singleton analysis.
## Twin births
A total of 5194 twins were born following blastocyst transfer and 16 746 were born following cleavage stage transfer. Women in the blastocyst group were slightly older, had a higher proportion of previous livebirths, had more eggs retrieved and were more likely to have had a single ET compared to the cleavage stage group (Table III).
**Table III**
| MATERNAL/COUPLE CHARACTERISTICS | Live births following blastocyst stage ET (N = 5194) n (%) | Live births following cleavage stage ET (N = 16 746) n (%) | P-value |
| --- | --- | --- | --- |
| Maternal age at treatment (years)@ | 33.9 (3.91) | 32.8 (3.86) | <0.001 |
| Cause of infertility | 860 (16.6) | 3287 (19.6) | <0.001 |
| Tubal disease | 860 (16.6) | 3287 (19.6) | <0.001 |
| Ovulatory disorder | 775 (14.9) | 2144 (12.8) | <0.001 |
| Male factor | 2133 (41.1) | 7507 (44.8) | <0.001 |
| Endometriosis | 337 (6.5) | 1148 (6.9) | 0.359 |
| Unexplained | 1578 (30.4) | 4481 (26.8) | <0.001 |
| Duration of infertility (years)@@ | 4 (3, 6) | 4 (3, 6) | 0.402 |
| Missing | 3941 | 6154 | 0.402 |
| Previous live birth | 608 (11.7) | 1410 (8.41) | <0.001 |
| Type of fertilization | | | <0.001 |
| IVF | 2171 (41.5) | 8291 (49.5) | <0.001 |
| ICSI | 3023 (58.2) | 8458 (50.5) | <0.001 |
| Number of eggs collected | 13 (10, 17) | 10 (7, 14) | <0.001 |
| Number of embryos transferred | | | <0.001 |
| 1 | 298 (5.7) | 74 (0.4) | <0.001 |
| 2 | 4797 (92.4) | 15 465 (92.3) | <0.001 |
| 3 | 99 (1.9) | 1210 (7.2) | <0.001 |
| Elective single ET | | | <0.001 |
| Yes | 233 (4.5) | 25 (0.2) | <0.001 |
| No | 4961 (95.5) | 16 724 (99.9) | <0.001 |
After adjustment for confounding factors, the risk of preterm birth (aRR 1.05; $95\%$ CI 1.02, 1.10) was slightly higher among twins conceived following blastocyst transfer compared to those born following cleavage ET (Table IV). There was no statistically significant difference in the risk of very preterm birth, low birthweight, high birthweight, and congenital anomaly between the groups. However, the chance of healthy twins was lower for those born as a result of blastocyst compared to those born following cleavage stage transfer (aRR = 0.90; $95\%$ CI 0.86, 0.95).
**Table IV**
| Unnamed: 0 | Twin 1 (n = 21 943) | Twin 1 (n = 21 943).1 | Twin 2 (n = 21 943) | Twin 2 (n = 21 943).1 | Unnamed: 5 | Unnamed: 6 |
| --- | --- | --- | --- | --- | --- | --- |
| MATERNAL/COUPLE CHARACTERISTICS | Live births following blastocyst stage ET (N = 5194) n (%) | Live births following cleavage stage ET (N = 16 749) n (%) | Live births following blastocyst stage ET (N = 5194) n (%) | Live births following cleavage stage ET (N = 16 749) n (%) | Crude RR 95% CI | Adjusted RR@ 95% CI |
| Gestational age at birth | | | | | | |
| Very preterm birth (vs full-term birth) | 470 (9.1) | 1490 (8.9) | 468 (9.0) | 1496 (8.9) | 1.12 (1.00, 1.26) | 1.01 (0.89, 1.16) |
| Preterm birth (vs full-term birth) | 2819 (54.3) | 8259 (49.3) | 2815 (54.2) | 8257 (49.3) | 1.10 (1.06, 1.15) | 1.05 (1.02, 1.10) |
| Birthweight categories | | | | | | |
| Low birthweight (vs normal birthweight) | 2914 (56.1) | 8907 (53.2) | 3132 (60.3) | 9676 (57.8) | 1.12 (1.06, 1.18) | 1.03 (0.97, 1.10) |
| High birthweight (vs normal birthweight) | 13 (0.3) | 43 (0.3) | 14 (0.3) | 42 (0.3) | 1.09 (0.64, 1.84) | 1.20 (0.65, 2.18) |
| Congenital anomaly | 184 (3.5) | 765 (4.6) | 185 (3.6) | 715 (4.3) | 0.80 (0.69, 0.94) | 1.06 (0.91, 1.25) |
| Healthy baby | 1518 (29.4) | 5798 (34.6) | 1397 (26.9) | 5295 (31.6) | 0.85 (0.81, 0.89) | 0.90 (0.86, 0.95) |
In the secondary analysis, the association between stage of transfer and congenital anomaly was different in the two time periods, 2000–2008 and 2009–2017 (interaction $P \leq 0.001$). In the time period 2000–2008, the risk of congenital anomaly was higher for blastocyst stage ET (aRR 1.59; $95\%$ CI 1.32, 1.91) and between 2009 and 2017, the risk of congenital anomaly was marginally lower for blastocyst stage ET (aRR 0.68; $95\%$ CI 0.50, 0.93). No significant interaction was found for the other outcomes (not shown).
## Singleton sibling pairs
Inclusion of the first two singleton siblings born following IVF treatment resulted in 5384 sibling pairs. Apart from maternal age and order of birth, other maternal characteristics (such as cause of infertility) were similar between the two comparison groups. Of the sibling pairs, 3158 ($58.6\%$) were born following two cleavage stage ETs, 936 ($17.3\%$) were born following two blastocyst ETs, 1028 ($19.1\%$) were born following a cleavage ET for the first sibling and blastocyst ET for the second sibling, and 262 ($4.9\%$) were born following blastocyst ET for the first sibling and cleavage stage ET for the second sibling (not shown). Only those singleton pairs ($$n = 1290$$) in which each sibling was born following a different ET strategy were included in the analysis. Singletons born following blastocyst transfer had a lower risk of congenital anomaly ($2.8\%$ versus $4.5\%$; aRR 0.52; $95\%$ CI 0.28, 0.97) and a higher risk of being LGA ($10.7\%$ versus $9.8\%$; aRR 1.57; $95\%$ CI 1.01, 2.46) compared to their siblings born following cleavage stage transfer (Fig. 2). There was no statistically significant difference in the risk of very preterm birth ($1.2\%$ versus $1.7\%$; aRR = 1.32; $95\%$ CI 0.46, 3.79), preterm birth ($8.2\%$ versus $7.4\%$; aRR = 1.47; $95\%$ CI 0.97, 2.23), low birthweight ($6.4\%$ versus $7.6\%$; aRR = 1.35; $95\%$ CI 0.81, 2.27), and high birthweight ($11.6\%$ versus $8.3\%$; aRR = 1.19; $95\%$ CI 0.80, 1.77) and being SGA ($6.6\%$ versus $9.6\%$; aRR = 0.71; $95\%$ CI 0.46, 1.11) between singletons born following a blastocyst transfer and their siblings born following a cleavage stage transfer (Table V). There was not enough evidence from the data to suggest a statistically significant difference in the chance of having a healthy baby between siblings ($76.1\%$ versus $78.1\%$; aRR = 0.97; $95\%$ CI 0.86, 1.09).
**Figure 2.:** *Association between blastocyst versus cleavage stage embryo transfer and perinatal outcomes.
Singletons Twins Siblings. Data are risk ratios ($95\%$ CI) (see also Table V).* TABLE_PLACEHOLDER:Table V
## Discussion
Our results show that singleton babies born following the transfer of a fresh blastocyst are at greater risk of being LGA but at lower risk of being born with a congenital anomaly than their siblings conceived from fresh cleavage stage embryos. Our sibling comparison removes much of the time-invariant residual confounding observed in earlier studies on this topic.
Singletons conceived following a blastocyst transfer were marginally less likely to be SGA than those born following a cleavage stage transfer. Singletons conceived following blastocyst transfer were less likely to be born with a congenital anomaly, which agrees with our singleton sibling finding. Twins conceived following a blastocyst transfer were marginally more likely to be preterm than those born following a cleavage stage transfer.
## Strengths of the study
A major strength of the study is the use of population-based national registry data over a 17-year period and inclusion of a complete birth cohort of singleton, twins and siblings. The capacity to link women with their IVF cycles allowed us to adjust for the clustering effect of multiple singletons born from the same women (Marconi et al., 2019) and also to compare outcomes between siblings to disentangle the effects of the ET strategy itself from those related to maternal characteristics (Seggers et al., 2016). By adjusting for order, the analysis accounts for differences in care between the first and second born sibling, for example mode of delivery.
## Limitations of the study
While we were able to adjust for a number of confounders, such as maternal age, cause of infertility, previous livebirths, number of eggs retrieved, type of insemination, and year of treatment, we were unable to adjust for BMI, ethnicity, race, smoking, and occurrence of vanishing twins as they are not reported in the HFEA dataset, as well as duration of infertility which was missing for more than $70\%$ of women. As parity was not available in the registry data, previous livebirth status was used as a proxy in the adjusted model. As there has been a significant improvement in laboratory techniques and culture conditions during the 17-year study period, we have adjusted for the year of treatment.
As obstetric complications, such as pre-eclampsia and antepartum haemorrhage, are not recorded in the HFEA database, we were unable to include them in our analysis and it was also not possible to distinguish between spontaneous and iatrogenic preterm births.
Consent for research using IVF data changed from ‘presumed’ to ‘active opt in’ in October 2009. Thus, only data from patients who provided explicit consent for research were available in the linked HFEA database. Prior to 2009, 70–$80\%$ of linked patient records were available for research, but after 2009 this figure dropped to 40–$50\%$.
Despite the use of appropriate statistical methods to mitigate against it, a degree of residual bias is inevitable in all observational studies and this is true for this study. Although our HFEA dataset allowed us to link cycles within each woman and undertake our sibling analysis to control for unmeasured maternal characteristics, there are still multiple limitations in our approach, not least because the ET strategy (blastocyst versus cleavage stage) was not allocated at random.
Though we were able to categorizes babies into SGA and LGA based on the UK centile chart of birthweight available for our singleton and siblings (Bonellie et al., 2008), we could not do the same for twins (Briffa et al., 2021) because the twin centile chart of birthweight for gestational age was stratified by chorionicity, which was not available in the HFEA dataset.
Frozen-thawed ET was not included in the study because the age of the ET was not available for frozen cycles in the dataset. With modern day IVF, the proportion of cycles adopting the freeze-all approach is increasing. The HFEA needs to take into consideration this limitation and capture the age of the frozen-thawed embryos when transferred for future research.
Our analysis of singleton siblings did not confirm the decreased risk of low birthweight but confirms the small decreased risk of congenital anomaly following blastocyst transfer found in singleton analysis. The precision of the aRR was low, because of the small number of events.
Finally, in the sibling analysis, while we were able to control for some factors that varied over time, such as maternal age, treatment type, and number of eggs collected, we were unable to control for other time varying factors. These could include maternal BMI, duration of subfertility and treatment-related factors, such as changes in IVF culture media over time and ovarian stimulation details. Such unmeasured time-varying factors may have resulted in residual confounding.
## Explanation of the findings
Exposing embryos to extended culture and blastocyst transfer appears to result in babies who are LGA (Mäkinen et al., 2013). Though the actual mechanism is unclear, the literature suggests that embryo culture media could be influential in the genesis of the large offspring syndrome (Young et al., 1998) in animals and LGA in humans (Dumoulin et al., 2010; Nelissen et al., 2012). We also found a lower risk of congenital anomaly between a singleton born following blastocyst transfer and their singleton sibling born following cleavage stage transfer. However, it is worth noting that the precision around this finding is reduced by the smaller sample size available for the sibling analysis. The change in the risk of congenital anomaly between the time period 2000–2008 and 2009–2017 may be due to the availability of better techniques in the laboratory to improve the quality of embryos.
## Other studies in singleton siblings
To our knowledge, there are a limited number of studies that have compared perinatal outcomes between siblings (Kalra et al., 2012; Luke et al., 2017). However, they did not investigate the association between extended culture and outcomes such as congenital anomaly and LGA in siblings owing to limited sample size.
## Other studies in singletons
Our study found a weak association between ET stage and low birthweight (aRR 0.92; $95\%$ CI 0.86, 0.99) in singleton live births. However, the findings were not consistent with two similar studies (Kalra et al., 2012; Chambers et al., 2015). Kalra et al. [ 2012] showed limited evidence of an increase in risk of low birthweight for singletons born following extended embryo culture (aOR 1.23; $95\%$ CI 0.99, 1.30). A population-based study of all ART cycles undertaken in Australia and New Zealand during 2009–2012 did not show an association between ET stage and low birthweight (aOR 1.00; $95\%$ CI 0.92, 1.09) (Chambers et al., 2015). Many other studies reported no association between embryo strategy and low birthweight (Dar et al., 2014; Oron et al., 2014; De Vos et al., 2015; Litzky et al., 2018; Marconi et al., 2019). Our finding of a lower risk of being SGA after fresh blastocyst transfer in singletons was consistent with a number of previous studies (Ishihara et al., 2014; Zhu et al., 2014; Ginström Ernstad et al., 2016), as well as meta-analyses (Maheshwari et al., 2013; Martins et al., 2016; Wang et al., 2017; Alviggi et al., 2018).
In our study, the risk of congenital anomaly was lower in blastocyst stage ET compared with cleavage stage ET. This is in line with our findings in singleton siblings. In contrast to our findings, a Swedish register-based study, which partly adjusted for confounders such as maternal age, parity, smoking, BMI, and year of birth, reported a higher risk of congenital anomaly (aOR 1.43; $95\%$ CI 1.14, 1.81) in infants born following blastocyst stage ET when compared to infants born following cleavage stage ET (Källén et al., 2010). Other studies have found no evidence of an association between blastocyst versus cleavage-stage ET which may be due to limited sample size (Dar et al., 2013; Oron et al., 2014, 2015; Ginström Ernstad et al., 2016; Marconi et al., 2019; Shi et al., 2019).
## Other studies in twins
Our finding of a small increased risk of preterm birth in twins born after blastocyst transfer is consistent with the US national level Society for Assisted Reproductive Technology database during 2004–2006 (aOR 1.39; $95\%$ CI 1.29–1.50) (Kalra et al., 2012). In contrast to this finding, data from Australia and New Zealand (Chambers et al., 2015) showed that blastocyst transfer was associated with a lower odds of preterm birth among twins (aOR 0.80; $95\%$ CI 0.70–0.93) born after blastocyst stage ET compared to cleavage stage ET. Both the studies (Kalra et al., 2012; Chambers et al., 2015) included additional potential confounders, such as number of prior assisted ART cycles, history of prior miscarriage, reduction in foetal heart rate on ultrasonography, and implantation rate, which were not available in the HFEA database.
## Implications for clinical practice and research
Our results provide some reassurance for the default position of extended culture as the absolute risks associated with this strategy are low.
The ideal option for generating unbiased data on perinatal outcomes is through follow-up studies of offspring born to women randomized to blastocyst or cleavage stage ETs. However, as such trials were conducted a while ago (Coskun et al., 2000; Emiliani et al., 2003; Papanikolaou et al., 2005), follow-up is likely to be difficult owing to challenges associated with consent as well as logistics. New trials may not be feasible because of a lack of clinical equipoise.
Sibling studies are able to address the issue of confounding caused by unmeasured maternal factors but such analyses are not feasible on anonymized datasets, which are the norm for most national registries (Sunkara et al., 2014; Marconi et al., 2019). Further studies are required with singleton siblings in order to confirm the findings of birthweight, adjusted for sex and gestational age. Even where linking of cycles to women is possible, a number of factors which can influence outcomes, such as BMI, ethnicity, race, smoking, and duration of infertility, may not be recorded. Meta-analyses of published data on sibling outcomes are not possible owing to the very small number of studies reported (Kalra et al., 2012; Luke et al., 2017). Individual patient data meta-analysis of registry data across the world, which are able to provide a link between maternal and cycle level data, may overcome these shortcomings and provide an answer closer to the truth. However, such an endeavour will require collaboration, data governance and funding.
Our findings of lower risk of congenital anomaly is reassuring for couples seeking treatment for infertility, the physician and for IVF practice at the time when blastocyst transfer is being used widely across the sector. The lower risk of SGA is associated with a diminished risk of hospital admission for neonatal care and risk of chronic diseases in later life, including hypertension and cardiovascular diseases (Barker et al., 2009). On the other hand, the perinatal risks of LGA include higher rates of caesarean delivery, postpartum haemorrhage, and neonatal shoulder dystocia and hypoglycaemia, as well as longer periods of hospitalization for newborn infants (Weissmann-Brenner et al., 2012). LGA babies remain taller and heavier throughout childhood and have a high risk of developing adulthood obesity (Dietz, 1994; Parsons et al., 1999).
## Conclusion
Our analysis of data from singleton siblings, partially corrected for maternal factors, suggests that babies conceived from blastocysts are at higher risk of being LGA but are less likely to have a congenital anomaly than those born after cleavage stage ET. However, the absolute risks of these outcomes are relatively low and there is insufficient evidence to challenge the practice of extended culture of embryos.
## Data availability
The final dataset used in our analysis from this particular work is not available owing to HFEA strict privacy and confidentiality rules. The details of the original dataset can be found here: https://www.hfea.gov.uk/about-us/our-data/#ar, and may be requested by contacting the HFEA, [email protected].
## Authors’ roles
D.J.M., S.B., and A.M. conceived the study, helped design the study, interpreted the results, and edited/commented on the manuscript drafts. E.A.R. performed analyses, interpreted the results, and drafted the manuscript. All authors appraised and approved the final manuscript.
## Funding
This project is financed by an NHS Grampian Endowment Research Grant, project number $\frac{17}{052.}$
## Conflict of interest
One of the authors, S.B., was the Editor in Chief of HROpen until 31 December 2022 and would have been in that role when the paper was first submitted. As an invited speaker, S.B. has received travel expenses, accommodation and honoraria from Merck, Organon, and Ferring. A.M. has received travel expenses, accommodation, and honoraria from Merck Serono, Cook Medical, Pharmasure, Gedeon Richter, and Ferring. D.J.M. is currently a HROpen Associate Editor.
## References
1. Alviggi C, Conforti A, Carbone IF, Borrelli R, de Placido G, Guerriero S.. **Influence of cryopreservation on perinatal outcome after blastocyst- versus cleavage-stage embryo transfer: systematic review and meta-analysis**. *Ultrasound Obstet Gynecol* (2018) **51** 54-63. PMID: 29077229
2. Banker M, Dyer S, Chambers GM, Ishihara O, Kupka M, de Mouzon J, Zegers-Hochschild F, Adamson GD.. **International Committee for Monitoring Assisted Reproductive Technologies (ICMART): world report on assisted reproductive technologies, 2013**. *Fertil Steril* (2021) **116** 741-756. PMID: 33926722
3. Barker DJP, Osmond C, Kajantie E, Eriksson JG.. **Growth and chronic disease: findings in the Helsinki Birth Cohort**. *Ann Hum Biol* (2009) **36** 445-458. PMID: 19562567
4. Bonellie S, Chalmers J, Gray R, Greer I, Jarvis S, Williams C.. **Centile charts for birthweight for gestational age for Scottish singleton births**. *BMC Pregnancy Childbirth* (2008) **8** 5. PMID: 18298810
5. Briffa C, Stirrup O, Huddy C, Richards J, Shetty S, Reed K, Khalil A.. **Twin chorionicity-specific population birth-weight charts adjusted for estimated fetal weight**. *Ultrasound Obstet Gynecol* (2021) **58** 439-449. PMID: 33538373
6. Brown WR.. **Association of preterm birth with brain malformations**. *Pediatr Res* (2009) **65** 642-646. PMID: 19218881
7. Carlin JB, Gurrin LC, Sterne JAC, Morley R, Dwyer T.. **Regression models for twin studies: a critical review**. *Int J Epidemiol* (2005) **34** 1089-1099. PMID: 16087687
8. Chamberlain G.. **Analysis of covariance with qualitative data**. *Rev Econ Stud* (1980) **47** 225
9. Chambers GM, Chughtai AA, Farquhar CM, Wang YA.. **Risk of preterm birth after blastocyst embryo transfer: a large population study using contemporary registry data from Australia and New Zealand**. *Fertil Steril* (2015) **104** 997-1003. PMID: 26253819
10. Cohen J, Simons RF, Edwards RG, Fehilly CB, Fishel SB.. **Pregnancies following the frozen storage of expanding human blastocysts**. *J In Vitro Fert Embryo Transf* (1985) **2** 59-64
11. Coskun S, Hollanders J, Al-Hassan S, Al-Sufyan H, Al-Mayman H, Jaroudi K.. **Day 5 versus day 3 embryo transfer: a controlled randomized trial**. *Hum Reprod* (2000) **15** 1947-1952. PMID: 10966992
12. Dar S, Lazer T, Shah PS, Librach CL.. **Neonatal outcomes among singleton births after blastocyst versus cleavage stage embryo transfer: a systematic review and meta-analysis**. *Hum Reprod Update* (2014) **20** 439-448. PMID: 24480786
13. Dar S, Librach CL, Gunby J, Bissonnette F, Cowan L. **Increased risk of preterm birth in singleton pregnancies after blastocyst versus Day 3 embryo transfer: Canadian ART Register (CARTR) analysis**. *Hum Reprod* (2013) **28** 924-928. PMID: 23349411
14. De Vos A, Janssens R, Van de Velde H, Haentjens P, Bonduelle M, Tournaye H, Verheyen G.. **The type of culture medium and the duration of in vitro culture do not influence birthweight of ART singletons**. *Hum Reprod* (2015) **30** 20-27. PMID: 25406185
15. Dietz WH.. **Critical periods in childhood for the development of obesity**. *Am J Clin Nutr* (1994) **59** 955-959. PMID: 8172099
16. Dumoulin JC, Land JA, Van Montfoort AP, Nelissen EC, Coonen E, Derhaag JG, Schreurs IL, Dunselman GA, Kester AD, Geraedts JP. **Effect of in vitro culture of human embryos on birthweight of newborns**. *Hum Reprod* (2010) **25** 605-612. PMID: 20085915
17. Emiliani S, Delbaere A, Vannin A-S, Biramane J, Verdoodt M, Englert Y, Devreker F.. **Similar delivery rates in a selected group of patients, for day 2 and day 5 embryos both cultured in sequential medium: a randomized study**. *Hum Reprod* (2003) **18** 2145-2150. PMID: 14507836
18. Farquhar CM, Wang YA, Sullivan EA.. **A comparative analysis of assisted reproductive technology cycles in Australia and New Zealand 2004-2007**. *Hum Reprod* (2010) **25** 2281-2289. PMID: 20650968
19. Ginström Ernstad E, Bergh C, Khatibi A, Källén KBM, Westlander G, Nilsson S, Wennerholm UB.. **Neonatal and maternal outcome after blastocyst transfer: a population-based registry study. Presented orally at the 36th Society for Maternal-Fetal Medicine Annual Pregnancy Meeting, Atlanta, GA, Feb. 1-6, 2016**. *Am J Obstet Gynecol* (2016) **214** 378.e1-378.e10
20. Glujoversusky D, Farquhar C, Quinteiro Retamar AM, Alvarez Sedo CR, Blake D.. **Cleavage stage versus blastocyst stage embryo transfer in assisted reproductive technology**. *Cochrane Database Syst Rev* (2016)
21. Hardin JW, Hilbe JM.. *Generalized Estimating Equations: Overview* (2003)
22. Henningsen AKA, Pinborg A, Lidegaard Ø, Vestergaard C, Forman JL, Andersen AN.. **Perinatal outcome of singleton siblings born after assisted reproductive technology and spontaneous conception: Danish national sibling-cohort study**. *Fertil Steril* (2011) **95** 959-963. PMID: 20813359
23. HFEA. Human Fertilisation and Embryology Act, UK. 1990. https://www.legislation.gov.uk/ukpga/1990/37/contents (12 March 2020, date last accessed).. (1990)
24. HFEA. Fertility Treatment in 2014. Trends and Figures. 2016. http://www.hfea.gov.uk/docs/Fertility Treatment 2012 TrendsFigures.pdf (29 June 2016, date last accessed).. (2016)
25. HFEA. Fertility Treatment 2019: Trends and Figures. 2021a. https://www.hfea.gov.uk/about-us/publications/research-and-data/fertility-treatment-2019-trends-and-figures/#Section4 (21 Sept 2020, date last accessed).. (2021a)
26. HFEA. Human Fertilisation & Embryology Authority. 2021b. https://www.hfea.gov.uk/about-us/publications/research-and-data/ (15 February 2021, date last accessed).. (2021b)
27. Holden EC, Kashani BN, Morelli SS, Alderson D, Jindal SK, Ohman-Strickland PA, McGovern PG.. **Improved outcomes after blastocyst-stage frozen-thawed embryo transfers compared with cleavage stage: a Society for Assisted Reproductive Technologies Clinical Outcomes Reporting System study**. *Fertil Steril* (2018) **110** 89-94.e2. PMID: 29908769
28. Ishihara O, Araki R, Kuwahara A, Itakura A, Saito H, Adamson GD.. **Impact of frozen-thawed single-blastocyst transfer on maternal and neonatal outcome: an analysis of 277,042 single-embryo transfer cycles from 2008 to 2010 in Japan**. *Fertil Steril* (2014) **101** 128-133. PMID: 24268706
29. Källén B, Finnström O, Lindam A, Nilsson E, Nygren K-G, Olausson PO.. **Blastocyst versus cleavage stage transfer in in vitro fertilization: differences in neonatal outcome?**. *Fertil Steril* (2010) **94** 1680-1683. PMID: 20137785
30. Kalra SK, Ratcliffe SJ, Barnhart KT, Coutifaris C.. **Extended embryo culture and an increased risk of preterm delivery**. *Obstet Gynecol* (2012) **120** 69-75. PMID: 22914393
31. Kissin DM, Kulkarni AD, Mneimneh A, Warner L, Boulet SL, Crawford S, Jamieson DJ. **Embryo transfer practices and multiple births resulting from assisted reproductive technology: an opportunity for prevention**. *Fertil Steril* (2015) **103** 954-961. PMID: 25637480
32. Litzky JF, Boulet SL, Esfandiari N, Zhang Y, Kissin DM, Theiler RN, Marsit CJ.. **Birthweight in infants conceived through in vitro fertilization following blastocyst or cleavage-stage embryo transfer: a national registry study**. *J Assist Reprod Genet* (2018) **35** 1027-1037. PMID: 29633148
33. Luke B, Brown MB, Wantman E, Stern JE, Toner JP, Coddington CC.. **Increased risk of large-for-gestational age birthweight in singleton siblings conceived with in vitro fertilization in frozen versus fresh cycles**. *J Assist Reprod Genet* (2017) **34** 191-200. PMID: 27909843
34. Maheshwari A, Kalampokas T, Davidson J, Bhattacharya S.. **Obstetric and perinatal outcomes in singleton pregnancies resulting from the transfer of blastocyst-stage versus cleavage-stage embryos generated through in vitro fertilization treatment: a systematic review and meta-analysis**. *Fertil Steril* (2013) **100** 1615-1621. PMID: 24083875
35. Mäkinen S, Söderström-Anttila V, Vainio J, Suikkari A-M, Tuuri T.. **Does long in vitro culture promote large for gestational age babies?**. *Hum Reprod* (2013) **28** 828-834. PMID: 23232355
36. Marconi N, Amalraj Raja E, Bhattacharya S, Maheshwari A.. **Perinatal outcomes in singleton live births after fresh blastocyst-stage embryo transfer: a retrospective analysis of 67 147 IVF/ICSI cycles**. *Hum Reprod* (2019) **34** 1716-1725. PMID: 31418775
37. Martins WP, Nastri CO, Rienzi L, Poel SZ, Van Der Gracia CR, Racowsky C.. **Obstetrical and perinatal outcomes following blastocyst transfer compared to cleavage transfer: a systematic review and meta-analysis**. *Hum Reprod* (2016) **31** 2561-2569. PMID: 27907898
38. McCullagh P, Nelder J.. *Generalized Linear Models* (1989)
39. Nelissen EC, Montfoort AP, Van Coonen E, Derhaag JG, Geraedts JP, Smits LJ, Land JA, Evers JL, Dumoulin JC.. **Further evidence that culture media affect perinatal outcome: findings after transfer of fresh and cryopreserved embryos**. *Hum Reprod* (2012) **27** 1966-1976. PMID: 22552689
40. Neuhaus JM.. *Fixed Effects Regression Methods for Longitudinal Data Using SAS* (2006) **101**
41. Oron G, Nayot D, Son WY, Holzer H, Buckett W, Tulandi T.. **Obstetric and perinatal outcome from single cleavage transfer and single blastocyst transfer: a matched case-control study**. *Gynecol Endocrinol* (2015) **31** 469-472. PMID: 25898158
42. Oron G, Sokal-Arnon T, Son WY, Demirtas E, Buckett W, Zeadna A, Holzer H, Tulandi T.. **Extended embryo culture is not associated with increased adverse obstetric or perinatal outcome**. *Am J Obstet Gynecol* (2014) **211** 165.e1-165.e7
43. Papanikolaou EG, Camus M, Kolibianakis EM, Van Landuyt L, Van Steirteghem A, Devroey P.. **In vitro fertilization with single blastocyst-stage versus single cleavage-stage embryos**. *Obstet Gynecol Surv* (2006) **61** 523-525
44. Papanikolaou EG, D’Haeseleer E, Verheyen G, Velde H, Van De Camus M, Steirteghem A, Van Devroey P, Tournaye H.. **Live birth rate is significantly higher after blastocyst transfer than after cleavage-stage embryo transfer when at least four embryos are available on day 3 of embryo culture. A randomized prospective study**. *Hum Reprod* (2005) **20** 3198-3203. PMID: 16055454
45. Parsons TJ, Power C, Logan S, Summerbell CD.. **Childhood predictors of adult obesity: a systematic review**. *Int J Obes Relat Metab Disord* (1999) **23** S1-S107
46. Pforr K.. **Femlogit—implementation of the multinomial logit model with fixed effects**. *Stat J* (2014) **14** 847-862
47. Romundstad LB, Romundstad PR, Sunde A, von Düring V, Skjærven R, Gunnell D, Vatten LJ.. **Effects of technology or maternal factors on perinatal outcome after assisted fertilisation: a population-based cohort study**. *Lancet* (2008) **372** 737-743. PMID: 18674812
48. SART. National Summary Report. 2021. https://www.sartcorsonline.com/rptCSR_PublicMultYear.aspx?reportingYear=2018 (20 May 2019, date last accessed).. (2021)
49. Seggers J, Pontesilli M, Ravelli ACJ, Painter RC, Hadders-Algra M, Heineman MJ, Repping S, Mol BWJ, Roseboom TJ, Ensing S.. **Effects of in vitro fertilization and maternal characteristics on perinatal outcomes: a population-based study using siblings**. *Fertil Steril* (2016) **105** 590-598.e2. PMID: 26658132
50. Shi W, Zhang W, Li N, Xue X, Liu C, Qu P, Shi J, Huang C.. **Comparison of perinatal outcomes following blastocyst and cleavage-stage embryo transfer: analysis of 10 years’ data from a single centre**. *Reprod Biomed Online* (2019) **38** 967-978. PMID: 30975585
51. Spangmose AL, Ginström Ernstad E, Malchau S, Forman J, Tiitinen A, Gissler M, Opdahl S, Romundstad LB, Bergh C, Wennerholm UB. **Obstetric and perinatal risks in 4601 singletons and 884 twins conceived after fresh blastocyst transfers: a Nordic study from the CoNARTaS group**. *Hum Reprod* (2020) **35** 805-815. PMID: 32294185
52. Sunkara S, Khalaf Y, Maheshwari A, Seed P, Coomarasamy A.. **Association between response to ovarian stimulation and miscarriage following IVF: an analysis of 124 351 IVF pregnancies**. *Hum Reprod* (2014) **29** 1218-1224. PMID: 24651128
53. Wang YA, Chapman M, Costello M, Sullivan EA.. **Better perinatal outcomes following transfer of fresh blastocysts and blastocysts cultured from thawed cleavage embryos: a population-based study**. *Hum Reprod* (2010) **25** 1536-1542. PMID: 20356898
54. Wang X, Du M, Guan Y, Wang B, Zhang J, Liu Z.. **Comparative neonatal outcomes in singleton births from blastocyst transfers or cleavage-stage embryo transfers: a systematic review and meta-analysis**. *Reprod Biol Endocrinol* (2017) **15** 36. PMID: 28472983
55. Wang SS, Sun HX.. **Blastocyst transfer ameliorates live birth rate compared with cleavage-stage embryos transfer in fresh in vitro fertilization or intracytoplasmic sperm injection cycles: reviews and meta-analysis**. *Yonsei Med J* (2014) **55** 815-825. PMID: 24719153
56. Weissmann-Brenner A, Simchen MJ, Zilberberg E, Kalter A, Weisz B, Achiron R, Dulitzky M.. **Maternal and neonatal outcomes of large for gestational age pregnancies**. *Acta Obstet Gynecol Scand* (2012) **91** 844-849. PMID: 22471810
57. Wilcox AJ, Weinberg CR, Basso O.. **On the pitfalls of adjusting for gestational age at birth**. *Am J Epidemiol* (2011) **174** 1062-1068. PMID: 21946386
58. Young LE, Sinclair KD, Wilmut I.. **Large offspring syndrome in cattle and sheep**. *Rev Reprod* (1998) **3** 155-163. PMID: 9829550
59. Zhu J, Lin S, Li M, Chen L, Lian Y, Liu P, Qiao J.. **Effect of in vitro culture period on birthweight of singleton newborns**. *Hum Reprod* (2014) **29** 448-454. PMID: 24408317
|
---
title: A process model account of the role of dopamine in intertemporal choice
authors:
- Alexander Soutschek
- Philippe N Tobler
journal: eLife
year: 2023
pmcid: PMC9995109
doi: 10.7554/eLife.83734
license: CC BY 4.0
---
# A process model account of the role of dopamine in intertemporal choice
## Abstract
Theoretical accounts disagree on the role of dopamine in intertemporal choice and assume that dopamine either promotes delay of gratification by increasing the preference for larger rewards or that dopamine reduces patience by enhancing the sensitivity to waiting costs. Here, we reconcile these conflicting accounts by providing empirical support for a novel process model according to which dopamine contributes to two dissociable components of the decision process, evidence accumulation and starting bias. We re-analyzed a previously published data set where intertemporal decisions were made either under the D2 antagonist amisulpride or under placebo by fitting a hierarchical drift diffusion model that distinguishes between dopaminergic effects on the speed of evidence accumulation and the starting point of the accumulation process. Blocking dopaminergic neurotransmission not only strengthened the sensitivity to whether a reward is perceived as worth the delay costs during evidence accumulation (drift rate) but also attenuated the impact of waiting costs on the starting point of the evidence accumulation process (bias). In contrast, re-analyzing data from a D1 agonist study provided no evidence for a causal involvement of D1R activation in intertemporal choices. Taken together, our findings support a novel, process-based account of the role of dopamine for cost-benefit decision making, highlight the potential benefits of process-informed analyses, and advance our understanding of dopaminergic contributions to decision making.
## Introduction
Many decisions require trading-off potential benefits (rewards) against the costs of actions, such as the time one has to wait for reward to occur (Soutschek and Tobler, 2018). The neurotransmitter dopamine is thought to play a central role in such cost-benefit trade-offs by increasing the tolerance for action costs in order to maximize net subjective benefits (Beeler, 2012; Robbins and Everitt, 1992; Salamone and Correa, 2012; Schultz, 2015). Tonic dopaminergic activity was hypothesized to implement a ‘cost control’ which moderates whether a reward or goal is considered to be worth its costs (Beeler and Mourra, 2018). Prominent accounts of dopaminergic functioning thus predict that dopamine should strengthen the preference for costly larger-later (LL) over less costly smaller-sooner (SS) rewards. However, empirical studies modulating dopaminergic neurotransmission during intertemporal decision making provided inconsistent evidence for these hypotheses (for a review, see Webber et al., 2021). Blocking dopaminergic activation even seems to increase rather than to reduce the preference for delayed outcomes (Arrondo et al., 2015; Soutschek et al., 2017; Wagner et al., 2020; Weber et al., 2016), in apparent contrast to accounts proposing that lower dopaminergic activity should decrease the attractiveness of costly rewards (Beeler and Mourra, 2018; Robbins and Everitt, 1992; Salamone and Correa, 2012). Thus, the link between dopamine and cost-benefit weighting in intertemporal choice remains elusive. Yet, a plausible account of how dopamine affects cost-benefit weighting is important given that deficits in delay of gratification belong to the core symptoms of several psychiatric disorders and that dopaminergic medication plays a central role in the treatment of these and other disorders (Hasler, 2012; MacKillop et al., 2011).
To account for the conflicting findings on the role of dopamine in intertemporal choice, recent proximity accounts hypothesized that dopamine – in addition to strengthening the pursuit of valuable goals – also increases the preference for proximate over distant rewards (first formulated by Westbrook and Frank, 2018; see also Soutschek et al., 2022). While proximity and action costs often correlate negatively (as cost-free immediate rewards are typically more proximate than costly delayed rewards), they can conceptually be distinguished: perceived costs depend on an individual’s internal state (e.g., available resources to wait for future rewards), whereas proximity is determined by situational factors like familiarity or concreteness (Westbrook and Frank, 2018). The hypothesis that dopamine increases the proximity advantage of sooner over later rewards is consistent with the observed stronger preference for LL options after D2R blockade, which could not be explained by standard accounts of the role of dopamine in cost-benefit decisions (Beeler and Mourra, 2018; Salamone and Correa, 2012).
Still, the question remains as to how the proximity account can be reconciled with the large body of evidence for a motivating role of dopamine in other domains than intertemporal choice (Webber et al., 2021). We recently suggested that both accounts may be unified within the framework of computational process models like the drift diffusion model (DDM) (Soutschek et al., 2022). DDMs assume that decision makers accumulate evidence for two reward options until a decision boundary is reached. The dopamine-mediated cost control may be implemented via dopaminergic effects on the evaluation of reward magnitudes and delay costs during the evidence accumulation process (drift rate), while a proximity advantage for sooner over delayed rewards may shift the starting bias toward the decision boundary for sooner rewards (Soutschek et al., 2022; Westbrook and Frank, 2018). Such proximity effects on the starting bias could reflect an automatic bias toward immediate rewards as posited by dual process models of intertemporal choice (Figner et al., 2010; McClure et al., 2004), whereas the influence of reward and delay on the drift rate involves more controlled and attention-demanding weighting of costs and benefits. Combining these two, in their consequences on overt choices partially opposing, but independent, effects of dopamine in a unified and tractable account could reconcile conflicting findings. In turn, such a process account might provide a knowledge basis to advance our understanding of the neurochemical basis of the decision-making deficits in clinical disorders and improve the effectiveness of pharmaceutical interventions.
Here, we tested central assumptions of the proposed account by re-analyzing the data from two previous studies that investigated how the dopamine D2 receptor antagonist amisulpride and the D1 agonist PF-06412562 impact cost-benefit weighting in intertemporal choice (Soutschek et al., 2017; Soutschek et al., 2020a). D1Rs are prevalent in the direct ‘Go’ pathway and facilitate action selection via mediating the impact of phasic bursts elicited by high above-average rewards (Evers et al., 2017; Kirschner et al., 2020). D2Rs, in contrast, dominate the indirect ‘Nogo’ pathway (which suppresses action) and are more sensitive to small concentration differences in tonic dopamine levels (Missale et al., 1998), which is thought to encode the background, average reward rate (Kirschner et al., 2020; Volkow and Baler, 2015; Westbrook and Frank, 2018; Westbrook et al., 2020). Comparing the influences of the two compounds on the choice process during intertemporal decisions allowed us to test the hypothesized dissociable roles of D1Rs and D2Rs for decision making. Previously reported analyses of these data had shown no influence of D2R blockade or D1R stimulation on the mean preferences for LL over SS options (Soutschek et al., 2017; Soutschek et al., 2020a). However, they had not asked whether the pharmacological agents moderate the influences of reward magnitudes and delay costs on subcomponents of the decision process within the framework of a DDM. We re-analyzed the data sets with hierarchical Bayesian drift diffusion modeling to test central assumptions of the proposed account on dopamine’s role in cost-benefit weighting. First, if D2R activation implements a cost threshold moderating the evaluation of whether a reward is worth the action costs, then blocking D2R activation with amisulpride should increase the influence of reward magnitude on the speed of evidence accumulation, with costly small rewards becoming less acceptable than under placebo. Second, if D2R-mediated tonic dopaminergic activity also moderates the impact of proximity on choices (which affects the starting bias rather than the speed of the evidence accumulation process), D2R blockade should attenuate the effects of waiting costs on the starting bias. Third, we expected D1R stimulation to modulate the sensitivity to rewards during evidence accumulation (via increasing activity in the direct ‘Go’ pathway), without affecting proximity costs which were related to tonic rather than phasic dopaminergic activity (Westbrook and Frank, 2018).
## Results
To disentangle how dopamine contributes to distinct subcomponents of the choice process, we re-analyzed a previously published data set where 56 participants had performed an intertemporal choice task under the D2 antagonist amisulpride (400 mg) and placebo in two separate sessions (Soutschek et al., 2017; Figure 1). First, we assessed amisulpride effects on intertemporal choices with conventional model-based and model-free analyses, as they are employed by other pharmacological studies on cost-benefit weighting. Hyperbolic discounting of future rewards was not significantly different under amisulpride (mean log-k=–2.07) compared with placebo (mean log-k=–2.19), Bayesian t-test, HDImean = 0.21, HDI$95\%$ = [–0.28; 0.70], and there were also no drug effects on choice consistency (inverse temperature), HDImean = –0.28, HDI$95\%$ = [–0.71; 0.13]. Model-free Bayesian mixed generalized linear models (MGLMs) revealed a stronger preference for LL over SS options with increasing differences in reward magnitudes, HDImean = 6.32, HDI$95\%$ = [5.03; 7.83], and with decreasing differences in delay of reward delivery, HDImean = –1.27, HDI$95\%$ = [–1.87; –0.60]. The impact of delays on choices was significantly reduced under amisulpride compared with placebo, HDImean = 0.75, HDI$95\%$ = [0.02; 1.67] (Figure 1C/D and Table 1). When we explored whether dopaminergic effects changed over the course of the experiment, we observed a significant main effect of trial number (more LL choices over time), HDImean = 0.58, HDI$95\%$ = [0.19; 0.99]. However, this effect was unaffected by the pharmacological manipulation, HDImean = –0.06, HDI$95\%$ = [–0.61; 0.48]. We also re-computed the MGLM reported above on log-transformed decision times, adding predictors for choice (SS vs. LL option) and Magnitudesum (combined magnitudes of SS and LL rewards). Participants made faster decisions the higher the sum of the two rewards, HDImean = –0.12, HDI$95\%$ = [–0.18; –0.06], however we observed no significant drug effects on decision times. Thus, based on these conventional analyses one would conclude that reduction of D2R neurotransmission lowers the sensitivity to delay costs, which on the one hand agrees with one line of previous findings (Arrondo et al., 2015; Wagner et al., 2020; Weber et al., 2016). On the other hand, this result seems to contradict the widely held assumption that dopamine increases the preference for costly over cost-free outcomes (Beeler and Mourra, 2018; Webber et al., 2021; Westbrook et al., 2020), because according to this view lower dopaminergic activity should increase, rather than decrease, the impact of waiting costs on LL choices. However, analyses that consider only the observed choices do not allow disentangling dopaminergic influences on distinct subcomponents of the choice process.
**Figure 1.:** *Task design and experimental procedures.(A) Participants made choices between alternatives that provided smaller-sooner rewards (e.g., 100 Swiss francs in 0 day) or larger-later rewards (e.g., 250 Swiss francs in 60 days). (B) *In a* double-blind crossover design, participants performed the intertemporal decision task after administration of the D2 antagonist amisulpride or placebo on two separate days. (C) Model-free Bayesian analyses revealed weaker influences of delay costs on decision making under amisulpride compared with placebo, consistent with previous findings that D2R antagonism strengthens the preference for delayed rewards. (D) Individual coefficients for the impact of delay on choices in the amisulpride and placebo conditions. (E) Illustration of the choice process in the framework of a drift-diffusion model. After a non-decision time τ (not shown here), evidence is accumulated from a starting point ζ with the weighted difference between benefits and action costs determining the speed of the accumulation process (drift rate v) toward the boundaries for the larger-later or smaller-sooner option. (F) Delay discounting under placebo (log-k; dots correspond to individual participants, with more negative values indicating weaker delay discounting) decreased with the difference in weights assigned to rewards and delay costs during evidence accumulation, replicating previous findings (Amasino et al., 2019).* TABLE_PLACEHOLDER:Table 1.
DDMs paint a fuller picture of the decision process than pure choice data by integrating information from observed choices and decision times. DDMs assume that agents accumulate evidence for the choice options (captured by the drift parameter v) from a starting point ζ until the accumulated evidence reaches a decision threshold (boundary parameter a; Figure 1E). Following previous procedures analyzing intertemporal choices with DDMs (Amasino et al., 2019), we assumed that the drift rate ν integrates reward magnitudes and delays of choice options via attribute-wise comparisons (DDM-1). In addition, we also allowed the starting bias to vary as a function of differences in delay costs, in line with recent proximity accounts of dopamine (Westbrook and Frank, 2018).
A sanity check revealed that larger differences between the reward magnitudes of the LL and SS options bias evidence accumulation toward the LL option, HDImean = 2.41, HDI$95\%$ = [1.93; 2.95], whereas larger differences in delays bias accumulation in favor of the SS option, HDImean = –1.13, HDI$95\%$ = [–1.53; –0.78]. Moreover, we assessed the relationship between the difference in DDM parameters (reward magnitude – delay) and hyperbolic discount parameters log-k as purely choice-based indicator of impulsiveness. Replicating previous findings, we found that across individuals the weights relate to delay discounting, r=–0.61, $p \leq 0.001$ (Amasino et al., 2019; Figure 1F), such that individuals weighting reward magnitudes more strongly over delays make more patient choices. Thus, our model parameters capture essential subprocesses of intertemporal decision making.
Next, we tested the impact of our dopaminergic manipulation on evidence accumulation: D2R blockade strengthened the impact of differences in reward magnitude on evidence accumulation, Drug×Magnitudediff: HDImean = 0.81, HDI$95\%$ = [0.04; 1.71], while the contribution of differences in delay costs remained unchanged, Drug×Delaydiff: HDImean = –0.30, HDI$95\%$ = [–0.85; 0.20] (Figure 2A–F and Table 2). The drug-induced increase in sensitivity to variation in reward magnitude suggests that low rewards are considered less valuable under amisulpride compared with placebo (Figure 2C). This finding is consistent with the cost control hypothesis (Beeler and Mourra, 2018) according to which low dopamine levels reduce the attractiveness of smaller, below-average rewards.
**Figure 2.:** *D2R blockade affects multiple components of the intertemporal decision process.(A) Larger differences in reward magnitude between the larger-later (LL) and smaller-sooner (SS) option increased drift rates, speeding up evidence accumulation toward LL options under placebo. (B) The impact of differences in reward magnitude was significantly stronger under amisulpride than under placebo. (C) Drug-dependent impact of differences in reward magnitude on the drift rate. Because the sensitivity to differences in reward magnitude was stronger under amisulpride than under placebo (steeper slope), D2R blockade sped up evidence accumulation toward the boundary for LL choices if differences in reward magnitude between the LL and SS options were large. In contrast, if the difference in reward magnitude was small, the drift rate was more negative under amisulpride compared with placebo, speeding up evidence accumulation toward the SS option. (D) Larger differences between delay of reward promoted evidence accumulation toward the (negative) boundary for SS choices under placebo, (E, F) but the impact of delay was not significantly altered by amisulpride. (G) The starting point of the accumulation processes was closer to the boundary for LL than SS choices under placebo, (H) and this starting bias toward the LL option was significantly reduced by amisulpride. (I) For larger differences in waiting costs, the starting point of the evidence accumulation process was increasingly shifted toward the SS option under placebo. (J) This impact of delay costs on the starting bias was significantly reduced under amisulpride. As illustrated in (K), reducing dopaminergic action on D2R with amisulpride shifted the starting bias toward the boundary for the SS option predominantly if no option possessed a clear proximity advantage (small difference between delays). In A, B, D, E, G, H, I, and J, yellow bars close to x-axis indicate $95\%$ HDIs.* TABLE_PLACEHOLDER:Table 2.
When we assessed dopaminergic effects on the starting bias, we observed that under placebo increasing differences in delay shifted the starting point toward the SS option, HDImean = 0.81, HDI$95\%$ = [0.04; 1.71], suggesting that the bias parameter is closer to the proximate (SS) option the stronger the proximity advantage of the SS over the LL option. Amisulpride shifted the starting bias toward the SS option for smaller differences in delay, main effect of Drug: HDImean = –0.04, HDI$95\%$ = [–0.08; -0.001], but also attenuated the impact of delay, Drug×Delaydiff: HDImean = 0.02, HDI$95\%$ = [0.001; 0.04]. Thus, dopamine appears to moderate the impact of temporal proximity on the starting bias (Figure 2G–K), providing support for recent proximity accounts of dopamine (Soutschek et al., 2022; Westbrook and Frank, 2018). Moreover, compared to the model-free analysis, our process model (which uses not only binary choice but also response time data) provides a fuller picture of the subcomponents of the choice process affected by the dopaminergic manipulation.
Next, we investigated the relation between the drug effects on the drift rate and on the starting bias. We found no evidence that the two effects correlated, $r = 0.07$, $$p \leq 0.60$$, suggesting that amisulpride effects on these subprocesses were largely independent of each other. Control analyses revealed no effects of amisulpride on non-decision times, HDImean = –0.10, HDI$95\%$ = [–0.24; 0.03], or the decision threshold, HDImean = 0.17, HDI$95\%$ = [–0.11; 0.46]. Thus, the results of DDM-1 suggest that dopamine moderates the influence of choice attributes on both the speed of evidence accumulation and on the starting bias, consistent with recent accounts (Soutschek et al., 2022; Westbrook and Frank, 2018) of dopamine’s role in cost-benefit weighting.
To test the robustness of our DDM findings, we computed further DDMs where we either removed the impact of Delaydiff on the starting bias (DDM-2) or the impact of Magnitudediff and Delaydiff on the drift rate (DDM-3). In a further model (DDM-4), we explored whether the starting bias is affected by the overall proximity of the options (sum of delays, Delaysum) rather than the difference in proximity (Delaydiff; see Table 3 for an overview over the parameters included in the various models). Importantly, our original DDM-1 (DIC = 9478) explained the data better than DDM-2 (DIC = 9481), DDM-3 (DIC = 10,224), or DDM-4 (DIC = 9492; Figure 3A). Nevertheless, amisulpride moderated the impact of Magnitudediff on the drift rate also in DDM-2, HDImean = 0.86, HDI$95\%$ = [0.18; 1.64], and DDM-4, HDImean = 0.83, HDI$95\%$ = [0.04; 1.75], and amisulpride also lowered the impact of Delaydiff on the starting bias in DDM-3, HDImean = –0.02, HDI$95\%$ = [–0.04; –0.001]. Thus, the dopaminergic effects on these subcomponents of the choice process are robust to the exact specification of the DDM.
**Figure 3.:** *Model comparison.(A) The deviance information criterion (DIC; lower numbers correspond to better fit) suggests that DDM-1 explained the data slightly better than DDM-2, DDM-4, and DDM-6 and clearly outperformed DDM-3 and DDM-5. (B, C) Posterior predictive checks on the group level (collapsed across all participants), separately for (B) placebo and (C) amisulpride. Particularly DDM-1, DDM-2, and DDM-3 described the empirically observed data well, whereas decisions simulated based on DDMs 4–6 more strongly deviated from observed behavior.* TABLE_PLACEHOLDER:Table 3.
We compared the winning account also with alternative process models of intertemporal choice. While in DDM-1 the drift rate depends on separate comparisons between choice attributes, one might alternatively assume that they compare the discounted subjective reward values of both options (Wagner et al., 2020), as given by the hyperbolic discount functions. However, a DDM where the drift rate was modeled as the difference between the hyperbolically discounted reward values (with the discount factor as free parameter; DDM-5) showed a worse model fit (DIC = 10,720) than DDM-1. This replicates previous findings according to which intertemporal choices can better be explained by attribute-wise than by option-wise comparison strategies (Amasino et al., 2019; Dai and Busemeyer, 2014; Reeck et al., 2017).
Next, we investigated an alternative to the proposal that differences in delay affect the starting bias via proximity effects. Specifically, we tested whether evidence for delay costs are accumulated earlier than for reward magnitude (relative-starting-time (rs)DDM; Amasino et al., 2019; Lombardi and Hare, 2021). From the perspective of rsDDMs, evidence accumulation for delays would start after a shorter non-decision time than for rewards, which is expressed by the variable τdiff (if τdiff > 0, non-decision time is shorter for delays than rewards, and vice versa if τdiff < 0). However, also this rsDDM (DDM-6) explained the data less well (DIC = 9,548) than DDM-1. Thus, DDM-1 explains the current data better than alternative DDMs.
The currently used dose of amisulpride (400 mg) is thought to have predominantly postsynaptic effects on D2Rs, while lower doses (50–300 mg) might show presynaptic rather than postsynaptic effects (Schoemaker et al., 1997). Given that we used the same dose in all participants, one might argue that we may have studied presynaptic effects in individuals with relatively high body mass (which lowers the effective dose). However, we observed no evidence that individual random coefficients for the drug effects on the drift rate or on the starting bias correlated with body weight, all r<0.22, all $p \leq 0.10.$ There were also no significant correlations between DDM parameters and performance in the digit span backward task as proxy for baseline dopamine synthesis capacity (Cools et al., 2008), all r<0.17, all $p \leq 0.22.$ There was thus no evidence that pharmacological effects on intertemporal choices depended on body weight as proxy of effective dose or working memory performance as proxy for baseline dopaminergic activity.
As further check of the explanatory adequacy of DDM-1, we performed posterior predictive checks and parameter recovery analyses. Plotting the observed RTs (split into quintiles according to Magnitudediff and Delaydiff) against the simulated RTs based on the parameter estimates from the different DDMs suggests that the DDMs provide reasonable accounts of the observed data both on the group and the individual level, at least for DDMs 1–3 (Figure 3B/C and Figure 4). Moreover, the squared differences between observed and simulated RTs were smaller for DDM-1 (0.83) than for alternative DDMs (DDM-2: 0.85; DDM-3: 0.98; DDM-4: 1.05, DDM-5: 0.89; DDM-6: 1.63). To assess parameter recovery, we re-computed DDM-1 on 10 simulated data sets based on the original DDM-1 parameter estimates. All group-level parameters from the simulated data were within the $95\%$ HDI of the original parameter estimates, except for the non-decision time τ (which suggests that our model tends to overestimate the duration of decision-unrelated processes). Nevertheless, all parameters determining the outcome of the decision process (i.e., the choice made) as well as the dopaminergic effects on the parameters could reliably be recovered by DDM-1.
**Figure 4.:** *Posterior predictive checks.For each individual participant (p1–p56), observed RTs (in black) are plotted against the RTs simulated based on the parameters for drift diffusion model (DDM) 1–6, separately for differences in (A) reward magnitude and (B) delay (quintiles). The plots suggest that the DDMs provide reasonable accounts of the observed RTs.*
To assess the receptor specificity of our findings, we conducted the same analyses on the data from a study (published previously in Soutschek et al., 2020a) testing the impact of three doses of a D1 agonist (6, 15, 30 mg) relative to placebo on intertemporal choices (between-subject design). In the intertemporal choice task used in this experiment, the SS reward was always immediately available (delay = 0), contrary to the task in the D2 experiment where the delay of the SS reward varied from 0 to 30 days. Again, the data in the D1 experiment were best explained by DDM-1 (DICDDM-1=19,657) compared with all other DDMs (DICDDM-2=20,934; DICDDM-3=21,710; DICDDM-5=21,982; DICDDM-6=19,660; note that DDM-4 was identical with DDM-1 for the D1 agonist study because the delay of the SS reward was 0). Neither the best-fitting nor any other model yielded significant drug effects on any drift diffusion parameter (see Table 4 for the best-fitting model). Also model-free analyses conducted in the same way as for the D2 antagonist study revealed no significant drug effects (all HDI$95\%$ included zero). There was thus no evidence for any influence of D1R stimulation on intertemporal decisions.
**Table 4.**
| Parameter | Regressor | Mean | 2.5% | 97.5% |
| --- | --- | --- | --- | --- |
| Drift rate:Delaydiff | Placebo (0 mg) | –0.42 | –0.74 | –0.20 |
| Drift rate:Delaydiff | 6 mg vs. 0 mg | –0.02 | –0.35 | 0.28 |
| Drift rate:Delaydiff | 15 mg vs. 0 mg | –0.15 | –0.52 | 0.19 |
| Drift rate:Delaydiff | 30 mg vs. 0 mg | –0.12 | –0.50 | 0.14 |
| Drift rate:Magnitudediff | Placebo (0 mg) | 0.86 | 0.49 | 1.31 |
| Drift rate:Magnitudediff | 6 mg vs. 0 mg | 0.17 | –0.26 | 0.63 |
| Drift rate:Magnitudediff | 15 mg vs. 0 mg | 0.11 | –0.53 | 0.73 |
| Drift rate:Magnitudediff | 30 mg vs. 0 mg | 0.23 | –0.23 | 0.83 |
| Drift rate: vmax | Placebo (0 mg) | 1.72 | 1.15 | 2.66 |
| Drift rate: vmax | 6 mg vs. 0 mg | –0.36 | –1.37 | 0.60 |
| Drift rate: vmax | 15 mg vs. 0 mg | 0.02 | –1.18 | 1.35 |
| Drift rate: vmax | 30 mg vs. 0 mg | –0.39 | –1.60 | 0.75 |
| Decision threshold | Placebo (0 mg) | 2.82 | 2.59 | 3.04 |
| Decision threshold | 6 mg vs. 0 mg | –0.13 | –0.46 | 0.20 |
| Decision threshold | 15 mg vs. 0 mg | –0.11 | –0.45 | 0.24 |
| Decision threshold | 30 mg vs. 0 mg | –0.06 | –0.39 | 0.25 |
| Starting bias:Intercept | Placebo (0 mg) | 0.59 | 0.54 | 0.64 |
| Starting bias:Intercept | 6 mg vs. 0 mg | –0.01 | –0.07 | 0.06 |
| Starting bias:Intercept | 15 mg vs. 0 mg | –0.01 | –0.08 | 0.06 |
| Starting bias:Intercept | 30 mg vs. 0 mg | –0.03 | –0.09 | 0.04 |
| Starting bias:Delaydiff | Placebo (0 mg) | 0.00 | –0.01 | 0.02 |
| Starting bias:Delaydiff | 6 mg vs. 0 mg | –0.01 | –0.03 | 0.02 |
| Starting bias:Delaydiff | 15 mg vs. 0 mg | 0.01 | –0.01 | 0.04 |
| Starting bias:Delaydiff | 30 mg vs. 0 mg | –0.01 | –0.03 | 0.01 |
| Non-decision time | Placebo (0 mg) | 0.85 | 0.78 | 0.93 |
| Non-decision time | 6 mg vs. 0 mg | 0.03 | –0.09 | 0.15 |
| Non-decision time | 15 mg vs. 0 mg | –0.02 | –0.13 | 0.09 |
| Non-decision time | 30 mg vs. 0 mg | 0.03 | –0.06 | 0.13 |
## Discussion
Dopamine is hypothesized to play a central role in human cost-benefit decision making, but existing empirical evidence does not conclusively support the widely shared assumption that dopamine promotes the pursuit of high benefit-high cost options (for reviews, see Soutschek et al., 2022; Webber et al., 2021). By manipulating dopaminergic activity with the D2 antagonist amisulpride, we provide empirical evidence for a novel process model of cost-benefit weighting that reconciles conflicting views by assuming dissociable effects of dopamine on distinct subcomponents of the decision process.
D2R blockade (relative to placebo) increased the sensitivity to variation in reward magnitudes during evidence accumulation, such that only relatively large future rewards were considered to be worth the waiting cost, whereas small delayed rewards were perceived as less valuable than sooner rewards. This dopaminergic impact on the drift rate is consistent with the view that D2R-mediated tonic dopamine levels implement a cost control determining whether a reward is worth the required action costs (Beeler and Mourra, 2018). From this perspective, lowering D2R activity with amisulpride resulted in a stricter cost control such that only rather large delayed rewards were able to overcome D2R-mediated cortical inhibition (Lerner and Kreitzer, 2011). While this effect is consistent with the standard view according to which dopamine increases the preference for large costly rewards (Robbins and Everitt, 1992; Salamone and Correa, 2012; Schultz, 2015), the dopaminergic effects on the starting bias parameter yielded a different pattern. Here, inhibition of D2R activation reduced the impact of delay costs on the starting bias, such that for shorter delays (where the immediate reward has only a small proximity advantage) D2R inhibition shifts the bias toward the SS option. This finding represents first evidence for the hypothesis that tonic dopamine moderates the impact of proximity (e.g., more concrete vs. more abstract rewards) on cost-benefit decision making (Soutschek et al., 2022; Westbrook and Frank, 2018). Pharmacological manipulation of D1R activation, in contrast, showed no significant effects on the decision process. This provides evidence for the receptor specificity of dopamine’s role in intertemporal decision making (though as caveat it is worth keeping the differences between the tasks administered in the D1 and the D2 studies in mind).
Conceptually, the assumption of proximity effects on the starting bias is consistent with dual process models of intertemporal choice assuming that individuals are (at least partially) biased toward selecting immediate over delayed rewards (Figner et al., 2010; McClure et al., 2004). This automatic favoring of immediate rewards is reflected in a shift of the starting bias and thus occurs before the evidence accumulation process, which relies on attention-demanding cost-benefit weighting (Zhao et al., 2019). In agreement with this notion, DDM-1 with temporal proximity-dependent bias showed better fit than DDM-5 with variable non-decision times for rewards and delays. We note that the hierarchical modeling approach allowed us to compare models on the group-level only, such that in some individuals behavior might better be explained by a different model than DDM-1. Such model comparisons on the individual level, however, were beyond the scope of the current study and might not yield robust results given the limited number of trials per individual. We also emphasize that alternative process models like the linear ballistic accumulator (LBA) model make different assumptions than DDMs, for example by positing the existence of separate option-specific accumulators rather than only one as assumed by DDMs. However, proximity effects as investigated in the current study might be incorporated in LBA models as well by varying the starting points of the accumulators as function of proximity.
A dopaminergic modulation of proximity effects provides an elegant explanation for the fact that in most D2 antagonist studies D2R reduction increased the preference for LL options (Arrondo et al., 2015; Soutschek et al., 2017; Wagner et al., 2020; Weber et al., 2016), contrary to the predictions of energization accounts (Beeler and Mourra, 2018; Salamone and Correa, 2012). Noteworthy, the dopaminergic effects on evidence accumulation and on the starting bias promote potentially different action tendencies, as the impact of amisulpride on evidence accumulation lowered the weight assigned to small future rewards, whereas the amisulpride effects on the starting bias increased the likelihood of LL options being chosen. Rather than generally biasing impulsive or patient choices, the impact of dopamine on decision making may therefore crucially depend on the rewards at stake and the associated waiting costs (Figure 4). In our model, lower dopamine levels strengthen the preference for high reward-high cost options predominantly in two situations. First, if differences in reward magnitude are high (e.g., choosing between your favorite meal vs. a clearly less liked dish) and, second, if the less costly option has a clear proximity advantage over the costlier one (having dinner in a restaurant close-by or a preferred restaurant on the other side of town). Conversely, if differences in both expected reward and waiting costs are small, lower dopamine may bias choices in favor of low-cost rewards over high-cost rewards. By extension, higher dopamine levels should increase the preference for an SS option if the SS option has a pronounced proximity advantage over the LL option, and bias the acceptance of LL options if both options are associated with similar waiting costs. We note though that the effects of increasing dopamine levels are less predictable than the effects of lowering dopaminergic activity due to possible inverted-U-shaped dopamine-response curves (Floresco, 2013); potentially, the dopaminergic effects on drift rate and starting bias might even follow different dose-response functions. Taken together, our process model of the dopaminergic involvement in cost-benefit decisions allows reconciling conflicting theoretical accounts and (apparently) inconsistent empirical findings by showing that dopamine moderates the effects of reward magnitudes and delay costs on different subcomponents of the choice process.
We note that the moderating roles of differences in delays are also reflected in the significant interaction between drug and delay from the model-free analysis, although this analysis could provide no insights into which subcomponents of the choice process are affected by dopamine. As the influence of dopamine on decision making varies as a function of the differences in reward magnitude and waiting costs, the outcomes of standard analyses like mean percentage of LL choices or hyperbolic discount parameters may be specific to the reward magnitudes and delays administered in a given study. For example, if an experimental task includes large differences between rewards and delays, dopamine antagonists may reduce delay discounting, whereas studies with smaller differences between these choice attributes may observe no effect of dopaminergic manipulations (Figure 5). Standard analyses that measure patience by one behavioral parameter only (e.g., discount factors) may thus result in misleading findings. In contrast, process models of decision making do not just assess whether a neural manipulation increases or reduces patience; instead, they quantify the influence of a manipulation on the weights assigned to rewards and waiting costs during different phases of the choice process, with these weights being less sensitive to the administered choice options in a given experiment. Process models may thus provide a less option-specific picture of the impact of pharmacological and neural manipulations.
**Figure 5.:** *Illustration of how dopaminergic effects on intertemporal choices depend on differences in both reward magnitude and delay in the proposed framework, separately for (A) placebo, (B) amisulpride, and (C) the difference between amisulpride and placebo.Plots are based on simulations assuming the group-level parameter estimates we observed under placebo and amisulpride. As dopaminergic effects on decision making affect both reward processing (via the drift rate) and cost processing (via the starting bias), the specific combination of rewards and delays determines whether D2R blockade increases or decreases the probability of larger-later (LL) choices. Low dopamine levels reduce the proximity advantage of smaller-sooner (SS) over LL options particularly if differences in action costs between reward options are large, promoting choices of the LL option. In contrast, if no option possesses a proximity advantage (small differences between delays), dopaminergic effects on evidence accumulation dominate, such that the LL option is perceived as less worth the waiting costs, particularly if its reward magnitude differs only little from that of the alternative SS option.*
As potential alternative explanation for the enhanced influence of reward magnitude under amisulpride, one might argue that D2R blockade generally increases the signal-to-noise ratio for decision-relevant information. However, this notion is inconsistent with the proposed role of D2R activation for precise action selection (Keeler et al., 2014), because this view would have predicted amisulpride to result in noisier (less precise action selection) rather than less noisy evidence accumulation. Moreover, our data provide no evidence for drug effects on the inverse temperature parameter measuring choice consistency, and there were also no significant correlations between amisulpride effects on reward and delay processing, contrary to what one should expect if these effects were driven by the same mechanism.
While higher doses of amisulpride (as administered in the current study) antagonize postsynaptic D2Rs, lower doses (50–300 mg) were found to primarily block presynaptic dopamine receptors (Schoemaker et al., 1997), which may result in amplified phasic dopamine release and thus increased sensitivity to benefits (Frank and O’Reilly, 2006). At first glance, the stronger influence of differences in reward magnitude on drift rates under amisulpride compared with placebo might therefore speak in favor of presynaptic (higher dopamine levels) rather than postsynaptic mechanisms of action in the current study. However, amisulpride vs. placebo increased evidence accumulation toward LL rewards (more positive drift rate) only for larger differences between larger (later) and smaller (sooner) rewards, whereas for smaller reward differences amisulpride enhanced evidence accumulation toward SS choices (more negative drift rate; see Figure 2C). The latter finding appears inconsistent with presynaptic effects, as higher dopamine levels are thought to increase the preference for costly larger rewards (Webber et al., 2021). Instead, the stronger influence of reward differences on drift rates under amisulpride could be explained by a stricter cost control (Beeler and Mourra, 2018). In this interpretation, individuals more strongly distinguish between larger rewards that are worth the waiting costs (large difference between LL and SS rewards) and larger rewards that are not worth the same waiting costs (small difference between LL and SS rewards). While this speaks in favor of postsynaptic effects, we acknowledge that the amisulpride effects for larger reward differences are compatible with presynaptic mechanisms.
The result pattern for the starting bias parameter, in turn, suggests the presence of two distinct response biases, reflected by the intercept and the delay-dependent slope of the bias parameter (see Figure 2K), which are both under dopaminergic control but in opposite directions. First, participants seem to have a general bias toward the LL option in the current task (intercept), which is reduced under amisulpride compared with placebo, consistent with the assumption that dopamine strengthens the preference for larger rewards (Beeler and Mourra, 2018; Salamone and Correa, 2012; Schultz, 2015). Second, amisulpride reduced the impact of increasing differences in delay on the starting bias, as predicted by the proximity account of tonic dopamine (Westbrook and Frank, 2018). Both of these effects are compatible with postsynaptic effects of amisulpride. However, we note that in principle one might make the assumption that proximity effects are stronger for smaller than for larger differences in delay, and under this assumption the results would be consistent with presynaptic effects. On balance, the current results thus appear more likely under the assumption of postsynaptic rather than presynaptic effects but the latter cannot be entirely excluded. Unfortunately, the lack of a significant amisulpride effect on decision times (which should be reduced or increased as consequence of presynaptic or postsynaptic effects, respectively) sheds no additional light on the issue. Lastly, while the actions of amisulpride on D2/D3 receptors are relatively selective, it also affects serotonergic 5-HT7 receptors (Abbas et al., 2009). Because serotonin has been related to impulsive behavior (Mori et al., 2018), it is worth keeping in mind that amisulpride effects on serotonergic, in addition to dopaminergic, activity might contribute to the observed result pattern.
An important question refers to whether our findings for delay costs can be generalized to other types of costs as well, including risk, social costs (i.e., inequity), effort, and opportunity costs. We recently proposed that dopamine might also moderate proximity effects for reward options differing in risk and social costs, whereas the existing literature provides no evidence for a proximity advantage of effort-free over effortful rewards (Soutschek et al., 2022). However, these hypotheses need to be tested more explicitly by future investigations. Dopamine has also been ascribed a role for moderating opportunity costs, with lower tonic dopamine reducing the sensitivity to opportunity costs (Niv et al., 2007). While this appears consistent with our finding that amisulpride (under the assumption of postsynaptic effects) reduced the impact of delay on the starting bias, it is important to note that choosing delayed rewards did not involve any opportunity costs in our paradigm, given that participants could pursue other rewards during the waiting time. Thus, it needs to be clarified whether our findings for delayed rewards without experienced waiting time can be generalized to choice situations involving experienced opportunity costs.
To conclude, our findings may shed a new light on the role of dopamine in psychiatric disorders that are characterized by deficits in impulsiveness or cost-benefit weighting in general (Hasler, 2012), and where dopaminergic drugs belong to the standard treatments for deficits in value-related and other behavior. Dopaminergic manipulations yielded mixed results on impulsiveness in psychiatric and neurologic disorders (Acheson and de Wit, 2008; Antonelli et al., 2014; Foerde et al., 2016; Kayser et al., 2017), and our process model regarding the role of dopamine for delaying gratification explains some of the inconsistencies between empirical findings (on top of factors like non-linear dose-response relationships). As similarly inconsistent findings were observed also in the domains of risky and social decision making (Soutschek et al., 2022; Webber et al., 2021), the proposed process model may account for the function of dopamine in these domains of cost-benefit weighting as well. By deepening the understanding of the role of dopamine in decision making, our findings provide insights into how abnormal dopaminergic activation, and its pharmacological treatment, in psychiatric disorders may affect distinct aspects of decision making.
## D2 antagonist study
In a double-blind, randomized, within-subject design, 56 volunteers (27 female, Mage = 23.2 years, SDage = 3.1 years) received 400 mg amisulpride or placebo in two separate sessions (2 weeks apart) as described previously (Soutschek et al., 2017). Participants gave informed written consent before participation. The study was approved by the Cantonal ethics committee Zurich [2012-0568].
## D1 agonist study
Detailed experimental procedures for the D1 experiment are reported in Soutschek et al., 2020a. A total of 120 participants (59 females, mean age = 22.57 years, range 18–28) received either placebo or one of three different doses (6, 15, 30 mg) of the D1 agonist PF-06412562 (between-subject design). The study was approved by the Cantonal ethics committee Zurich [2016-01693] and participants gave informed written consent prior to participation. The D1 agonist study was registered on ClinicalTrials.gov (identifier: NCT03181841).
## Task design
In the D2 antagonist study, participants made intertemporal decisions 90 min after drug or placebo intake. We used a dynamic version of a delay discounting task in which the choice options were individually selected such that the information provided by each decision was optimized (dynamic experiments for estimating preferences; Toubia et al., 2013). On each trial, participants decided between an SS (reward magnitude 5–250 Swiss francs, delay 0–30 days) and an LL option (reward magnitude 15–300 Swiss francs, delay 3–90 days). Participants pressed the left or right arrow keys on a standard keyboard to choose the option presented on the left or right side of the screen. On each trial, the reward options were presented until participants made a choice. The next choice options were displayed after an intertrial interval of 1 s. Participants made a total of 20 choices between SS and LL options.
In the D1 agonist experiment, participants performed a task battery including an intertemporal decision task 5 hr after drug administration (the procedures and results for the other tasks are described in Soutschek et al., 2020b, and Soutschek et al., 2020b). In the intertemporal decision task, the magnitude of the immediate reward option varied between 0 and 16 Swiss francs (in steps of 2 Swiss francs), while for the LL option a fixed amount of 16 Swiss francs was delivered after a variable delay of 0–180 days. A total of 54 trials was administered where each combination of SS and LL reward options was presented once. SS and LL options were randomly presented on either the right or left screen side until a choice was made, and participants indicated their choices by pressing the right arrow key (for the option presented on the right side) or the left arrow key (for the option on the left side).
## Drift diffusion modeling
We analyzed drug effects on intertemporal decision making with hierarchical Bayesian drift diffusion modeling using the JAGS software package (Plummer, 2003). JAGS utilizes Markov Chain Monte Carlo sampling for Bayesian estimation of drift diffusion parameters (drift rate ν, boundary α, bias ζ, and non-decision time τ) via the Wiener module (Wabersich and Vandekerckhove, 2014) on both the group and the participant level. In our models, the upper boundary (decision threshold) was associated with a choice of the LL option, the lower boundary with a choice of the SS option. A positive drift rate thus indicates evidence accumulation toward the LL option, a negative drift rate toward the SS option. We first describe how the models were set up for the D2 antagonist study. As we were interested in how dopamine modulates different subcomponents of the choice process, in DDM-1 we assumed that the drift rate v is influenced by the comparisons of reward magnitudes and delays between the SS and LL options (Amasino et al., 2019; Dai and Busemeyer, 2014):[1]ν′=β1 (Magnitudediff)+β2 (Drug×Magnitudediff)+β3 (Delaydiff)+β4 (Drug×Delaydiff) Magnitudediff indicates the difference between the reward magnitudes of the LL and SS options, Delaydiff indicates the difference between the corresponding delays. Both Magnitudediff and Delaydiff were z-transformed to render the size of the parameter estimates comparable (Amasino et al., 2019). Following previous procedures, we transformed v’ with a sigmoidal link function as this procedure explains observed behavior better than linear link functions (Fontanesi et al., 2019; Wagner et al., 2020). Indeed, also the current data were better explained by a DDM with (DIC =9478) than without (DIC =10,283) a sigmoidal link (where vmax indicates the upper and lower borders of the drift rate):[2]ν=2×β5(vmax) + β6(Drug×vmax)1+exp(−v′)−(β5(vmax) + β6(Drug×vmax)) Next, we assessed whether delay costs affect the starting bias parameter ζ, as assumed by proximity accounts (Soutschek et al., 2022; Westbrook and Frank, 2018):[3]ζ=β7 (Intercept)+β8 (Drug)+β9 (Delaydiff)+β10 (Drug × Delaydiff) We also investigated whether the drug affected the decision threshold parameter α (Equation 4) or the non-decision time τ (Equation 5):[4]α=β11 (Intercept)+β12 (Drug)[5]τ=β13 (Intercept)+β14 (Drug)
As the experiment followed a within-subject design, we modeled all parameters both on the group level and on the individual level by assuming that individual parameter estimates are normally distributed around the mean group-level effect with a standard deviation λ (which was estimated separately for each group-level effect). We tested for significant effects by checking whether the $95\%$ HDIs of the posterior samples of group-level estimates contained zero. Note that all statistical inferences were based on assessment of group-level estimates, as individual estimates might be less reliable due to the limited number of trials for each participant. We excluded the trials with the $2.5\%$ fastest and $2.5\%$ slowest response times to reduce the impact of outliers on parameter estimation (Amasino et al., 2019; Wagner et al., 2020). As priors, we assumed standard normal distributions for all group-level effects (with mean = 0 and standard deviation = 1) and gamma distributions for λ (Wagner et al., 2020). For model estimation, we computed two chains with 500,000 samples (burning = 450,000, thinning = 5). R was used to assess model convergence in addition to visual inspection of chains. For all effects, R was below 1.01, indicating model convergence.
We compared DDM-1 also with alternative process models. DDM-2 was identical to DDM-1 but did not estimate starting bias as free parameter, assuming ζ=0.5 instead, whereas DDM-3 left out the influences of Magnitudediff and Delaydiff on the drift rate. DDM-4 assessed whether the starting bias is modulated by the sum of the delays (as measure of overall proximity, Delaysum) rather than Delaydiff. In DDM-5 we assumed that the drift rate depends on the comparison of the hyperbolically discounted subjective values of the two choice options rather than on the comparison of choice attributes (Konovalov and Krajbich, 2019). In particular, the drift rate ν’ (prior to being passed through the sigmoidal link function) was calculated with:[6]v′=LL reward magnitude1+(β1+β2(Drug))×LL delay−SS reward magnitude1+(β1+β2(Drug))×SS delay Here, β1 corresponds to the hyperbolic discount factor, which determines the hyperbolically discounted subjective values of the available choice options.
Finally, we considered a model without influence of Delaydiff on the starting bias but with separate non-decision times for rewards and delays. In more detail, DDM-6 included an additional parameter τdiff which indicated whether the accumulation process started earlier for delays than for rewards (τdiff > 0) or vice versa (τdiff < 0). For example, if τdiff > 0, evidence accumulation for delays starts directly after the non-decision time τ, whereas the accumulation process for reward magnitudes starts at τ + τdiff (and then influences the drift rate together with Delaydiff until the decision boundary is reached). A recent study showed that such time-varying drift rates can be calculated as follows (Lombardi and Hare, 2021):[7]v′={β1(Mdiff)+β2(Drug×Mdiff) ifτdiff<0 & τdiff+τ<RTβ3(Ddiff)+β4(Drug×Ddiff)ifτdiff>0& τdiff+τ<RTτ+τdiffRT−τ+τdiff×(β1(Mdiff)+β2(Drug×Mdiff))+RT−τRT−τ+τdiff×(β1(Mdiff)+β2(Drug×Mdiff)+β3(Ddiff)+β2(Drug×Ddiff))ifτdiff<0 & τ<RTτdiffRT−τ×(β3(Ddiff)+β4(Drug×Ddiff))+RT−τ−τdiffRT−τ×(β1(Mdiff)+β2(Drug×Mdiff)+β3(Ddiff)+β2(Drug×Ddiff))ifτdiff>0 & τdiff+τ<RT For the ease of reading, Magnitudediff and Delaydiff are abbreviated as Mdiff and Ddiff, respectively.
For the D1 agonist study, we computed the same DDMs as for the D2 antagonist study. However, because the D1 agonist experiment followed a between-subject design, we estimated separate group-level parameters for the four between-subject drug groups (placebo, 6, 15, 30 mg). We tested for significant group differences by computing the $95\%$ HDI for the differences between the posterior samples of group-level estimates. For model estimation, we computed two chains with 100,000 samples (burning = 50,000, thinning = 5), which ensured that R values for all group-level effects were below 1.01.
We compared model fits between the different DDMs with the deviance information criterion (DIC) as implemented in the Rjags package. We note that JAGS does not allow computing more recently developed model selection criteria such as the Pareto smoothed importance sampling leave-one-out (PSIS-LOO) approach. However, a recent comparison of model selection approaches found that PSIS-LOO had a slightly higher false detection rate than DIC, but in general both PSIS-LOO and DIC led to converging conclusions (Lu et al., 2017). There is therefore good reason to assume that our findings were not biased by the employed model selection approach.
## Posterior predictive checks and parameter recovery analyses
We performed posterior predictive checks to assess whether the DDMs explained key aspects of the empirical data. For this purpose, we simulated 1000 RT distributions based on the individual parameter estimates from all DDMs. We then binned trials into quintiles based on differences in reward magnitude and plotted the observed empirical data and the simulated data (averaged across the 1000 simulations) as a function of these bins, separately for each individual participant. We performed the same analysis by binning trials based on differences in delay instead of reward magnitude.
We conducted a parameter recovery analysis by re-computing DDM-1 on 10 randomly selected data sets which were simulated based on the original DDM-1 parameters. We checked parameter recovery by assessing whether group-level parameters from the simulated data lie within the $95\%$ HDI of the original parameter estimates.
## Model-free analyses
We analyzed choice data also in a model-free manner and with a hyperbolic discounting model. In the model-free analysis of the D2 antagonist study, we regressed choices of LL vs. SS options on fixed-effect predictors for Drug, Magnitudediff, Delaydiff, and the interaction terms using Bayesian mixed models as implemented in the brms package in R (Bürkner, 2017). For the D1 agonist study, the same MGLM was used with the only difference that Drug (0, 6, 15, 30 mg) represented a between- rather than a within-subject factor. All predictors were also modeled as random slopes in addition to participant-specific random intercepts. Finally, the hyperbolic discounting model was fit using the hBayesDM toolbox (Ahn et al., 2017), using a standard hyperbolic discounting function:[8]SVdiscounted=reward magnitude1+k×delay To translate subjective value into choices, we fitted a standard softmax function to each participant’s choices:[9]P(choice of LL option)=11+e−βtemp×(SVLL−SVSS) We estimated parameters capturing the strength of hyperbolic discounting (k) and choice consistency (βtemp) separately for each participant and experimental session by computing two chains of 4000 iterations (burning = 2000). We then performed a Bayesian t-test on the log-transformed individual parameter estimates under placebo vs. amisulpride using the BEST package (Kruschke, 2013).
## Funding Information
This paper was supported by the following grants:
## Data availability
The data supporting the findings of this study and the data analysis code are available on Open Science Framework (https://osf.io/dp2me/).
The following dataset was generated: SoutschekA 2023Intertemporal Choice_amisulprideOpen Science Frameworkdp2me
## References
1. Abbas AI, Hedlund PB, Huang XP, Tran TB, Meltzer HY, Roth BL. **Amisulpride is a potent 5-HT7 antagonist: relevance for antidepressant actions in vivo**. *Psychopharmacology* (2009) **205** 119-128. DOI: 10.1007/s00213-009-1521-8
2. Acheson A, de Wit H. **Bupropion improves attention but does not affect impulsive behavior in healthy young adults**. *Experimental and Clinical Psychopharmacology* (2008) **16** 113-123. DOI: 10.1037/1064-1297.16.2.113
3. Ahn WY, Haines N, Zhang L. **Revealing neurocomputational mechanisms of reinforcement learning and decision-making with the hbayesdm package**. *Computational Psychiatry* (2017) **1** 24-57. DOI: 10.1162/CPSY_a_00002
4. Amasino DR, Sullivan NJ, Kranton RE, Huettel SA. **Amount and time exert independent influences on intertemporal choice**. *Nature Human Behaviour* (2019) **3** 383-392. DOI: 10.1038/s41562-019-0537-2
5. Antonelli F, Ko JH, Miyasaki J, Lang AE, Houle S, Valzania F, Ray NJ, Strafella AP. **Dopamine-agonists and impulsivity in Parkinson’s disease: impulsive choices vs. impulsive actions**. *Human Brain Mapping* (2014) **35** 2499-2506. DOI: 10.1002/hbm.22344
6. Arrondo G, Aznárez-Sanado M, Fernández-Seara MA, Goñi J, Loayza FR, Salamon-Klobut E, Heukamp FH, Pastor MA. **Dopaminergic modulation of the trade-off between probability and time in economic decision-making**. *European Neuropsychopharmacology* (2015) **25** 817-827. DOI: 10.1016/j.euroneuro.2015.02.011
7. Beeler JA. **Thorndike’s law 2.0: dopamine and the regulation of thrift**. *Frontiers in Neuroscience* (2012) **6**. DOI: 10.3389/fnins.2012.00116
8. Beeler JA, Mourra D. **To do or not to do: dopamine, affordability and the economics of opportunity**. *Frontiers in Integrative Neuroscience* (2018) **12**. DOI: 10.3389/fnint.2018.00006
9. Bürkner PC. **Brms: an R package for bayesian multilevel models using stan**. *Journal of Statistical Software* (2017) **80** 1-28. DOI: 10.18637/jss.v080.i01
10. Cools R, Gibbs SE, Miyakawa A, Jagust W, D’Esposito M. **Working memory capacity predicts dopamine synthesis capacity in the human striatum**. *The Journal of Neuroscience* (2008) **28** 1208-1212. DOI: 10.1523/JNEUROSCI.4475-07.2008
11. Dai J, Busemeyer JR. **A probabilistic, dynamic, and attribute-wise model of intertemporal choice**. *Journal of Experimental Psychology. General* (2014) **143** 1489-1514. DOI: 10.1037/a0035976
12. Evers EA, Stiers P, Ramaekers JG. **High reward expectancy during methylphenidate depresses the dopaminergic response to gain and loss**. *Social Cognitive and Affective Neuroscience* (2017) **12** 311-318. DOI: 10.1093/scan/nsw124
13. Figner B, Knoch D, Johnson EJ, Krosch AR, Lisanby SH, Fehr E, Weber EU. **Lateral prefrontal cortex and self-control in intertemporal choice**. *Nature Neuroscience* (2010) **13** 538-539. DOI: 10.1038/nn.2516
14. Floresco SB. **Prefrontal dopamine and behavioral flexibility: shifting from an “ inverted-U” toward a family of functions**. *Frontiers in Neuroscience* (2013) **7**. DOI: 10.3389/fnins.2013.00062
15. Foerde K, Figner B, Doll BB, Woyke IC, Braun EK, Weber EU, Shohamy D. **Dopamine modulation of intertemporal decision-making: evidence from Parkinson disease**. *Journal of Cognitive Neuroscience* (2016) **28** 657-667. DOI: 10.1162/jocn_a_00929
16. Fontanesi L, Gluth S, Spektor MS, Rieskamp J. **A reinforcement learning diffusion decision model for value-based decisions**. *Psychonomic Bulletin & Review* (2019) **26** 1099-1121. DOI: 10.3758/s13423-018-1554-2
17. Frank MJ, O’Reilly RC. **A mechanistic account of striatal dopamine function in human cognition: psychopharmacological studies with cabergoline and haloperidol**. *Behavioral Neuroscience* (2006) **120** 497-517. DOI: 10.1037/0735-7044.120.3.497
18. Hasler G. **Can the neuroeconomics revolution revolutionize psychiatry?**. *Neuroscience and Biobehavioral Reviews* (2012) **36** 64-78. DOI: 10.1016/j.neubiorev.2011.04.011
19. Kayser AS, Vega T, Weinstein D, Peters J, Mitchell JM. **Right inferior frontal cortex activity correlates with tolcapone responsivity in problem and pathological gamblers**. *NeuroImage. Clinical* (2017) **13** 339-348. DOI: 10.1016/j.nicl.2016.12.022
20. Keeler JF, Pretsell DO, Robbins TW. **Functional implications of dopamine D1 vs. D2 receptors: a “ prepare and select ” model of the striatal direct vs. indirect pathways**. *Neuroscience* (2014) **282** 156-175. DOI: 10.1016/j.neuroscience.2014.07.021
21. Kirschner M, Rabinowitz A, Singer N, Dagher A. **From apathy to addiction: insights from neurology and psychiatry**. *Progress in Neuro-Psychopharmacology & Biological Psychiatry* (2020) **101**. DOI: 10.1016/j.pnpbp.2020.109926
22. Konovalov A, Krajbich I. **Revealed strength of preference: inference from response times**. *Judgment and Decision Making* (2019) **14** 381-394. DOI: 10.1017/S1930297500006082
23. Kruschke JK. **Bayesian estimation supersedes the T test**. *Journal of Experimental Psychology. General* (2013) **142** 573-603. DOI: 10.1037/a0029146
24. Lerner TN, Kreitzer AC. **Neuromodulatory control of striatal plasticity and behavior**. *Current Opinion in Neurobiology* (2011) **21** 322-327. DOI: 10.1016/j.conb.2011.01.005
25. Lombardi G, Hare T. **Piecewise Constant Averaging Methods Allow for Fast and Accurate Hierarchical Bayesian Estimation of Drift Diffusion Models with Time-Varying Evidence Accumulation Rates**. *PsyArXiv* (2021). DOI: 10.31234/osf.io/5azyx
26. Lu ZH, Chow SM, Loken E. **A comparison of Bayesian and frequentist model selection methods for factor analysis models**. *Psychological Methods* (2017) **22** 361-381. DOI: 10.1037/met0000145
27. MacKillop J, Amlung MT, Few LR, Ray LA, Sweet LH, Munafò MR. **Delayed reward discounting and addictive behavior: a meta-analysis**. *Psychopharmacology* (2011) **216** 305-321. DOI: 10.1007/s00213-011-2229-0
28. McClure SM, Laibson DI, Loewenstein G, Cohen JD. **Separate neural systems value immediate and delayed monetary rewards**. *Science* (2004) **306** 503-507. DOI: 10.1126/science.1100907
29. Missale C, Nash SR, Robinson SW, Jaber M, Caron MG. **Dopamine receptors: from structure to function**. *Physiological Reviews* (1998) **78** 189-225. DOI: 10.1152/physrev.1998.78.1.189
30. Mori M, Tsutsui-Kimura I, Mimura M, Tanaka KF. **5-Ht3 antagonists decrease discounting rate without affecting sensitivity to reward magnitude in the delay discounting task in mice**. *Psychopharmacology* (2018) **235** 2619-2629. DOI: 10.1007/s00213-018-4954-0
31. Niv Y, Daw ND, Joel D, Dayan P. **Tonic dopamine: opportunity costs and the control of response vigor**. *Psychopharmacology* (2007) **191** 507-520. DOI: 10.1007/s00213-006-0502-4
32. Plummer M.. **JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling**. (2003)
33. Reeck C, Wall D, Johnson EJ. **Search predicts and changes patience in intertemporal choice**. *PNAS* (2017) **114** 11890-11895. DOI: 10.1073/pnas.1707040114
34. Robbins TW, Everitt BJ. **Functions of dopamine in the dorsal and ventral striatum**. *Seminars in Neuroscience* (1992) **4** 119-127. DOI: 10.1016/1044-5765(92)90010-Y
35. Salamone JD, Correa M. **The mysterious motivational functions of mesolimbic dopamine**. *Neuron* (2012) **76** 470-485. DOI: 10.1016/j.neuron.2012.10.021
36. Schoemaker H, Claustre Y, Fage D, Rouquier L, Chergui K, Curet O, Oblin A, Gonon F, Carter C, Benavides J, Scatton B. **Neurochemical characteristics of amisulpride, an atypical dopamine D2/D3 receptor antagonist with both presynaptic and limbic selectivity**. *The Journal of Pharmacology and Experimental Therapeutics* (1997) **280** 83-97. PMID: 8996185
37. Schultz W. **Neuronal reward and decision signals: from theories to data**. *Physiological Reviews* (2015) **95** 853-951. DOI: 10.1152/physrev.00023.2014
38. Soutschek A, Burke CJ, Raja Beharelle A, Schreiber R, Weber SC, Karipidis II, Ten Velden J, Weber B, Haker H, Kalenscher T, Tobler PN. **The dopaminergic reward system underpins gender differences in social preferences**. *Nature Human Behaviour* (2017) **1** 819-827. DOI: 10.1038/s41562-017-0226-y
39. Soutschek A., Tobler PN. **Motivation for the greater good: neural mechanisms of overcoming costs**. *Current Opinion in Behavioral Sciences* (2018) **22** 96-105. DOI: 10.1016/j.cobeha.2018.01.025
40. Soutschek A, Gvozdanovic G, Kozak R, Duvvuri S, de Martinis N, Harel B, Gray DL, Fehr E, Jetter A, Tobler PN. **Dopaminergic d**. *Biological Psychiatry* (2020a) **87** 678-685. DOI: 10.1016/j.biopsych.2019.09.002
41. Soutschek A, Kozak R, de Martinis N, Howe W, Burke CJ, Fehr E, Jetter A, Tobler PN. **Activation of D1 receptors affects human reactivity and flexibility to valued cues**. *Neuropsychopharmacology* (2020b) **45** 780-785. DOI: 10.1038/s41386-020-0617-z
42. Soutschek A, Jetter A, Tobler PN. **Toward a unifying account of dopamine’s role in cost-benefit decision making**. *Biological Psychiatry Global Open Science* (2022). DOI: 10.1016/j.bpsgos.2022.02.010
43. Toubia O, Johnson E, Evgeniou T, Delquié P. **Dynamic experiments for estimating preferences: an adaptive method of eliciting time and risk parameters**. *Management Science* (2013) **59** 613-640. DOI: 10.1287/mnsc.1120.1570
44. Volkow ND, Baler RD. **Now vs later brain circuits: implications for obesity and addiction**. *Trends in Neurosciences* (2015) **38** 345-352. DOI: 10.1016/j.tins.2015.04.002
45. Wabersich D, Vandekerckhove J. **Extending JAGS: a tutorial on adding custom distributions to JAGS (with a diffusion model example)**. *Behavior Research Methods* (2014) **46** 15-28. DOI: 10.3758/s13428-013-0369-3
46. Wagner B, Clos M, Sommer T, Peters J. **Dopaminergic modulation of human intertemporal choice: a diffusion model analysis using the D2-receptor antagonist haloperidol**. *The Journal of Neuroscience* (2020) **40** 7936-7948. DOI: 10.1523/JNEUROSCI.0592-20.2020
47. Webber HE, Lopez-Gamundi P, Stamatovich SN, de Wit H, Wardle MC. **Using pharmacological manipulations to study the role of dopamine in human reward functioning: a review of studies in healthy adults**. *Neuroscience and Biobehavioral Reviews* (2021) **120** 123-158. DOI: 10.1016/j.neubiorev.2020.11.004
48. Weber SC, Beck-Schimmer B, Kajdi M-E, Müller D, Tobler PN, Quednow BB. **Dopamine D2/3- and μ-opioid receptor antagonists reduce cue-induced responding and reward impulsivity in humans**. *Translational Psychiatry* (2016) **6**. DOI: 10.1038/tp.2016.113
49. Westbrook A, Frank M. **Dopamine and proximity in motivation and cognitive control**. *Current Opinion in Behavioral Sciences* (2018) **22** 28-34. DOI: 10.1016/j.cobeha.2017.12.011
50. Westbrook A, van den Bosch R, Määttä JI, Hofmans L, Papadopetraki D, Cools R, Frank MJ. **Dopamine promotes cognitive effort by biasing the benefits versus costs of cognitive work**. *Science* (2020) **367** 1362-1366. DOI: 10.1126/science.aaz5891
51. Zhao WJ, Diederich A, Trueblood JS, Bhatia S. **Automatic biases in intertemporal choice**. *Psychonomic Bulletin & Review* (2019) **26** 661-668. DOI: 10.3758/s13423-019-01579-9
|
---
title: Targeting the fatty acid binding proteins disrupts multiple myeloma cell cycle
progression and MYC signaling
authors:
- Mariah Farrell
- Heather Fairfield
- Michelle Karam
- Anastasia D'Amico
- Connor S Murphy
- Carolyne Falank
- Romanos Sklavenitis Pistofidi
- Amanda Cao
- Catherine R Marinac
- Julie A Dragon
- Lauren McGuinness
- Carlos G Gartner
- Reagan Di Iorio
- Edward Jachimowicz
- Victoria DeMambro
- Calvin Vary
- Michaela R Reagan
journal: eLife
year: 2023
pmcid: PMC9995119
doi: 10.7554/eLife.81184
license: CC BY 4.0
---
# Targeting the fatty acid binding proteins disrupts multiple myeloma cell cycle progression and MYC signaling
## Abstract
Multiple myeloma is an incurable plasma cell malignancy with only a $53\%$ 5-year survival rate. There is a critical need to find new multiple myeloma vulnerabilities and therapeutic avenues. Herein, we identified and explored a novel multiple myeloma target: the fatty acid binding protein (FABP) family. In our work, myeloma cells were treated with FABP inhibitors (BMS3094013 and SBFI-26) and examined in vivo and in vitro for cell cycle state, proliferation, apoptosis, mitochondrial membrane potential, cellular metabolism (oxygen consumption rates and fatty acid oxidation), and DNA methylation properties. Myeloma cell responses to BMS309403, SBFI-26, or both, were also assessed with RNA sequencing (RNA-Seq) and proteomic analysis, and confirmed with western blotting and qRT-PCR. Myeloma cell dependency on FABPs was assessed using the Cancer Dependency Map (DepMap). Finally, MM patient datasets (CoMMpass and GEO) were mined for FABP expression correlations with clinical outcomes. We found that myeloma cells treated with FABPi or with FABP5 knockout (generated via CRISPR/Cas9 editing) exhibited diminished proliferation, increased apoptosis, and metabolic changes in vitro. FABPi had mixed results in vivo, in two pre-clinical MM mouse models, suggesting optimization of in vivo delivery, dosing, or type of FABP inhibitors will be needed before clinical applicability. FABPi negatively impacted mitochondrial respiration and reduced expression of MYC and other key signaling pathways in MM cells in vitro. Clinical data demonstrated worse overall and progression-free survival in patients with high FABP5 expression in tumor cells. Overall, this study establishes the FABP family as a potentially new target in multiple myeloma. In MM cells, FABPs have a multitude of actions and cellular roles that result in the support of myeloma progression. Further research into the FABP family in MM is warrented, especially into the effective translation of targeting these in vivo.
## eLife digest
Multiple myeloma is a type of blood cancer for which only a few treatments are available. Currently, only about half the patients with multiple myeloma survive for five years after diagnosis. Because obesity is a risk factor for multiple myeloma, researchers have been studying how fat cells or fatty acids affect multiple myeloma tumor cells to identify new treatment targets.
Fatty acid binding proteins (FABPs) are one promising target. The FABPs shuttle fatty acids and help cells communicate. Previous studies linked FABPs to some types of cancer, including another blood cancer called leukemia, and cancers of the prostate and breast. A recent study showed that patients with multiple myeloma, who have high levels of FABP5 in their tumors, have worse outcomes than patients with lower levels. But, so far, no one has studied the effects of inhibiting FABPs in multiple myeloma tumor cells or animals with multiple myeloma.
Farrell et al. show that blocking or eliminating FABPs kills myeloma tumor cells and slows their growth in a dish (in vitro) and in some laboratory mice. In the experiments, the researchers treated myeloma cells with drugs that inhibit FABPs or genetically engineered myeloma cells to lack FABPs. They also show that blocking FABPs reduces the activity of a protein called MYC, which promotes tumor cell survival in many types of cancer. It also changed the metabolism of the tumor cell. Finally, the team examined data collected from several sets of patients with multiple myeloma and found that patients with high FABP levels have more aggressive cancer.
The experiments lay the groundwork for more studies to determine if drugs or other therapies targeting FABPs could treat multiple myeloma. More research is needed to determine why inhibiting FABPs worked in some mice with multiple myeloma but not others, and whether FABP inhibitors might work better if combined with other cancer therapies. There were no signs that the drugs were toxic in mice, but more studies must prove they are safe and effective before testing the drugs in humans with multiple myeloma. Designing better or more potent FABP-blocking drugs may also lead to better animal study results.
## Introduction
Fatty acid binding protein (FABP) family members are small (12–15 kDa) proteins that reversibly bind lipids (Hotamisligil and Bernlohr, 2015). The 10 human FABP isoforms are functionally and spatially diverse, consisting of ten anti-parallel beta sheets, which form a beta barrel that shuttles fatty acids across membranes of organelles including peroxisomes, mitochondria, nuclei, and the endoplasmic reticulum (Furuhashi and Hotamisligil, 2008). FABPs influence cell structure, intracellular and extracellular signaling, metabolic and inflammatory pathways (Hotamisligil and Bernlohr, 2015), and maintain mitochondrial function (Field et al., 2020). While most cell types express a single FABP isoform, some co-express multiple FABPs that can functionally compensate for each other if needed (Hotamisligil et al., 1996; Shaughnessy et al., 2000), suggesting that broad FABP targeting may be necessary. FABP insufficiencies in humans and mice induce health benefits (eg. protection from cardiovascular disease, atherosclerosis, and obesity-induced type 2 diabetes), suggesting these to be safe therapeutic targets (Cao et al., 2006; Maeda et al., 2005; Tuncman et al., 2006).
Multiple myeloma (MM), a clonal expansion of malignant plasma cells, accounts for ~$10\%$ of hematological neoplasms (Rajkumar, 2020). Myeloma cell growth initiates in and spreads throughout the bone marrow, leading to aberrant cell proliferation and destruction of the bone (Fairfield et al., 2016). Treatments for myeloma patients have greatly improved within the past two decades (American cancer institute, 2022), but most patients eventually relapse, demonstrating the need to pursue more novel types of MM treatment. Few therapies are designed to specifically target molecules involved in the MM cell metabolism, despite recent findings that MM cells uptake fatty acids through fatty acid transport proteins, which can enhance their proliferation (Panaroni et al., 2022). Links between FABP4 and cancer have been demonstrated in prostate, breast, and ovarian cancer, and acute myeloid leukemia (AML; Al-Jameel et al., 2017; Carbonetti et al., 2019; Herroon et al., 2013; Lan et al., 2011; Mukherjee et al., 2020; Shafat et al., 2017; Yan et al., 2018; Zhou et al., 2019). FABP5 has been less widely studied in cancer, but is known to transport ligands to PPARD (Tan et al., 2002), which can intersect with many pro-tumor pathways that increase proliferation, survival (Adhikary et al., 2013; Di-Poï et al., 2002; Tan et al., 2001), and angiogenesis (Wang et al., 2006), and decrease tumor suppressor expression (Tan et al., 2001). Herein we explored the oncogenic function of the FABPs in MM by examining therapeutic targeting with FABP inhibitors (FABPi) in multiple cell lines in vitro, and using genetic knockout of FABP5, pre-clinical models, large cell line datasets, and multiple patient datasets. Our results suggest FABPs are a novel target in MM due to the plethora of important biological functions that FABPs modulate to control cellular processes at multiple levels.
## FABP5 is vital for MM cells and genetic knockout results in reduced cell number
We first examined FABP gene expression in MM cell lines and found that FABP5 was the most highly-expressed FABP isoform in GFP+/Luc+MM.1S and RPMI-8226 cells (Supplementary file 1, Fairfield et al., 2021) and that some other FABPs were also expressed to a lesser extent (eg. FABP3, FABP4, and FABP6). FABP5 protein was also robustly expressed in these cells (Figure 1A, Figure 1—figure supplement 1A), and FABP5 consistently showed the expression in haematopoetic/lymphoid lineage lines within the Cancer Cell Line Encyclopedia (CCLE) at the gene level (Figure 1—figure supplement 1B) and protein level (Figure 1—figure supplement 1C, “DepMap 22Q2, 2022; Ghandi et al., 2019; Nusinow et al., 2020). In MM cell lines specifically, FABP5 was the most highly expressed at the gene level (Figure 1B) and FABP5 and FABP6 were the most highly expressed at the protein level (Figure 1—figure supplement 1D). In the Broad Institute’s Cancer Dependency Map (DepMap; Tsherniak et al., 2017), of all the FABPs, only FABP5 exhibited a negative CERES Score (–0.30) in all 20 MM cell lines, demonstrating a strong reliance on FABP5 for their survival (Figure 1—figure supplement 2A). Interestingly, all cancer types within the DepMap database had negative FABP5 CERES values (Figure 1—figure supplement 2B). Importantly, many fatty acid metabolism genes, including FABP5, had negative CERES scores (shown in blue) in MM cells (Figure 1—figure supplement 2C).
**Figure 1.:** *FABPi significantly impair MM cell growth and induces apoptosis.(A) Confocal overlay immunofluorescence images show FABP5 (red) expressed in cytoplasm of GFP+/Luc+ MM.1S cells. Nuclei identified with DAPI (blue), cells stained with secondary antibody alone (control) or primary plus secondary antibodies (FABP5 staining), scale bar = 200 µm. (B) Comparison of basal gene expression of FABP isoforms in 30 myeloma cell lines. Data extracted from the Cancer Cell Line Encyclopedia (CCLE; DepMap, Broad (2022): DepMap 22Q2 Public. figshare. dataset. https://doi.org/10.6084/m9.figshare.19700056.v2), filtered in excel, and graphs made in Graphpad PRISM (v7.04) using scatter dot plots (mean ± SEM). (C, D) MM cell numbers after being exposed to (C) BMS309403 and (D) SBFI-26 for 72 hr; 50 µM dose (~EC50) indicated by arrows. (E) GFP+/Luc+MM.1S cell numbers after treatment with inhibitors in combination (50 µM each). Vehicle vs BMS309403 (24 hr, *; 48 hr, ****; 72 hr, ****). Vehicle vs SBFI-26 (24 hr, *; 48 hr, ****; 72 hr, ****). Vehicle vs BMS309403 +SBFI-26 (24 hr, ***; 48 hr, ****; 72 hr, ****). BMS309403 vs BMS309403 +SBFI-26 (48 hr, **; 72 hr, ****). SBFI-26 vs BMS309403 +SBFI-26 (48 hr, **; 72 hr, ****). Two-way ANOVA analysis with Tukey’s multiple comparisons test analysis. (F) CellTiter-Glo analysis of human mesenchymal stem cells after treatment with BMS309403 or SBFI-26 for 72 hr. Data are mean ± SEM and represent averages or representative runs of at least three experimental repeats. One-way ANOVA with Dunnett’s multiple comparison test significance shown as *p<0.05. **p<0.01. ***p<0.001. ****p<0.0001. **** p<0.0001. Please see 8 supplements to Figure 1.*
Based on these initial findings, we next examined the effect of FABP5 knockout (KO) in MM cells. FABP5 KO (FABP5KO) MM.1R cells exhibited a $94\%$ editing efficiency with a ~$59\%$ KO efficiency after expansion (Figure 1—figure supplement 3A and B). We observed an $84\%$ reduction in FABP5 expression in the edited pool (Figure 1—figure supplement 3C), confirming functional FABP5 knockdown. FABP4 expression was not altered (Figure 1—figure supplement 3D), but FABP6 expression was increased in the edited cells (Figure 1—figure supplement 3E). FABP5 KO cells showed a slight reduction in cell numbers at 48, 72, and 96 hr, versus controls (Figure 1—figure supplement 3F).
## Pharmacological inhibition of FABPs reduces myeloma cell proliferation in vitro
Having observed potential compensation among FABP family members in the FABP5KO cells, we next used two well-known FABP inhibitors (FABPi): BMS309403 and SBFI-26, which specifically and potently inhibit FABPs by binding their canonical ligand-binding pockets, or inducing conformational changes, for example by binding their substrate entry portal region (Hsu et al., 2017). Ligand-binding assays determined that BMS309403 has Ki values in solution of <2, 250, and 350 nM for FABP4, FABP3, and FABP5, and that SBFI-26 has Ki values of 900 and 400 nM for FABP5 and FABP7, respectively, as reported on the manufacturers’ datasheets (Hsu et al., 2017). BMS309403 and SBFI-26 consistently demonstrated dose-dependent decreases in myeloma cell numbers, in all 7 MM lines screened, at 72 hr (Figure 1C and D; Supplementary files 2 and 3) and earlier (Figure 1—figure supplement 4). BMS309403 (50 µM), SBFI-26 (50 µM), or the combination (50 µM BMS309403 +50 µM SBFI-26) reduced cell numbers at 24, 48, and 72 hr by $39\%$, $42\%$, and $83\%$, respectively in GFP+/Luc+MM.1S cells (Figure 1E), suggesting that targeting different FABPs, or using different FABP inhibitors, could be beneficial. Non-cancerous cells were much less sensitive to FABPi (Figure 1F), intimating the potential clinical translation of these or similar FABP inhibitors, as supported by prior literature showing the safety of FABP inhibitors (Al-Jameel et al., 2017; Mukherjee et al., 2020). No change in amount or localization of FABP5 protein after treatment with FABPi was observed by immunofluorescence (Figure 1—figure supplements 5 and 6) at 24 hr in GFP+/Luc+MM.1S or RPMI-8226 cells, or by western blotting at 24, 48, or 72 hr in GFP+/Luc+MM.1S cells (Figure 1—figure supplement 7A and B). Gene expression of FABP3, FABP4, FABP5, and FABP6 were also not consistently altered with treatments (Figure 1—figure supplement 7C) as assessed by qRT-PCR. These data suggest that FABP activity, but not protein expression, is decreased by these FABP inhibitors. Recombinant FABP4 and FABP5 did not affect MM.1S cell number (Figure 1—figure supplement 8A,B).
## FABPi induce gene expression changes in myeloma cells that affect a range of cellular processes and pathways linked to survival
To identify transcriptional changes that may mediate the effects of FABP inhibition on cell number, we treated GFP+/Luc+MM.1S cells with a vehicle control, the single FABP inhibitors alone (50 µM), or the combination of FABPi (50 µM of each) for 24 hr in vitro, isolated total RNA, and performed RNA-Seq. Principal component analysis (PCA) demonstrated that the FABP inhibitor groups exhibited distinct gene expression profiles, and that the combination treatment differed the most from vehicle-treated cells (Figure 2A). Over 14,000 genes were analyzed, revealing 93 significant differentially expressed (DE) genes within all three treatment groups, compared to the vehicle control (FDR <0.2): 90 downregulated and 3 upregulated (Figure 2B; Supplementary file 4). Consistent with decreased levels of transcription, we also observed significantly lower levels of 5-hydroxymethylcytosine in cells treated with FABPi compared to vehicle-treated cells (Figure 2C), suggesting decreases in active chromatin. This finding is consistent with previous reports linking FABP depletion to DNA methylation signatures in other cancers (Mukherjee et al., 2020; Yan et al., 2018).
**Figure 2.:** *RNA sequencing analysis of MM1S cells treated with FABPi for reveals unique gene expression patterns.(A) Principal component analysis of cells after 24 hr treatments. (B) Venn diagram displays the overlapping and specific genes dysregulated with FABPi (FDR cutoff of 0.2). (C) Global hydroxymethylation DNA analysis of MM.1S cells after 24 hr of combination treatment. Data represent mean and +/- SEM using n=3 biological repeats, and * p<0.05 using an unpaired, two-tailed Student t-test. (D) Ingenuity pathway analysis of RNA-Seq results (p-value of overlap by Fisher’s exact test, significance threshold value of p<0.05(-log value of 1.3)). Stringdb (FDR cutoff of 0.2) of the combination therapy versus control showing (E) the 1 upregulated pathway and (F) 5 of the many downregulated pathways. MYC, a central node, is circled for emphasis. GFP+/Luc +MM.1 S cells were used for these experiments. Please see 3 supplements to Figure 2.*
To further understand the mechanisms of action of FABPi, we investigated which pathways were impacted in our RNA-*Seq data* using STRINGdb and IPA (Ingenuity Pathway Analysis). IPA was specifically used to investigate canonical pathways, while STRINGdb was used to examine connectivity of DE genes and enrichment for specific gene ontology terms, as well as molecules in Reactome and KEGG pathways. In total, 15 IPA canonical pathways were commonly dysregulated in all three treatment groups including Cell Cycle: G2/M DNA Damage Checkpoint Regulation, EIF2 Signaling, Sirtuin Signaling Pathway, and the NER pathway (Figure 2D; Supplementary file 5). The one upregulated pathway according to STRING was ‘cellular response to interferon gamma signaling’ in the combination group (Figure 2E; Supplementary file 6). The top downregulated pathways in the combination treatment by STRING analysis are in Supplementary file 7.
Interestingly, both IPA and STRING databases revealed commonly downregulated pathways related to the unfolded protein response (UPR) or ER stress responses for BMS309403 (Figure 2—figure supplement 1A–C), SBFI-26 (Figure 2—figure supplement 3, and the combination Figure 2D and F). Three of the five downregulated Reactome pathways in the combination group were related to UPR or ER stress (Figure 2F), driven by molecular players such as XBP1, BIP (HSPA5), and IRE1 (ERN1) (Figure 2—figure supplement 3A). Downregulation of total XBP1 by the combination treatment was confirmed after 24 hr (Figure 2—figure supplement 3B) and heatmaps visually demonstrated the downregulation of genes involved in XBP1 signaling (Figure 2—figure supplement 3C) and the UPR (Figure 2—figure supplement 3D) as determined by IPA. Interestingly, MYC, a known oncogene, was a central node in STRING analysis (Figure 2F) and among the top 10 most downregulated genes in RNA-Seq from combination treatments (Supplementary file 8).
## FABPi induces protein changes in MM cells that affect a range of cellular processes and pathways linked to survival
To identify protein changes resulting from FABPi, we treated GFP+/Luc+MM.1S cells with the single inhibitors (50 µM) or the combination (50 µM of each) for 48 hr, isolated total cell lysate proteins, and performed a mass spectrometry-based proteomic analysis. ( Numbers of significant proteins, Supplementary file 9; protein names, Supplementary files 10-15). PCA analysis showed a tight grouping of samples based on treatments (Figure 3—figure supplement 1A); 15 proteins were commonly upregulated and 15 were commonly downregulated between all treatments (Figure 3—figure supplement 1B, C; Supplementary files 16 and 17).
We then compared significant genes and proteins identified by both RNA-Seq and proteomics (Figure 3A and B). CCL3, a chemokine for monocytes, macrophages, and neutrophils, was upregulated by SBFI-26, BMS309403, and their combination in proteomics, and upregulated by the combination treatments in RNA-Seq. Ki67, a proliferation marker, and PTMA, a negative regulator of apoptosis, were both significantly downregulated in the combination treatment in RNA-Seq and proteomics, and in the single drug treatments in proteomics (Figure 3B), indicating cell death and cell cycle arrest likely result from FABPi.
**Figure 3.:** *Forty-eight hr proteomic analysis of MM1S cells treated with FABPi reveals a unique protein signature.MM.1S cells were assessed by proteomics after 48 hr treatments with BMS309403 (50 µM), SBFI-26 (50 µM) or the combination, and compared to results from RNA-Seq. N=3 biological replicates and three technical replicates Venn diagram comparison of (A) upregulated genes and (B) downregulated proteins in proteomics and RNA-Seq among BMS309403 and SBFI-26 treated cells compared to vehicle. (C–F) Pathway analysis of proteomic data of significantly upregulated or downregulated proteins in MM.1S cells treated with both FABPi (BMS309403 +SBFI-26). (C, D) String analysis of upregulated (C) or downregulated (D) pathways. (E) Top 10 significantly changed pathways with FABP inhibition. For IPA analysis, orange represents positive z-score, blue indicates a negative z-score, gray represents no activity pattern detected and white represents a z-score of 0. (F) Ingenuity pathway analysis of the Cell Death and Survival heatmap. Numbers in boxes represent: (1) Cell death of melanoma lines; (2) Cell death of carcinoma cell lines; (3) Cell death of neuroblastoma cell lines; (4) Cell death of breast cancer cell lines; (5) Cell death of connective tissue cells; (6) Cell death of fibroblast cell lines; (7) Cell viability of myeloma cell lines; (8) Apoptosis of tumor cell lines; (9) Apoptosis of carcinoma cell lines. GFP+/Luc +MM.1 S cells were used for these experiments. Please see 7 supplements to Figure 3.*
STRING analysis of proteomic data suggested many other systemic changes (eg, downregulation of DNA replication and other viability/proliferation processes and upregulation of lysosome, carboxylic acid catabolic process, and mitochondrial pathways) induced by the FABPi combination treatments (Figure 3C and D). STRING analysis also revealed interesting up- and downregulated pathways by BMS309403 or SBFI-26 treatments alone (Figure 3—figure supplement 2, Figure 3—figure supplement 3). IPA analysis revealed ‘EIF2 Signaling’ to have the highest negative Z-score for all FABPi treatments in proteomics (Figure 3E; Figure 3—figure supplement 4A, Figure 3—figure supplement 5A). IPA ‘Cell Death and Survival’ heatmap analysis showed increases in cell death and apoptosis pathways and decreases in cell viability pathways after FABPi combination treatment (Figure 3F; Figure 3—figure supplements 4B and 5B). Interestingly, MYC was the most significant predicted upstream regulator, found to be strongly inhibited in the BMS309403, SBFI-26, and combination treatments from IPA proteomic analysis (Supplementary files 18-20).
Since MYC was found as a central node or commonly downregulated gene/pathway in our RNA-Seq and proteomic data analyses, we investigated MYC’s role in FABP signaling in myeloma cells. We confirmed decreased MYC expression in GFP+/Luc+MM.1S cells treated with the FABPi combination, and also saw a trend for this in 5TGM1-TK cells treated with SBFI-26 (Figure 3—figure supplement 6A, B). MYC protein level was also decreased in GFP+/Luc+MM.1S cells at 24, 48, and 72 hr with FABPi (Figure 4A and B), with similar trends observed in 5TGM1-TK myeloma cells (Figure 3—figure supplement 6C, D). The decrease in MYC-regulated genes with FABPi was also visualized in both the RNA-Seq (Figure 4C) and proteomic data (Figure 4D) by heatmap analysis. In RNA-Seq data, treatment with BMS309403 induced aberrant gene expression of 171 genes known to be regulated by MYC (Supplementary file 21), with 138 of those having expression patterns consistent with MYC inhibition. Similarly, co-treatment induced changes in 91 genes modulated by MYC (Figure 3—figure supplement 7; 68 consistent with MYC downregulation), while 29 MYC targets were aberrantly expressed with SBFI-26 treatment (Figure 2—figure supplement 2D; 18 consistent with MYC downregulation).
**Figure 4.:** *FABPi target MYC and the MYC pathway.(A) Representative western blot and (B) quantification of MYC protein and β-actin (housekeeping control) at 24, 48, and 72 hr after treatment with BMS309403 (50 µM), SBFI-26 (50 µM), or the combination. (C) RNA-seq and (D) Proteomic analysis of expression of genes/proteins involved in MYC signaling shown as heatmap visualizations. Curated lists are based on IPA MYC Pathway list, known MYC-regulated genes, and proteins present in proteomics. (E) 72 hr BMS309403 dose curve with and without Myc inhibitor 10058-F4 (37.5 µM) in MM.1S cells. (F) 72 hr SBFI-26 dose curve with and without 10058-F4 (37.5 µM) in MM.1S cells. Data represent mean ± SEM from n=3 biological repeats, analyzed with one-way ANOVA with significance shown as *p<0.05. **p<0.01. ****p<0.0001. GFP+/Luc +MM.1 S cells were used for these experiments. Please see 1 supplement to Figure 4.*
To test if MYC inhibition was a major cause of the FABPi effects on MM cells, we then pharmacologically inhibited MYC and tested a range of doses of FABPi. MYC inhibition alone dramatically reduced cell numbers at 72 hr, as expected, and FABP inhibition had less of an effect on MM cells when MYC was already inhibited (seen by a slope of ~0 for the black lines) (Figure 4E and F). This suggests that much of the effect of FABPi is through decreased MYC signaling, although the strong effect of the MYC inhibitor makes this difficult to determine unhesitantly. Similar results were seen at 24 and 48 hr (Figure 4—figure supplement 1).
## FABPi impair MM cell metabolism, mitochondrial function, and cell viability
Having observed effects of the inhibitors on metabolic processes such as mitochondrial function and oxidative phosphorylation in the proteomic data, we next assessed mitochondrial function and metabolic changes using a Cell Mito Stress Test (Figure 5—figure supplement 1A). After 24 hr treatments, all FABPi treatments decreased basal mitochondrial oxygen consumption rates (OCR) and OCR dedicated to ATP production (Figure 5—figure supplement 1B). Maximal respiration and spare respiratory capacity were decreased with SBFI-26 and combination treatments, suggesting FABP inhibition reduces the ability of MM cells to meet their energetic demands.
To determine the effects of FABPi on fatty acid oxidation (FAO) specifically, we treated tumor cells with etoxomir, an FAO inhibitor, with or without the combination FABPi treatment (Figure 5—figure supplement 2). The combination of FABPi alone again strongly reduced mitochondrial respiration in most of the parameters assessed. Interestingly, etoxomir treatment caused a slight, but significant reduction in OCR when it was administered, demonstrating some reliance of MM cells on FAO for mitochondrial respiration. However, the FABPi had a much greater effect on MM mitochondrial respiration than etoxomir alone, suggesting that FABPi treatment inhibited mitochondrial respiration through another mechanism. Also, since maximal respiration was decreased in the Etox +FABPi combination compared to FABPi alone, it appears that FABPi treatment does not completely block FAO when used alone. Overall, the data demonstrate that mitochondrial respiration is inhibited by FABPi. To assess whether metabolic dysfunction could be caused by damaged mitochondria, we utilized tetramethylrhodamine, ethyl ester (TMRE) staining and flow cytometric analysis to assess mitochondrial transmembrane potential. GFP+/Luc+MM.1S cells treated with BMS309403 or the combination (BMS309403 +SBFI-26) had decreased TMRE staining (Figure 5—figure supplement 3), suggesting that BMS309403 damages MM cell mitochondria.
We next investigated if reactive oxygen species (ROS), a major byproduct of the electron transport chain, were changing in MM cells after FABPi treatment. CellROX staining showed that the combination FABPi treatment significantly increased total ROS at 24, 48, or 72 hr in MM.1S (ATCC), U266 and OPM2 cells (Figure 5A, Figure 5—figure supplements 4A and 5A, 6 A). We also found changes in superoxide, a ROS subspecies measured by MitoSOX, after FABP inhibition; in MM.1S (ATCC), BMS309403 and the FABPi combination increased superoxides over 72 hr (Figure 5B, Figure 5—figure supplement 4B). In U266, the FABPi combination increased superoxides at each time point, and BMS309403 increased superoxides at 48 and 72 hr (Figure 5—figure supplement 5B). In OPM2, all FABPi treaments increased superoxides at all timepoints (Figure 5—figure supplement 6B). Overall, FABP proteins are vital to MM cells for normal oxygen consumption, mitochondrial potential maintenance and ATP production, adaption to increased demands for energy, and control of ROS, including superoxides.
**Figure 5.:** *FABPi significantly induce reactive oxygen species, impair MM cell growth and induce apoptosis.(A) Reactive oxygen species measured by MFI (mean fluorescent intensity) with CellROX Green staining at 72 hr in MM.1S cells. TBHP is positive control. (B) Superoxide levels shown as MFI, determined with MitoSOX staining, at 72 hr in MM.1S cells. (C) MM.1S cell cycle states with the FABPi alone (50 µM) or in combination (50 µM of each). (D) Apoptosis in MM.1S cells with FABPi as in C. Data are mean ± SEM unless otherwise stated and represent averages or representative runs of at least three experimental repeats. One-way ANOVA with Dunnett’s multiple comparison test significance shown as *p<0.05. **p<0.01. ***p<0.001. ****p<0.0001. ATCC MM.1S cells were used for these experiments. Please see 10 supplements to Figure 5.*
We next investigated FABP inhibitor effects on MM cell cycle and apoptosis. In GFP+/Luc +MM.1 S, FABPi combination treatment increased the G0/G1 population at 24, 48, and 72 hr, and decreased G2/M at 48 and 72 hr, suggesting a G0/G1 arrest and a negative impact on cell cycle progression (Figure 5C, Figure 5—figure supplement 7). FABPi combination treatment also increased apoptosis in GFP+/Luc +MM.1 S cells at all three time points, and SBFI-26 did as well at 72 hr (Figure 5D). To determine if effects of the combination treatment were reflective purely of a higher level of inhibition, or a synergism of the different FABP inhibitors, we assessed apoptosis, cell cycle, and proliferation using a range of doses and FABP inhibitor combinations (Figure 5—figure supplements 8 and 9). Interestingly, in GFP+/Luc +MM.1 S, 100 µM of BMS309403 induced larger impacts on apoptosis, cell cycle arrest, and Ki67 expression than all other treatments (Figure 5—figure supplement 8) suggesting it may be more effective than SBFI-26 in this cell line. In RPMI-8226 cells, apoptosis and cell cycle arrest were also induced with the combination or single inhibitors (Figure 5—figure supplement 9). Interestingly, in this cell line, 100 µM of single inhibitors elicited similar responses to combination treatment inhibitors (50 µM BMS309403 +50 µM SBFI-26), suggesting that FABP inhibitors may have slightly different efficacies in different MM cells. We subsequently investigated the combination of FABPi with dexamethasone, a first-line therapy for MM patients. Dexamethasone and FABPi showed promising, additive effects on cell numbers and apoptosis in GFP+/Luc+MM.1S, OPM2, and RPMI-8226 cells (Figure 5—figure supplement 10), suggesting a potential to combine FABP inhibition with current therapies. In summary, FABPi treatment in vitro elicited multitudinous changes in MM cell transcriptomes and proteomes, resulting in alterations in cell cycle progression, cell viability, apoptosis, MYC signaling, cellular metabolism.
## FABPi has variable effects on tumor burden and survival in myeloma mouse models
To investigate the efficacy of FABPi in vivo, we utilized two murine myeloma models. First, we examined the efficacy of FABPi in the GFP+/Luc+MM.1S SCID-beige xenograft model. Treatments began with 5 mg/kg BMS309403, 1 mg/kg SBFI-26, the combination, or vehicle 3 X/week (Figure 6—figure supplement 1A) one day after GFP+/Luc+MM.1S tail vein inoculation. Bone mineral density (BMD), but not bone mineral content (BMC), was slightly lower after BMS309403 treatment (Figure 6—figure supplement 1B, C), although this group also started with a slightly lower BMD, and fat mass, but not lean mass was decreased with the combination treatment (Figure 6—figure supplement 1D, E). FABPi did not influence mouse weight (Figure 6A), but a difference in tumor burden assessed by BLI was detected at day 21 with all FABPi versus vehicle, and this difference continued throughout the study (Figure 6B and C). Consistent with reduced tumor burden, mice receiving FABPi survived longer than the vehicle-treated mice (Figure 6D). Similarly, in the GFP+/Luc+ 5TGM1-TK/KaLwRij syngeneic model (Figure 6—figure supplement 2A), mice treated with 5 mg/kg BMS309403 showed increased survival (Figure 6E) without significant body weight changes (Figure 6—figure supplement 2B). However, due to variable responses to different doses of FABP inhibitors in mice of different ages (publication in preparation), we repeated the GFP+/Luc+MM.1S SCID-Beige study. As in our first study, mice gained weight over the course of the study with no treatment effect (Figure 7A). However, in this cohort, treatments had on slight, non-significant effects on tumor burden (Figure 7B and C), and no effect on survivial (Figure 7D). The in vivo data thus demonstrate a need to explore and identify factors currently limiting the efficacy of these FABP inhibitors in vivo.
**Figure 6.:** *FABPi do not consistently increase survival or decrease tumor burden in myeloma xenograft (cohort 1) and syngeneic mouse models.(A) Mouse weights from the first cohort of SCID-beige- GFP+/Luc +MM.1 S mice treated with BMS309403, SBFI-26, or the combination from day of injection plotted as Mean ± SEM. (B) Tumor burden from cohort 1 of SCID-beige GFP+/Luc +MM.1 S mice assessed by bioluminescence imaging (BLI) in MM.1S model. In panel B, One-way ANOVA with Dunnett’s multiple comparison test significance shown as *p<0.05. **p<0.01. ***p<0.001. ****p<0.0001. Vehicle vs BMS309403 (24 days, ****; 28 days, ****). Vehicle vs SBFI-26 (24 days ****; 28 days, ****). Vehicle vs BMS309403 +SBFI-26 (24 hr, ****; 28 days, ****). BMS309403 vs BMS309403 +SBFI-26 (24 days NS; 28 days, ***). SBFI-26 vs BMS309403 +SBFI-26 (24 and 28 days, NS). BMS309403 vs SBFI-26 (24 hr, NS 28 days, **). (C) Representative BLI images from cohort 1 of SCID-Beige MM.1Sgfp+luc+ mice at days 24 and 28. (D) Survival of SCID-Beige MM.1Sluc+ mice from first cohort; analysis performed by Kaplan-Meier Survival Analysis, Log-Rank (Mantel-Cox) test, p<0.0001, n=11. (E) Survival of KaLwRij mice injected with 5TGM1 cells. Survival analysis performed by Kaplan-Meier Survival Analysis, Log-Rank (Mantel-Cox) test, p=0.0023, Vehicle n=8, BMS309403 n=9. Please see 2 supplements to Figure 6.* **Figure 7.:** *FABPi do not consistently increase survival or decrease tumor burden in myeloma xenograft mice (cohort 2).(A) Mouse weights from the second cohorts of SCID-beige- GFP+/Luc +MM.1 S mice treated with BMS309403, SBFI-26, or the combination from day of injection plotted as Mean ± SEM. (B) Tumor burden from two separate cohorts of SCID-beige GFP+/Luc +MM.1 S assessed by bioluminescence imaging (BLI) in MM.1S model. No significance detected with One-way ANOVA with Dunnett’s multiple comparison test. (C) Representative BLI images from second cohort of SCID-Beige MM.1Sluc+ mice at days 29 and 33. (D) Survival of SCID-Beige GFP+/Luc +MM.1 S mice from second cohort- no significance observed. Analysis performed by Kaplan-Meier Survival Analysis, Log-Rank (Mantel-Cox) test, no significance in panel D, n=10.*
## Elevated expression of FABP5 in MM cells corresponds to worse clinical outcomes for patients
To establish potential clinical relevancy, we next tested for an association between FABP5 and MM in independent patient datasets using Multiple Myeloma Research Foundation (MMRF) CoMMpass and OncoMine. In the CoMMpass database, ~$70\%$ of myeloma patient cases exhibited moderate-to-high expression of FABP5 (defined as >10 counts; Figure 8—figure supplement 1A). FABP3, FABP4, and FABP6 were expressed by MM cells at lower levels (Figure 8—figure supplement 1A, insert). We next tested for an association between FABP5 and MM in independent microarray datasets using OncoMine. The Zhan dataset indicated that patients with higher MM cell FABP5 expression had significantly shorter overall survival (OS) than those with lower expression (Zhan et al., 2006), (Figure 8A and B), which was confirmed in the Mulligan dataset (Mulligan et al., 2007, Figure 8C). Similarly, the Carrasco dataset showed a shorter progression-free survival (PFS) in MM patients with high versus low FABP5 expression (Figure 8D, Carrasco et al., 2006). Moreover, patients of the high-risk/poor prognosis subtype had higher FABP5 expression than those in the more favorable subtypes (Zhan et al., 2006, Figure 8E). In the Chng dataset (Chng et al., 2007), relapsed patients showed increased FABP5 expression versus newly-diagnosed patients (Figure 8F). Worse PFS and OS in patients with elevated FABP5 expression levels was then confirmed in the CoMMpass dataset (log-rank-value for high vs. low expression,<0.0001 for both PFS and OS; Figure 8—figure supplement 1B, C). In the Cox proportional hazards model, high FABP5 expression was associated with a $64\%$ increased risk of disease progression or death (HR: 1.64; CI: 1.34, 2.00), and a twofold increased risk of early death (HR: 2.19; CI: 1.66, 2.88).
**Figure 8.:** *FABP proteins are clinically relevant in MM.(A, B) Kaplan-Meier analysis of overall survival (OS) of MM patients in Zhan et al. dataset stratified as top (n=100) or bottom (n=100) FABP5 expressing, or all patients above (n=207) or below (n=207) the median. (C) Kaplan–Meier analysis of relapse-free survival of MM patient groups in Mulligan et al. dataset stratified as top (n=100) or bottom (n=100) FABP5 expressing. (D) Kaplan–Meier analysis of relapse-free survival of MM patient groups in Carrasco et al. dataset: high (n=20) and low (n=20) FABP5 relative to median. (E) Molecular subtypes of MM cells were analyzed for FABP5 expression and significance between all groups and the highly aggressive subtype (PR) was observed using a one-way ANOVA with Dunnett’s multiple comparison testing. (CD1 or CD2 of cyclin D translocation; HY: hyperdiploid; LB: low bone disease; MF or MS with activation of MAF, MAFB, or FGRF3/MMSET; PR: proliferation. From reference Zhan et al., 2006). (F) Data from Chng et al. dataset from newly-diagnosed (ND) (n=73) and relapsed MM patients (n=28) as mean with 95% confidence interval (CI), with statistical analysis performed using a Mann Whitney test. Data are mean ± SD unless otherwise stated. *p<0.05. **p<0.01. ***p<0.001. ****p<0.0001. Please see 2 supplements to Figure 8.*
Since obesity is a known MM risk factor (Marinac et al., 2018) and FABP5 can regulate diet-induced obesity (Shibue et al., 2015), we explored the influence of body mass index (BMI) on our findings in the CoMMpass dataset. BMI was not associated with FABP5 in a general linear model adjusting for age or sex, and the addition of BMI to the Cox model of FABP5 expression described above did not materially attenuate the effect estimates, suggesting FABP5 expression is a BMI-independent biomarker for MM aggressiveness. We also examined genes correlated with FABP5 and found none ontologically related to obesity, again suggesting that FABP5 effects are BMI-independent (Figure 8—figure supplement 1D; Supplementary file 22). When all other FABPs expressed in MM cells (FABP6, FABP3, and FABP4) were examined, only FABP6 also affected hazard ratios (although effect sizes were not as large as FABP5) for PFS (HR:1.48; CI 1.172, 1.869) and OS (HR:1.837, CI: 1.347, 2.504), indicating that it may also be a biomarker for worse outcomes (Figure 8—figure supplement 2). Overall, these data across multiple datasets provide rationale to explore the molecular and functional roles of the FABPs in the MM setting.
## Discussion
Herein, we describe our finding that the FABPs are a family of targetable proteins that support myeloma cells. Targeting the FABP family may be a new, efficacious method to inhibit MM progression that necessitates further investigation. FABP inhibition induced apoptosis, cell cycle arrest, and inhibition of proliferation of numerous MM cell lines in vitro, while having negligible effects on non-MM cells. In vivo, FABP inhibition caused no weight loss or other overt toxicities, supporting similar findings in other pre-clinical oncology studies (Al-Jameel et al., 2017; Bosquet et al., 2018; Herroon et al., 2013; Mukherjee et al., 2020). Further analysis and experiments are still needed (e.g. histological analysis of major organs and quantification of serum toxicity markers) before targeting FABPs can be translated to humans. Myeloma cell proliferation also decreased with genetic knockout of FABP5, although FABP signaling compensation may have occurred via upregulation of FABP6. Clinical datasets and DepMap analyses also demonstrated the importance of the FABPs, specifically FABP5, and perhaps FABP6, in MM. A recent publication also analyzed patient datasets and similarly found correlations between high FABP5 expression and worse MM patient survival, and between FABP5 mRNA levels and different immune microenvironment properties, suggesting a role for FABP5 in immunomodulation, an important hypothesis that we have not yet further explored (Jia et al., 2021).
FABP inhibition decreased expression of genes and pathways related to ER stress, XBP1, and the UPR. For example, EIF5B was downregulated by all FABPi in proteomic analysis and RNA-Seq. EIF5B is a translation initiation factor that promotes the binding of subunits and antagonizes cell cycle arrest via modulations of p21 and p27, and depletion of EIF5B could contribute to activation of ER stress (Ross et al., 2019). eIF5B has been implicated as a oncoprotein that aids in managing ER stress and evading apoptosis (Ross et al., 2019). Myeloma cells constitutively activate the UPR to protect themselves from ER stress-induced death that would otherwise result from the continuous production and secretion of immunoglobulins. Therefore, the inhibition of the protective UPR appears to be one mechanism by which FABP inhibition damages MM cells. We also observed decreased XBP1 expression and decreased XBP1 pathway activation with FABPi. Based on studies demonstrating the IRE/XBP1 pathway is required for differentiation and survival of MM cells (White-Gilbertson et al., 2013), this could be a driver of the decreased UPR and MM cell death resulting from FABPi.
Interestingly, decreased UPR and XBP1 signaling could result from decreased MYC expression directly, since MYC directly controls IRE1 transcription by binding to its promoter and enhancer (Zhao et al., 2018). While others have shown that BMS309403 reduces UPR in skeletal muscle cells (Bosquet et al., 2018), this has not previously been shown in tumor cells before now. As a transcription factor, c-MYC can act as an activator or repressor through either direct binding to regulatory regions, or through chromatin modulation. A MYC activation signature is seen in $67\%$ of MM patients (Chng et al., 2011), and this signature influences the progression from monoclonal gammopathy of undetermined significance (MGUS) to MM. Targeting MYC in MM cells by knockdown (Cao et al., 2021) or treatment with a small molecule inhibitor (Holien et al., 2012) induces cell death; however, the importance of MYC in many healthy cell types make targeting it difficult. Thus, our study represents a novel approach to reducing MYC by targeting the FABP family. This work also builds upon data that myeloma cells exhibit aberrant amino acid, lipid, and energy metabolism (Steiner et al., 2018), and data revealing the importance of metabolic enzymes in myeloma tumorigenesis (Li et al., 2021) and drug resistance (Lipchick et al., 2021) by demonstrating the role of FABPs in MM cell metabolism and mitochondrial integrity. In sum, we demonstrated that FABPs are a new protein family potentially important in MM.
Herein we demonstrated the pivotal role of FABPs in myeloma cell survival in vitro and in clinical datasets. However, in vivo results were mixed, and followup analysis needs to be performed before clinical work can be initiated, such as optimizing doses or delivery mechanisms and determing if any effects in vivo were due to the early drug administration (which could affect homing). More systemic analysis of mice, such as testing immune cell effects that could reduced efficacy of FABPi, is also needed since FABPi alter a plethora of phenotypes across the body, including glucose metabolism, lipid metabolism, and inflammation (Bosquet et al., 2018; Lan et al., 2011; Shibue et al., 2015) – all of which have potential implications for myeloma disease progression. Demonstration of efficacy of FABPi on established MM tumors in vivo, as well as effects of FABPi on primary MM cells, which we were not able to obtain in our laboratory, must also preceed clinical translation. Lastly, an assessment of the FABPi effects on tumor cells in vivo (e.g. effects on proliferation markers (proliferating cell nuclear antigen or Ki67) or apoptosis) would be reveal in vivo effects of FABPi.
## Conclusion
Pharmacologic or genetic inhibition of FABPs result in reduced growth, decreased UPR and MYC signaling, decreased metabolism, and induction of apoptosis in myeloma cells in vitro. FABP inhibition in vivo had variable effects. Patients that have high FABP5 expression within their myeloma cells have worse outcomes and high FABP5 is seen in MM clinical subtypes that have a more aggressive phenotype. Collectively, these data demonstrate the anti-myeloma effects of FABP inhibition, suggest different mechanisms driving this, and thus describe a potentially new target for MM therapy.
## Materials and reagents
Recombinant FABP4 [10009549] and FABP5 [10010364] were purchased from Caymen Chemical (Ann Arbor, MI). Dexamethasone (dex) (VWR), BMS3094013 (Caymen Chemical), SBFI-26 (Aobious, Gloucester, MA), and the MYC inhibitor 10058-F4 (Abcam, Cambridge, UK) were dissolved in DMSO. In vitro, dex was used at 80 µM; BMS309403 and SBFI-26 were used at 50 µM either as single treatments or in combination, unless otherwise stated.
## Cell culture
Human myeloma cell lines GFP+/Luc+MM.1S, MM.1S (ATCC, Manassas, VA), RPMI-8226 (ATCC), MM.1R (ATCC), OPM2 (DSMZ), and mouse cell line GFP+/Luc+ 5TGM1-TK (5TGM1-TK) were maintained in standard MM cell media: RPMI-1640 medium, $10\%$ FBS (Atlanta Biologicals, Flowery Branch, GA), and 1 X Antibiotic-Antimycotic (100 U/ml penicillin, 100 μg/ml streptomycin, 0.25 μg/ml fungizone) (ThermoFisher Scientific, Grand Island, NY). U266 (ATCC) cells were maintained in MM growth medium +$15\%$ FBS (Atlanta Biologicals). NCI-H929 (H929, ATCC) cells were maintained in MM growth medium plus 0.05 mM 2-mercaptoethanol. Vk*MYC cells were maintained in RPMI-1640 medium +$20\%$ FBS. Vk*MYC, and GFP+/Luc+MM.1S cells were generously provided by Dr. Ghobrial (Dana-Farber Cancer Institute). GFP+/Luc+ 5TGM1-TK cells were generously provided by Dr. Roodman (Indiana University). FABP5 WT and KO MM.1R (ATCC) cells were generated by Synthego (Menlo Park, CA). Primary human MSCs were isolated from deidentified cancellous bone from the acetabulum received from donors (men and women) after total hip arthroplasty through the MaineHealth Biobank after IRB approval and informed consent (Biobank IRB # 2526). Human MSCs were isolated by surface adherence and cultured with a growth media of DMEM, $10\%$ FBS, and $1\%$ an antibiotic-antimycotic as previously described (Fairfield et al., 2018; Reagan et al., 2014; Schutze et al., 2005).
## Cell number quantification, cell cycle, and apoptosis in vitro assays
Cell numbers were measured by bioluminescence imaging (BLI), CellTiter Glo (Promega, Madison, WI), or RealTime Glo (Promega) assays, according to the manufacturer’s instructions, and read on a GLOMAX microplate reader (Promega). Cell cycle analysis was measured with DAPI (0.5 µg/ml) and Ki67 staining (Alexa Fluor 647 Ki67 antibody, 350510, BioLegend). Apoptosis was measured using an annexin V/APC and DAPI Kit (BioLegend); total apoptotic cells were defined as annexin V+/DAPI++annexin V+/DAPI- populations. Data were acquired on a Miltenyi MACSquant flow cytometer and data analysis was performed using FlowJo software (BD Life Sciences). For BLI in vitro imaging of luciferase expressing cells, sterile luciferin (10 µL/well from a 7.5 mg/mL stock, VivoGlo, Promega) is added to white, 96 well plates of cells, given 5 min to reach equilibrium, and read in a GLOMAX microplate reader (Promega). For flow cytometry, a minimum of 10,000 events was collected and gated off forward and side scatter plots.
## Immunofluorescence and confocal microscopy
Myeloma cells were fixed and permeabilized using the Nuclear Factor Fixation and Permeabilization Buffer Set (Biolegend, San Diego CA), stained with DAPI (20 µg/ml), antibodies against FABP5 (MA5-2402911215, 1.25 µg/mL, ThermoFisher), and Alexa Fluor 647 anti-rabbit secondary antibody (A-21244, 1.25 µg/mL, ThermoFisher). Cells were then rinsed twice with PBS and imaged on a Leica SP5X laser scanning confocal microscope (Leica Microsystems, Buffalo Grove, IL) with Leica LAS acquisition software, using settings as previously described (Fairfield et al., 2021) using a 20×dry objective on 1.5 mm glass-bottomed dishes (MatTek Corporation, Ashland, MA).
## CRISPR/Cas9 FABP5-knockout MM.1R cell line development and characterization
An FABP5-KO pool of MM.1R cells and controls were generated by Synthego using the Guide target ACTTAACATTCTACAGGAGT, Guide sequence ACUUAACAUUCUACAGGAGU and PAM recognition sequence GGG. MM.1R were used as they were found to be the most amenable to CRISPR-*Cas9* genetic targeting technology. MM.1R cells were obtained from ATCC by Synthego and confirmed as mycoplasma-negative and free from microbial contamination. Control and KO cell pools were provided to the Reagan lab at passage 4 and passage 5, respectively. Single-cell clones were not able to be expanded and thus the pooled sample was used. PCR and sequencing primers used for confirmation were: Fwd: TTTCATATATGTAAAGTGCTGGCTC and Rev:TGATACAGCCTATCATTCTAGAAGCT.
Wild type and edited cells were thawed and allowed to grow for 1 week prior to seeding (5000 cells/well; 96-well plate with Real Time Glo (RTG)). Cells from both pools were seeded at ~1 million cells/T25 for 96 hr prior to harvest for RNA (Qiazol). The expression of FABP family members in both experiments was assessed by qRT-PCR.
## Western blotting
Protein from cell lysates was extracted using RIPA buffer (Santa Cruz, 24948) or Minute Total Protein Extraction Kit (Invent Biotechnology, SD-001/SN-002) and quantified using a DC protein assay kit II (Bio-Rad, 5000112). Samples were denatured in 4 x laemmli buffer (Bio-Rad, 1610747) with β-mercaptoethanol (VWR, 97064–880) for 5 min at 95 °C, run on $12\%$ polyacrylamide gels (Bio-Rad, 5671043), and transferred onto PVDF membranes (Bio-Rad, 1704156). Blots were blocked for 2 hr in $5\%$ non-fat milk (VWR, 10128–602). Staining protocols with antibody details are in Supplementary file 23. All antibodies were incubated at 4 °C. Blots were imaged after adding ECL reagents (Biorad, 1705060) for 5 min and visualized using Azure c600 (Azure biosystems).
## Seahorse metabolic assays
GFP+/Luc +MM.1 S cells were cultured for 24 hr with BMS309403 (50 µM), SBFI-26 (50 µM), or both and then adhered to Cell Tak (Corning)-coated Seahorse XF96 V3 PS cell culture microplates (Agilent, #101085–004) at a density of 60,000 cells/well in XF DMEM medium pH, 7.4 (Aglient #103576–100) supplemented with 1 mM sodium pyruvate, 2 mM glutamine and 10 mM glucose according to the manufacturer’s instructions (https://www.agilent.com/cs/library/technicaloverviews/public/5991-7153EN.pdf). Oxygen consumption rate in cells was then measured in basal conditions and in response to oligomycin (1.25 µM), FCCP (1 µM), and rotenone and antimycin A (0.5 µM). Data were analyzed using Wave Software V2.6 and Seahorse XF Cell Mito Stress Test Report Generators (https://www.agilent.com). A one-way ANOVA was used for each parameter with Uncorrected Fisher’s LSD multiple comparison post-hoc testing for significance. Results represent 5 independent experiments with 1 representative experiment shown with 20–24 wells per condition. In a separate set of experiments, cells were treated as above, however etomoxir or vehicle was added at a final concentration of 4 μM prior to subjecting the cells to the mitochondrial stress test. Due to artificial increases in OCR caused by further warming of the plate during ETOX measurements, the ETOX response data was normalized to MM.1S (vehicle, vehicle) control cells.
## TMRE mitochondrial membrane potential Assay
GFP+/Luc+ MM.1S cells were cultured for 24, 48, and 72 hr with BMS309403 (50 µM), SBFI-26 (50 µM), or combination before staining with 0.5 mM TMRE for 30 minutes per Caymen Chemical protocol. Data acquisition was performed on a Miltenyi MACSquant flow cytometer and data analysis was performed using FlowJo analysis software (BD Life Sciences) with a minimum of 10,000 events collected and gated off forward and side scatter plots.
## CellROX green oxidative stress and MitoSOX red mitochondrial superoxide assays
ATCC MM.1S cells were cultured for 24, 48, and 72 hr with BMS309403 (50 µM), SBFI-26 (50 µM), or combination before staining with 500 nM CellROX for 30 min or 5 µM MitoSOX for 10 min per Thermofisher Scientific protocol. Data acquisition was performed on a Miltenyi MACSquant flow cytometer and data analysis was performed using FlowJo analysis software (BD Life Sciences) with a minimum of 10,000 events collected and gated off forward and side scatter plots.
## Quantification of global 5-hydroxymethylcytosine levels
DNA was isolated from 1 million GFP+/Luc +MM.1 S cells after 24 hr of treatment with vehicle (DMSO) or 50 µM BMS309403 and 50 µM SBFI-26 using the DNeasy Blood and Tissue kit (Qiagen, Germantown, MD, USA) per the manufacturer’s instructions. DNA was quantified and tested for quality and contamination using a Nanodrop 2000 (Thermo Fisher Scientific) and subjected to quality control minimum standards of $\frac{260}{230}$>2 and $\frac{260}{280}$>1.8 prior to use in subsequent steps. 100 ng of DNA was then analyzed via MethylFlash Global DNA Hydroxymethylation (5-hmC) ELISA Easy Kit (Cat.# P-1032–48, Epigentek, Farmingdale, NY, USA) per the manufacturer’s instructions.
## Quantitative RT-PCR
GFP+/Luc +MM.1 S, MM.1S, 5TGM1-TK, OPM-2 and RPMI-8226 cells were cultured for 24 hr with treatments prior to mRNA isolation as described above. cDNA synthesis (Applied Biosciences High Capacity cDNA Kit, ThermoScientific, Waltham, MA, USA) was executed prior to quantitative PCR (qRT-PCR) using SYBR Master Mix (Bio-Rad, Hercules, CA, USA) and thermocycling reactions were completed using a CFX-96 (Bio-Rad Laboratories). Data were analyzed using Bio-Rad CFX Manager 3.1 and Excel (Microsoft Corp., Redmond, WA, USA) using the delta-delta CT method. Primer details are in Supplementary file 24. Two wells (technical duplicates) were used at the minimum, for qRT-PCR analysis for each biological data point.
## LC-MS/MS
All sample separations performed in tandem with mass spectrometric analysis are performed on an Eksigent NanoLC 425 nano-UPLC System (Sciex, Framingham, MA) in direct-injection mode with a 3 µL sample loop. Fractionation is performed on a reverse-phase nano HPLC column (Acclaim PepMap 100 C18, 75 µm×150 mm, 3 µm particle, 120 Å pore) held at 45 °C with a flow rate of 350 nL/min. Solvents are blended from LC-MS-grade water and acetonitrile (Honeywell Burdick & Jackson, Muskegon, MI). Mobile phase A is $2\%$ acetonitrile solution, while mobile phase B is $99.9\%$ acetonitrile. Both contain $0.1\%$ formic acid (Optima grade, Fisher Chemical, Waltham, MA). Approximately 1 µg of peptides are applied to the column equilibrated at $3\%$ B and loading continued for 12 min. The sample loop is then taken out of the flow path and the column washed for 30 s at starting conditions. A gradient to $35\%$ B is executed at constant flow rate over 90 min followed by a 3 min gradient to $90\%$ B. The column is washed for 5 min under these conditions before being returned to starting conditions over 2 min.
Analysis is performed in positive mode on a TripleTOF 6600 quadrupole time-of-flight (QTOF) mass spectrometer (Sciex, Framingham, MA). The column eluate is directed to a silica capillary emitter (SilicaTip, 20 µm ID, 10 µm tip ID, New Objective, Littleton, MA) maintained at 2400–2600 V. Nitrogen nebulizer gas is held at 4–6 psi, with the curtain gas at 21–25 psi. The source is kept at 150 °C.
Data acquisition performed by information-dependent analysis (IDA) is executed under the following conditions: a parent ion scan is acquired over a range of 400–1500 mass units using a 200 ms accumulation time. This is followed by MS/MS scans of the 50 most-intense ions detected in the parent scan over ranges from 100 to 1500 mass units. These ions must also meet criteria of a 2+–5+ charge state and of having intensities greater than a 350 counts-per-second (cps) threshold to be selected for MS/MS. Accumulation times for the MS/MS scans are 15 ms. Rolling collision energies are used according to the equation recommended by the manufacturer. Collision energy spread is not used. After an ion is detected and fragmented, its mass is excluded from subsequent analysis for 15 s.
SWATH analysis is performed according to previously-published optimized conditions tailored to the 6600 instrument (Schilling et al., 2017). Briefly, SWATH MS/MS windows of variable sizes are generated using Sciex-provided calculators. Rolling collision energies are used, as well as fragmentation conditions optimized for ions of a 2+ charge state. SWATH detection parameters are set to a mass range of m/$z = 100$–1500 with accumulation times of 25 ms in the high-sensitivity mode. A parent-ion scan is acquired over a range of 400–1500 mass units using a 250 ms accumulation time. The PRIDE (PRoteomics IDEtifications Database) was used to upload and share raw data (Perez-Riverol et al., 2019), and InteractiveVenn software was used to make Venn Diagrams to combine Proteomic and RNAseq data (http://www.interactivenn.net/#) (Heberle et al., 2015). Heatmaps of proteomic data were generated using centroid linkage and Kendall’s Tau distance measurement algorithms with http://www.heatmapper.ca/expression.
## Cell line validation
Cells were authenticated and validated as mycoplasma and virus negative by the Yale Comparative Pathology Research Core on the following dates.
## In vivo experiments
All experimental studies and procedures involving mice were performed in accordance with approved protocols from the MaineHealth Institute for Research (Scarborough, Maine, USA) Institutional Animal Care and Use Committee (#1812 and 2111). In cohort one, eight week old female SCID-beige (CB17.Cg-PrkdcscidLystbg-J/Crl, Charles River) mice were inoculated intravenously (IV) with 5x10^6 GFP+/Luc+MM.1S cells by a blinded investigator. Mice were randomized based on weight and body parameters, then treatments then began 3 X/week with either 5 mg/kg BMS309403, 1 mg/kg SBFI-26, the combination (5 mg/kg BMS309403 +1 mg/kg SBFI-26), or the vehicle ($5\%$ DMSO), intraperitoneally ($$n = 12$$/group), based on safe doses reported previously (Al-Jameel et al., 2017; Yan et al., 2018). Body parameters were assessed with piximus at day 1 and 30. In a second cohort of SCID-Beige mice ($$n = 10$$/group, randomized by weight), a near identical experimental schema was followed, except body parameters were not assessed. In a second animal model, 10–12 week old mice (both sexes, mixed equally between groups) of KaLwRij/C57Bl6 mice (from Dana-Farber Cancer Institute) were injected with 1x10^6 GFP+/Luc+ 5TGM1-TK cells IV by a blinded investigator, randomized by weight, and treated as in the SCID-Beige model with 5 mg/kg BMS309403 ($$n = 9$$) or vehicle ($$n = 8$$). Mice were frequently weighed and monitored for clinical signs of treatment-related side effects. “ Survival endpoints” were mouse death or euthanasia as required by IACUC, based on body conditioning score including weight loss and impaired hind limb use. Survival differences were analyzed by Kaplan-Meier methodology. For bioluminescent imaging, mice were injected with 150 mg/kg i.p. filter-sterilized D-luciferin substrate (VivoGlo, Promega) and imaged after 15 min in an IVIS Lumina LT (Perkin Elmer, Inc; Waltham, MA). Tail vein injector was blinded in all studies; BLI technician was blinded in second SCID-Beige study. Data were acquired and analyzed using LivingImage software 4.5.1. ( PerkinElmer). Body parameters (BMD, BMC, Lean Mass, and Fat Mass) were measured with PIXImus duel-energy X-ray densitometer (GE Lunar, Boston, MA, USA). The PIXImus was calibrated daily with a mouse phantom provided by the manufacturer. Mice were anesthetized using $2\%$ isoflurane via a nose cone and placed ventral side down with each limb and tail positioned away from the body. Full-body scans were obtained and DXA data were gathered and processed (Lunar PIXImus 2, version 2.1). BMD and BMC were calculated by extrapolating from a rectangular region of interest (ROI) drawn around one femur of each mouse, using the same ROI for every mouse, and lean and fat mass were also calculated for the entire mouse, exclusive of the head, using Lunar PIXImus 2.1 software default settings. Each mouse (single animals) was considered the experimental unit (rather than litters or cage of animals). Replicates numbers were decided from experience of the techniques performed and practical considerations. Mice that didn't have reliable IV injections were noted to be dropped, as agreed upon a priori. To minimize confounders, cages were chosen at random for IV tumor injections, and needles loaded with tumor cells were pre-loaded and laid out and then chosen at random. The ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments), a checklist of information to include in publications describing animal research, was followed.
## mRNA isolation and RNA-Seq
Three biological sets of GFP+/Luc+MM.1S cells were cultured for 24 hr with vehicle, 50 μM BMS309403, 50 μM SBFI-26, or the combination prior to mRNA isolation with Qiazol (Qiagen, Germantown, MD) and miRNeasy Mini Kit with on-column DNAse digestion (Qiagen) according to the manufacturer’s protocol. Samples underwent library preparation, sequencing, and analysis at the Vermont Integrative Genomics Resource. mRNA was quantified and tested for quality and contamination using a Nanodrop (Thermo Fisher Scientific) and subjected to quality control standards of $\frac{260}{230}$>2 and $\frac{260}{280}$>1.8 prior to library preparation. Partek Flow (version 10.0.21.0302) was used to analyze the sequence reads. Poorer quality bases from the 3’ end were trimmed (phred score <20), and the trimmed reads (ave. quality >36.7, ave. length 75 bp, ave. GC ~$56\%$) were aligned to the human reference genome hg38 using the STAR 2.6 aligner. Aligned reads were then quantified using an Expectation-Maximization model, and translated to genes. Genes that had fewer than 30 counts were then filtered, retaining 14,089 high count genes. Differentially expression comparisons were performed using DESeq2. Downstream comparisons of IPA canonical pathways and upstream regulators were executed in Excel (Microsoft, Redmond, WA). Data were analyzed through the use of IPA2 (QIAGEN, https://www.qiagenbioinformatics.com/products/ingenuitypathway-analysis) and STRING DB version 11.0. RNAseq heatmap of Myc pathway was generated on http://www.heatmapper.ca/expression applying clustering to rows and columns using single linkage and Pearson distance measurement algorithms.
## Cancer dependency map (DepMap) analysis
Genetic dependency data from the Dependency Map (DepMap) Portal’s CRISPR (Avana) Public20Q3 (https://depmap.org/portal/download/) of 20 human MM cell lines were analyzed and the dependency score (computational correction of copy-number effect in CRISPR-Cas9 essentiality screens (CERES)) of Hallmark Fatty Acid *Metabolism* genes from Gene Set Enrichment Analysis (https://www.gseamsigdb.org) were determined.
## Survival and expression analyses of clinical datasets
The (Zhan et al., 2006) (GSE132604), (Carrasco et al., 2006) (GSE4452), and (Mulligan et al., 2007) (GSE9782) datasets were analyzed using OncoMine (ThermoFisher). The Chng dataset (Chng et al., 2007) showing patient FABP5 mRNA transcript data was analyzed from accession number GEO:GSE6477. The relationship between FABP5 and MM progression was analyzed with Kaplan-*Meier analysis* using log-rank Hazard Ratio (HR) and Gehan-Breslow-Wilcoxon significance testing. Gene expression data were downloaded (GEO; GSE6477), log-transformed, and analyzed with an one-way ANOVA model using the aov() function in R, as previously described (Fairfield et al., 2021).
For survival analysis in the CoMMpass dataset, survival and Transcripts Per Million (TPM)-normalized gene expression data (IA15 data release) were downloaded from the Multiple Myeloma Research Foundation (MMRF)’s Researcher Gateway ($\frac{6}{16}$/2021). Patient samples drawn at timepoints other than the baseline were removed from consideration. Based on the histogram of FABP5 expression levels in the CoMMpass cohort, FABP5 expression follows a right-tailed distribution, whereby a subset of patient tumors exhibit higher levels of FABP5. We discretized FABP5 expression based on the cohort’s mean (10.838), stratified samples as FABP5-high and FABP5-low and plotted Kaplan-Meier curves to showcase its effect on OS and PFS. To derive effect estimates, we examined associations between FABP5-high (vs. FABP5-low) in a Cox proportional Hazards Model. *Exploratory* general linear models also examined the association between BMI and FABP5 expression levels, adjusting for age and sex. Based on the boxplot generated to identify related FABP gene expression levels, FABP3, FABP4 and FABP6 were also significantly expressed in myeloma cells. Thus, following similar procedures, analyses were also conducted based on the cohort’s mean for FABP3 (3.2611), FABP4 (1.624), and FABP6 (0.786).
## Statistical analysis
Data were analyzed using GraphPad Prism v.6 or above, and unpaired Student’s t tests or one-way or two-way ANOVA using Tukey’s correction was performed, unless otherwise stated. Data are expressed as mean ± standard error of the mean (SEM) or standard deviation (SD); ****p≤0.0001; ***$p \leq 0.001$; **$p \leq 0.01$; *$p \leq 0.05.$
## Funding Information
This paper was supported by the following grants:
## Data availability
The clinical datasets used and analyzed during the current study are from Oncomine or data related to accession number GEO:GSE6477. RNA-seq data have been deposited in the NCBI Gene Expression Omnibus (GEO) database with the accession number GSE190699. The mass spectrometry proteomic data have been deposited to the ProteomeXchange *Consortium via* the PRIDE partner respository with the dataset identifier PXD032829.
The following datasets were generated: FarrellM FairfieldH KaramM D'amicoA MurphyCS FalankC PistofidisRS CaoA MarinacCR DragonJ McGuinnessL GartnerC IorioRD JachimowiczE DeMambroV VaryC ReaganMR 2022Fatty acid binding proteins contribute to multiple myeloma cell maintenance through regulation of Myc, the unfolded protein response, and metabolismNCBI Gene Expression OmnibusGSE190699 FarrellM FairfieldH KaramM D'amicoA MurphyCS FalankC PistofidisRS CaoA MarinacCR DragonJ McGuinnessL GartnerC IorioRD JachimowiczE DeMambroV VaryC ReaganMR 2023Fatty Acid Binding Protein 5 is a Novel Target in Multiple MyelomaProteomeXchangePXD032829 The following previously published dataset was used:
ChngWJ KumarS VanwierS AhmannG 2007Expression data from different stages of plasma cell neoplasmNCBI Gene Expression OmnibusGSE6477
## References
1. Adhikary T, Brandt DT, Kaddatz K, Stockert J, Naruhn S, Meissner W, Finkernagel F, Obert J, Lieber S, Scharfe M, Jarek M, Toth PM, Scheer F, Diederich WE, Reinartz S, Grosse R, Müller-Brüsselbach S, Müller R. **Inverse PPARβ/δ agonists suppress oncogenic signaling to the ANGPTL4 gene and inhibit cancer cell invasion**. *Oncogene* (2013) **32** 5241-5252. DOI: 10.1038/onc.2012.549
2. Al-Jameel W, Gou X, Forootan SS, Al Fayi MS, Rudland PS, Forootan FS, Zhang J, Cornford PA, Hussain SA, Ke Y. **Inhibitor SBFI26 suppresses the malignant progression of castration-resistant PC3-M cells by competitively binding to oncogenic FABP5**. *Oncotarget* (2017) **8** 31041-31056. DOI: 10.18632/oncotarget.16055
3. American cancer institute
2022Survival rates for multiple myelomaAmerican Cancer Institutehttps://www.cancer.org/cancer/multiple-myeloma/detection-diagnosis-staging/survival-rates.html. *American Cancer Institute* (2022)
4. Bosquet A, Girona J, Guaita-Esteruelas S, Heras M, Saavedra-García P, Martínez-Micaelo N, Masana L, Rodríguez-Calvo R. **Fabp4 inhibitor BMS309403 decreases saturated-fatty-acid-induced endoplasmic reticulum stress-associated inflammation in skeletal muscle by reducing p38 MAPK activation**. *Biochimica et Biophysica Acta. Molecular and Cell Biology of Lipids* (2018) **1863** 604-613. DOI: 10.1016/j.bbalip.2018.03.004
5. Cao H, Maeda K, Gorgun CZ, Kim HJ, Park SY, Shulman GI, Kim JK, Hotamisligil GS. **Regulation of metabolic responses by adipocyte/macrophage fatty acid-binding proteins in leptin-deficient mice**. *Diabetes* (2006) **55** 1915-1922. DOI: 10.2337/db05-1496
6. Cao Y, Shan H, Liu M, Liu J, Zhang Z, Xu X, Liu Y, Xu H, Lei H, Yu M, Zhang X, Liu W, Bu Z, Fang Z, Ji Y, Yan H, Gu W, Wu Y. **Directly targeting c-myc contributes to the anti-multiple myeloma effect of anlotinib**. *Cell Death & Disease* (2021) **12**. DOI: 10.1038/s41419-021-03685-w
7. Carbonetti G, Wilpshaar T, Kroonen J, Studholme K, Converso C, d’Oelsnitz S, Kaczocha M. **Fabp5 coordinates lipid signaling that promotes prostate cancer metastasis**. *Scientific Reports* (2019) **9**. DOI: 10.1038/s41598-019-55418-x
8. Carrasco DR, Tonon G, Huang Y, Zhang Y, Sinha R, Feng B, Stewart JP, Zhan F, Khatry D, Protopopova M, Protopopov A, Sukhdeo K, Hanamura I, Stephens O, Barlogie B, Anderson KC, Chin L, Shaughnessy JD, Brennan C, Depinho RA. **High-Resolution genomic profiles define distinct clinico-pathogenetic subgroups of multiple myeloma patients**. *Cancer Cell* (2006) **9** 313-325. DOI: 10.1016/j.ccr.2006.03.019
9. Chng WJ, Kumar S, Vanwier S, Ahmann G, Price-Troska T, Henderson K, Chung TH, Kim S, Mulligan G, Bryant B, Carpten J, Gertz M, Rajkumar SV, Lacy M, Dispenzieri A, Kyle R, Greipp P, Bergsagel PL, Fonseca R. **Molecular dissection of hyperdiploid multiple myeloma by gene expression profiling**. *Cancer Research* (2007) **67** 2982-2989. DOI: 10.1158/0008-5472.CAN-06-4046
10. Chng WJ, Huang GF, Chung TH, Ng SB, Gonzalez-Paz N, Troska-Price T, Mulligan G, Chesi M, Bergsagel PL, Fonseca R. **Clinical and biological implications of Myc activation: A common difference between MGUS and newly diagnosed multiple myeloma**. *Leukemia* (2011) **25** 1026-1035. DOI: 10.1038/leu.2011.53
11. DepMap 22Q2
2022DepMap 22Q2Figsharehttps://figshare.com/articles/dataset/DepMap_22Q2_Public/19700056/2. *Figshare* (2022)
12. Di-Poï N, Tan NS, Michalik L, Wahli W, Desvergne B. **Antiapoptotic role of PPARbeta in keratinocytes via transcriptional control of the Akt1 signaling pathway**. *Molecular Cell* (2002) **10** 721-733. DOI: 10.1016/s1097-2765(02)00646-9
13. Fairfield H, Falank C, Avery L, Reagan MR. **Multiple myeloma in the marrow: pathogenesis and treatments**. *Annals of the New York Academy of Sciences* (2016) **1364** 32-51. DOI: 10.1111/nyas.13038
14. Fairfield H, Falank C, Harris E, Demambro V, McDonald M, Pettitt JAJ, Mohanty ST, Croucher P, Kramer I, Kneissel M, Rosen CJ, Reagan MR. **The skeletal cell-derived molecule sclerostin drives bone marrow adipogenesis**. *Journal of Cellular Physiology* (2018) **233** 1156-1167. DOI: 10.1002/jcp.25976
15. Fairfield H, Dudakovic A, Khatib CM, Farrell M, Costa S, Falank C, Hinge M, Murphy CS, DeMambro V, Pettitt JA, Lary CW, Driscoll HE, McDonald MM, Kassem M, Rosen C, Andersen TL, van Wijnen AJ, Jafari A, Reagan MR. **Myeloma-modified adipocytes exhibit metabolic dysfunction and a senescence-associated secretory phenotype**. *Cancer Research* (2021) **81** 634-647. DOI: 10.1158/0008-5472.CAN-20-1088
16. Field CS, Baixauli F, Kyle RL, Puleston DJ, Cameron AM, Sanin DE, Hippen KL, Loschi M, Thangavelu G, Corrado M, Edwards-Hicks J, Grzes KM, Pearce EJ, Blazar BR, Pearce EL. **Mitochondrial integrity regulated by lipid metabolism is a cell-intrinsic checkpoint for Treg suppressive function**. *Cell Metabolism* (2020) **31** 422-437. DOI: 10.1016/j.cmet.2019.11.021
17. Furuhashi M, Hotamisligil GS. **Fatty acid-binding proteins: role in metabolic diseases and potential as drug targets**. *Nature Reviews. Drug Discovery* (2008) **7** 489-503. DOI: 10.1038/nrd2589
18. Ghandi M, Huang FW, Jané-Valbuena J, Kryukov GV, Lo CC, McDonald ER, Barretina J, Gelfand ET, Bielski CM, Li H, Hu K, Andreev-Drakhlin AY, Kim J, Hess JM, Haas BJ, Aguet F, Weir BA, Rothberg MV, Paolella BR, Lawrence MS, Akbani R, Lu Y, Tiv HL, Gokhale PC, de Weck A, Mansour AA, Oh C, Shih J, Hadi K, Rosen Y, Bistline J, Venkatesan K, Reddy A, Sonkin D, Liu M, Lehar J, Korn JM, Porter DA, Jones MD, Golji J, Caponigro G, Taylor JE, Dunning CM, Creech AL, Warren AC, McFarland JM, Zamanighomi M, Kauffmann A, Stransky N, Imielinski M, Maruvka YE, Cherniack AD, Tsherniak A, Vazquez F, Jaffe JD, Lane AA, Weinstock DM, Johannessen CM, Morrissey MP, Stegmeier F, Schlegel R, Hahn WC, Getz G, Mills GB, Boehm JS, Golub TR, Garraway LA, Sellers WR. **Next-generation characterization of the cancer cell line encyclopedia**. *Nature* (2019) **569** 503-508. DOI: 10.1038/s41586-019-1186-3
19. Heberle H, Meirelles GV, da Silva FR, Telles GP, Minghim R. **InteractiVenn: a web-based tool for the analysis of sets through venn diagrams**. *BMC Bioinformatics* (2015) **16**. DOI: 10.1186/s12859-015-0611-3
20. Herroon MK, Rajagurubandara E, Hardaway AL, Powell K, Turchick A, Feldmann D, Podgorski I. **Bone marrow adipocytes promote tumor growth in bone via FABP4-dependent mechanisms**. *Oncotarget* (2013) **4** 2108-2123. DOI: 10.18632/oncotarget.1482
21. Holien T, Våtsveen TK, Hella H, Waage A, Sundan A. **Addiction to c-myc in multiple myeloma**. *Blood* (2012) **120** 2450-2453. DOI: 10.1182/blood-2011-08-371567
22. Hotamisligil GS, Johnson RS, Distel RJ, Ellis R, Papaioannou VE, Spiegelman BM. **Uncoupling of obesity from insulin resistance through a targeted mutation in AP2, the adipocyte fatty acid binding protein**. *Science* (1996) **274** 1377-1379. DOI: 10.1126/science.274.5291.1377
23. Hotamisligil GS, Bernlohr DA. **Metabolic functions of FABPs -- mechanisms and therapeutic implications**. *Nature Reviews. Endocrinology* (2015) **11** 592-605. DOI: 10.1038/nrendo.2015.122
24. Hsu HC, Tong S, Zhou Y, Elmes MW, Yan S, Kaczocha M, Deutsch DG, Rizzo RC, Ojima I, Li H. **The antinociceptive agent SBFI-26 binds to anandamide transporters FABP5 and FABP7 at two different sites**. *Biochemistry* (2017) **56** 3454-3462. DOI: 10.1021/acs.biochem.7b00194
25. Jia H, Zhang X, Liu X, Qiao R, Liu Y, Lv S, Zhu H, Wang J, Kong Q, Zhang H, Zhang Z. **FABP5, a novel immune-related mrna prognostic marker and a target of immunotherapy for multiple myeloma**. *Frontiers in Medicine* (2021) **8**. DOI: 10.3389/fmed.2021.667525
26. Lan H, Cheng CC, Kowalski TJ, Pang L, Shan L, Chuang CC, Jackson J, Rojas-Triana A, Bober L, Liu L, Voigt J, Orth P, Yang X, Shipps GW, Hedrick JA. **Small-Molecule inhibitors of FABP4/5 ameliorate dyslipidemia but not insulin resistance in mice with diet-induced obesity**. *Journal of Lipid Research* (2011) **52** 646-656. DOI: 10.1194/jlr.M012757
27. Li Z, Liu H, He J, Wang Z, Yin Z, You G, Wang Z, Davis RE, Lin P, Bergsagel PL, Manasanch EE, Wong STC, Esnaola NF, Chang JC, Orlowski RZ, Yi Q, Yang J. **Acetyl-Coa synthetase 2: a critical linkage in obesity-induced tumorigenesis in myeloma**. *Cell Metabolism* (2021) **33** 78-93. DOI: 10.1016/j.cmet.2020.12.011
28. Lipchick BC, Utley A, Han Z, Moparthy S, Yun DH, Bianchi-Smiraglia A, Wolff DW, Fink E, Liu L, Furdui CM, Lee J, Lee KP, Nikiforov MA. **The fatty acid elongase ELOVL6 regulates bortezomib resistance in multiple myeloma**. *Blood Advances* (2021) **5** 1933-1946. DOI: 10.1182/bloodadvances.2020002578
29. Maeda K, Cao H, Kono K, Gorgun CZ, Furuhashi M, Uysal KT, Cao Q, Atsumi G, Malone H, Krishnan B, Minokoshi Y, Kahn BB, Parker RA, Hotamisligil GS. **Adipocyte/macrophage fatty acid binding proteins control integrated metabolic responses in obesity and diabetes**. *Cell Metabolism* (2005) **1** 107-119. DOI: 10.1016/j.cmet.2004.12.008
30. Marinac CR, Birmann BM, Lee IM, Rosner BA, Townsend MK, Giovannucci E, Rebbeck TR, Buring JE, Colditz GA. **Body mass index throughout adulthood, physical activity, and risk of multiple myeloma: a prospective analysis in three large cohorts**. *British Journal of Cancer* (2018) **118** 1013-1019. DOI: 10.1038/s41416-018-0010-4
31. Mukherjee A, Chiang CY, Daifotis HA, Nieman KM, Fahrmann JF, Lastra RR, Romero IL, Fiehn O, Lengyel E. **Adipocyte-induced FABP4 expression in ovarian cancer cells promotes metastasis and mediates carboplatin resistance**. *Cancer Research* (2020) **80** 1748-1761. DOI: 10.1158/0008-5472.CAN-19-1999
32. Mulligan G, Mitsiades C, Bryant B, Zhan F, Chng WJ, Roels S, Koenig E, Fergus A, Huang Y, Richardson P, Trepicchio WL, Broyl A, Sonneveld P, Shaughnessy JD, Bergsagel PL, Schenkein D, Esseltine D-L, Boral A, Anderson KC. **Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib**. *Blood* (2007) **109** 3177-3188. DOI: 10.1182/blood-2006-09-044974
33. Nusinow DP, Szpyt J, Ghandi M, Rose CM, McDonald ER, Kalocsay M, Jané-Valbuena J, Gelfand E, Schweppe DK, Jedrychowski M, Golji J, Porter DA, Rejtar T, Wang YK, Kryukov GV, Stegmeier F, Erickson BK, Garraway LA, Sellers WR, Gygi SP. **Quantitative proteomics of the cancer cell line encyclopedia**. *Cell* (2020) **180** 387-402. DOI: 10.1016/j.cell.2019.12.023
34. Panaroni C, Fulzele K, Mori T, Siu KT, Onyewadume C, Maebius A, Raje N. **Multiple myeloma cells induce lipolysis in adipocytes and uptake fatty acids through fatty acid transporter proteins**. *Blood* (2022) **139** 876-888. DOI: 10.1182/blood.2021013832
35. Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, Inuganti A, Griss J, Mayer G, Eisenacher M, Pérez E, Uszkoreit J, Pfeuffer J, Sachsenberg T, Yilmaz S, Tiwary S, Cox J, Audain E, Walzer M, Jarnuczak AF, Ternent T, Brazma A, Vizcaíno JA. **The pride database and related tools and resources in 2019: improving support for quantification data**. *Nucleic Acids Research* (2019) **47** D442-D450. DOI: 10.1093/nar/gky1106
36. Rajkumar SV. **Multiple myeloma: 2020 update on diagnosis, risk-stratification and management**. *American Journal of Hematology* (2020) **95** 548-567. DOI: 10.1002/ajh.25791
37. Reagan MR, Mishima Y, Glavey SV, Zhang Y, Manier S, Lu ZN, Memarzadeh M, Zhang Y, Sacco A, Aljawai Y, Shi J, Tai Y-T, Ready JE, Kaplan DL, Roccaro AM, Ghobrial IM. **Investigating osteogenic differentiation in multiple myeloma using a novel 3D bone marrow niche model**. *Blood* (2014) **124** 3250-3259. DOI: 10.1182/blood-2014-02-558007
38. Ross JA, Dungen KV, Bressler KR, Fredriksen M, Khandige Sharma D, Balasingam N, Thakor N. **Eukaryotic initiation factor 5b (eIF5B) provides a critical cell survival switch to glioblastoma cells via regulation of apoptosis**. *Cell Death & Disease* (2019) **10**. DOI: 10.1038/s41419-018-1283-5
39. Schilling B, Gibson BW, Hunter CL. **Generation of high-quality swath acquisition data for label-free quantitative proteomics studies using tripletof mass spectrometers**. *Methods in Molecular Biology* (2017) **1550**. DOI: 10.1007/978-1-4939-6747-6_16
40. Schutze N, Noth U, Schneidereit J, Hendrich C, Jakob F. **Differential expression of CCN-family members in primary human bone marrow-derived mesenchymal stem cells during osteogenic, chondrogenic and adipogenic differentiation**. *Cell Communication and Signaling* (2005) **3**. DOI: 10.1186/1478-811X-3-5
41. Shafat MS, Oellerich T, Mohr S, Robinson SD, Edwards DR, Marlein CR, Piddock RE, Fenech M, Zaitseva L, Abdul-Aziz A, Turner J, Watkins JA, Lawes M, Bowles KM, Rushworth SA. **Leukemic blasts program bone marrow adipocytes to generate a protumoral microenvironment**. *Blood* (2017) **129** 1320-1332. DOI: 10.1182/blood-2016-08-734798
42. Shaughnessy S, Smith ER, Kodukula S, Storch J, Fried SK. **Adipocyte metabolism in adipocyte fatty acid binding protein knockout mice (ap2-/-) after short-term high-fat feeding: Functional compensation by the keratinocyte [correction of keritinocyte] fatty acid binding protein**. *Diabetes* (2000) **49** 904-911. DOI: 10.2337/diabetes.49.6.904
43. Shibue K, Yamane S, Harada N, Hamasaki A, Suzuki K, Joo E, Iwasaki K, Nasteska D, Harada T, Hayashi Y, Adachi Y, Owada Y, Takayanagi R, Inagaki N. **Fatty acid-binding protein 5 regulates diet-induced obesity via GIP secretion from enteroendocrine K cells in response to fat ingestion**. *American Journal of Physiology. Endocrinology and Metabolism* (2015) **308** E583-E591. DOI: 10.1152/ajpendo.00543.2014
44. Steiner N, Müller U, Hajek R, Sevcikova S, Borjan B, Jöhrer K, Göbel G, Pircher A, Gunsilius E. **The metabolomic plasma profile of myeloma patients is considerably different from healthy subjects and reveals potential new therapeutic targets**. *PLOS ONE* (2018) **13**. DOI: 10.1371/journal.pone.0202045
45. Tan NS, Michalik L, Noy N, Yasmin R, Pacot C, Heim M, Flühmann B, Desvergne B, Wahli W. **Critical roles of PPAR beta/delta in keratinocyte response to inflammation**. *Genes & Development* (2001) **15** 3263-3277. DOI: 10.1101/gad.207501
46. Tan NS, Shaw NS, Vinckenbosch N, Liu P, Yasmin R, Desvergne B, Wahli W, Noy N. **Selective cooperation between fatty acid binding proteins and peroxisome proliferator-activated receptors in regulating transcription**. *Molecular and Cellular Biology* (2002) **22** 5114-5127. DOI: 10.1128/MCB.22.14.5114-5127.2002
47. Tsherniak A, Vazquez F, Montgomery PG, Weir BA, Kryukov G, Cowley GS, Gill S, Harrington WF, Pantel S, Krill-Burger JM, Meyers RM, Ali L, Goodale A, Lee Y, Jiang G, Hsiao J, Gerath WFJ, Howell S, Merkel E, Ghandi M, Garraway LA, Root DE, Golub TR, Boehm JS, Hahn WC. **Defining a cancer dependency MAP**. *Cell* (2017) **170** 564-576. DOI: 10.1016/j.cell.2017.06.010
48. Tuncman G, Erbay E, Hom X, De Vivo I, Campos H, Rimm EB, Hotamisligil GS. **A genetic variant at the fatty acid-binding protein AP2 locus reduces the risk for hypertriglyceridemia, type 2 diabetes, and cardiovascular disease**. *PNAS* (2006) **103** 6970-6975. DOI: 10.1073/pnas.0602178103
49. Wang D, Wang H, Guo Y, Ning W, Katkuri S, Wahli W, Desvergne B, Dey SK, DuBois RN. **Crosstalk between peroxisome proliferator-activated receptor delta and VEGF stimulates cancer progression**. *PNAS* (2006) **103** 19069-19074. DOI: 10.1073/pnas.0607948103
50. White-Gilbertson S, Hua Y, Liu B. **The role of endoplasmic reticulum stress in maintaining and targeting multiple myeloma: A double-edged sword of adaptation and apoptosis**. *Frontiers in Genetics* (2013) **4**. DOI: 10.3389/fgene.2013.00109
51. Yan F, Shen N, Pang JX, Zhao N, Zhang YW, Bode AM, Al-Kali A, Litzow MR, Li B, Liu SJ. **A vicious loop of fatty acid-binding protein 4 and DNA methyltransferase 1 promotes acute myeloid leukemia and acts as a therapeutic target**. *Leukemia* (2018) **32** 865-873. DOI: 10.1038/leu.2017.307
52. Zhan F, Huang Y, Colla S, Stewart JP, Hanamura I, Gupta S, Epstein J, Yaccoby S, Sawyer J, Burington B, Anaissie E, Hollmig K, Pineda-Roman M, Tricot G, van Rhee F, Walker R, Zangari M, Crowley J, Barlogie B, Shaughnessy JD. **The molecular classification of multiple myeloma**. *Blood* (2006) **108** 2020-2028. DOI: 10.1182/blood-2005-11-013458
53. Zhao N, Cao J, Xu L, Tang Q, Dobrolecki LE, Lv X, Talukdar M, Lu Y, Wang X, Hu DZ, Shi Q, Xiang Y, Wang Y, Liu X, Bu W, Jiang Y, Li M, Gong Y, Sun Z, Ying H, Yuan B, Lin X, Feng XH, Hartig SM, Li F, Shen H, Chen Y, Han L, Zeng Q, Patterson JB, Kaipparettu BA, Putluri N, Sicheri F, Rosen JM, Lewis MT, Chen X. **Pharmacological targeting of MYC-regulated Ire1/XBP1 pathway suppresses Myc-driven breast cancer**. *The Journal of Clinical Investigation* (2018) **128** 1283-1299. DOI: 10.1172/JCI95873
54. Zhou Y, Elmes MW, Sweeney JM, Joseph OM, Che J, Hsu HC, Li H, Deutsch DG, Ojima I, Kaczocha M, Rizzo RC. **Identification of fatty acid binding protein 5 inhibitors through similarity-based screening**. *Biochemistry* (2019) **58** 4304-4316. DOI: 10.1021/acs.biochem.9b00625
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---
title: Erxian herbal pair enhances bone formation in infected bone nonunion models
and attenuates lipopolysaccharide-induced osteoblastinhibition by regulating miRNA-34a-5p
authors:
- Li Zhang
- Yang Zhang
- Maomao Miao
- Shaoqi Hu
- Xuping Wang
- Lisha Zhao
- Xiaowen Huang
- Gang Cao
- Dan Shou
journal: Bioengineered
year: 2023
pmcid: PMC9995130
doi: 10.1080/21655979.2022.2085388
license: CC BY 4.0
---
# Erxian herbal pair enhances bone formation in infected bone nonunion models and attenuates lipopolysaccharide-induced osteoblastinhibition by regulating miRNA-34a-5p
## ABSTRACT
Bacterium-induced inflammatory responses cause bone nonunion. Although antibiotics suppress infection, bone loss after antibacterial treatment remains a critical challenge. Erxian herbal pair (EHP) has been proven effective in promoting bone formation. Our study aimed to investigate the effect of EHP on bone repair after anti-infection treatment, explore its effect on a lipopolysaccharide (LPS)-induced osteoblast. We evaluated effects of EHP on bone repair with Micro-CT, and morphology detecting. Chemical constituents of EHP and EHP-containing serum (EHP-CS) were identified by UHPLC-Q/TOF-MS. In addition, osteoblast induced by LPS was established and administrated with EHP-CS. Cell proliferationwas assessed by MTT. Target prediction identified SMAD2 as a potential target of miRNA-34a-5p. MiRNA mimic, inhibitor and siRNA were transiently transfected into osteoblasts. The mRNA levels and protein expressions of miRNA-34a-5p, BMP2, Runx2, SMAD2 were assessed. The results showed that the main biocactivity ingredients in EHP-CS were Baohuoside Ι and Orcinol Glucoside. EHP could promote bone remolding after anti-infection therapy and restore the activity of LPS-induced osteoblasts. Moreover, miRNA-34a-5p was dramatically downregulated and SMAD2 was upregulated after LPS stimulation, while EHP resisted the inhibition of LPS by promoting miRNA-34a-5p, ALP, and BMP2 expressions. Whereas downregulation of miRNA-34a-5p reversed these effects. Silencing endogenous SMAD2 expression markedly promoted BMP2 and ALP activity and enhanced osteogenesis. Taken together, EHP restored LPS-induced bone loss by regulating miRNA-34a-5p levels and repressing its target gene SMAD2. EHP might be a potential adjuvant herbal remedy for the treatment of bone nonunion, and miRNA-34a-5p is a novel target for controlling bone and metabolic diseases.
## Graphical abstract
## Highlights
EHP could promoted bone remolding after anti-infection therapy. The main biocactivity ingretients in EHP-CS were Baohuoside Ι and Orcinol Glucoside. EHP protected LPS-induced osteoblast. EHP resisted inhibitory action of LPS on osteoblast by regulating miR-34a-5p, and BMP2/SMAD2/Runx2 pathway.
## Introduction
Infectious bone disease is mainly caused by bacterium invading and characterized by inflammation-associated bone defect with concurrent new bone formation [1]. There are approximately 10,000 fracture patients in China each year, and the incidence of nonunion is approximately $5\%$-$10\%$, most cases of which are caused by open fracture and traffic accidents inducing infection, as well as the regularity use of orthopedic devices. The predominant therapies for infectious bone disease are surgery to thoroughly remove dead bones and inflammatory granulation tissue and to provide local and systemic application of sensitive antibiotics to control infection [2]. With the widespread application of antibiotics, many pathogenic bacteria have developed resistance, which is difficult to treat owing to its higher risk of infection recurrence. Recent treatments require complicated operations; however result in long-term disablement, contributing to high costs for the patients [3]. Currently, infected bone nonunion remains one of the most challenging disorders for orthopedic surgeons. Therefore, there is a need to seek effective treatments for infected bone nonunion.
Previous resarches have suggested that natural herbal products have shown promising potential for enhancing bone formation [4]. EHP, a traditional Chinese herbal pair that includes Epimediumbrevicornu Maxim. and Curculigo orchioides Gaertn., has been widespread for treating bone metabolism diseases, such as osteoporosis, osteoarthritis, and chronic osteomyelitis [5,6]. EHP has been reported to display inhibitory effects on osteoclastic bone resorption and positive effects on osteoblast proliferation [7]. Recently, Wong [8] et al. reported that EHP had a definite anti-osteoporotic effect similar to estrogen. Zhu [9] et al. reported that EHP promotes osteoblasts proliferation, and inhibits apoptosis. However, the accurate mechanism for mediating osteoblast differentiation and bone repair has not yet been studied.
MicroRNAs (miRNA) are small noncoding RNAs, and have been proved to regulate gene expression by binding to 3'-untranslated regions (UTRs) of their target genes to induce their cleavage or inhibit translation [10]. Previous researches were shown that miRNAs play crucial roles in diverse biological processes and diseases [11]. Some miRNAs have been confirmed to have bone remolding effects, miR-214-3p, miR-29b, and miR-34a [12–14]. In our previous study, sequencing the transcriptome in a rabbit model of bone infection found that miRNA-34a-5p played crucial role in the bone remodeling in infected bone nonunion animal models. In the previous studies, miR-34a had been proved to regulate tumor development, such as breast cancer, nonsmall cell lung cancer, colorectal cancer, and prostate cancer [15–18]. However, whether miRNA-34a-5p regulates bone formation after bone infection has not been explored.
We hypothesized that EHP promoted bone remolding in infected bone nonunion during post infection treatment, and its molecular mechanism was involved with miRNA-34a-5p. In this study, Micro-CT, and morphology detecting were carried out to reveal bone healing effect of EHP in infected bone nonunion animal models. We observed down-regulation of miRNA-34a-5p in LPS-induced osteoblasts, and verificated molecular mechanism by which EHP affected miRNA-34a-5p and regulated osteogenic factors. Thus, the aim of this study is to provide a new and supplementary remedy for bone remolding in infected bone nonunion during post infection treatment.
## Erxian herbal pair (EHP) extract preparation and chemical components identification
Two plant materials *Epimedium sagittatum* Maxim. and Curculigo orchioides Gaertn. were mixed with ratio of 1:1, according to clinical experience and TCM theory [19]. The dried materials were extracted with 10 x (v/w) distilled water at 100°C for 2 h twice. A freeze drier was used to lyophilize the water extracts. At last, the freeze-dry powder was kept at 4°C.
The UPLC-Q/TOF-MS (Ultra performance liquid chromatography-quadrupole-time of flight-mass spectrometer) system consisted of an AcquityTM ultra performance liquid chromatography (UPLC) system (Waters Corporation, Milford, MA, United States) and a Synapt G2 mass spectrometer (MS) (Waters MS-Technologies, Manchester, United Kingdom) equipped with an electro spray ion (ESI) source. Chromatography was performed according to the following parameter: Acquity UPLC BEH C18 column (2.1 × 150 mm, 1.7 µm, Waters Corporation, Milford, MA, United States), flow rate was 0.3 mL/min, column temperature was set as 40°C, mobile phases were set as A (HCOOH: CH3CN = 0.1: 100, v/v) and B (HCOOH: H2O = 0.1: 100, v/v): 0–14 min, 99–$50\%$ B; 14–15 min, 50–$40\%$ B; 15–15.5 min, 40–$1\%$ B; 15.5–18 min, maintained at $1\%$ B.
For MS analysis, the ESI source was operated in both positive and negative ion modes with the following parameters: ion source gas 1 was 45 psi, ion source gas 2 was 55 psi, curtain gas was 35 psi, source temperature was 600°C, ion sapary voltage floating was 5500 V for positive ion mode and −4500 V for negative ion mode. The IDA MS/MS experiments acquired spectra in the high sensitivity mode with ± 60 psi declustering potential, 35 eV collision energy with ± 15 eV collision.
## Preparation of infected bone nonunion rabbit models
For preparation of infected bone nonunion rabbit models, we used New Zealand white male rabbits (SYXK 2019–0010), which were obtained from the Animal Experiment Center of Zhejiang Academy of Traditional Chinese Medicine. The rabbits ($$n = 10$$/group; age, 3 months, 3.0–3.5 kg weight) were housed in individual cages and offered the same feed and distilled water during the experimental period. The rabbit models were prepared according to established method [20,21]. Briefly, animals were punched a bone defect of 2 mm-diameter at tibia plateau. Subsequently, the holes were sealed with bone wax, and 1 × 106 CFU/mL S. aureus suspension (China General Microbiological Culture Collection Center, CGMCC) was injected into the bone marrow by penetrating the bone wax layer. At the end of 4 weeks after infection, $90\%$ rabbits were diagnosed with bone infection. Then animal groups and treatments timetable are indicated in Table 1. The rabbits in the Van-CS (vancomycin-calcium sulfate) and Van-CS+EHP (vancomycin-calcium sulfate+EHP) groups were debrided dead bone tissue, punched two adjacent 4-mm-diameter holes at tibia plateau, then implanted Van-CS in the holes. After 4 weeks of implanting Van-CS, rabbits in Van-CS+EHP group were administered EHP intragastrically at 384.56 mg/kg/day for 8 weeks. This administration dosage was confirmed by our preliminary experiments. All animal experiments were approved by the Animal Ethics Committee of Zhejiang Academy of Traditional Chinese Medicine (Approval No. [ 2019]004).Table 1.Animal groups and treatment. Treatment(T) GroupNumberModel(M)VancomycinEHPEuthanasia time point after M or TControl5NoneNoneNone4 w after M, 8 w after TModel5InfectionNoneNone4 w after M 10InfectionNoneNone8 w after TVan-CS10Infection12.5 mgNone8 w after TVan-CS+EHP10Infection12.5 mg384.56 mg/kg/day8 w after T
## Evaluation of bone formation
At the end of the 8 weeks after treatment with EHP, blood samples were collected from the ear vein, white blood cell (WBC) counting was detected by Automated Hematology Analyzer (Sysmex, XN-1000 V, Japan), and alkaline phosphatase (ALP) level was detected by using ELISA kit (Beyotime Biotechnology Co. Ltd., Shanghai, China). The rabbits were euthanized and tibia specimens were examined by micro computed tomography (micro-CT, SkyScan-1172, Bruker, Switzerland). The indexes such as bone volume/tissue volume ratio (BV/TV) and bone mineral density (BMD) were analyzed to evaluate bone formation [22].
## Medicated serum harvesting and main ingredient quantification
In total, 40 SD rats ($$n = 20$$/group) were administered 2 mL of Erxian extract gastrogavage (150 mg/kg/d) once a day for 1 week to prepare Erxian-containing serum. The control animals were administered equal volumes of distilled water. Venous blood was taken from the abdominal aorta, after the last administration, and centrifuged at 4000 r/min for 15 min. Then the separated serum was inactivated in 56°C for 30 min.
For main ingredient quantification in EHP-containing serum (EHP-CS), 900 μL methanol-acetonitrile solution was added into 300 μL EHP-CS or blank serum (Con), then vortexed for 1 min, centrifuged at 14000 r/min for 15 min at 4°C to obtain supernatant. Standard substances of Baohuoside Ι and Orcinol Glucoside (Shanghai yuanye Bio-Technology Co., Ltd, Shanghai, China) were dissolved in methanol to make mother stock solutions. Further, combined spiking stock solutions of the two reference substances were prepared by stepwise dilution from the mother stock solutions, and filtered with 0.22 µm membranes. Then 2 µL volume solutions were injected into the UPLC-Q/TOF-MS system for analysis [23,24]. The UPLC conditions were the same as those previously described. For MS analysis, the ESI source was operated in negative ion modes,the parameters were set as followed: ion source gas 1 was 55 psi, ion source gas 2 was 55 psi, curtain gas was 35 psi, source temperature was 600°C, ion sapary voltage floating was −4500 V. MRM (multiple reaction monitoring) spectrum scanning method was used to acquire spectra in the high sensitivity mode, the MRM parameters for Baohuoside Ι and Orcinol Glucoside were listed in Table 2.Table 2.Sequences of primers used in real-time polymerase chain reaction. GeneForward primerReverse primerRunx2CATGGCCGGGAATGATGAGTGTGAAGACCGTTATGGTCAAAGTGALPCATCGCCTATCAGCTAATGCACAATGAGGTCCAGGCCATCCAGBMP2TGGAAGTGGCCCATTTAGAGGCTTTTCTCGTTTGTGGAGCSMAD2CTCTCCGGCTGAACTGTCTCGCCGTCTACAGTGAGTGAGGβ-actinGGAGATTACTGCCCTGGCTCCTAGACTCATCGTACTCCTGCTTGCTGMiR-34a-5pTGCGCTGGCAGTGTCTTAGCTGCCAGTGCAGGGTCCGAGGTAU6CGCTTCACGAATTTGCGTGTCATCAAAGTGCTTACAGTGCAGGTAG
## Osteoblasts preparation and treatment
Osteoblasts were isolated from rat calvarias and cultured in incubator at 37°C, with $5\%$ CO2 [25]. The blank serum group was given DMEM medium + blank serum; the model group was given DMEM medium + blank serum + LPS (10 μg/mL); the EHP-CS group was given DMEM medium + EHP containing serumat four different doses ($2.5\%$, $5\%$, $10\%$, and $20\%$) + LPS (10 μg/mL). The results of MTT assays showed that when the EHP-CS was $10\%$, the drug had the best effect [26]. Subsequent experiments were conducted at this concentration.
In order to investigate effect of miRNA-34a-5p mimic/inhibitor and SMAD2 siRNA on osteoblasts, cells were first serum-deprived for 6–8 h, plated at 60–$70\%$ confluence and then transfected with SMAD2 siRNA (GenePharm Pharmaceutical Technology Co., Ltd., Shanghai, China) or miRNA-34a-5p mimic/inhibitor (Sangon Biotech Co., Ltd., Shanghai, China) for 6 h with Lipofectamine 2000 (Invitrogen, Waltham, MA, USA).
## Evaluation of cell proliferation
MTT assay was used to measure the proliferation rates of osteoblasts. Osteoblasts were dispensed into 96-well plate with 100 μL of fresh medium. At the end of the 24 h culture, a total of 50 μL of MTT solution was put into the cell and incubated at 37°C for4 h. Then, the solution was discarded, 100 μL DMSO was added to each well. At last, the absorbance was measured at 490 nm.
## ALP staining and alizarin red staining
ALP staining was performed following the ALP staining kit protocols (KGI Biotechnology Co., Ltd., JiangSu, China). For calcification induction, osteoblasts were cultured in induction medium for 14 days, and the medium was changed after 48 h. After induction, cells were fixed in $4\%$ formaldehyde and washed with PBS, then stained with $0.1\%$ alizarin red. The orange or red nodes were identified as calcium nodules [27].
## Dual-luciferase assay
HEK 293 cells were seeded into dark 96-well plates, and cultured in incubator at 37°C, with $5\%$ CO2. Cells were transfected with 50 nM miRNA-34a-5p mimic or scrambled mimic and 400 ng of dual luciferase vector expressing the wild-type or mutant SMAD2 (GenePharm Pharmaceutical Technology Co., Ltd., Shanghai, China) by using the Lipofectamine 2000 (Invitrogen, Waltham, MA, USA). After 48 h of incubation, luciferase reporter gene assay was performed using a dual-luciferase reporter assay kit (Yeasen Biotechnology Co., Ltd., Shanghai, China) [27]. The ratios of renilla luciferase activity to firefly luciferase activity were calculated to evaluate target relationship of miRNA-34a-5p and SMAD2.
## qRT-PCR analysis
Total RNA of osteoblasts was extracted with 0.5 mL TRIzol reagent (Invitrogen Life Technologies, Carlsbad, CA, United States). The quality of the extracted RNA was identified with a spectrophotometer (NanoDrop 2000, Thermo Scientific, MA, United States), and RNA purity was determined by the A$\frac{260}{280}$ ratio range of 1.8–2.1. All RNA samples were stored at −80°C for the further expriments. Reverse transcription was carried out by using a cDNA first strand synthesis kit (Bioer Technology Co., Ltd., Hangzhou, China), and quantitative reverse transcription PCR were performed by using SYBR Green I real time PCR kit (Bioer Technology Co., Ltd., Hangzhou, China). MiRNAs were validated with reverse transcription kit and qRT-PCR quantitation kit (Sangon Biotech Co., Ltd., Shanghai, China). The fluorescence quantitative PCR reaction system was performed as followed: [1] mRNA: 10 μL SYBR Premix (Diamond, USA), 1 μL each the upstream and downstream primers, 2 μL cDNA and 6 μL ddH2O; [2] miRNA: 10 μL 2*miRNA qPCR master mix, 0.5 μL forward Primer (10 μM), 0.5 μL reverse Primer (10 μM), 1 μL ROX Reference (L), forward Primer (10 μM), 2 μL cDNA and 6 μL ddH2O. The reaction was carried out on a 7500 fluorescent quantitative PCR instrument (Applied biosystems, USA). The amplification program was as follows: [1] mRNA: 94°C for 2 min, 94°C for 10s, 60°C for 15s, 72°C for 30s, 40 cycles; [2] miRNA: 95°C for 30s, 95°C for 5s, 60°C for 30s, 72°C for 30s, 40 cycles. MiRNA expression was normalized to U6 expression, and the fold changes of mRNAs and miRNAs in each group were normalized to the control groups. Primers sequences in this study were listed in Table 2. The expression of detected genes was analyzed using the 2−ΔΔ CT method.
## Western blot analysis
In brief, osteoblasts were lysed in RIPA lysis buffer (KeyGEN BioTech Co., Ltd., Jiangsu, China), and total proteins in osteoblasts were quantified using BCA Protein Assay kit (KeyGEN BioTech Co., Ltd., Jiangsu, China). The proteins were subjected to $12\%$ or $10\%$ SDS-PAGE and were then transferred to 0.45-µm PVDF membranes (Bio-Rad Co., Ltd., CA, USA), which were blocked with $5\%$ BSA in PBST for 1 h. After incubation with specific primary antibodies at 4°C overnight, HRP-conjugated secondary antibodies were added. Then the PVDF membranes were visualized in ChemiDocTM MP Imaging system (BioRad Co., Ltd., CA, USA) using a chemiluminescent ECL reagent (BioRad Co., Ltd., CA, USA). Anti-BMP2 polyclonal rabbit antibody, anti-ALP polyclonal rabbit antibody (bs-522526, 1:1000, BiossBiotechnology Co.,Ltd. ,Beijing, China), and anti-SMAD2 polyclonal rabbit antibody (bs-0718 R, 1:500, Bioss Biotechnology Co., Ltd., Beijing, China) served as primary antibodies. GAPDH (ab181602, 1:1000, Abcam, Cambridge, USA,) was used as control.
## Statistical analysis
The data are presented as the mean ± SD, SPSS software 22.0 (SPSS, Inc., Chicago, IL, United States) was used for statistical analyses. Student’s t-test was used to analyze the comparison between two groups of unpaired data with normal distribution and homogeneity of variance. One-way analysis of variance (ANOVA) and Tukey’s post-hoc test were used to compare between multiple groups, $p \leq 0.05$ was considered as significant.
## Results
Here, we hypothesized that EHP promoted bone repair after antibiotic treatment, by increasing bone volumn and bonemineral density. MiRNA-34a-5p promoted the proliferation and mineralization abilities of osteoblasts, by inhibiting target gene SMAD2. Moreover, EHP reversed the LPS inhibition in osteoblasts, by increasing miRNA-34a-5p expression. The results suggested that targeting the miRNA-34a-5p/SMAD2 axis might be a new therapeutic strategy in treatment of bone nonunion after anti-infection treatment in infected bone nonunion.
## Chemical constituents characterization in Erxian herbal pair (EHP) extract
The composition profiles of EHP extract were analyzed by UPLC-Q/TOF-MS. The element compositions were calculated and confirmed by using MarkerLynx (4.1) software. Total ion chromatogram (TIC) was obtained in the positive ion and negative ion modes (Figure 1). In comparison with the database of TCM MS/MS Library of SCIEX OS software, 38 constituents were assigned in the positive ion mode (Supplementary table S1), and 53 constituents were assigned in the negative ion mode (Supplementary table S2). A total of 79 constituents in EHP extract were identified by combining the constituents of positive and negative ion mode and removing duplicate ones (Supplementary table S3). As the results shown, Baohuoside I, Curculigoside, Epimedin A, Epimedin B, Epimedin C, Icarrin, Orcinol glucosid, Quinic acid were the main constituents in EHP extract. Figure 1.TIC of EHP extract of 0–20 min. ( a) Postive mode. ( b) Negative mode.
## Erxian herbal pair (EHP) promoted bone formation
It can be seen that some rabbits gradually weakened, with a decreased appetite, after modeling process. Most rabbits had obvious tissue swelling around the calf wound, with purulent secretions and white and yellowpus overflowing from the wounds, and there was no hair growth around the wound, thus confirming that infected bone models had been established. At the end of the 8 weeks after treatment with EHP, the rabbits in model group had severe bone infection. The tibia plateaus of the Van-CS+EHP groups seemed flatter than those of the Van-CS groups (Figure 2(a)). The WBC levels in the model group were higher than control group ($P \leq 0.01$), WBC levels in Van-CS and Van-CS+EHP treatments decreased than the model group, and Van-CS+EHP treatments significantly inhibited WBC levels ($P \leq 0.05$, Figure 2(b)). ALP in serum decreased in the model groups, compared with control groups ($P \leq 0.05$), and was improved by Van-CS+EHP at the end of the 8 weeks after treatment, compared with model groups ($P \leq 0.05$,Figure 2(c)). With 8 weeks treatment, BV/TV and BMD in control groups were significantly higher than the model groups ($P \leq 0.01$), which indicated s.aureus infection caused the number of bone decreasing. Moreover, BV/TV indicator in Van-CS+EHP group was higher than that in the Van-CS group significantly. These results indicated EHP could noticeably increase bone volumns (Figure 2(d,e)). Figure 2.EHP promoted bone formation in the rabbit after infection with Staphylococcus aureus. ( a)Three-dimensional reconstruction of bone defects of the rabbit tibia 8 weeks after infection with *Staphylococcus aureus* and treatment with Van-CS+EHP. ( b and c) The results of WBC and ALP activity assays in the rabbit serum were performed at the eighth week after treatment. ( d and e) Microarchitecture of the rabbit tibias 8 weeks after treatment with Van-CS+EHP. Data depict the mean ± standard deviation (mean ± SD) and are representative of three independent experiments. * $P \leq 0.05$, **$P \leq 0.01$ compared with the control groups; #$P \leq 0.05$, ##$P \leq 0.01$ compared with the model groups; ♦$P \leq 0.05$ compared with the Van-CS groups.
## Quantification of ingredients in Erxian herbal pair-containing serum
As the UPLC-Q/TOF-MS results of EHP-CS shown, Baohuoside Ι and Orcinol Glucoside were main ingredients that absorbed in blood. The established quantitative method was applied to determine the contents of main ingredients Baohuoside Ι and Orcinol Glucoside in EHP-CS. The ingredients quantification results were concluded by matching the accurate mass data of references substances. The Linear parameters and concentrations of the two ingredients in serum were listed in Table 3 and Table 4. Typical chromatograms of the two ingredients were shown in Figure 3. In our following study, Baohuoside Ι and Orcinol Glucoside were considerated as quality indexes for EHP-CS.Table 3.Linearity and concentration for the two ingredients. Compound nameFormulaStandard curveLinear range (ng/mL)rAreaActual Concentration (ng/mL)Baohuoside ΙC27H30O10y = 722521x – 1726.43.2–4000.99996.28E+023.258Orcinol GlucosideC13H18O7y = 742612x – 3107.23.2–4000.99994.61E+0466.222 Table 4.The MRM parameters. CompoundAdductPrecursor ion(m/z)Fragment(m/z)DP(V)CE(V)Accumulation(sec)Retention time(min)Orcinol GlucosideM+ HCOOH-H331.10123.0453−80−250.14.6Baohuoside ΙM-H513.18366.1107−80−350.114.9 Figure 3.Typical chromatograms of Baohuoside Ι and Orcinol Glucoside in EHP-CS. ( a) MRM of Blank serum. ( b) MRM of EHP-CS. ( c) MRM of standard. ( d) Chromatograms of Baohuoside Ι and Orcinol Glucoside in blank serum, EHP-CS and standard solution.
## Erxian herbal pair (EHP) reversed the inhibition of osteoblasts caused by LPS
The survival rate of osteoblast cells after intervention with EHP-CS showed that doses of $2.5\%$, $5\%$ and $10\%$ EHP-CS had no cytotoxicity to osteoblasts, and $20\%$ EHP-CS had inhibitory effect to osteoblasts ($P \leq 0.05$, Figure 4(a)). Therefore, EHP-CS doses of $2.5\%$, $5\%$ and $10\%$ were selected for further experiments. The survival rates of osteoblasts in the model group were decreased significantly. In osteoblasts treated with EHP-CS, the cell morphology and cell proliferation exhibited considerable changes, and the $10\%$ EHP-CS group had the best effect ($P \leq 0.01$, Figure 4 (b)). Therefore, subsequent experiments were conducted at this concentration. LPS at 10 μg/mL significantly increased the IL-6, IL-β, and TNF-α expression levels in osteoblasts. EHP-CS was reduced to approximately $80\%$ compared with the model group ($P \leq 0.05$, Figure 4(c)). As shown in Figure 4 (d) and (e), ($P \leq 0.01$), $10\%$ EHP-CS distinctly increased ALP activity and staining, and the ALP activity was almost 1.3 times that of the model group. The qRT-PCR data show that the mRNA expressions of ALP, RUNX2, and BMP2 were decreased in LPS groups. Osteoblasts co-cultured with LPS and EHP-CS significantly enhanced the mRNA levels of all these four genes ($P \leq 0.01$, figure 4(f)). The Western Blotting results shown that the inhibition protein expressions of BMP2, ALP, and Runx2 induced by LPS were rescued by EHP-CS ($P \leq 0.01$, $P \leq 0.05$, $P \leq 0.01$, Figure 4(g,h)). Taken together, EHP treatment can counteract the inhibitory effect of LPS on osteoblasts. Figure 4.LPS inhibited osteoblast proliferation and differentiation, and EHP-CS controlled the effect of LPS on osteoblast differentiation. ( a and b) Rat osteoblasts were treated with or without LPS and different concentrations of EHP-CS, and the survival rate of osteoblasts was measured by MTT. ( c) IL-6, IL-1β, and TNF-α activity levels were detected by ELISA. ( d and e) ALP staining and activity assays were performed on day 2. ( f) RT-PCR was performed to analyze the expression levels of RUNX2, ALP,and BMP2 mRNA. ( g and h) western blot analysis showed the protein levels of RUNX2, ALP, and BMP2. Data depict the mean ± standard deviation(mean ± SD) and are representative of three independent experiments. * $P \leq 0.05$, **$P \leq 0.01$ compared with the control groups; #$P \leq 0.05$, ##$P \leq 0.01$ compared with the model groups.
## Erxian herbal pair (EHP) regulated osteoblasts by increasing miRNA-34a-5p expression
We measured miRNA-34a-5p expression in LPS-induced inflammatory responses of osteoblasts by using qRT-PCR. As shown in Figure 5(a), miRNA-34a-5p was dramatically down regulated after LPS application, however, EHP-CS up regulated miRNA-34a-5p levels ($P \leq 0.05$). To investigate biological function of miRNA-34a-5p in osteogenic differentiation, osteoblasts were transfected with miRNA-34a-5p inhibitor for 6 h, and then co-cultured in the presence of 10 μg/mL LPS and $10\%$ EHP-CS. QRT-PCR results confirmed that miRNA-34a-5p inhibitor effectively decreased miRNA-34a-5p expressions in osteoblasts, compared with inhibitor N.C group ($P \leq 0.01$, Figure 5(b)). As well, ALP expressions and activities were decreased by the miRNA-34a-5p inhibitor ($P \leq 0.01$, Figure 5(c,e)). Alizarin red staining results indicated that osteoblasts mineralization abilities were decreased under miRNA-34a-5p inhibitor treatment ($P \leq 0.05$, Figure 5(d,f)). QRT-PCR analysis showed that miRNA-34a-5p inhibitor-infected osteoblasts exhibited lower levels of ALP, RUNX2 and BMP2 than that in the EHP group (Figure 5(g)). Western blot analysis indicated thatmiRNA-34a-5p inhibitor transfecting dramatically downregulated the protein levels of ALP and BMP2 ($P \leq 0.01$, $P \leq 0.05$, Figure 5(h,i)). Figure 5.Effects of EHP-CS on RUNX2, ALP, and SAMD2 mRNA and protein expression in LPS-induced osteoblasts after inhibition of miRNA-34a-5p. ( a) Relative level of miRNA-34a-5p in rat osteoblasts treated with 10 μg/mL LPS and LPS with $10\%$ EHP-CS. ( b) Stem-loop RT-PCR was performed to analyze the expression of miRNA-34a-5p after transfection with a miRNA-34a-5p-specific inhibitor. ( C andE) ALP staining and activity assays were performed on day 2 of drug intervention. ( d and f) Alizarin red S staining and activity assays were performed on day 14 of drug intervention. ( g, h and i) RT-PCR and western blotting were performed to analyze the mRNA and protein levels of osteogenic-specific markers after miRNA-34a-5p inhibitor transfection. Data depict the mean ± standard deviation (mean ± SD) and are representative of three independent experiments. * $P \leq 0.05$, **$P \leq 0.01$ compared with control groups; #$P \leq 0.05$, ##$P \leq 0.01$ compared with model groups; ΔP<0.05, ΔΔP<0.01 compared with EHP-containing groups.
## MiRNA-34a-5p directly targets SMAD2
We used TargetScan (http://www.targetscan.org/vert_80/) to predict the targets genes of miRNA-34a-5p. Among the candidate genes, osteoblast differentiation-related gene SMAD2 has miRNA-34a-5p binding sites in its 3'UTR (Figure 6(a)). The Dual luciferase reporter analysis showed that overexpression of miRNA-34a-5p significantly inhibited the luciferase reporter activity of the vector containing the WT SMAD2 3'UTR ($P \leq 0.01$, Figure 6(b)). The qRT-PCR and western blot results indicated that the mRNA and protein expression levels of SMAD2 in osteoblast cells were significantly decreased by transfecting with overexpression of miRNA-34a-5p ($P \leq 0.01$, Figure 6(c,d)). In order to confirm SMAD2 was a direct target gene of miRNA-34a-5p, we suppressed the expression of SMAD2 by transfecting osteoblasts with siRNAs against SMAD2. The data showed that the mRNA and protein levels of SMAD2 was significantly suppressed by transfected with siRNA SMAD2-3 (5'GCCUAAGUGAUAGUGCGAUTT3’) ($P \leq 0.01$, Figure 6(e,f)). Therefore, siRNA SMAD2-3 was chosen for the subsequent functional experiments. Taken together, our data suggested that SMAD2 could be downstream target gene of miRNA-34a-5p in osteoblast cells. Figure 6.SMAD2 is a direct target of miRNA-34a-5p.(a and b) Diagram of putative miRNA-34a-5p binding sequence in SMAD2 3'UTR and its mutant in luciferase reporter assay. A luciferase reporter assay was performed to measure luciferase activity in osteoblast cells; WT = wild-type, MUT = mutant-type. ( c and d) Western blot and qRT-PCR analyses of the expression of SMAD2 after overexpression of miRNA-34a-5p. ( e and f) The knockdown efficiency of three SMAD2 siRNAs was confirmed by qRT-PCR and western blot. Data depict the mean ± standard deviation (mean ± SD) and are representative of three independent experiments. * $P \leq 0.05$; **$P \leq 0.01$ compared with the control groups.
## siRNA SMAD2 remedy reduction effect of miRNA-34a-5p on osteogenesis
We further confirmed that the effect of miRNA-34a-5p during the LPS-induced bone loss mechanism was mediated by targeting SMAD2. Osteoblasts were silenced miRNA-34a-5p with miRNA-34a-5p inhibitor and knocked down SMAD2 with siRNA SNAD2 for 6 h, and then treated with 10 µg/mL LPS and EHP-CS at $10\%$. Compared with miR-inhibitor, after SMAD2 knockdown, ALP staining and activity were significantly up regulated ($P \leq 0.05$, Figure 7(a,c)). As well, siRNA SMAD2 was proved to reverse the inhibition effect of miRNA-34a-5p inhibitor on osteogenesis differentiation, as examined by alizarin red staining, and markedly increased mineralized nodule formation at 14 days ($P \leq 0.01$, Figure 7(b,d)). The mRNA and protein expression levels of ALP and BMP2 were dramatically up regulated in SMAD2 knockdown cells ($P \leq 0.01$, Figure 7(e–g)). Further, EHP counteracted LPS-induced osteoblasts suppression by increasing miRNA-34a-5p expression, which regulates osteogenesis by directly targeting SMAD2. Figure 7.SMAD2 knockdown reverses the effect of the miRNA-34a-5p inhibitor on osteogenesis in LPS-induced bone loss. ( A andC) ALP staining and activity assays were performed on day 2 of drug intervention. ( b and d) Alizarin red S staining and activity assays were performed on day 14 of drug intervention. ( E, F and G) western blotting and qRT-PCR were used to analyze osteogenic factor protein and mRNA expression after different treatments. Data depict the mean ± standard deviation (mean ± SD) and are representative of three independent experiments. * $P \leq 0.05$; **$P \leq 0.01$ compared with the control group; #$P \leq 0.05$, ##$P \leq 0.01$ compared with the EHP-containing groups; ΔP<0.05, ΔΔP<0.01 compared with the miR-inhibitor groups. LPS groups were treated with LPS, EPH-CS groups were treated with EPH-CS, miR-Inhibitor groups were transfected with miRNA-34a-5p inhibitor for 6 h, and then treatedwith LPS and EPH-CS, miR-Inhibitor+siSMAD2 groups were transfected with miRNA-34a-5p inhibitor and siSMAD2 for 6 h, and then treatedwith LPS and EPH-CS.
## Discussion
Staphylococcus aureus (S. aureus) is the most common bacterial species involved in infected bone nonunion. The inflammatory response caused by bacteria induces imbalance between bone resorption and bone formation, which can lead to bone nonunion and fracture healing delay [28]. The bone formation process is the process of balancing the activity of osteoblasts and osteoclasts. Chronic persistent inflammation can stimulate bone resorption. The increased expression of TNF-α has been proved to promote osteoclastic differentiation from bone marrow mesenchymal stem cells and bone resorption [29]. IL-1 inhibits the production and function of osteoblasts [30]. At present, there is still a lack of prevention or intervention methods for bone loss.
Curculigo orchioides Gaertn. is a well-known Chinese herbal medicine and is considered a major active compound [31]. It has been used to promote bone healing, which preventsosteoporosis by stimulating osteoblasts proliferation and differentiation, as well as attenuating adipogenic differentiationfrom mesenchymal stem cells [32]. Epimedin A, epimedin B, epimedin C and icariin are major bioactive compounds in *Epimedium sagittatum* Maxim. Epimedium sagittatum Maxim. can significantly increase the expression of apoptotic proteins of osteoclasts and protective proteins of osteoblasts and enhance the BMD of femoral heads, preventing osteoporosis and leading to collapse [33]. In previous studies, *Erxian formula* consisting of Curculigo orchioides Gaertn. and *Epimedium sagittatum* Maxim. was shown to be the major contributor to preventing osteoblast apoptosis [7], promoting osteoblast proliferation [34], and self-renewal and osteoblastic differentiation of bMSCs [35].
As the ingredients profile of EHP-CS shows, the main ingredients Baohuoside Ι and Orcinol Glucoside were detected in the EHP-CS with high bioavailability. Although the polarity of Orcinol *Glucoside is* very high, the molecular weight of Orcinol *Glucoside is* small, so it can enter the blood through the cell membrane. Baohuoside Ι is the main metabolite of Icariin, Epimedin A, Epimedin B and Epimedin C, and is also one of the main components absorbed into the blood [36]. Orcinol Glucoside was shown to promote bone repair by increasing osteoblasts differentiation genes and decreasing adipogenic differentiation related genes from BMSCs. Baohuoside Ι was shown to be related to the induction of BMSCs differentiation into osteoblasts [37]. It was speculated that Baohuoside Ι and Orcinol Glucoside may be effective components in the EHP-CS. Therefore, we chose these two ingredients as quantitative indicators of the EHP-CS to ensure the homogeneity of EHP-CS.
The present study demonstrated that EHP have effect on bone repair in S. aureus-induced rabbit infected bone nonunion model. In vivo, micro-CT results suggested bone destruction and increased sequestration in the model group and increased infected bone nonunion. Van-CS+EHP provide a good environment for the elimination of inflammation and bone formation. EHP treatment increased BMD in the area of the bone defect and restored bone tissue by promoting volumes of new bone around the bone defect areas when compared with Van-CS groups at 8 weeks. ALP activity is a phenotypic marker of mature osteoblasts [38]. Therefore, we chose ALP as an index to evaluate changes in osteogenic capacity. The decreasing serum ALP levels in the model group indicated the inhibitory effects of bacterium on metabolic activityof osteoblasts. This shows that S.aureus infection inhibits the osteogenesis reaction, resulting in bone mass. However, EHP treatment increased ALP levels after antibiotic administration and accelerated bone union. However, the specific mechanism is unclear, and we are evaluating EHP-mediated bone remodeling at the cellular level.
MiRNAs are powerful modulators of fracture healing in the previous researches. MiRNA-223-3p is highly expressed in fracture patients, and regulates osteoblast cell by targeting fibroblast growth factor receptor 2 [39]. MiR-874-3p has been proved to promote the proliferation and differentiation of hBMSCs by downregulating the expression of target gene, thus may be used as a novel strategy for treatment of osteoporosis [40]. In our study, we speculated that miRNA-34a, a crucial regulator of osteoclast bone resorption and stem cell osteogenic differentiation, played an important role in new bone growth and bone repair. As the recent literatures shown, miRNA-34a inhibits osteoclasts differentiation by inhibiting the OPG/RANK/RANKL pathway. Additionally, miR-34a was previously found to improve osteoblastic differentiation and to promote new bone volumes [41]. MiRNA-34a can promote the osteogenic differentiation of BMSCs and increase the expression of the osteogenic differentiation marker genes RUNX2 and OCN, ALP activity and matrix mineralization ability. In addition, inhibition targeting of miRNA-34a in hMSCs increased bone formation in heterotopic bone formation mice models [42].
In our study, we carried out LPS-induced osteoblasts. LPS, a component of the outer membranes of gram-negative bacteria, has been verified to be capable of inducing bone resorption and inhibiting osteoblasts differentiation and function in vivo or in vitro [43]. LPS stimulates osteoblasts to secrete inflammatory cytokines, such as interleukin-6 (IL-6), prostagrandin (PG) E2 and receptoractivator of nuclear factor-kappa B ligand (RANKL), all of which induce osteoclasts activation [44], Furtherore, several recent studies revealed that the expression of IL-6, tumor necrosis factor-α (TNF-α) were increased by LPS stimulation in osteoblast, and osteogenic differentiation of osteoblasts is inhibited by LPS, which may be consideredas mechanism of inflammation induced bone loss [45,46]. To sum up, LPS-induced osteoblasts injury model was constructed in this study to investigate the mechanism of EHP on osteoblasts. As the results shown, miRNA-34a-5p was dramatically down regulated after treatment with LPS, and EHP restored the expression level of miRNA-34a-5p to normal. The improvement of osteogenic differentiation by EHP treatment was inverted by transfecting with miRNA-34a-5p inhibitor. The expressions of ALP and BMP2 were significantly reduced, and ALP activity analysis. Also, alizarin red staining results exhibited a trend consistent with the qPCR results that EHP treatment significantly reversed mineral nodule formation by down regulating miRNA-34a-5p in osteoblasts. It was concluded that EHP counteracted LPS-induced bone loss by up-regulating miRNA-34a-5p expression.
It is well known that miRNAs play a role in regulating the target genes. TargetScan prediction implied that SMAD2 was target gene of miRNA-34a-5p. Tranasforming growth factor β (TGF-β) signaling and BMP superfamily pathways have been proved to regulate osteoblasts proliferation and differentiation. SMAD2 is a member of the SMAD protein family, which play important role in BMP pathways [47]. Activated receptor-regulated SMADs (R-SMADs) include SMADs 1, 2, 3, 5 and 8. Both SMAD2 and SMAD3 are essential for chondrogenesis in vivo, SMAD3 suppresses chondrocyte hypertrophy, and overexpression of SMAD2 can regulates chondrocyte maturation [48]. In addition, activation of SMAD$\frac{2}{3}$ and p38 MAPK can increase the transcriptional activity of Runx2 and then enhance the osteoblastic differentiation of mesenchymal stem cells [49]. During osteogenesis, increased p-SMAD2 and decreased p-SMAD1 were observed in MSCs [50]. The previous studies indicated that the relationships between SMAD protein osteoblasts proliferation and bone healing.
In the present study, we concluded that miRNA-34a-5p directly regulated SMAD2 in osteoblast differentiation, and down regulation of SMAD2 up regulated BMP2 expression, while knockdown of SMAD2 rescued the inhibitory effect of miRNA-34a-5p on osteogenesis. EHP counteracted LPS-induced bone loss by suppressing SMAD2 expression. However, we did not clarify whether miRNA-34a-5p have effect on the other genes in TGF-β signaling pathway in this study, which should be verified in subsequent experiments. Whether EHP directly regulates the BMP2/Runx2 pathway by suppressing the expression of SMAD2 regulates bone formation needs to be further investigated.
## Conclusions
In summary, our data indicate that EHP can increase the number of osteoblasts to promote bone formation ininfected bone nonunion rabbit models after anti-infection treatment. MiRNA-34a-5p has a positive function in osteogenesis, and EHP promotes bone remodeling and osteoblast differentiation by increasing miRNA-34a-5p levels and osteogenic gene expression. By targeting SMAD2, miRNA-34a-5p rescued LPS-induced BMP2 down regulation. All the results implied that EHP has potential as a therapeutic agent in the treatment of bone nonunion after anti-infection treatment in infected bone nonunion.
## Disclosure statement
No potential conflict of interest was reported by the author(s).
## Data availability statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. For original data, please contact [email protected].
## Author contributions
Dan Shou and Yang Zhang conceived and designed the experiments; Li Zhang, Yang Zhang, Maomao Miao, Shaoqi Hu, Xuping Wang, Lisha Zhao, and Xiaowen Huang performed the experiments, analysed the data; Li Zhang and Yang Zhang wrote the manuscript; Dan Shou administered and supervised the experimental work. All authors read and approved the final manuscript.
## Abbreviations
BV/TV: Bone volume/tissue volume ratio BMD: bone mineral density; EHP: Erxian herbal pair EHP-CS:EHP-containing serum LPS: lipopolysaccharide; S. aureus: Staphylococcus aureus
TIC: Total ion chromatogram UPLC-Q/TOF-MS: Ultra performance liquid chromatography-quadrupole-time of flight-mass spectrometer Van-CS: vancomycin-calcium sulfate.
## Ethics statement
The animal study was reviewed and approved by The Animal Ethics Committee of Zhejiang Academy of Traditional Chinese Medicine.
## Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/$\frac{10.1080}{21655979.2022.2085388}$
## References
1. Hajishengallis G, Moutsopoulos NM, Hajishengallis E. **Immune and regulatory functions of neutrophils in inflammatory bone loss**. *Semin Immunol* (2016) **28** 146-158. PMID: 26936034
2. Heinz W.. **Treatment of chronic orthopaedic infection**. *EFORT Open Rev* (2017) **2** 110-116. PMID: 28630748
3. Lan Y, Xie H, Shi Y. **NEMObinding domain peptide ameliorates inflammatory bone destruction in a Staphylococcusaureusinduced chronic osteomyelitis model**. *Mol Med Rep* (2019) **19** 3291-3297. PMID: 30816459
4. Zhang D, Gao S, Niu J. **Herba epimedii: an ancient Chinese herbal medicine in the prevention and treatment of osteoporosis**. *Curr Pharm Des* (2015) **22** 328-349
5. Zhu Z, Li HL, Jin X. **Er-Xian decoction, a traditional Chinese herbal formula, intervening early in hypothalamic-pituitary axis of male rats with delayed puberty**. *Pharmacogn Mag* (2014) **10** 517-521. PMID: 25422555
6. Chou YJ, Chuu JJ, Peng YJ. **The potent anti-inflammatory effect of Guilu Erxian Glue extracts remedy joint pain and ameliorate the progression of osteoarthritis in mice**. *J Orthop Surg Res* (2018) **13** 1. PMID: 29298726
7. Wang N, Xin H, Xu P. **Erxian decoction attenuates TNF-α induced osteoblast apoptosis by modulating the Akt/Nrf2/HO-1 signaling pathway**. *Front Pharmacol* (2019) **10** 988. PMID: 31551787
8. Wong KC, Lee KS, Luk HK. **Er-xian Decoction exerts estrogen-like osteoprotective effects in vivo and in vitro**. *Am J Chin Med* (2014) **42** 409-426. PMID: 24707871
9. Zhu Z, Xue LM, Han T. **Antiosteoporotic effects and proteomic characterization of the target and mechanism of an Er-Xian decoction on osteoblastic UMR-106 and osteoclasts induced from RAW264.7**. *Molecules* (2010) **15** 4695-4710. PMID: 20657386
10. Li Z, Sun Y, He M. **Differentially-expressed mRNAs, microRNAs and long noncoding RNAs in intervertebral disc degeneration identified by RNA-sequencing**. *Bioengineered* (2021) **12** 1026-1039. PMID: 33764282
11. Wu Y, Wang Y, Liu Q. **Conserved microRNA mediates heating tolerance in germ cells versus surrounding somatic cells**. *RNA Biol* (2019) **16** 1494-1503. PMID: 31276432
12. Li D, Liu J, Guo B. **Osteoclast-derived exosomal miR-214-3p inhibits osteoblastic bone formation**. *Nat Commun* (2016) **7** 10872. PMID: 26947250
13. Lee WY, Li N, Lin S. **miRNA-29b improves bone healing in mouse fracture model**. *Mol Cell Endocrinol* (2016) **15** 97-107
14. Jing YK, Wei W, Huynh HD. **miR-34a blocks osteoporosis and bone metastasis by inhibiting osteoclastogenesis and Tgif2**. *Nature* (2020) **582** 134. PMID: 32483375
15. Jiang L, Hermeking H.. **miR-34a and miR-34b/c suppress intestinal tumorigenesis**. *Cancer Res* (2017) **77** 2746-2758. PMID: 28363996
16. Ruan L, Chen J, Ruan L. **miR-34a inhibits tumorigenesis of NSCLC via targeting SIRT6**. *Int J Clin Exp Pathol* (2018) **11** 1135-1145. PMID: 31938208
17. Rokavec M, Öner MG, Li H. **IL-6R/STAT3/miR-34a feedback loop promotes EMT-mediated colorectal cancer invasion and metastasis**. *J Clin Invest* (2014) **124** 1853-1867. PMID: 24642471
18. Wang G, Zhao F, Yang D. **Human amniotic epithelial cells regulate osteoblast differentiation through the secretion of TGFβ1 and microRNA-34a-5p**. *Int J Mol Med* (2018) **41** 791-799. PMID: 29207015
19. Li JY, Jia YS, Chai LM. **Effects of Chinese herbal formula Erxian decoction for treating osteoporosis: a systematic review**. *Clin Interv Aging* (2018) **12** 45-53
20. Zhang Y, Shen L, Wang P. **Treatment with vancomycin loaded calcium sulphate and autogenous bone in an improved rabbit model of bone infection**. *J Vis Exp* (2019) **145** e57294
21. Boyle KK, Sosa B, Osagie L. **Vancomycin-laden calcium phosphate-calcium sulfate composite allows bone formation in a rat infection model**. *PLoS One* (2019) **14** e0222034. PMID: 31536540
22. Nazarian A, Von Stechow D, Zurakowski D. **Bone volume fraction explains the variation in strength and stiffness of cancellous bone affected by metastatic cancer and osteoporosis**. *Calcif Tissue Int* (2008) **83** 368-379. PMID: 18946628
23. Qin Z, Wang W, Liao D. **UPLC-Q/TOF-MS-based serum metabolomics reveals hypoglycemic effects of rehmannia glutinosa, coptis chinensis and their combination on high-fat-diet-induced diabetes in KK-Ay mice**. *Int J Mol Sci* (2018) **19** 3984. PMID: 30544908
24. Cheng T, Sheng T, Yi Y. **Metabolism profiles of icariin in rats using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry and in vitro enzymatic study**. *J Chromatogr B Analyt Technol Biomed Life Sci* (2016) **1033-1034** 353-360
25. Wu GJ, Wang W, Lin YL. **Oxidative stress-induced apoptotic insults to rat osteoblasts are attenuated by nitric oxide pretreatment via GATA-5-involved regulation of Bcl-X L gene expression and protein translocation**. *Arch Toxicol* (2016) **90** 905-916. PMID: 25726415
26. Nagao M, Tanabe N, Manaka S. **LIPUS suppressed LPS-induced IL-1α through the inhibition of NF-κB nuclear translocation via AT1-PLCβ pathway in MC3T3-E1 cells**. *J Cell Physiol* (2017) **232** 3337-3346. PMID: 28063227
27. Wang Q, Miao Y, Qian Z. **MicroRNA-15a-5p plays a role in osteogenic MC3T3-E1 cells differentiation by targeting PDCD4 (programmed cell death 4) via Wnt/β-catenin dependent signaling pathway**. *Bioengineered* (2021) **12** 8173-8185. PMID: 34672248
28. Zhao Y, Gao J, Zhang Y. **Cyclosporine A promotes bone remodeling in LPS-related inflammation via inhibiting ROS/ERK signaling: studies in vivo and in vitro**. *Oxid Med Cell Longev* (2021) **2021** 8836599. PMID: 33505590
29. Marahleh A, Kitaura H, Ohori F. **TNF-α directly enhances osteocyte RANKL expression and promotes osteoclast formation**. *Front Immunol* (2019) **10** 2925. PMID: 31921183
30. Furman BD, Kimmerling KA, Zura RD. **Brief report: articular ankle fracture results in increased synovitis, synovial macrophage infiltration, and synovial fluid concentrations of inflammatory cytokines and chemokines**. *Arthritis Rheumatol* (2015) **67** 1234-1239. PMID: 25707992
31. Bian Q, Yang H, Chan CO. **Fingerprint analysis and simultaneous determination of phenolic compounds in extracts of Curculiginis Rhizoma by HPLC-diode array detector**. *Chem Pharm Bull (Tokyo)* (2013) **61** 802-808. PMID: 23902862
32. Zhou X, Liu Z, Huang B. **Orcinol glucoside facilitates the shift of MSC fate to osteoblast and prevents adipogenesis via Wnt/beta-catenin signaling pathway**. *Drug Des Devel Ther* (2019) **13** 2703-2713
33. Wang L, Li Y, Guo Y. **Herba Epimedii: an ancient Chinese Herbal medicine in the prevention and treatment of osteoporosis**. *Curr Pharm Des* (2016) **22** 328-349. PMID: 26561074
34. Nian H, Qin LP, Zhang QY. **Antiosteoporotic activity of Er-Xian Decoction, a traditional Chinese herbal formula, in ovariectomized rats**. *J Ethnopharmacol* (2006) **108** 96-102. PMID: 16765003
35. Liu S, Huang J, Wang J. **Er-Xian decoction stimulates osteoblastic differentiation of bone mesenchymal stem cells in ovariectomized mice and its gene profile analysis**. *Stem Cells Int* (2016) **2016** 4079210. PMID: 27064269
36. Zhou J, Chen Y, Wang Y. **A comparative study on the metabolism of epimedium koreanum Nakai-Prenylated flavonoids in rats by an intestinal enzyme (Lactase Phlorizin Hydrolase) and intestinal flora**. *Molecules* (2014) **19** 177-203
37. Xi YH, Jiang TW, Yu JM. **Preliminary studies on the anti-osteoporosis activity of Baohuoside I**. *Biomed Pharmacother* (2019) **115** 108850. PMID: 31004988
38. Maagensen H, Junker AE, Jørgensen NR. **Bone turnover markers in patients with nonalcoholic fatty liver disease and/or Type 2 diabetes during oral glucose and isoglycemic intravenous glucose**. *J Clin Endocrinol Metab* (2018) **103** 2042-2049. PMID: 29506157
39. Wang B, Wu W, Xu K. **MicroRNA-223-3p is involved in fracture healing by regulating fibroblast growth factor receptor 2**. *Bioengineered* (2021) **12** 12040-12048. PMID: 34753389
40. Mei L, Li M, Zhang T. **MicroRNA miR-874-3p inhibits osteoporosis by targeting leptin (LEP)**. *Bioengineered* (2021) **12** 11756-11767. PMID: 34818977
41. Liu H, Dong Y, Feng X. **miR-34a promotes bone regeneration in irradiated bone defects by enhancing osteoblastic differentiation of mesenchymal stromal cells in rats**. *Stem Cell Res Ther* (2019) **10** 180. PMID: 31215466
42. Chen L, Holmstrom K, Qiu W. **MicroRNA-34a inhibits osteoblast differentiation and in vivo bone formation of human stromal stem cells**. *Stem Cells* (2014) **32** 902-912. PMID: 24307639
43. Lee JH, Kim H, Shim JH. **Platycarya strobilacea leaf extract inhibits tumor necrosis factor-α production and bone loss induced by Porphyromonas gingivalis-derived lipopolysaccharide**. *Arch Oral Biol* (2018) **96** 46-51. PMID: 30172945
44. Matsumoto A, Takami M, Urano E. **Lipopolysaccharide (LPS) inhibits ectopic bone formation induced by bone morphogenetic protein-2 and TGF-β1 through IL-1β production**. *J Oral Biosci* (2020) **62** 44-51. PMID: 31987892
45. Cai P, Cai T, Li X. **Herbacetin treatment remitted LPS induced inhibition of osteoblast differentiation through blocking AKT/NF-κB signaling pathway**. *Am J Transl Res* (2019) **11** 865-874. PMID: 30899386
46. Zhang K, He S, Dai Z. **Axin 1 knockdown inhibits osteoblastic apoptosis induced by porphyromonas gingivalis lipopolysaccharide**. *Arch Oral Biol* (2020) **112** 104667. PMID: 32092441
47. Tando T, Hirayama A, Furukawa M. **Smad2/3 proteins are required for immobilization-induced skeletal muscle atrophy**. *J Biol Chem* (2016) **291** 12184-12194. PMID: 27129272
48. Song B, Estrada KD, Lyons KM. **Smad signaling in skeletal development and regeneration**. *Cytokine Growth Factor Rev* (2009) **20** 379-388. PMID: 19926329
49. Inubushi T, Kosai A, Yanagisawa S. **Bovine lactoferrin enhances osteogenesis through Smad2/3 and p38 MAPK activation**. *J Oral Biosci* (2020) **62** 147-154. PMID: 32464258
50. Choi JY, Han KM, Kim D. **Impaired osteogenesis of disease-specific induced pluripotent stem cells derived from a CFC syndrome patient**. *Int J Mol Sci* (2017) **18** 2591. PMID: 29194391
|
---
title: Geniposide promotes splenic Treg differentiation to alleviate colonic inflammation
and intestinal barrier injury in ulcerative colitis mice
authors:
- Yun Yu
- Yong Bian
- Jian-Xin Shi
- Yu Gu
- Dong-Ping Yuan
- Bin Yu
- Le Shi
- Da-Hai Dou
journal: Bioengineered
year: 2023
pmcid: PMC9995132
doi: 10.1080/21655979.2022.2092678
license: CC BY 4.0
---
# Geniposide promotes splenic Treg differentiation to alleviate colonic inflammation and intestinal barrier injury in ulcerative colitis mice
## ABSTRACT
Geniposide has been proven to have a therapeutic effect on ulcerative colitis (UC) in animals, but its potential mechanism in UC remains to be clarified. The purpose of this study was to confirm the efficacy of geniposide in UC and to investigate the possible mechanism of geniposide in UC treatment. In vivo, geniposide relieved weight loss and reduced intestinal tissue damage in UC mice. Geniposide decreased the levels of IL-1β and TNF-α and increased IL-10 levels in the colon and serum of UC mice. Geniposide increased FOXP3 expression in the colon and the number of CD4+ FOXP3+ cells in the spleen of UC mice. BD750 abolished the above regulatory effect of GE on UC mice. In vitro, geniposide increased the number of CD4+ FOXP3+ cells in spleen cells from normal mice, decreased the levels of IL-1β, CCL2 and TNF-α in the supernatant of LPS-treated Caco-2 cells, and decreased the protein expression of Beclin-1 and Occludin in cacO-2 cells. Epirubicin inhibited the effect of geniposide on increasing the number of CD4+ FOXP3+ cells in spleen cells, attenuated the inhibitory effect of geniposide on proinflammatory factors and attenuated the upregulation of geniposide on tight junction proteins in LPS-treated Caco-2 cells in the coculture system. In conclusion, geniposide has an effective therapeutic effect on UC. Increasing Treg differentiation of spleen cells is the mechanism by which geniposide alleviates intestinal inflammation and barrier injury in UC.
## Graphical Abstract
## Highlights
Geniposide alleviates colonic damage in ulcerative colitis miceGeniposide improves immune function and decreases inflammation in ulcerative colitis miceGeniposide promotes Treg differentiation to reduce inflammation and colonic damage
## Introduction
UC is a chronic nonspecific inflammatory disease, mainly in the colon [1]. The etiology and pathogenesis of UC remain unclear and are mainly closely related to genetic susceptibility, epithelial barrier defects, immune disorders and environmental factors [2]. In recent years, influenced by unhealthy living habits, great mental stress, genetics and other factors, the global incidence of UC has increased significantly [3]. The incidence of UC in Asia and the Middle *East is* approximately 0.63‰ [4]. The incidence of UC is 2.14‰ in the United States, 2.48‰ in Canada, and even 5.05‰ in Europe [5]. The treatment of UC is an urgent global health problem.
The goal of UC treatment is to minimize the use of corticosteroids and surgical treatment and to achieve clinical and endoscopic remission with minimal adverse reactions [6,7]. At present, the treatment drugs for UC patients mainly include the following categories: 1) amino-salicylic acid preparations that are most commonly used in UC, 2) glucocorticoids that are first-line treatment in patients with severe UC, and 3) immunosuppressants and biological agents that are used in the treatment of UC patients with ineffective traditional drugs [7,8]. The above drugs for the treatment of UC often have faint efficacy or serious adverse reactions, especially adverse reactions such as hypertension, hypoglycemia, infection and osteoporosis caused by corticosteroids, which limit their long-term application in UC [9]. Therefore, the exploration of potential drugs for the treatment of UC has attracted wide attention [10]. To seek safe and effective drugs to treat UC, researchers have turned their focus to traditional drugs and their extracts [11,12].
Geniposide (CAS No: 24,512–63-8) is a kind of iridoid glycoside purified from Gardenia jasminoides Ellis that has significant anti-inflammatory and antioxidant effects [13,14]. Studies have shown that geniposide can suppress intestinal inflammation. In a trinitro-benzene-sulfonic acid (TNBS)-induced UC model in rats, geniposide reduced intestinal inflammation and repaired the impaired intestinal barrier [15]. In a mouse model of acute colitis induced by sodium dextran sulfate (DSS), geniposide also reduced intestinal inflammation and enhanced the intestinal barrier [16]. The therapeutic effect of geniposide on the intestinal inflammation model in DSS mice may involve the Nrf-2/HO-1 [17], PPARγ [18], AMPK/Sirt1 and NLRP3 [19] signaling pathways. These studies demonstrate that geniposide is a candidate for the treatment of UC, but the therapeutic mechanism of geniposide on UC remains to be further revealed.
The occurrence of UC is the result of the interaction of various factors, among which immune factors (immune abnormalities) are key factors in UC pathogenesis [20]. Intestinal inflammation occurs when immune imbalance is present, where pathogens will destroy the intestinal barrier and cause UC in severe cases [21]. A review article summarized the therapeutic effects and potential molecular mechanisms of geniposide in chronic inflammatory diseases [13]. Although the anti-inflammatory effect of geniposide has been widely reported, its role in immune regulation has rarely been disclosed. In the current study, we hypothesized that geniposide can improve UC by regulating immunity. We focused on whether geniposide can inhibit intestinal inflammation and ameliorate intestinal barrier injury by promoting Treg differentiation in the spleen.
## Reagents
BD750 (#S0981) was purchased from Selleck (Shanghai, China). Epirubicin (#HY-13624) was purchased from MCE (New Jersey, USA). PrimeScript RT Master Mix (#RR036A) and TB Green® Fast qPCR Mix (#RR430A) were purchased from Takara (Dalian, China). Antibodies against IL-10 (#bs-0698 R), TNF-α (#bs-10,802 R), IL-1β (#bs-0812 R), and FoxP3 (#bs-10,211 R) were purchased from Bioss (Beijing, China). Antibodies against Claudin 1 (#13050-1-AP), Occludin (#27260-1-AP), and GAPDH (#10494-1-AP) were purchased from Proteintech (Wuhan, China). HRP-conjugated AffiniPure goat anti-rabbit IgG (H + L) (#SA00001-2) and FITC-conjugated AffiniPure goat anti-rabbit IgG (H + L) (#SA00003-2) were purchased from Proteintech (Wuhan, China). FITC-CD4 antibody (#553046) and PE-FOXP3 (#562466) antibody were purchased from BD Pharmingen (New York, USA). A mouse IL-10 ELISA kit (#ZC-37962), mouse IL-1β ELISA kit (#ZC-37974), mouse TNF-α ELISA kit (#ZC-39024), and mouse CCL2 ELISA kit (#ZC-385882) were purchased from Zcibio (Shanghai, China).
## Replication, grouping and treatment of the UC model
The animal experiments were conducted with approval from the Ethics Committee of Nanjing University of Chinese Medicine (NO. ACU210404). Animal experiments were carried out in accordance with China’s Guidelines for Ethical Review of Experimental Animal Welfare (GB/T 35892–2018) and strictly abided by international experimental animal welfare ethics.
C57BL/6 mice purchased from GemPharmatech Co., Ltd. The mice were divided into five groups by the random number table method: normal group, UC group, geniposide 10 mg/kg group, geniposide 20 mg/kg group, and geniposide 20 mg/kg+BD750 20 μg/kg group, with 10 mice in each group. Except for the normal group, UC was induced in mice in the other groups by TNBS intracolonic administration. Specific procedures were as follows: Mice were fasted overnight and mildly anesthetized with ether. TNBS (3 mg) was dissolved in 0.1 ml $50\%$ ethanol, and then the mixed solution was slowly injected into the colon with a No. 16 irrigation needle [22]. Control mice were treated with 0.1 mL $50\%$ ethanol. On the day of modeling, geniposide was intragastrically administered to the mice, and BD750 was intraperitoneally injected. The mice in the control group and model group were given an equal volume of normal saline once a day for 4 weeks. The mice were weighed weekly, and the volume of administration was adjusted according to the weight. The mortality of the mice was recorded, and 20–$40\%$ of the model mice in the different groups died. After administration, the blood, spleen and colon of mice were collected and stored or treated immediately according to experimental requirements.
## Isolation of spleen cells and coculture with Caco-2 cells
The noncontact coculture method was used in this study according to previous study [23]. Spleen tissues of normal mice were collected, and a single-cell suspension of mouse spleen was obtained using a mouse spleen mononuclear cell isolation kit. Cells were suspended in DMEM and MEM containing $2\%$ FBS, and 50,000 spleen cells were added to a Transwell chamber. Cultured cacO-2 cells were collected and suspended in $2\%$ FBS DMEM and MEM medium, and 100,000 Caco-2 cells were added to 6-well plates. Transwell chambers with spleen cells were placed into 6-well plates containing Caco-2 cells for noncontact coculture. Spleen cells in the transwell chamber were induced to differentiate with 100 U/mL IL-2 and 10 ng/mL TGF-β and treated with 25 μM geniposide and 20 nM epirubicin. Cell damage was induced in Caco-2 cells in 6-well plates using 10 nM LPS. Under different intervention conditions, the cells were cocultured for 48 hours.
## HE staining
The mice were sacrificed to collect colon tissues. HE staining of the colon tissues was performed with a $4\%$ formaldehyde solution fixed and routine paraffin embedding. The histological morphology of colon tissues was observed under a light microscope. The pathological score was calculated as follows: 0 was divided into no damage, 1 was divided into slight damage, 3 was divided into moderate damage, and 5 was divided into severe erosion. The higher the score, the more severe the lesion [24].
## ELSIA
At the end of the experimental period, blood was collected from the orbit of the mice. The serum was separated by centrifugation. For cocultured cells, the supernatant in the 6-well plate was collected, that is, the liquid in the wells where Caco-2 cells were cultured. The levels of IL-1β, IL-10 and TNF-α in the serum of mice were detected by ELISA kits. The levels of IL-1β, CCL2 and TNF-α in the supernatant of Caco-2 cell culture wells were detected by an ELISA kit. The detection procedures were performed according to the kit instructions provided by the kit manufacturer [25].
## Real-time quantitative-polymerase chain reaction (RT-PCR)
At the end of the experimental period, the mice were sacrificed to collect colon tissues.
Colonic tissue was lysed using an EP tube without RNA enzyme and Trizol reagent on an ultrasonic crusher. A reverse transcription kit was used to make the extracted mRNA transformed to cDNA. The obtained cDNA was amplified according to the instructions of the RT-PCR kit. The ABI7500 system was used for cDNA amplification. The mRNA expression levels of IL-1β, IL-10 and TNF-α in colon tissues of mice were calculated by 2−ΔΔCT method [25]. The primers were synthesized by Sangon Biotech (Shanghai, China). Forward primer of IL-1β is 5’-CAACCAACAAGTGATATTCTCCATG-3’. Reverse primer of IL-1β is 5’-GATCCACAC TCTCCAGCTGCA-3’. Forward primer of TNF-α is 5’-CTCCACTTGGTGGTTTGCTAC-3’. Reverse primer of TNF-α is 5’-CTTCCCTCTCATCAGTTCTATGG-3’. Forward primer of IL-10 is 5’-ACCTGGTACAAGTGATGCC-3’. Reverse primer of IL-10 is 5’-CAAGGAGTTGTTTCCGTTA-3’. Forward primer of GPADH is 5’-CATGGCCTTCCGTGTTCCTA-3’. Reverse primer of GAPDH is 5’-GCGGCAC GTCAGATCCA-3’
## Immunohistochemical staining
The mice were sacrificed to collect colon tissues. The sections were fixed with $4\%$ formaldehyde solution and routinely embedded in paraffin. The tissues were sliced into 3 μm sections, and immunohistochemistry was performed by the SP method. Colonic tissues were incubated with primary antibody enhancer after antigen repair and blocking. Then, the colonic tissues were incubated with IL-1β, IL-10, TNF-α primary antibodies and HRP-IGg secondary antibodies. After coloration with a DAB kit, the nuclei were re-stained with hematoxylin. The expression levels of L-1β, IL-10 and TNF-α in colon tissue were observed and analyzed by microscopy and Image J [26].
## Immunofluorescence staining
The mice were sacrificed to collect colon tissues. The sections were fixed with $4\%$ formaldehyde solution and routinely embedded in paraffin. The tissues were sliced into 3 μm sections. Colonic tissues were incubated with primary antibody enhancer. Then, the colonic tissues were incubated with OFXP3 primary antibody and TRITC-IGg secondary antibody. The nuclei were restained with DAPI. The OFXP3 expression level in colon tissue was observed and analyzed by microscopy and ImageJ.
Cocultured Caco-2 cells were fixed in $4\%$ formaldehyde solution and then incubated with primary antibody enhancer. Then, Caco-2 cells were incubated with Cluadin-1 and Occludin primary antibodies and FITC-IGg secondary antibodies. The nucleus was restained with DAPI. The expression levels of Cluadin-1 and Occludin in Caco-2 cells were observed and analyzed by microscopy and Image J [27].
## Flow cytometry
Mouse spleens were collected, and a single-cell suspension of spleens was obtained using a mouse spleen mononuclear cell isolation kit. Spleen cells were collected from Transwell chambers in a coculture system. FITC-labeled CD4 monoclonal antibody and PE-labeled FOXP3 monoclonal antibody were diluted according to the instructions, and the antibodies were added to a 50 µL spleen cell suspension. IGg1-PE and IGg1-FITC were added to the control tubes and incubated at room temperature without light for 30 min. Then, 100 µL of stationary liquid was added to the cells from light and incubated for 15 min. After washing with PBS, 100 µL of membrane penetrating solution was added to detect the proportion of Treg cells (CD4+ FOXP3 + T cells) in the cells on a flow cytometer [28].
## Western blot
Colon tissue extracts were prepared by lysis in pro-prep protein extraction buffer. Protein concentration was quantified using a protein concentration determination kit. Proteins were mixed with sample buffer and heated at 100°C for 5 min before loading. Total protein samples (30 μg) were subjected to SDS–PAGE, and electrophoresis was performed at 100–120 V for 90 min. The isolated proteins were transferred to PVDF membranes. After blocking the proteins in $5\%$ skim milk at room temperature for 1 h, the membranes were incubated with diluted claudin-1 (1:1000) and Occludin (1:1000) primary antibodies overnight. Then, IgG secondary antibody (diluted at 1:10,000) was added at room temperature and incubated for 1 hour. The intensity of Cluadin-1 and Occludin was detected by an enhanced chemiluminescence kit. The relative expression of Cluadin-1 and Occludin was measured and analyzed by a gel imaging system and Imager Lab software [29].
## Statistical analysis
SPSS 23.0 software was used for data analysis. Measurement data are expressed as the mean ± SD. One-way ANOVA (OneWAY-ANOVA) was used for comparisons between groups, LSD-T was used for multiple comparisons, and $P \leq 0.05$ was considered statistically significant.
## Geniposide alleviates weight loss and colonic tissue damage in UC mice
TNBS injection into the colon resulted in significant weight loss in mice after 1 week ($P \leq 0.01$). After 2 weeks of treatment with 10 mg/kg or 20 mg/kg geniposide, the weight loss of TNBS colonic injection mice was significantly alleviated, and the effect of geniposide on weight loss was maintained until the end of treatment for 4 weeks (Figure 1(a)). HE staining results showed that no injury was observed in the colon tissues of mice in the normal group. Intestinal mucosa epithelial cells were reduced, neutrophil cells were infiltrated, and the crypt structure was deformed in mice with TNBS colonic injection. After 4 weeks of treatment with 10 mg/kg or 20 mg/kg geniposide, TNBS colonic injection mice had less pathological damage and significantly lower histopathological scores ($P \leq 0.05$) (Figure 1(b,c)). Western blot results showed that the protein expression levels of claudin-1 and occludin in colon tissue of UC model mice were significantly decreased. After treatment with 10 mg/kg or 20 mg/kg geniposide for 4 weeks, the loss of Cluadin-1 and Occludin was significantly reduced in the colon tissue of UC model mice (Figure 1(d,e)). Figure 1.Geniposide alleviates weight loss and colonic tissue damage in UC mice. UC model mice were treated with different doses of geniposide (10 mg/kg or 20 mg/kg) for 4 weeks. ( a) The body weight of mice in each group was recorded weekly. ( b) HE staining was used to detect the histological changes in colon tissues in each group. ( c) related to B. The pathological score of colon tissues of mice in each group was calculated according to HE staining. ( d) Representative western blot showing the protein expression levels of Claudin-1 and Occludin in colon tissues of each group of mice. ( e) related to D. Relative quantitative analysis of the protein expression levels of Claudin-1 and Occludin in colon tissues of each group. A-C, $$n = 6$.$ D-E, $$n = 3$.$ * $p \leq 0.05$, **$p \leq 0.01.$
## Geniposide improves immune function and decreased inflammation in UC mice
Immunohistochemical staining results showed that the expression levels of IL-1β and TNF-α in colon tissue of UC model mice were significantly increased compared to normal mice, while the expression levels of IL-10 were significantly decreased. After 4 weeks of treatment with 10 mg/kg or 20 mg/kg garinoside, the protein levels of IL-1β and TNF-α in colon tissues of UC model mice were significantly decreased, and the protein expression level of IL-10 was increased (Figure 2(a,b)). The changes in serum IL-1β, IL-10 and TNF-α levels, and the changes in colonic IL-1β, IL-10 and TNF-α mRNA expression in each group showed similar changes to protein expression in mouse colon tissues (Figure 2(c,d)). Immunofluorescence staining of colon tissues showed that FOXP3 protein expression levels in UC model mice were significantly decreased compared to those in normal mice. After 4 weeks of treatment with 10 mg/kg or 20 mg/kg geniposide, FOXP3 protein expression levels in colon tissues of UC model mice were significantly increased (Figure 2(e,f)). Flow cytometry results showed that the number of CD4+ FOXP3+ cells in the spleen of UC model mice was significantly decreased compared to that in normal mice. After 4 weeks of treatment with 10 mg/kg or 20 mg/kg garposide, the number of CD4+ FOXP3+ cells in the spleen of UC model mice were significantly increased (Figure 2(g,h)). Figure 2.Geniposide improves immune function and decreased inflammation in UC mice. UC model mice were treated with different doses of geniposide (10 mg/kg or 20 mg/kg) for 4 weeks. ( a) Representative images of immunohistochemistry of IL-1β, IL-10, and TNF-α in colon tissues of each group of mice. ( b) related to A. The mean optical density analysis of IL-1β, IL-10 and TNF-α expression in colon tissues of mice in each group. ( c) Serum IL-1β, IL-10, TNF-α levels were detected by ELISA. ( d) The IL-1β, IL-10 and TNF-α mRNA expression in colon tissues of mice in each group. ( e) Representative images of immunohistofluorescence of FOX3 in colon tissues of each group of mice. ( f) related to E. The mean optical density analysis of FOX3 expression in colon tissues of mice in each group. ( g) Representative flow cytometry images showing the percentage of CD4-positive and FOX3-positive cells in the spleen cells of each group of mice. ( h) related to G. The percentage of CD4 and FOX3 double-positive cells in mouse spleen cells was calculated. A-B and D-H $$n = 3$.$ C, $$n = 6$.$ * $p \leq 0.05$, **$p \leq 0.01.$
## Geniposide reduces LPS-induced inflammation and tight joint damage in colon cells by promoting Treg differentiation of spleen cells
To determine whether gardenitin can reduce tight junction injury caused by inflammation by promoting Treg differentiation of spleen cells, we isolated normal spleen cells from normal mice and used 100 U/mL IL-2 and 10 ng/mL TGF-β to induce Treg differentiation in spleen cells. The spleen cells were then placed in a Transwell chamber, and cacO-2 cells were placed in 6-well plates for noncontact coculture. Geniposide (25 μM) was added to differentiation-induced spleen cells, and LPS (10 nM) was used to induce inflammatory injury in Caco-2 cells (Figure 3(a)). Flow cytometry results showed that in vitro, induction of Treg differentiation in spleen cells increased the number of CD4+ FOXP3+ cells, while the coapplication of geniposide and differentiation induction reagent further increased the number of CD4+ FOXP3+ cells (Figure 3(b,c)). The levels of IL-1β, CCL-2 and TNF-α were detected by ELISA in the supernatant of the lower compartment in the coculture system. The results showed that the levels of IL-1β, CCl-2 and TNF-α were significantly increased in the LPS-treated Caco-2 culture medium compared with the control medium. The levels of IL-1β, CCL-2 and TNF-α were significantly decreased when the differentiation-induced spleen cells were cocultured with LPS-treated Caco-2 cells. The expression of IL-1β, CCL-2, and TNF-α in the lower compartment was significantly decreased when the cells were cocultured with LPS-treated cacO-2 cells after geniposide was used with differentiation induction reagent (Figure 3(d-f). The levels of Cluadin-1 and Occludin in cocultured Caco-2 cells were detected by immunoassay, and the results showed that compared with the control group, the levels of Cluadin-1 and Occludin in LPS-treated Caco-2 cells were significantly reduced. The coculture of differentiation-induced spleen cells and LPS-treated cacO-2 cells increased the expression levels of Cluadin-1 and Occludin in Caco-2 cells. After geniposide and differentiation induction reagent were applied to spleen cells, the coculture of the cells further increased the expression levels of Cluadin-1 and Occludin in Caco-2 cells (Figure 3(g-j)). Figure 3.Geniposide reduces LPS-induced inflammation and tight joint damage in colon cells by promoting Treg differentiation of spleen cells. Spleen cells were isolated from normal mice, and Treg differentiation was induced by 100 U/mL IL-2 and 10 ng/mL TGF-β. The spleen cells were placed in the upper compartment of the coculture chamber, and garinoside (25 μM) was added to treat the differentiation-induced cells. Caco-2 cells were placed in the lower chamber of the coculture chamber, and LPS (10 nM) was used to induce Caco-2 cell damage. ( a) Schematic diagram of coculture spleen cells and Caco-2 cells indicating the experimental grouping and cell intervention. ( b) Upper compartment spleen cells were taken from the coculture system. Representative flow cytometry images show the percentage of CD4-positive and FOX3-positive cells in spleen cells of each group. ( c) related to B. The percentage of CD4 and FOX3 double-positive cells in mouse spleen cells was calculated. ( d) In the coculture system, the culture medium in the lower chamber was taken, and the level of IL-1β was detected by ELISA. ( e) In the coculture system, the culture medium in the lower chamber was taken, and the level of CCL-2 was detected by ELISA. ( f) In the coculture system, the culture medium in the lower chamber was taken, and the level of TNF-α was detected by ELISA. ( g) Representative images of immunohistofluorescence of Claudin-1 in cacO-2 cells from each group. ( h) related to G. The mean optical density analysis of Claudin-1 in the cacO-2 cells of each group. ( i) Representative images of immunohistofluorescence of Occludin in cacO-2 cells of each group. ( j) related to I. The mean optical density analysis of Occludin in cacO-2 cells of each group. B-C and G-J, $$n = 3$.$ D-F, $$n = 6$.$ * $p \leq 0.05$, **$p \leq 0.01.$
## Immunosuppressive agents cancel the alleviating effect of garinoside on weight loss and colonic tissue damage in UC mice
To confirm whether the alleviating effect of geniposide on weight loss and colonic tissue damage is related to its immunomodulatory effect, 20 mg/kg geniposide and 20 μg/kg BD750 were combined in UC mice. BD750 is an effective immunosuppressant inhibitor, which can inhibit IL-2-induced JAK3/STAT5-dependent T cell proliferation. The results showed that BD750 eliminated the alleviating effect of 20 mg/kg geniposide on weight loss in UC mice after 4 weeks (Figure 4(a)). HE staining showed that compared with the 20 mg/kg geniposide group, the colonic tissues of UC mice treated with 20 mg/kg geniposide and 20 μg/kg BD750 showed reduced intestinal mucosa epithelial cells, infiltrated neutrophil cells and deformed crypt structure, which was similar to the histopathological changes observed in the model group. BD750 cancels the palliative effect of geniposide on pathological colonic injury in mice (Figure 4(b,c)). Western blot results showed that compared with the 20 mg/kg group, the protein expression levels of Cluadin-1 and Occludin in colon tissue of UC mice treated with 20 mg/kg geniposide and 20 μg/kg BD750 were significantly decreased, and the protein expression levels of Cluadin-1 and Occludin in colon tissues of UC mice treated with garinoside and BD750 showed no significant difference compared with the model group (Figure 4(d,e)). Figure 4.Immunosuppressive agents cancel the alleviating effect of garinoside on weight loss and colonic tissue damage in UC mice. UC model mice were treated with geniposide (20 mg/kg) alone or in combination with the immunosuppressive agent BD750 (20 μg/kg) for 4 weeks. UC model mice were treated with different doses of geniposide (10 mg/kg or 20 mg/kg) for 4 weeks. ( a) The body weight of mice in each group was recorded weekly. ( b) HE staining was used to detect the histological changes in colon tissues in each group. ( c) related to B. The pathological score of colon tissues of mice in each group was calculated according to HE staining. ( d) Representative western blot showing the protein expression levels of Claudin-1 and Occludin in colon tissues of each group of mice. ( e) related to D. Relative quantitative analysis of the protein expression levels of Claudin-1 and Occludin in colon tissues of each group. A-C, $$n = 6$.$ D-E, $$n = 3$.$ * $p \leq 0.05$, **$p \leq 0.01.$
## Immunosuppressive agents cancel the promotion effect on splenic cell Treg differentiation and the inhibitory effect of geniposide on inflammation in UC mice
To determine whether the effect of geniposide on immune regulation and inflammation in UC mice is related to its promotion of splenic cell Treg differentiation in UC mice, 20 mg/kg geniposide and 20 μg/kg BD750 were combined in UC mice. Compared with the 20 mg/kg geniposide group, the expression levels of IL-1β and TNF-α in colon tissue of UC mice treated with 20 mg/kg geniposide and 20 μg/kg BD750 were significantly increased, while the expression levels of IL-10 were significantly decreased. There was no significant difference in the expression levels of IL-10 and TNF-α in colon tissues between the UC group and geniposide 20 mg/kg+BD750 20 μg/kg group (Figure 5(a,b)). The changes in serum IL-1β, IL-10 and TNF-α levels in each group showed similar changes to those in mouse colon tissues (Figure 5(c-e)). Compared with the 20 mg/kg geniposide group, 20 mg/kg geniposide combined with 20 μg/kg BD750 significantly reduced FOXP3 protein expression in colon tissues of UC mice, and there was no significant difference in FOXP3 protein expression in colon tissues between the UC group and geniposide 20 mg/kg+BD750 20 μg/kg group (Figure 5(f,g)). Flow cytometry results showed that compared with the 20 mg/kg geniposide group, the number of CD4+ FOXP3+ cells in the spleen of UC mice treated with 20 mg/kg geniposide and 20 μg/kg BD750 was significantly reduced. There was no significant difference in CD4+ FOXP3+ cells in the spleen between the UC group and the geniposide 20 mg/kg+BD750 20 μg/kg group (Figure 5(h,i)). Figure 5.Immunosuppressive agents cancel the promotion effect on splenic cell Treg differentiation and the inhibitory effect of geniposide on inflammation in UC mice. UC model mice were treated with geniposide (20 mg/kg) alone or in combination with the immunosuppressive agent BD750 (20 μg/kg) for 4 weeks. ( a) Representative images of immunohistochemistry of IL-1β, IL-10, and TNF-α in colon tissues of each group of mice. ( b) related to A. The mean optical density analysis of IL-1β, IL-10 and TNF-α expression in colon tissues of mice in each group. ( C) Serum IL-1β levels were detected by ELISA. ( d) Serum IL-10 levels were detected by ELISA. ( e) Serum TNF-α levels were detected by ELISA. ( f) Representative images of immunohistofluorescence of FOX3 in colon tissues of each group of mice. ( g) related to F. The mean optical density analysis of FOX3 expression in colon tissues of mice in each group. ( h) Representative flow cytometry images showing the percentage of CD4-positive and FOX3-positive cells in the spleen cells of each group of mice. ( i) related to F. The percentage of CD4 and FOX3 double-positive cells in mouse spleen cells was calculated. A-B and F-I, $$n = 3$.$ C-E, $$n = 6$.$ * $p \leq 0.05$, **$p \leq 0.01.$
## The relief of inflammation and tight joint damage by geniposide in colonic cells requires its induction of Treg differentiation in spleen cells
To confirm whether the relief of geniposide on the tight joint damage caused by inflammation in Caco-2 cells requires the induction of Treg differentiation in spleen cells, we used 20 nM epirubicin to inhibit FOXP3 in spleen cells in a coculture system. The flow chart shows the grouping and treatment of spleen cells and Caco-2 cells in the coculture system (Figure 6(a)). Flow cytometry results showed that in vitro, the increased percentage of CD4+ FOXP3+ cells in spleen cells by the combined application of geniposide and differentiation induction reagent was inhibited by epirubicin intervention (Figure 6(b,c)). The levels of IL-1β, CCL-2 and TNF-α in the supernatant of the lower compartment of the coculture system were determined by ELISA. The results showed that the combination of geniposide and differentiation induction reagent used in splenic cells significantly decreased the levels of IL-1β, CCL-2, and TNF-α in the LPS-induced Caco-2 culture medium, but these effects were significantly attenuated by epirubicin added to spleen cells (Figure 6(d-f)). Immunofluorescence was used to detect claudin-1 and occludin expression in the cocultured Caco-2 cells. The results showed that after the treatment of splenic cells with geniposide and differentiation induction reagent, the cocultured LPS-treated Caco-2 cells showed significantly increased expression levels of Cluadin-1 and Occludin, but the protective effect of Treg differentiation on tight junctions in spleen cells was significantly inhibited by the application of epirubicin in spleen cells (Figure 6(g-j)). Figure 6.The relief of inflammation and tight joint damage by geniposide in colonic cells requires its induction of Treg differentiation in spleen cells. Spleen cells were isolated from normal mice, and Treg differentiation was induced by 100 U/mL IL-2 and 10 ng/mL TGF-β. The spleen cells were placed in the upper compartment of the coculture chamber, and garinoside (25 μM) was added to treat the differentiation-induced cells. Epirubicin (20 nM) was combined with geniposide (25 μM) in one group. Caco-2 cells were placed in the lower chamber of the coculture chamber, and LPS (10 nM) was used to induce Caco-2 cell damage. ( a) Schematic diagram of coculture spleen cells and Caco-2 cells indicating the experimental grouping and cell intervention. ( b) Upper compartment spleen cells were taken from the coculture system. Representative flow cytometry images show the percentage of CD4-positive and FOX3-positive cells in spleen cells of each group. ( c) related to B. The percentage of CD4 and FOX3 double-positive cells in mouse spleen cells was calculated. ( d) In the coculture system, the culture medium in the lower chamber was taken, and the level of IL-1β was detected by ELISA. ( e) In the coculture system, the culture medium in the lower chamber was taken, and the level of CCL-2 was detected by ELISA. ( f) In the coculture system, the culture medium in the lower chamber was taken, and the level of TNF-α was detected by ELISA. ( g) Representative images of immunohistofluorescence of Claudin-1 in cacO-2 cells from each group. ( h) related to G. The mean optical density analysis of Claudin-1 in the cacO-2 cells of each group. ( i) Representative images of immunohistofluorescence of Occludin in cacO-2 cells of each group. ( j) related to I. The mean optical density analysis of Occludin in cacO-2 cells of each group. B-C and G-J, $$n = 3$.$ D-F, $$n = 6$.$ * $p \leq 0.05$, **$p \leq 0.01.$
## Discussion
UC is a refractory intestinal disease involving the terminal ileum or the whole colon in severe cases that causes abdominal pain, diarrhea, bloody stools, lower gastrointestinal bleeding, perforation and other symptoms and even endangers lives [30]. The pathogenesis of UC has not been fully elucidated, and its clinical treatment currently focuses on anti-inflammatory and immunomodulatory therapy [31]. Geniposide, as an important component of the traditional medicine Gardenia jasminoides Ellis, has attracted great attention for its anti-inflammatory effect [13,32]. Our current study aims to confirmed the therapeutic effect of geniposide on UC and to provide experimental evidence for the potential mechanism of geniposide on UC treatment. We proved that geniposide regulates immunity by promoting Treg differentiation in the spleen to inhibit intestinal inflammation and ameliorate intestinal barrier injury to alleviate UC.
By exploring changes in body weight, colon tissue injury, inflammatory cytokines and Treg differentiation of spleen cells in model mice, we confirmed that geniposide inhibited weight loss, reduced inflammation and intestinal tissue injury, and promoted Treg differentiation of spleen cells in UC mice. In contrast, the therapeutic effect of geniposide on UC mice was blocked by the immunosuppressive agent BD750. More importantly, we stimulated mouse spleen cells with geniposide and used the stimulated spleen cells to coculture with intestinal epithelial cells, confirming that geniposide reduces inflammation-induced tight junction injury of colon cells by promoting Treg differentiation. The coapplication of FOXP3 inhibitor and geniposide in mouse spleen cells confirmed that the relief of geniposide on inflammatory colonic barrier injury requires the correct induction of Treg differentiation in spleen cells.
Malnutrition caused by UC is the most common clinical symptom [33]. Malnutrition is one of the most important factors for poor clinical outcomes in patients with intestinal inflammatory diseases [34]. The severity of UC depends on disease activity, duration, and degree, especially by proinflammatory cytokines such as TNF-α, IL-1, and IL-6, which may increase catabolism and lead to anorexia [35] and may be responsible for weight loss in UC patients [33]. As we observed in UC mice, geniposide significantly inhibited weight loss. Intestinal tissue injury is not only the cause of intestinal nutritional function decline but also a standard indicator of clinical colonoscopy and histological examination of UC [5]. We found that the colon tissue of the weight-losing UC mice was severely damaged, while gardenitin significantly improved the pathological changes in the colon tissue of UC mice, which may restore the nutritional function of the gut. As previously reported that geniposide is an adipose thermogenesis inhibitor that can ameliorate metabolic disease [36].
Increased intestinal permeability occurs at the early stage of inflammatory bowel diseases and severely impairs the nutritional function of the intestine [37]. As an important part of the intestinal mucosal barrier, the intestinal epithelial barrier is composed of complete intestinal epithelial cells and cell junctions, and tight junctions are considered to be the structural basis of the intestinal epithelial barrier [38]. Tight junctions include at least 50 membrane-related proteins [39], and the key proteins include Claudins, Occludin and ZO⁃1 [40]. Claudins and Occludin are transmembrane proteins, and they are connected to each other to form the main structure of tight connections [41]. Our results confirmed that in the colon tissues of UC mice, the expression levels of Claudins and Occludin were significantly decreased, and this decrease was significantly alleviated by geniposide. The intestinal mucosal barrier is the most important functional and morphological structure to prevent bacterial translocation in the intestine, and tight junction damage promotes the occurrence of deep inflammation in the intestinal tissue [42]. We found that IL-1β and TNF-α levels were significantly increased in the injured colon tissue and serum of UC mice, and gardenitin decreased inflammatory factors in colon tissue and circulation while improving colon barrier function. Our experimental results on the effects of geniposide on intestinal inflammation and the intestinal barrier are consistent with previous reports [17–19].
The imbalance between proinflammatory factors and anti-inflammatory factors is one of the important pathogeneses of UC, which is one of the most important factors leading to intestinal barrier damage and permeability increase [43]. IL-1β and TNF-α have been reported to promote UC, as we observed here. IL-10, as an important anti-inflammatory cytokine in the cellular immune response, can inhibit antigen presentation and proinflammatory cytokines, including TNF-α and IL-12, thus reducing mucosal inflammation [44]. Our results showed that geniposide increased IL-10 levels in colon tissue and serum of UC mice. In the past, studies have shown that chronic ileocolitis in IL-10 gene deletion mice is aggravated, the therapeutic effect of anti-inflammatory drugs is canceled, and loss of IL-10 receptor function also leads to severe UC [45,46]. Study have shown that geniposide can promote the expression of IL-10 in injured tissues of diabetic rats. The expression of IL-10 induced by geniposide helps to inhibit the expression of proinflammatory factors [47].
IL-10 can be secreted by a variety of cells involved in innate and adaptive immune responses, including monocytes, B cells, T cells, natural killer cells, macrophages and dendritic cells [48]. However, the production of IL-10 in UC is almost always from CD4 + T cells, and the small amount of IL-10 in the normal colon is mainly produced by other white blood cells rather than T cells [49]. The imbalance of T-cell subsets leads to an inappropriate immune response, resulting in cell damage and long-term inflammation, which is of great significance in the pathogenesis of UC [50,51]. Treg cells are essential for self-tolerance and immune homeostasis, and Treg cells can inhibit intestinal autoimmune inflammation in the lamina propria [52]. Treg cells in the peripheral blood of UC patients were reduced, but FOXP3 was highly expressed in the intestinal mucosa of UC patients, indicating that Treg cells may be enriched in the intestinal mucosa to inhibit the proinflammatory immune response of UC patients [53,54]. Our results showed that in the mice, Treg cells in spleen and colon tissues were both significantly reduced after long-term UC induction, which may indicate the weakening of the anti-inflammatory immune response in colon tissues of UC mice.
Moreover, we confirmed that geniposide can reduce inflammation and tight junction injury in colon cells by inducing Treg differentiation. We isolated mouse spleen cells and conducted noncontact coculture with colon epithelial cells in vitro. In spleen cells, we induced Treg differentiation in purified spleen cells using IL-2 and TGF-β. We confirmed that geniposide increased Treg differentiation in spleen cells and enhanced the resistance of cocultured Caco-2 cells to LPS stimulation, showing the decreased loss of claudins and occludin in Caco-2 cells. More importantly, in vivo, when we combined BD750 (a potent immunosuppressant that inhibits IL-2-induced T-cell activation) with geniposide in UC mice, the alleviating effects of geniposide on weight loss, intestinal inflammation, and intestinal barrier damage in UC mice were abolished. These results confirm that the therapeutic effect of geniposide on UC depends on its immunomodulatory effect.
Study shows that geniposide upregulates FOXP3 expression to promote the number and function of Treg cells, in part through lipid regulation and immune regulation to ameliorate the progression of atherosclerotic lesions in mice [55]. As a transcription factor, FOXP3 plays an important role in the development and function of CD4+ Treg cells [56]. Normalization of FOXP3+ Treg cells in the lamina propria inhibits the development of colitis in mice [56]. FOXP3+ Treg cells secrete cytokines, including IL-10, and inhibit intestinal inflammatory responses related to innate or acquired immunity [57]. These studies suggest the potential therapeutic effect of enhanced FOXP3+ Treg cells in UC. At the end of this study, FOXP3 inhibitor and geniposide were used to treat mouse spleen cells, and we found that the induction of Treg differentiation by geniposide was inhibited by FOXP3 inhibitor, as well as the alleviating effect on inflammation and injury of geniposide on colon cells. Our experiment confirmed that the relief of geniposide on colon inflammatory barrier damage depends on its induction of Treg differentiation in spleen cells.
## Conclusion
In the current study, we confirmed the effective therapeutic effect of geniposide on UC and proved that increasing Treg differentiation of spleen cells may be the mechanism by which geniposide alleviates intestinal inflammation and barrier damage in UC. Geniposide is a candidate drug for UC treatment.
## Disclosure statement
No potential conflict of interest was reported by the author(s).
## Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/$\frac{10.1080}{21655979.2022.2092678}$
## References
1. Eisenstein M.. **Ulcerative colitis: towards remission**. *Nature* (2018) **563** S33. PMID: 30405234
2. Gajendran M, Loganathan P, Jimenez G. **A comprehensive review and update on ulcerative colitis()**. *Dis Mon* (2019) **65** 100851. PMID: 30837080
3. Du L, Ha C. **Epidemiology and pathogenesis of ulcerative colitis**. *Gastroenterol Clin North Am* (2020) **49** 643-654. PMID: 33121686
4. Molodecky NA, Soon IS, Rabi DM. **Increasing incidence and prevalence of the inflammatory bowel diseases with time, based on systematic review**. *Gastroenterology* (2012) **142** 46-54 e42. PMID: 22001864
5. Ungaro R, Mehandru S, Allen PB. **Ulcerative colitis**. *Lancet* (2017) **389** 1756-1770. PMID: 27914657
6. Singh S, Allegretti JR, Siddique SM. **AGA technical review on the management of moderate to severe ulcerative colitis**. *Gastroenterology* (2020) **158** 1465-1496 e1417. PMID: 31945351
7. Bressler B, Marshall JK, Bernstein CN. **Clinical practice guidelines for the medical management of nonhospitalized ulcerative colitis: the Toronto consensus**. *Gastroenterology* (2015) **148** 1035-1058 e1033. PMID: 25747596
8. Feuerstein JD, Cheifetz AS. **Ulcerative colitis: epidemiology, diagnosis, and management**. *Mayo Clin Proc* (2014) **89** 1553-1563. PMID: 25199861
9. Alsoud D, Verstockt B, Fiocchi C. **Breaking the therapeutic ceiling in drug development in ulcerative colitis**. *Lancet Gastroenterol Hepatol* (2021) **6** 589-595. PMID: 34019798
10. Hirten RP, Sands BE. **New therapeutics for ulcerative colitis**. *Annu Rev Med* (2021) **72** 199-213. PMID: 33502898
11. Naganuma M, Sugimoto S, Mitsuyama K. **Efficacy of indigo naturalis in a multicenter randomized controlled trial of patients with ulcerative colitis**. *Gastroenterology* (2018) **154** 935-947. PMID: 29174928
12. Cao SY, Ye SJ, Wang WW. **Progress in active compounds effective on ulcerative colitis from Chinese medicines**. *Chin J Nat Med* (2019) **17** 81-102. PMID: 30797423
13. Ran D, Hong W, Yan W. **Properties and molecular mechanisms underlying geniposide-mediated therapeutic effects in chronic inflammatory diseases**. *J Ethnopharmacol* (2021) **273** 113958. PMID: 33639206
14. Li N, Li L, Wu H. **Antioxidative property and molecular mechanisms underlying geniposide-mediated therapeutic effects in diabetes mellitus and cardiovascular disease**. *Oxid Med Cell Longev* (2019) **2019** 7480512. PMID: 31089416
15. Xu B, Li YL, Xu M. **Geniposide ameliorates TNBS-induced experimental colitis in rats via reducing inflammatory cytokine release and restoring impaired intestinal barrier function**. *Acta Pharmacol Sin* (2017) **38** 688-698. PMID: 28260798
16. Lu Y, Chen J, He X. **Combined administration of vitamin D3 and geniposide is less effective than single use of vitamin D3 or geniposide in the treatment of ulcerative colitis**. *Front Pharmacol* (2021) **12** 714065. PMID: 34650431
17. Yang H, Yue Y, Li Y. **Geniposide attenuates dextran sulfate sodium-induced colitis in mice via Nrf-2/HO-1/NF-kappaB pathway**. *Ann Palliat Med* (2020) **9** 2826-2836. PMID: 32787366
18. Zhang Z, Li Y, Shen P. **Administration of geniposide ameliorates dextran sulfate sodium-induced colitis in mice via inhibition of inflammation and mucosal damage**. *Int Immunopharmacol* (2017) **49** 168-177. PMID: 28595080
19. Pu Z, Liu Y, Li C. **Using network pharmacology for systematic understanding of geniposide in ameliorating inflammatory responses in colitis through suppression of NLRP3 inflammasome in macrophage by AMPK/Sirt1 dependent signaling**. *Am J Chin Med* (2020) **48** 1693-1713. PMID: 33202149
20. Hindryckx P, Jairath V, D’Haens G. **Acute severe ulcerative colitis: from pathophysiology to clinical management**. *Nat Rev Gastroenterol Hepatol* (2016) **13** 654-664. PMID: 27580687
21. Neurath MF. **Targeting immune cell circuits and trafficking in inflammatory bowel disease**. *Nat Immunol* (2019) **20** 970-979. PMID: 31235952
22. Chen G, Ran X, Li B. **Sodium butyrate inhibits inflammation and maintains epithelium barrier integrity in a TNBS-induced inflammatory bowel disease mice model**. *EBioMedicine* (2018) **30** 317-325. PMID: 29627390
23. Lian P, Braber S, Varasteh S. **Hypoxia and heat stress affect epithelial integrity in a Caco-2/HT-29 co-culture**. *Sci Rep* (2021) **11** 13186. PMID: 34162953
24. Meng Q, Li Y, Ji T. **Estrogen prevent atherosclerosis by attenuating endothelial cell pyroptosis via activation of estrogen receptor alpha-mediated autophagy**. *J Adv Res* (2021) **28** 149-164. PMID: 33364052
25. Zhang H, Yuan Z, Zhu Y. **Th17/Treg imbalance mediates hepatic intolerance to exogenous lipopolysaccharide and exacerbates liver injury in triptolide induced excessive immune response**. *J Ethnopharmacol* (2022) **295** 115422. PMID: 35654348
26. Meng Q, Yu X, Chen Q. **Liuwei Dihuang soft capsules inhibits the phenotypic conversion of VSMC to prevent the menopausal atherosclerosis by up-regulating the expression of myocardin**. *J Ethnopharmacol* (2020) **246** 112207. PMID: 31476440
27. Meng Q, Li J, Chao Y. **beta-estradiol adjusts intestinal function via ERbeta and GPR30 mediated PI3K/AKT signaling activation to alleviate postmenopausal dyslipidemia**. *Biochem Pharmacol* (2020) **180** 114134. PMID: 32628929
28. Luz-Crawford P, Kurte M, Bravo-Alegria J. **Mesenchymal stem cells generate a CD4+CD25+Foxp3+ regulatory T cell population during the differentiation process of Th1 and Th17 cells**. *Stem Cell Res Ther* (2013) **4** 65. PMID: 23734780
29. Miao Y, Zheng Y, Geng Y. **The role of GLS1-mediated glutaminolysis/2-HG/H3K4me3 and GSH/ROS signals in Th17 responses counteracted by PPARgamma agonists**. *Theranostics* (2021) **11** 4531-4548. PMID: 33754076
30. Conrad K, Roggenbuck D, Laass MW. **Diagnosis and classification of ulcerative colitis**. *Autoimmun Rev* (2014) **13** 463-466. PMID: 24424198
31. Kobayashi T, Siegmund B, Le Berre C. **Ulcerative colitis**. *Nat Rev Dis Primers* (2020) **6** 74. PMID: 32913180
32. Zhou YX, Zhang RQ, Rahman K. **Diverse pharmacological activities and potential medicinal benefits of geniposide**. *Evid Based Complement Alternat Med* (2019) **2019** 4925682. PMID: 31118959
33. Balestrieri P, Ribolsi M, Guarino MPL. **Nutritional aspects in inflammatory bowel diseases**. *Nutrients* (2020) **12** 372. PMID: 32023881
34. Mizukawa K, Takayama H, Sato H. **Alterations of muscarinic cholinergic receptors in the hippocampal formation of stressed rat: in vitro quantitative autoradiographic analysis**. *Brain Res* (1989) **478** 187-192. PMID: 2924116
35. Cabre E, Gassull MA. **Nutrition in inflammatory bowel disease: impact on disease and therapy**. *Curr Opin Gastroenterol* (2001) **17** 342-349. PMID: 17031181
36. Li Y, Zhang K, Liu J. **Geniposide suppresses thermogenesis via regulating PKA catalytic subunit in adipocytes**. *Toxicology* (2021) **464** 153014. PMID: 34718029
37. Michielan A, D’Inca R. **Intestinal permeability in inflammatory bowel disease: pathogenesis, clinical evaluation, and therapy of leaky gut**. *Mediators Inflamm* (2015) **2015** 628157. PMID: 26582965
38. Buckley A, Turner JR. **Cell biology of tight junction barrier regulation and mucosal disease**. *Cold Spring Harb Perspect Biol* (2018) **10** a029314. PMID: 28507021
39. Gunzel D, Fromm M. **Claudins and other tight junction proteins**. *Compr Physiol* (2012) **2** 1819-1852. PMID: 23723025
40. Gunzel D, Yu AS. **Claudins and the modulation of tight junction permeability**. *Physiol Rev* (2013) **93** 525-569. PMID: 23589827
41. Otani T, Furuse M. **Tight junction structure and function revisited**. *Trends Cell Biol* (2020) **30** 805-817. PMID: 32891490
42. Sharma D, Malik A, Guy CS. **Pyrin inflammasome regulates tight junction integrity to restrict colitis and tumorigenesis**. *Gastroenterology* (2018) **154** 948-964 e948. PMID: 29203393
43. Steinbach EC, Plevy SE. **The role of macrophages and dendritic cells in the initiation of inflammation in IBD**. *Inflamm Bowel Dis* (2014) **20** 166-175. PMID: 23974993
44. Saraiva M, Vieira P, O’Garra A. **Biology and therapeutic potential of interleukin-10**. *J Exp Med* (2020) **217**. DOI: 10.1084/jem.20190418
45. Geng Y, Yue Y, Guan Q. **Cereal vinegar sediment alleviates spontaneous ulcerative colitis in Il-10 deficient mice**. *Mol Nutr Food Res* (2021) **65** e2001227. PMID: 34699119
46. Murphy SF, Rhee L, Grimm WA. **Intestinal epithelial expression of TNFAIP3 results in microbial invasion of the inner mucus layer and induces colitis in IL-10-deficient mice**. *Am J Physiol Gastrointest Liver Physiol* (2014) **307** G871-882. PMID: 25234043
47. Chen XY, Jiang WW, Liu YL. **Anti-inflammatory action of geniposide promotes wound healing in diabetic rats**. *Pharm Biol* (2022) **60** 294-299. PMID: 35130118
48. Yao Y, Simard AR, Shi FD. **IL-10-producing lymphocytes in inflammatory disease**. *Int Rev Immunol* (2013) **32** 324-336. PMID: 23617759
49. Melgar S, Yeung MM, Bas A. **Over-expression of interleukin 10 in mucosal T cells of patients with active ulcerative colitis**. *Clin Exp Immunol* (2003) **134** 127-137. PMID: 12974765
50. Geremia A, Biancheri P, Allan P. **Innate and adaptive immunity in inflammatory bowel disease**. *Autoimmun Rev* (2014) **13** 3-10. PMID: 23774107
51. Corridoni D, Antanaviciute A, Gupta T. **Single-cell atlas of colonic CD8(+) T cells in ulcerative colitis**. *Nat Med* (2020) **26** 1480-1490. PMID: 32747828
52. Cook L, Stahl M, Han X. **Suppressive and gut-reparative functions of human type 1 T regulatory cells**. *Gastroenterology* (2019) **157** 1584-1598. PMID: 31513797
53. Schmitt H, Ulmschneider J, Billmeier U. **The TLR9 AGONIST COBITOLIMOD INDUCES IL10-producing wound healing macrophages and regulatory T cells in ulcerative colitis**. *J Crohns Colitis* (2020) **14** 508-524. PMID: 31630153
54. Rosenzwajg M, Lorenzon R, Cacoub P. **Immunological and clinical effects of low-dose interleukin-2 across 11 autoimmune diseases in a single, open clinical trial**. *Ann Rheum Dis* (2019) **78** 209-217. PMID: 30472651
55. Liao P, Liu L, Wang B. **Baicalin and geniposide attenuate atherosclerosis involving lipids regulation and immunoregulation in ApoE-/- mice**. *Eur J Pharmacol* (2014) **740** 488-495. PMID: 24991786
56. Deng G, Song X, Fujimoto S. **Foxp3 post-translational modifications and treg suppressive activity**. *Front Immunol* (2019) **10** 2486. PMID: 31681337
57. Long Y, Wang C, Xia C. **Recovery of CD226-TIGIT(+)FoxP3(+) and CD226-TIGIT-FoxP3(+) regulatory T cells contributes to clinical remission from active stage in ulcerative colitis patients**. *Immunol Lett* (2020) **218** 30-39. PMID: 31883787
|
---
title: β‑aminoisobutyric acid ameliorates hypertensive vascular remodeling via activating
the AMPK/SIRT1 pathway in VSMCs
authors:
- Bo Yin
- Yu-Bin Wang
- Xiang Li
- Xu-Wei Hou
journal: Bioengineered
year: 2023
pmcid: PMC9995136
doi: 10.1080/21655979.2022.2085583
license: CC BY 4.0
---
# β‑aminoisobutyric acid ameliorates hypertensive vascular remodeling via activating the AMPK/SIRT1 pathway in VSMCs
## ABSTRACT
Excessive proliferation and migration of vascular smooth muscle cells (VSMCs) play a fundamental role in the pathogenesis of hypertension-related vascular remodeling. β-aminoisobutyric acid (BAIBA) is a nonprotein β-amino acid with multiple pharmacological actions. Recently, BAIBA has been shown to attenuate salt‑sensitive hypertension, but the role of BAIBA in hypertension-related vascular remodeling has yet to be fully clarified. This study examined the potential roles and underlying mechanisms of BAIBA in VSMC proliferation and migration induced by hypertension. Primary VSMCs were cultured from the aortas of Wistar-Kyoto rats (WKY) and spontaneously hypertensive rats (SHR). Our results showed that BAIBA pretreatment obviously alleviated the phenotypic transformation, proliferation, and migration of SHR-derived VSMCs. Exogenous BAIBA significantly inhibited the release of inflammatory cytokines by diminishing phosphorylation and nuclear translocation of p65 NFκB, retarding IκBα phosphorylation and degradation, as well as erasing STAT3 phosphorylation in VSMCs. Supplementation of BAIBA triggered Nrf2 dissociation from Keap1 and inhibited oxidative stress in VSMCs from SHR. Mechanistically, activation of the AMPK/sirtuin 1 (SIRT1) axis was required for BAIBA to cube hypertension-induced VSMC proliferation, migration, oxidative damage and inflammatory response. Most importantly, exogenous BAIBA alleviated hypertension, ameliorated vascular remodeling and fibrosis, abated vascular oxidative burst and inflammation in SHR, an effect that was abolished by deficiency of AMPKα1 and SIRT1. BAIBA might serve as a novel therapeutic agent to prevent vascular remodeling in the context of hypertension.
## Graphic Abstract
## Highlights
BAIBA attenuated the proliferation and migration of SHR-derived VSMCs. BAIBA inhibited VSMC inflammation and oxidative stress in SHR.BAIBA activated the LKB1/AMPK/SIRT1 axis to suppress hypertensive vascular injury. BAIBA ameliorated hypertension-related vascular fibrosis and inflammation.
## Introduction
Hypertension is still a serious public health issue worldwide since high blood pressure is believed to be a leading cause of people premature morbidity and death [1,2]. Uncontrolled hypertension is a predisposing factor for various life-threatening disorders, such as myocardial hypertrophy and infarction, aortic dissection, nephropathy and stroke [3,4]. *Both* genetics and environmental factors are involved in the etiologies of hypertension [5]. Although our current understanding of the pathogenesis of hypertension is greatly advanced and the treatment medications are widely available, a large number of hypertensive subjects are still suffering from uncontrolled blood pressure [6,7]. Mounting evidence suggests that disrupted vascular homeostasis plays a pathogenic role in the development and progression of hypertension [8]. Pathological vascular remodeling is not only a feature of hypertension, but also a pathological basis of maintaining hypertension [9,10]. Reversing vascular remodeling may provide a new strategy for the management of hypertension. In healthy vasculature, vascular smooth muscle cells (VSMCs) exhibit a contractile phenotype (a dedifferentiation phenotype) and serve as fundamental components for vascular cardiovascular homeostasis [11,12]. Under pathological conditions, including hypertensive irritants, VSMCs might undergo phenotypic conversion from a contractile phenotype to a synthetic phenotype (a differentiation phenotype), leading to excessive proliferation and migration of VSMCs, a core event of vascular remodeling [13–15]. Therefore, identification of novel molecular targets related to VSMC biological behaviors might be highly necessary to develop more effective treatments for hypertension.
β-aminoisobutyric acid (BAIBA) is produced from the skeletal muscle tissues during physical activities and acts as a skeletal muscle-derived myokine to induce numerous biological effects [16]. Specifically, BAIBA is able to ameliorate hyperglycemia-induced insulin resistance and inflammation in mice fed by a high-fat diet (HFD) in an AMP-activated protein kinase (AMPK)/peroxisome proliferator-activated receptor δ (PPARδ)-dependent manner [17]. Exogenous BAIBA is documented to inhibit lipopolysaccharide (LPS)-induced secretion of pro-inflammatory cytokines in differentiated 3 T3T-L1 mouse adipocytes through AMPK-dependent signaling [18]. Treatment of human umbilical vascular endothelial cells (HUVECs) and monocytes with BAIBA significantly suppresses LPS-induced proinflammatory cytokine production and endoplasmic reticulum stress via activating the AMPK signaling pathway [19]. Pretreatment with BAIBA blocks angiotensin II (Ang II)-induced extracellular matrix (ECM), inflammatory factor generation, and nicotinamide adenine dinucleotide phosphate oxidase (NOX2)-derived reactive oxygen species (ROS) production, ultimately ameliorating renal fibrosis [20]. BAIBA is reported to reverse hypothalamic inflammation in HFD-induced mice [21]. Recently, application of BAIBA represses ROS production and apoptosis in rat pheochromocytoma (PC12) cells exposed to hydrogen peroxide (H2O2), an effect that is mediated by the AMPK/phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) pathway [22]. Rats with myocardial infarction (MI) are accompanied by cardiac mitochondrial dysfunction, metabolic stress and apoptosis, whereas exercise largely attenuated these changes by induction of BAIBA [23]. Importantly, exogenous administration of BAIBA shows the similar cardiovascular protection as exercise in MI rats through the downstream target AMPK, as indicated by a combined transcriptomic with metabolomic analysis [23]. A recent study has shown that BAIBA supplementation reverses salt‑sensitive hypertension and attenuates oxidative stress in the renal medulla of Dahl salt‑sensitive rats via the phosphorylation of AMPK [24]. Based on the above findings, we hypothesized that BAIBA may serve as a potential antihypertensive candidate with strong anti-oxidative and anti-inflammatory effects. However, it remains to be addressed whether BAIBA is beneficial in ameliorating hypertension and its associated vascular remodeling in spontaneously hypertensive rats (SHR), a classical animal model of hypertension.
In this study, we aimed to investigate the effects of BAIBA on the proliferation, migration, inflammation and oxidative stress of VSMCs, and to explore whether BAIBA attenuated the development of hypertension and its associated vascular remodeling. In addition, we examined whether the AMPK/SIRT1 pathway contributed to the effects of BAIBA on VSMC functions and hypertension development in vitro and in vivo.
## Reagents and chemicals
The specific primers used in this study were synthesized by Sangon Biotech Co., Ltd (Shanghai, China). More information about the main reagents and chemicals used in this study was provided in Supplementary Table 1.
## Animals
Male Wistar-Kyoto rats (WKY) and SHR aged 12 weeks were purchased from the Vital River Laboratory Animal Technology Co. Ltd. (Beijing, China). All procedures were approved by the Institutional Animals Care and Use Ethics Committee at Jinzhou Medical University (No. 2,019,051) and conducted following the guidelines published by the China Animal Protection Association (Laboratory Animal Management Regulations). The ethical approval obtained for the study (No. 2,019,051) allowed us to investigate the effects of BAIBA on blood pressure in WKY and SHR. All animal experimental procedures were also in keeping with the ARRIVE guidelines (https://arriveguidelines.org/). Animals had free access to standard chow and tap water under a temperature and humidity room on a 12-h light/dark cycle. Six WKY were intraperitoneally injected with BAIBA (100 mg/kg) [17], and 6 WKY received the same volume of normal saline for 4 weeks. Simultaneously, 12 SHR ($$n = 6$$ for each group) were subjected to intraperitoneal injection of saline and BAIBA (100 mg/kg), respectively. To determine whether AMPKα1 and SIRT1 mediated the hypotensive effect of BAIBA, 12 SHR rats were injected with AMPKα1 and SIRT1 lentivirus particles (20 μl stock solution for each rat) through caudal vein three days prior to BAIBA treatment. After that, these hypertensive rats were treated with BAIBA (100 mg/kg) for subsequent 4 weeks. At the end of the experiments, the systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and heart rate (HR) were recorded, and the aortic samples were harvested for pathological staining and biochemical detection [25].
## Measurement of blood pressure and heart rate (HR)
The rats were warmed for 20 min at 28°C in order to detect the pulse of caudal artery and reach a stable pulse level. Systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and heart rate (HR) in conscious rats were determined using a noninvasive computerized tail-cuff IITC blood pressure system (MRBP-2, Woodland Hills, CA). The blood pressure and HR were calculated from 10 measurements.
## Histological examination
The aortic tissues were immersed in $4\%$ paraformaldehyde for at least 24 h. After embedded in paraffin wax, the aorta was cut into slices at 5 µm per section and stained with hematoxylin and eosin (H&E). Sections were observed with a phase-contrast microscope (80i, Nikon, Japan). Paraffinembedded sections of aortas were stained with Picro Sirius Red Stain Kit under standard protocols. The images were collected using a light microscope (80i, Nikon, Japan). The media thickness and lumen diameter were used as indicators of vascular remodeling.
## Primary cell isolation and culture
Primary VSMCs were isolated and cultured as previously depicted [25,26]. In short, the thoracic aortas were dissected in sterile PBS after removal of connective tissues and fat around the vessels. Each aorta was cut longitudinally and the intima was stripped. The aortas were then incubated with collagenase II ($0.2\%$) and pancreatic elastase (1.25 U/ml) for 30 min at 37°C. The adventitia was then stripped, and the vascular media was digested with collagenase II ($0.4\%$) in DMEM supplemented with $20\%$ FBS for 30 min. The isolated VSMCs were collected and cultured in DMEM medium containing $10\%$ FBS, 100 units/mL penicillin, and 100 μg/mL streptomycin in an incubator containing $95\%$ air and $5\%$ CO2. Cells in the third to fifth passages were used for mRNA/protein examinations or functional analysis.
## Cell proliferation and migration
The proliferation of VSMCs was assessed by CCK-8 assay and EdU staining according to the manufacturer’s protocols [26]. In brief, VSMCs were seeded onto the 96-well plates with 104 cells per well plate and cultured for 24 h. After treatment with BAIBA (3, 10, 30 μM) for 48 h, CCK-8 solution (10 μl) was added into the culture medium (100 μl) at the indicated wells and incubated for 2 h at 37°C. The absorbance of each sample was measured at 450 nm with a microplate reader (Varioskan Flash, Thermo Electron Corporation, Waltham, MA. USA). The cell viability was expressed by calculating the average optical density ratio of the experimental group/control group. For EdU incorporation assay, the collected VSMCs were co-incubated with EdU (50 mM) for 2 h, and were stained with Hoechst 33,342 for 30 min after fixation and permeabilization. The fluorescence images were captured by a fluorescence microscope (DP70, Olympus Optical, Tokyo, Japan), and the proliferating cells were green, and cell proliferation rate was expressed by a normalization of EdU-positive cells to the total number of cells from five random fields. The migration of VSMCs was evaluated by using a transwell chamber, and the cells migrated to the lower surface of the filter were stained with crystal violet ($1\%$) and captured using a phase-contrast microscope (80i, Nikon, Japan). The number of stained VSMCs was counted from five randomly chosen fields in each well.
## Real time fluorescence quantitative reverse transcription polymerase chain reaction (qRT-PCR)
The total RNA was separated by using TRIzol™ Reagent (15,596,026, Thermo Fisher Scientific, Carlsbad, CA, USA) in accordance with the manufacturer’s suggestions [27], and total RNA in each sample was subjected to reverse transcriptase reactions using the PrimeScript RT reagent Kit, and qRT-PCR was performed by using ChamQTM SYBR® qPCR Master Mix (Vazyme, Nanjing, China) under a StepOnePlus Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). The sequences of specific primers were provided in the online supplementary tables (Supplementary Table S2).
## Immunoblotting analysis
VSMCs and aortas were harvested and proteins in each sample were extracted by Western blot and IP cell lysates (P0013, Beyotime, Shanghai, China) for 30 min at 4°C. Equal amount of protein samples (30 μg) in each group were electrophoresed, transferred to a polyvinylidene fluoride membrane (GVHP04700, Millipore, Darmstadt, Germany), followed by secondary antibody conjugated with horseradish peroxidase. The blots were visualized by the enhanced chemiluminescent (WBKLS0500, Millipore, Billerica, MA, USA) and quantified by scanning densitometry with the aid of Western blot analysis system (Tanon, Shanghai, China). The phosphorylated proteins were determined after 30 min of treatment, and the non-phosphorylated proteins were detected after 48 h of treatment.
## Enzyme-Linked Immunosorbent Assay (ELISA)
Cell-secreted TNF-α, IL-1β, IL-6 and MCP-1 were measured with each ELISA kit according to the manufacturer’s instructions as previously described [28,29]. For TNF-α ELISA kits, the detection range was 15.6 pg/ml to 1000 pg/ml, the sensitivity was less than 2 pg/ml, the coefficient of variation (CV) was $2.5\%$, and the samples are generally not diluted for TNF-α measurement. For IL-1β ELISA kits, the detection range was 31.2 pg/ml to 2000 pg/ml, the sensitivity was less than 1 pg/ml, the CV was $4.8\%$, and the samples are generally not diluted for IL-1β measurement. For MCP-1 ELISA kits, the detection range was 15.6 pg/ml to 1000 pg/ml, the sensitivity was less than 0.5 pg/ml, the CV was $2.5\%$, and the samples are diluted in a ratio of 1:2 for MCP-1 measurement. For IL-6 ELISA kits, the detection range was 12.5 pg/ml to 4000 pg/ml, the sensitivity was less than 5 pg/ml, the CV was $2.1\%$, and the samples are generally not diluted for IL-6 measurement.
## Nuclear and cytoplasmic protein extraction
NE-PER™ Nuclear and cytoplasmic extraction reagents were utilized for the isolation and purification of nuclear and cytoplasmic proteins [30]. The harvested cells were transferred to a 1.5 mL microcentrifuge tube and pelleted by centrifugation at 500 × g for 2–3 minutes. The supernatant was carefully removed and discarded using a pipette to carefully remove and discard the supernatants, and ice-cold CER I was added into the dry cell pellet, and the complex was vortexed to fully suspend the cell pellet, and incubated on ice for 10 min. The ice-cold CER II was added to the tube and incubated on ice for 1 min, and the complex was centrifuged for 5 minutes at maximum speed in a microcentrifuge (~16,000 × g). The supernatant (cytoplasmic extract) was immediately transferred to a clean pre-chilled tube. The insoluble (pellet) fraction (containing the nuclei contents) was then suspended in ice-cold NER and vortexed for 15 seconds every 10 minutes, for a total of 40 minutes. The supernatant (nuclear extract) fraction to a clean pre-chilled tube after centrifuging the tube at maximum speed (~16,000 × g) in a microcentrifuge for 10 minutes. The nuclear and cytoplasmic proteins were then subjected to Western blot.
## Membrane protein extraction
For the extraction of the membrane proteins, Mem-PER™ Plus Membrane Protein Extraction Kit was used in conformance to the manufacturer’s procedures. The cells were harvested, washed by 3 mL of cell wash solution, centrifuged at 300 × g for 5 minutes. The suspended cell pellet was incubated with 0.75 mL of Permeabilization Buffer for 10 minutes at 4°C with constant mixing. The permeabilized cells were centrifuged for 15 minutes at 16,000 × g, and 0.5 mL of Solubilization Buffer was added to the pellet and resuspended by pipetting up and down for 30 minutes at 4°C. Finally, the supernatant containing solubilized membrane and membrane-associated proteins were obtained by centrifuging tubes at 16,000 × g for 15 minutes at 4°C. The cytosolic and membrane fractions were stored as aliquots at −80°C for future use.
## Measurement of oxidative markers
Intracellular ROS was measured with DHE fluorescent dye [31]. After fixation, the VSMCs and aortas were incubated with DHE (10 μM) for 30 min at 37°C under dark environment as previously reported [32,33]. The samples were then washed with phosphate buffered saline (PBS) and immediately observed on fluorescence microscope (DP70, Olympus Optical, Tokyo, Japan). The fluorescence signal intensity was quantified using the IMAGE-PRO PLUS 6.0 (Media Cybernetics, Bethesda, MD). The MDA levels were detected using a commercially available assay kit, the standard and samples were mixed with the three reagents provided by the kit, and the complex was boiled for 40 min at 95°C. Afterward, the cooled samples were centrifuged for 10 minutes at 4000 rpm to collect the supernatants, and the absorbance of the supernatants in each sample was then measured at 532 nm. Also, the activities of SOD were measured by a commercially available assay kit, and the optical density was examined at 550 nm by a microplate reader (Varioskan Flash, Thermo Electron Corporation, Waltham, MA. USA).
## Immunofluorescence staining
After treatment, cells were fixed with $4\%$ paraformaldehyde for 30 min, and permeabilized by Triton X-100 ($0.1\%$) for 15 min. The cells were then blocked by $10\%$ goat serum for 60 min at room temperature, followed by incubation with the primary Nrf2 antibody overnight at 4°C. After washing with PBS for 3 times, the cells were then incubated with a goat anti-rabbit IgG H&L Alexa Fluor® 594 in blocking buffer for 1 h at room temperature, and DAPI was used to mark the nucleus. Images were captured with a fluorescence microscope (80i, Nikon, Tokyo, Japan)
## Immunoprecipitation
The cells were harvested and lysed, and the protein concentration in each sample was measured by BCA protein assay kit (23225, Thermo Fisher Scientific, IL, USA). Equal amounts of proteins were immunoprecipitated by antibodies specific to Keap1 (1 μg) or acetyllysine (1 μg) for 2 hours at 4°C and the Protein G PLUS-Agarose (20 μl) was added into the corresponding tubes at 4°C on a rocker platform. The pellets were collected by centrifugation at 3,000 rpm for 30 seconds at 4°C. After aspirating and discarding the supernatant, the pellets were then resuspend in electrophoresis sample buffer (40 µl) and boiled for 3 minutes. Finally, the immune complexes were determined by Western blot with anti-Nrf2 antibody and anti-STAT3 antibody, respectively.
## Transfection with siRNA
For AMPK1α1 and LKB1 knockdown, VSMCs were plated the day before siRNA transfection grown to 30–$50\%$ confluence. A scrambled siRNA (100 nM), AMPK1α1 siRNA(100 nM), and LKB1 siRNA (100 nM) were transfected to VSMCs using Lipofectamine 2000 (11668019, Thermo Fisher Scientific, IL, USA) following the manufacturer’s instructions. Cells were then incubated with the corresponding treatments for additional detection.
## Statistical analysis
All data were expressed as mean ± SEM. Figure legends indicated the number of independent assays. The independent experiments were repeated at least four times. Comparisons between two groups were made by unpaired student’s t tests. One-way or two-way ANOVA followed by post-hoc Bonferroni test was used for multiple comparisons. Difference with a value of $P \leq 0.05$ was recognized as statistically significant.
## BAIBA attenuates the phenotypic conversion, proliferation and migration of SHR-derived VSMCs
The contractile-synthetic phenotype switch of VSMCs is a critical step for their acquisition of abnormal proliferative and migratory abilities [13]. Excessive proliferation and migration of VSMCs are driving forces for the development of hypertension and vascular remodeling [34]. To evaluate the potential role of BAIBA in hypertensive vascular remodeling in vitro, we first explored the actions of BAIBA on the proliferation and migration of VSMCs. CCK-8 assay showed that treatment with BAIBA attenuated the cell viability of VSMCs from SHR in which BAIBA at dose of 10 μM and 30 μM exhibited the comparable effects (Figure 1(a)). Therefore, the dose of BAIBA used in the following cellular experiments was 10 μM. The anti-proliferative effects of BAIBA (10 and 30 μM) were further confirmed by EdU staining (Figure 1(b,c)). In addition, the upregulated protein expression of PNCA, a marker of cell proliferation, in SHR-derived VSMCs, was obviously downregulated by administration of BAIBA (Figure 1(d)), suggesting that BAIBA had a strong capability to inhibit VSMC proliferation in SHR. Dysregulation of VSMC differentiation and dedifferentiation is a key event involving the development of vascular remodeling-related diseases, such as hypertension [35]. In response to hypertensive stimuli, VSMCs might undergo phenotype alterations from a differentiated phenotype to a dedifferentiated phenotype, a major initiating factor for the excessive proliferation and migration of VSMCs [36]. Thus, we further determined the effects of BAIBA on the phenotype switching in VSMCs. RT-PCR results showed that BAIBA restored the differentiated phenotype of SHR-derived VSMCs by upregulating the expression of contractile proteins α-smooth muscle actin (α-SMA) and smooth muscle 22α (SM22α), and downregulating the synthetic protein osteopontin (OPN) (Figure 1e), indicating that BAIBA negatively regulated the phenotypic transformation in aortic media of SHR. Consistent with the effects of BABAI on the proliferation of VSMCs, Boyden chamber assay demonstrated that the migration of SHR-derived VSMCs was diminished after BAIBA treatment although BAIBA had no significant effects on VSMC migration in WKY (Figure 1(f,g)). The inhibitory effects of BAIBA on VSMC migration were further ascertained by measurement of protein MMP-2 and MMP-9 expressions (Figure 1(h)). Thus, we reached a conclusion that BAIBA was capable of curbing hypertension-induced VSMC phenotypic conversion, proliferation, and migration. Figure 1.BAIBA attenuates the phenotypic transformation, proliferation and migration of SHR-derived VSMCs. ( a) VSMCs were treated with various doses of BABAI for 48 h, and the proliferation of VSMCs was determined by CCK-8. ( b) EdU-positive cells measured with Edu incorporation assay. Scale bar = 100 μm. ( c) Relative EdU-positive cells. ( d) Represented blots and relative quantification of PCNA. ( e) Relative mRNA levels of αsma, Sm22α, and Opn. ( f, g) Effects of curcumin (BAIBA) on VSMC migration measured with Boyden chamber assay. Scale bar = 200 μm (h) Represented blots and relative quantification of MMP-2 and MMP-9. Values are mean ± S.E. * $P \leq 0.05$ vs. WKY, † $P \leq 0.05$ vs. SHR. $$n = 4$$–5 for each group.
## BAIBA attenuates inflammation in SHR-derived VSMCs
Inflammation response in VSMCs is a pervasive characteristic during the development of hypertension and blockade of vascular inflammation is effectively attenuating hypertensive vascular remodeling [26,37]. On these grounds, we tested the potential anti-inflammatory effects of BAIBA on SHR-derived VSMCs. In keeping with a previous report [25], the expressions of inflammatory cytokines including Tnfα, Il1β, Mcp1, and IL-6 were remarkably elevated in SHR-derived VSMCs, effects that were strikingly blocked by BAIBA treatment (Figure 2(a-c)). Abberant activation of the p65 NFκB and STAT3 signaling pathways are critically involved in vascular inflammation and remodeling in hypertension [26,38]. Phosphoryla-tion and degradation of NF-κB inhibitor α (IκBα) are an upstream event that leads to p65 NFκB phosphorylation and its nuclear translocation, thus leading to upregulations of inflammatory factors [39,40]. The phosphorylation levels of p65 NF-κB, IκBα, and STAT3, the protein of IκBα, as well as subcellular localization of p65 NF-κB were examined in VSMCs of WKY and SHR. As expected, BAIBA therapy inhibited the phosphorylation levels of p65 NF-κB and IκBα, reduced the degradation of IκBα, and restrained p65 NF-κB in the cytoplasm in VSMCs isolated from SHR (Figure 2(d-e)). Parallelingly, BAIBA treatment resulted in a trend toward decreased STAT3 phosphorylation in SHR-originated VSMCs (Figure 2(f)). Collectively, we demonstrated that BAIBA exhibited anti-inflammatory effects on VSMCs through inhibiting p65 NF-κB and STAT3 pathways. Figure 2.BAIBA attenuates VSMC inflammation in SHR-derived VSMCs. ( a) Represented blots and relative quantification of TNF-α, IL-1β, MCP-1, and IL-6. ( b) Relative mRNA levels of Tnfα, Il1β, Mcp1, and Il6. ( c) ELISA assays for TNF-α, IL-1β, MCP-1, and IL-6. ( d) Represented blots and relative quantification of phosphorylated p65 NF-κB, IκBα, and phosphorylated IκBα. ( e) Represented blots and relative quantification of p65 NF-κB in the cytoplasm and nucleus. ( f) Represented blots and relative quantification of phosphorylated STAT3. Values are mean ± S.E. * $P \leq 0.05$ vs. WKY, † $P \leq 0.05$ vs. SHR. $$n = 4$$–5 for each group.
## BAIBA attenuates oxidative stress in SHR-derived VSMCs
Upon exposure to ROS overproduction, oxidative stress is activated in VSMCs, leading to the proliferation and migration of VSMCs, as well as vascular remodeling in hypertension-related diseases [41]. The phosphorylation of p47phox is required for its membrane translocation, and NADPH oxidase activation, as well as following ROS generation in cells [42]. We therefore examined whether or not BAIBA could ameliorate hypertension-evoked oxidative stress in VSMCs. The protein expression levels of NOX1, NOX4, phosphorylated p47phox, and membrane p47phox tended to be higher in SHR-derived VSMCs, whereas BAIBA treatment (30 μM) almost completely abolished these upregulations (Figure 3(a)). This was further reflected by measurement of DHE fluorescence, SOD activities and MDA contents as shown in Figure 3(b-d). Kelch-like ECH-associated protein 1 (Keap1) is a well-known inhibitor of nuclear factor (erythroid-derived 2)-like 2 (Nrf2) nuclear activity by forming the Keap1/Nrf2 complex and inducing subsequent ubiquitination and rapid proteasomal degradation of Nrf2 [43]. The Keap1/Nrf2 system is a key component of the oxidative stress response that acts as a dominator in maintaining redox homeostasis, and dissociation of Keap1 with Nrf2 leads to robust induction of a battery of cytoprotective antioxidant genes [44]. For this reason, we tested whether the antioxidative effects of BAIBA were related with the Keap1/Nrf2 system in VSMCs. In the presence of BAIBA, the nuclear abundance of Nrf2 was further augmented, but the protein expression of Keap1 was further inhibited in SHR-derived VSMCs (Figure 3(e,g)). Moreover, co-IP results showed that incubation of VSMCs with BAIBA promoted Nrf2 dissociation from Keap1 (Figure 3(f,h)). Immunofluorescence staining results further confirmed that BAIBA triggered the nuclear deposition of Nrf2 since BAIBA-treated VSMCs had a more proportion of nuclear fluorescence localization cells when compared to control cells (Figure 3(i)). Accordingly, BAIBA transcriptionally raises the mRNA levels of Nrf2 downstream target gene, including heme oxygenase 1 (Ho1), NAD(P)H: quinone oxidoreductase-1 (Nqo1), Glutamate-Cysteine Ligase Catalytic Subunit (Gclc), and Glutamate-Cysteine Ligase Modifier Subunit (Gclm) in VSMCs of WKY, and restored such reduced genes in SHR-derived VSMCs. To sum up, we provided ample evidence that BAIBA attenuated oxidative stress in SHR-derived VSMCs via inhibiting p47phox phosphorylation and membrane translocation, as well as activating the Nrf2 system. Figure 3.BAIBA attenuates VSMC oxidative stress in SHR-derived VSMCs. ( a) Represented blots and relative quantification of NOX1, NOX4, p47phox, and membrane p47phox. ( b) Represented images and relative quantification of DHE. Scale bar = 150 μm. ( c) SOD activities. ( d) MDA contents. ( e, g) Represented blots and relative quantification of nuclear Nrf-2 and Keap1. ( f, h) Cell lysates were immunoprecipitated with an anti-Keap1 antibody and blotted with an anti-Nrf2 antibody. ( i) Immunofluorescence images of Nrf-2. Scale bar = 200 μm. ( j) Relative mRNA levels of Ho1, Nqo1, Gclc, and Gclm. Values are mean ± S.E. * $P \leq 0.05$ vs. WKY, † $P \leq 0.05$ vs. SHR. $$n = 4$$–5 for each group.
## BAIBA activates the LKB1/AMPK/SIRT1 axis to suppress p65 NF-κB and STAT3 acetylation
AMP-activated protein kinase (AMPK) inactivation and downregulation of SIRT1 is associated with cardiovascular remodeling in hypertension [45,46]. As an agonist of SIRT1, resveratrol is established to induce VSMC differentiation through stimulation of AMPK signaling [47], indicating a SIRT1/AMPK axis involved in the regulation of vascular remodeling. Very recently, phosphorylation of AMPK underlies BAIBA’s antihypertensive effects in Dahl salt-sensitive rats [24]. Therefore, we examined whether activation of the AMPK/SIRT1 signaling pathway contributed to the actions of BAIBA on VSMCs. As shown in Figure 4(a), BAIBA time-dependently increased the phosphorylation levels of AMPK, with maximal effects after incubation of BAIBAI for 30 min. BAIBA stimulation elevated levels of P-AMPK and SIRT1 in SHR VSMCs (Figure 4(b)). Gene deficiency of AMPKα1 by small interfering RNA (siRNA) prevented the positive effects of BAIBA on the protein expression of SIRT1 in VSMCs from SHR, indicating an AMPK/SIRT1 signaling axis induced by BAIBA in VSMCs (Fig. S1a, b). Moreover, the higher levels of acetylated p65 NF-κB and STAT3 in SHR-derived VSMCs were abrogated by exposure of BAIBA (Figure 4(c)). Up to now, liver kinase B1 (LKB1), transforming growth-factor-β-activated kinase-1 (TAK1) and Ca2+/calmodulin-dependent protein kinase kinase-β (CaMKKβ) are well-established upstream kinases that results in AMPK activation [48,49]. Next, a selective CaMKKβ inhibitor STO-609, a potent and selective TAK1 inhibitor Takinib, and siRNA of LKB1 (Fig. S2) were used to address how BAIBA activates AMPK in VSMCs. Western blot results showed that neither STO-609 nor Takinib had no effect on the activation of AMPK induced by BAIBA (Figure 4(d,e)). However, ablation of LKB1 obviously prevented BAIBA-triggered AMPK activation (Figure 4(f)). In summary, these results suggested that BAIBA might activate AMPK in a LKB1-dependent manner, and the LKB1/AMPK/SIRT1 axis might partially contribute to the inhibitory effects of BAIBA on VSMC proliferation and migration in SHR. Figure 4.BAIBA activates the LKB1/AMPK/SIRT1 axis to suppress p65 NF-κB and STAT3 acetylation. ( a) Effects of BAIBA (10 μM) on the phosphorylation levels of AMPK. ( b) Represented blots and relative quantification of P-AMPK and SIRT1. ( c) Represented blots and relative quantification of acetylated p65 NF-κB and STAT3. ( d) VSMCs were pretreated with STO-609 (0.8 μM) for 30 min, and then were challenged by BAIBA (10 μM) for 30 min to determine the phosphorylated AMPK, and for 48 h to determine the SIRT1 protein expression. ( e) VSMCs were pretreated with Takinib (10 mM) for 30 min, and then were challenged by BAIBA (10 μM) for 30 min to determine the phosphorylated AMPK, and for 48 h to determine the SIRT1 protein expression. ( f) VSMCs were transfected with LKB1 siRNA (100 nM) for 24 h, and then were challenged by BAIBA (10 μM) for 30 min to determine the phosphorylated AMPK, and for 48 h to determine the SIRT1 protein expression. Values are mean ± S.E. * $P \leq 0.05$ vs. 0 min or WKY, † $P \leq 0.05$ vs. SHR, # $P \leq 0.05$ vs. SHR+BAIBA. $$n = 4$$ for each group.
## Inhibition of the AMPK/SIRT1 axis abolishes the effects of BAIBA on VSMCs
To further confirm whether the protective effects of BAIBA on hypertensive VSMC injury were dependent on the AMPK/SIRT1 axis, we used AMPK siRNA and SIRT1 inhibitor EX-527 to inhibit AMPK and SIRT1, respectively. As anticipated, the capabilities of BAIBA to inhibit VSMC proliferation and migration were attenuated when VSMCs were treated with AMPK siRNA and EX-527 (Figure 5(a)). Corresponding to this change, after knocking down AMPK and inhibiting SIRT1, the expression levels of PCNA, MMP-2, MMP-9 did not decrease in SHR-derived VSMCs after BAIBA administration (Figure 5(b)). In addition, the protective effects of BAIBA against hypertension-induced VSMC oxidative stress and inflammation were also interdicted by both deletion of AMPK and blockade of SIRT1 (Figure 5(b)). These observations indicated the fundamental involvement of the AMPK/SIRT1 axis in the benefits of BAIBA in SHR-derived VSMCs. Figure 5.Inhibition of AMPK/SIRT1 axis abolishes the effects of BAIBA on VSMCs. ( a) VSMCs were transfected with AMPKα1 siRNA (100 nM) or treated with EX-527 (10 μM) for 24 h, and then were challenged by BAIBA (10 μM) for 48 h. The proliferation and migration of VSMCs were assessed by EdU staining (upper, scale bar = 100 μm) and transwell assay (lower, scale bar = 200 μm). ( b) Represented blots and relative quantification of PCNA, MMP-2, and MMP-9. ( c) MDA contents and SOD activities. ( d) Relative mRNA levels of Tnfα and Il1β. Values are mean ± S.E. * $P \leq 0.05$ vs. WKY, † $P \leq 0.05$ vs. SHR, # $P \leq 0.05$ vs. SHR+BAIBA. $$n = 4$$–5 for each group.
## Activation of AMPK/SIRT1 signaling is required for BAIBA to ameliorate hypertension-related vascular remodeling and oxidative stress
Given the inhibitory effects of BAIBA on VSMC proliferation and migration, it is still interesting to know whether BAIBA could mitigate hypertension and its associated vascular remodeling in rodents. We next examined whether long-term intraperitoneal injection of BAIBA improved hypertension and its associated vascular remodeling and oxidative stress in SHR, and whether the effects of BAIBA were abrogated by inactivation of the AMPK/SIRT1 signaling pathway. Long-term infusion of BAIBA had no effects on blood pressure and heart rate in WKY, but caused significant reductions in SBP, DBP, and MAP in SHR (Figure 6(a)). Representative photographs of HE-stained aortas demonstrated that the media thickness, and ratio of media thickness to lumen diameter were lower in SHR treated with BAIBA (Figure 6(b,d)), hinting that vascular remodeling in SHR was significantly attenuated by BAIBA. DHE fluorescence staining showed that BAIBA treatment lessened ROS overproduction in arteries of SHR (Figure 6(c,e)). Similar to these observations, the protein expressions of PCNA and NOX1 in SHR aortas were substantially increased, while such increases were dropped after BAIBAI treatment for 4 weeks (Figure 6(f)). We next investigated whether knockdown of AMPKα1 and SIRT1 by shRNA could prevent the antihypertensive and vascular protective effects of BAIBA in animals. The interference efficiency of AMPKα1 shRNA and SIRT1 shRNA was confirmed by immunoblotting (Fig. S3). Inevitably, supplementation of BAIBA lost the protective effects against hypertension, vascular remodeling and oxidative stress in SHR subjected to knockdown of either AMPKα1 or SIRT1 (Figure 6(a-f)), indicating the involvement of AMPK/SIRT1 signaling in mediating the effects of BAIBA in SHR. Figure 6.Activation of AMPK/SIRT1 signaling is required for BAIBA ameliorates hypertension-related vascular remodeling and oxidative stress in SHR. ( a) Systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and heart rate (HR) in conscious state. ( b, d) Representative photographs and quantitative analysis of aortas by HE staining. Scale bar = 100 μm. ( c, e) Representative photographs and quantitative analysis of aortas by DHE staining. Scale bar = 100 μm. ( f) Represented blots and relative quantification of PCNA and NOX1. Values are mean ± S.E. * $P \leq 0.05$ vs. 0 min or WKY, † $P \leq 0.05$ vs. SHR, # $P \leq 0.05$ vs. SHR+BAIBA. $$n = 4$$–6 for each group.
## Activation of AMPK/SIRT1 signaling is required for BAIBA ameliorates hypertension-related vascular fibrosis and inflammation
Vascular fibrosis and inflammation are important components of vascular remodeling in hypertension, and reversing vascular fibrosis/inflammation plays a beneficial role in the management of hypertension [50]. Eventually, we explored whether BAIBA acted on the AMPK/SIRT1 signaling pathway to relieve hypertension-induced vascular fibrosis and inflammation in rats. In consistence with the effects of BAIBA on hypertensive vascular remodeling, administration of BAIBA for 4 weeks reduced vascular fibrosis in aortas of SHR, an observation that was eliminated by lentivirus mediated downregulations of AMPKα1 and SIRT1 in SHR (Figure 7(a,b)). These findings were further confirmed by measurement of mRNA levels of Tgfβ1 and collagen1 (Figure 7(c)). Furthermore, deficiency of AMPKα1 and SIRT1 removed the protective effects of BAIBA on vascular fibrosis and inflammation in response to hypertension, as indicated by protein expressions of TGF-β1, collagen I, TNF-α, IL-1β, MCP-1, and IL-6 (Figure 7(d)). In summary, the obtained data implied that the protective effects of BAIBA against hypertension-induced vascular fibrosis and inflammation were dependent on the AMPK/SIRT1 signaling pathway. Figure 7.Activation of AMPK/SIRT1 signaling is required for BAIBA ameliorates hypertension-related vascular fibrosis and inflammation. ( a, b) Representative photographs and quantitative analysis of aortas by sirius red staining. ( c) Relative mRNA levels of Tgfβ1 and Collagen1. ( d) Represented blots and relative quantification of TGF-β1, collagen I, TNF-α, IL-1β, MCP-1, and IL-6. Values are mean ± S.E. * $P \leq 0.05$ vs. 0 min or WKY, † $P \leq 0.05$ vs. SHR, # $P \leq 0.05$ vs. SHR+BAIBA. $$n = 4$$–6 for each group.
## Discussion
Reversing vascular remodeling is increasingly recognized as one of the potential strategies for the prevention and treatment of hypertension. In the present study, we demonstrated for the first time that exogenous BAIBI relieved the development of hypertension and its related vascular remodeling, fibrosis, inflammation, and oxidative stress in SHR, an effect that was AMPK/SIRT1-dependent. Cellular experiments displayed that BAIBI inhibits the proliferation and migration of VSMCs by downregulating the PCNA and MMP-2/MMP-9 expressions. Exposure of BAIBA suppressed inflammation response in VSMCs via blocking p65 NF-κB and IκBα phosphorylation, and IκBα degradation, as well as STAT3 phosphorylation. BAIBA treatment inhibited VSMC oxidative injury by disrupting the Keap1/Nrf2 complex and facilitating Nrf2 nuclear translocation. Importantly, the LKB1/AMPK/SIRT1 signaling pathway participated in the effects of BAIBA on VSMC proliferation, migration, oxidative stress and inflammation in the context of hypertension. Hence, BAIBAI might hold tremendous promise for the prevention and treatment of hypertension and vascular remodeling.
Abnormal proliferation and migration of VSMCs are critical events that are responsible for the pathologies of several vascular diseases, such as atherosclerosis, restenosis, and hypertension [51–53]. VSMC phenotype switching from the contractile phenotype to synthetic phenotype is a requisite step for the excessive proliferation and migration of VSMCs in a host of vascular disorders, including hypertension [54,55]. The phenotypic transformation of VSMCs is reflected by increased synthetic proteins including OPN and decreased contractile proteins such as α-SMA and SM22α in hypertensive vascular remodeling [56,57]. MMP-2 and MMP-9 play a crucial role in the initiation and development of VSMC migration by control of degradation of ECM proteins around VSMCs [58]. Our results showed that BAIBA had no effect on the proliferation and migration of VSMCs from normotensive rats, but attenuated VSMC proliferation and migration in SHR, with concomitant decreases in protein expressions of PCNA, MMP-2, and MMP-9. In addition, the mRNA levels of contractile proteins αsma and Sm22α were inhibited, while the mRNA level of synthetic protein Opn were lifted in SHR-derived VSMCs, which was markedly reversed by BAIBA treatment. The in vitro effects of BAIBA was phenocopied in BAIBA-treated SHR since chronic infusion of BAIBA decreased blood pressure and alleviated vascular remodeling in the aortas of SHR. These results collectively indicated that BAIBA plays a beneficial role in the development of hypertension and vascular remodeling in SHR by decelerating VSMC phenotypic transformation, and subsequent proliferation and migration.
Persistent low-grade VSMC inflammation and oxidative stress play vital roles in the initiation and development of hypertension-related vascular remodeling in experimental models [37,59]. Inhibition of inflammatory response and oxidative stress is closely related with decreased hypertension-related target organ damage, including vascular remodeling [60–63]. Activation of p65 NFκB and STAT3 signaling pathways is observed in inflammatory disorders, blockade of this signaling might benefit hypertension and its related vascular remodeling [64–66]. In this study, we found that BAIBA inhibited the release of proinflammatory factors at protein and mRNA levels in SHR-derived VSMCs by impeding p65 NFκB and STAT3 activation. In other words, BAIBI blocked the phosphorylation and degradation of IκBα, mitigated the phosphorylation and nuclear shift of p65 NFκB, coinciding with decreased STAT3 phosphorylation, thus suppressing the transcription/translation of related proinflammatory genes in VSMCs of SHR. NADPH oxidases are thought to be the main source of ROS in the vascular system, and its activation leads to oxidative stress in VSMCs, and subsequent VSMC proliferation and migration, a critical event of hypertensive vascular modeling [41]. In various oxidative stress-related diseases, the assembly and activation of NADPH oxidase could be induced by the phosphorylation of p47phox and exclusion of cytosolic p47phox [67,68]. Contrary to the pro-oxidative action ofp47phox activation, Nrf2 is an important transcription factor to positively regulate multiple antioxidant genes in the antioxidant defense system [69,70]. The Keap1-Nrf2 pathway is the major regulator of cytoprotective responses to oxidative stress, and the dissociation of this complex is exploited to treat oxidative stress-related diseases [71,72]. This study revealed a novel antioxidant mechanism in which BAIBA retarded VSMC oxidative stress by attenuating p47phox phosphorylation and its following membrane translocation, and promoting the isolation of Nrf2 from Keap1 and the subsequent nuclear accumulation of Nrf2. More importantly, we found that chronic administration of BAIBA obliterated vascular inflammation and oxidative stress in aortas of SHR. These above findings provide ample evidence that BAIBA plays a role in the treatment of hypertension and vascular remodeling, at least through anti-inflammatory and antioxidant effects.
AMPK and SIRT1 function as energy sensors that regulate the proliferation, survival and senescence of mammalian cells [73,74]. AMPK is generally activated by phosphorylation and its activation protects against hypertensive vascular remodeling [75]. SIRT1 has gained considerable attention as a therapeutic target for various diseases [76]. As a NAD(+)-dependent deacetylase, SIRT1 confers healthy benefits in various diseases by deacetylation and dephosphorylation of its target genes p65 NF-κB and STAT3 [77]. Activation of AMPK and SIRT1 is found to suppress the proliferation, migration, inflammation and oxidative stress of VSMCs [72,78–80]. In this study, we found that BAIBA time-dependently stimulated AMPK phosphorylation in WKY VSMCs and showed a trend of increase in phosphorylated AMPK and SITR1 of SHR VSMCs. Knockdown of AMPKα1 abolished the effects of BAIBA on the protein expression of SIRT1 in SHR-derived VSMCs, indicating a regulatory role of BAIBA in the AMPK/SIRT1 signaling cascaded. Moreover, we identified that the acetylation levels of p65 NF-κB and STAT3 tended to be normal in SHR VSMCs after BAIBA treatment. Intriguingly, we further demonstrated that BAIBA-mediated activation of AMPK is relied on LKB1, but not TAK1 or CaMKKβ. Furthermore, ectopic expression of AMPKα1 siRNA and EX-527-mediated inactivation of SIRT1 eliminated the ameliorating actions of BAIBA on VSMC proliferation, migration, oxidative injury and inflammation in SHR. In parallel to these in vitro results, the improvement of hypertension, vascular remodeling, fibrosis, oxidative stress and inflammation was profoundly reduced in BAIBA-treated SHR when AMPKα1 and SIRT1 in aortas were genetically deleted. These observations consistently suggested that BAIBA could activate the LKB1/AMPK/SIRT1 signaling pathway to prevent VSMC proliferation, migration, oxidative stress, and inflammation, thus attenuating the development of hypertension and vascular remodeling in SHR.
## Conclusion
In summary, the present study uncovers new insights that BAIBA could serve as a novel and potent therapeutic target for hypertension and its associated pathological vascular remodeling by activating the LKB1/AMPK/SIRT1 signaling pathway. Future studies are required to develop and confirm these findings in multiple in vivo models with hypertension. However, there are several limitations in the present study. Firstly, we did not detect the level of BAIBA in peripheral blood of hypertensive patients and the relationship between BAIBA level and vascular function in hypertensive patients. Secondly, the dose range of BAIBA should be expanded at the animal level to provide a basis for selecting the best antihypertensive effect of BAIBA in vivo. Thirdly, the pharmacodynamics and metabolism of BAIBA in vitro and in vivo were unknown, this deserved further studies. Fourthly, the advantages and disadvantages of BAIBA versus traditional antihypertensive drugs are currently unclear, and its side effects should be considered in future preclinical and clinical studies. Fifthly, the pathological phenotype of BAIBAI in the cardiovascular system is slightly insufficient. In future studies, we will try to clarify the exact role of BAIBA on the pathological phenotypes of the cardiovascular system through more preclinical and clinical studies.
## Author’s Contribution
B.Y., and X.W.H. designed the experiments. B.Y., Y.B.W., and X.L. conducted the experiments, statistics, and created figures and tables. The manuscript was prepared, written and reviewed by all the authors.
## Disclosure statement
No potential conflict of interest was reported by the author(s).
## Ethical approval and consent to participate
All procedures were approved by the Institutional Animals Care and Use Ethics Committee at Jinzhou Medical University (No. 2019051) and conducted following the guidelines published by the China Animal Protection Association (Laboratory Animal Management Regulations). All animal experimental procedures were also in keeping with the ARRIVE guidelines (https://arriveguidelines.org/).
## Data availability statement
The data analyzed during the current study are available from the corresponding author on reasonable request.
## Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/$\frac{10.1080}{21655979.2022.2085583}$
## References
1. Travieso-Gonzalez A, Núñez-Gil IJ, Riha H. **Management of arterial hypertension: 2018 ACC/AHA versus ESC guidelines and perioperative implications**. *J Cardiothorac Vasc Anesth* (2019) **33** 3496-3503. PMID: 31078374
2. Ye C, Zheng F, Wu N. **Extracellular vesicles in vascular remodeling**. *Acta Pharmacol Sin* (2022). DOI: 10.1038/s41401-021-00846-7
3. Sekar D, Shilpa BR, Das AJ.. **Relevance of microRNA 21 in different types of hypertension**. *Curr Hypertens Rep* (2017) **19** 57. PMID: 28597404
4. Zhang JR, Sun HJ.. **Extracellular vesicle-mediated vascular cell communications in hypertension: mechanism insights and therapeutic potential of ncRNAs**. *Cardiovasc Drugs Ther* (2022) **36** 157-172. PMID: 32964302
5. Bei Y, Das S, Rodosthenous RS. **Extracellular vesicles in cardiovascular theranostics**. *Theranostics* (2017) **7** 4168-4182. PMID: 29158817
6. Zhang JR, Sun HJ. **LncRNAs and circular RNAs as endothelial cell messengers in hypertension: mechanism insights and therapeutic potential**. *Molecular Biology Reports* (2020) **47** 5535-5547. PMID: 32567025
7. Zhang C, Niu K, Lian P. **Pathological bases and clinical application of long noncoding RNAs in cardiovascular diseases**. *Hypertension (Dallas, Tex.: 1979)* (2021) **78** 16-29. PMID: 34058852
8. Ren XS, Tong Y, Qiu Y. **MiR155-5p in adventitial fibroblasts-derived extracellular vesicles inhibits vascular smooth muscle cell proliferation via suppressing angiotensin-converting enzyme expression**. *J Extracell Vesicles* (2020) **9** 1698795. PMID: 31839907
9. Tong Y, Ye C, Ren XS. **Exosome-mediated transfer of ACE (Angiotensin-Converting Enzyme) from adventitial fibroblasts of spontaneously hypertensive rats promotes vascular smooth muscle cell migration**. *Hypertension (Dallas, Tex.: 1979)* (2018) **72** 881-888. PMID: 30354715
10. Ling L, Chen D, Tong Y. **Fibronectin type III domain containing 5 attenuates NLRP3 inflammasome activation and phenotypic transformation of adventitial fibroblasts in spontaneously hypertensive rats**. *J Hypertens* (2018) **36** 1104-1114. PMID: 29303830
11. Brown PA. **Differential and targeted vesiculation: pathologic cellular responses to elevated arterial pressure**. *Mol Cell Biochem* (2022) **477** 1023-1040. PMID: 34989921
12. Wang S, Li P, Jiang G. **Long non-coding RNA LOC285194 inhibits proliferation and migration but promoted apoptosis in vascular smooth muscle cells via targeting miR-211/PUMA and TGF-β1/S100A4 signal**. *Bioengineered* (2020) **11** 718-728. PMID: 32619136
13. Zhang JR, Sun HJ. **MiRNAs, lncRNAs, and circular RNAs as mediators in hypertension-related vascular smooth muscle cell dysfunction**. *Hypertens Res* (2021) **44** 129-146. PMID: 32985618
14. Jiang X, Ning Q. **Long noncoding RNAs as novel players in the pathogenesis of hypertension**. *Hypertens Res* (2020) **43** 597-608. PMID: 32020084
15. Cheng Q, Zhang M, Zhang M. **Long non-coding RNA LOC285194 regulates vascular smooth muscle cell apoptosis in atherosclerosis**. *Bioengineered* (2020) **11** 53-60. PMID: 31884873
16. Tanianskii DA, Jarzebska N, Birkenfeld AL. **Beta-aminoisobutyric acid as a novel regulator of carbohydrate and lipid metabolism**. *Nutrients* (2019) **11** 524. PMID: 30823446
17. Jung TW, Hwang HJ, Hong HC. **BAIBA attenuates insulin resistance and inflammation induced by palmitate or a high fat diet via an AMPK-PPARδ-dependent pathway in mice**. *Diabetologia* (2015) **58** 2096-2105. PMID: 26105792
18. Jung TW, Park HS, Choi GH. **β-aminoisobutyric acid attenuates LPS-induced inflammation and insulin resistance in adipocytes through AMPK-mediated pathway**. *J Biomed Sci* (2018) **25** 27. PMID: 29592806
19. Lee W, Yun S, Choi GH. **BAIBA attenuates the expression of inflammatory cytokines and attachment molecules and ER stress in HUVECs and THP-1 cells**. *Pathobiol: J Immunol Cell Biol* (2018) **85** 280-288
20. Wang H, Qian J, Zhao X. **β-Aminoisobutyric acid ameliorates the renal fibrosis in mouse obstructed kidneys via inhibition of renal fibroblast activation and fibrosis**. *J Pharmacol Sci* (2017) **133** 203-213. PMID: 28433566
21. Park BS, Tu TH, Lee H. **Beta-aminoisobutyric acid inhibits hypothalamic inflammation by reversing microglia activation**. *Cells* (2019) **8** 1609. PMID: 31835795
22. Minato T, Nakamura N, Saiki T. **β-Aminoisobutyric acid, L-BAIBA, protects PC12 cells from hydrogen peroxide-induced oxidative stress and apoptosis via activation of the AMPK and PI3K/Akt pathway**. *IBRO neuroscience reports 12* (2022) 65-72
23. Yu Y, Chen W, Yu M. **Exercise-generated β-Aminoisobutyric acid (BAIBA) reduces cardiomyocyte metabolic stress and apoptosis caused by mitochondrial dysfunction through the miR-208b/AMPK pathway**. *Front Cardiovasc Med* (2022) **9** 803510. PMID: 35282369
24. Zheng X, Zhou L, Jin Y. **β-Aminoisobutyric acid supplementation attenuated salt-sensitive hypertension in Dahl salt-sensitive rats through prevention of insufficient fumarase**. *Amino Acids* (2022) **54** 169-180. PMID: 34837556
25. Ye C, Geng Z, Zhang LL. **Chronic infusion of ELABELA alleviates vascular remodeling in spontaneously hypertensive rats via anti-inflammatory, anti-oxidative and anti-proliferative effects**. *Acta Pharmacol Sin* (2022). DOI: 10.1038/s41401-022-00875-w
26. Sun HJ, Ren XS, Xiong XQ. **NLRP3 inflammasome activation contributes to VSMC phenotypic transformation and proliferation in hypertension**. *Cell Death & Disease* (2017) **8** e3074. PMID: 28981106
27. Tu H, Wang W, Feng Y. **β-Patchoulene represses hypoxia-induced proliferation and epithelial-mesenchymal transition of liver cancer cells**. *Bioengineered* (2022) **13** 11907-11922. PMID: 35546067
28. Sun H, Zhu X, Cai W. **Hypaphorine attenuates lipopolysaccharide-induced endothelial inflammation via regulation of TLR4 and PPAR-γ dependent on PI3K/Akt/mTOR signal pathway**. *Int J Mol Sci* (2017) **18** 844. PMID: 28420166
29. Xia MZ, Liang YL, Wang H. **Melatonin modulates TLR4-mediated inflammatory genes through MyD88- and TRIF-dependent signaling pathways in lipopolysaccharide-stimulated RAW264.7 cells**. *J Pineal Res* (2012) **53** 325-334. PMID: 22537289
30. Cao X, Nie X, Xiong S. **Renal protective effect of polysulfide in cisplatin-induced nephrotoxicity**. *Redox Biol* (2018) **15** 513-521. PMID: 29413963
31. Ma W, Zhong T, Chen J. **Exogenous H(2)S reverses high glucose-induced endothelial progenitor cells dysfunction via regulating autophagy**. *Bioengineered* (2022) **13** 1126-1136. PMID: 35258406
32. Lu QB, Wan MY, Wang PY. **Chicoric acid prevents PDGF-BB-induced VSMC dedifferentiation, proliferation and migration by suppressing ROS/NFkappaB/mTOR/P70S6K signaling cascade**. *Redox Biol* (2018) **14** 656-668. PMID: 29175753
33. Sun HJ, Zhao MX, Ren XS. **Salusin-beta promotes vascular smooth muscle cell migration and intimal hyperplasia after vascular injury via ROS/NFkappaB/MMP-9 pathway**. *Antioxid Redox Signal* (2016) **24** 1045-1057. PMID: 26952533
34. Yang H, Liu J, Chen X. **Angptl2 gene knockdown is critical for abolishing angiotensin II-induced vascular smooth muscle cell proliferation and migration**. *Biochem Cell Biol* (2022) **100** 59-67. PMID: 34860608
35. Shi N, Chen SY. **Mechanisms simultaneously regulate smooth muscle proliferation and differentiation**. *J Biomed Res* (2014) **28** 40-46. PMID: 24474962
36. Zheng B, Han M, Wen JK. **Role of Krüppel-like factor 4 in phenotypic switching and proliferation of vascular smooth muscle cells**. *IUBMB Life* (2010) **62** 132-139. PMID: 20073036
37. Caillon A, Schiffrin EL. **Role of inflammation and immunity in hypertension: recent epidemiological, laboratory, and clinical evidence**. *Curr Hypertens Rep* (2016) **18** 21. PMID: 26846785
38. Lu QB, Wang HP, Tang ZH. **Nesfatin-1 functions as a switch for phenotype transformation and proliferation of VSMCs in hypertensive vascular remodeling, Biochimica et biophysica acta**. *Mol Basis Dis* (2018) **1864** 2154-2168
39. Kumar A, Chatterjee I, Gujral T. **Activation of nuclear factor-κB by tumor necrosis factor in intestinal epithelial cells and mouse intestinal epithelia reduces expression of the chloride transporter SLC26A3**. *Gastroenterology* (2017) **153** 1338-1350.e3. PMID: 28823863
40. Sun HJ, Zhao MX, Ren XS. **Salusin-β promotes vascular smooth muscle cell migration and intimal hyperplasia after vascular injury via ROS/NFκB/MMP-9 pathway**. *Antioxid Redox Signal* (2016) **24** 1045-1057. PMID: 26952533
41. Badran A, Nasser SA. **Reactive oxygen species: modulators of phenotypic switch of vascular smooth muscle cells**. *Int J Mol Sci* (2020) **21**
42. Belambri SA, Marzaioli V, Hurtado-Nedelec M. **Impaired p47phox phosphorylation in neutrophils from p67phox-deficient chronic granulomatous disease patients**. *Blood* (2022) **139** 2512-2522. PMID: 35108370
43. Manley S, Williams JA, Ding WX. **Role of p62/SQSTM1 in liver physiology and pathogenesis**. *Exp Biol Med (Maywood)* (2013) **238** 525-538. PMID: 23856904
44. Zhang DD, Hannink M. **Distinct cysteine residues in Keap1 are required for Keap1-dependent ubiquitination of Nrf2 and for stabilization of Nrf2 by chemopreventive agents and oxidative stress**. *Mol Cell Biol* (2003) **23** 8137-8151. PMID: 14585973
45. Xu Q, Si LY. **Protective effects of AMP-activated protein kinase in the cardiovascular system**. *J Cell Mol Med* (2010) **14** 2604-2613. PMID: 20874722
46. Xia L, Sun C, Zhu H. **Melatonin protects against thoracic aortic aneurysm and dissection through SIRT1-dependent regulation of oxidative stress and vascular smooth muscle cell loss**. *J pineal res* (2020) **69** e12661. PMID: 32329099
47. Thompson AM, Martin KA, Rzucidlo EM. **Resveratrol induces vascular smooth muscle cell differentiation through stimulation of SirT1 and AMPK**. *PloS one* (2014) **9** e85495. PMID: 24416418
48. Herrero-Martín G, Høyer-Hansen M, García-García C. **TAK1 activates AMPK-dependent cytoprotective autophagy in TRAIL-treated epithelial cells**. *EMBO J* (2009) **28** 677-685. PMID: 19197243
49. Guo Z, Tuo H, Tang N. **Neuraminidase 1 deficiency attenuates cardiac dysfunction, oxidative stress, fibrosis, inflammatory via AMPK-SIRT3 pathway in diabetic cardiomyopathy mice**. *Int J Biol Sci* (2022) **18** 826-840. PMID: 35002528
50. Intengan HD, Schiffrin EL. **Vascular remodeling in hypertension: roles of apoptosis, inflammation, and fibrosis**. *Hypertension (Dallas, Tex.: 1979)* (2001) **38** 581-587. PMID: 11566935
51. Huynh DTN, Heo KS. **Role of mitochondrial dynamics and mitophagy of vascular smooth muscle cell proliferation and migration in progression of atherosclerosis**. *Arch Pharm Res* (2021) **44** 1051-1061. PMID: 34743301
52. Li Z, Xu C, Sun D. **MicroRNA-488 serves as a diagnostic marker for atherosclerosis and regulates the biological behavior of vascular smooth muscle cells**. *Bioengineered* (2021) **12** 4092-4099. PMID: 34288824
53. Gan X, Zhao J, Chen Y. **Plin5 inhibits proliferation and migration of vascular smooth muscle cell through interacting with PGC-1α following vascular injury**. *Bioengineered* (2022) **13** 10665-10678. PMID: 35470759
54. Wang F, Zhen Y, Si C. **WNT5B promotes vascular smooth muscle cell dedifferentiation via mitochondrial dynamics regulation in chronic thromboembolic pulmonary hypertension**. *J Cell Physiol.* (2022;) **237** 789-803. PMID: 34368954
55. Wang S, Li J, Chen A. **Differentiated expression of long non-coding RNA-small nucleolar RNA host gene 8 in atherosclerosis and its molecular mechanism**. *Bioengineered* (2021) **12** 7167-7176. PMID: 34558393
56. Lai YM, Fukuda N, Su JZ. **Novel mechanisms of the antiproliferative effects of amlodipine in vascular smooth muscle cells from spontaneously hypertensive rats**. *Hypertens Res* (2002) **25** 109-115. PMID: 11924715
57. Xiang Y, Li L, Xia S. **Cullin3 (CUL3) suppresses proliferation, migration and phenotypic transformation of PDGF-BB-stimulated vascular smooth muscle cells and mitigates inflammatory response by repressing Hedgehog signaling pathway**. *Bioengineered* (2021) **12** 9463-9472. PMID: 34699319
58. Wang M, Kim SH, Monticone RE. **Matrix metalloproteinases promote arterial remodeling in aging, hypertension, and atherosclerosis**. *Hypertension (Dallas, Tex.: 1979)* (2015) **65** 698-703. PMID: 25667214
59. Camargo LL, Montezano AC, Hussain M. **Central role of c-Src in NOX5- mediated redox signalling in vascular smooth muscle cells in human hypertension**. *Cardiovascular Research* (2022) **118** 1359-1373. PMID: 34320175
60. Zhou B, Wu LL, Zheng F. **miR-31-5p promotes oxidative stress and vascular smooth muscle cell migration in spontaneously hypertensive rats via inhibiting FNDC5 expression**. *Biomedicines* (2021) **9** 1009. PMID: 34440213
61. Wenzel U, Turner JE, Krebs C. **Immune mechanisms in arterial hypertension**. *J Am Soc Nephrol* (2016) **27** 677-686. PMID: 26319245
62. Gan L, Liu D, Liu J. **CD38 deficiency alleviates Ang II-induced vascular remodeling by inhibiting small extracellular vesicle-mediated vascular smooth muscle cell senescence in mice**. *Signal Transduct Target Ther* (2021) **6** 223. PMID: 34112762
63. Tuo B, Xu J, Zhang W. **Upregulation of miR-140-5p uncouples mitochondria by targeting Bcl-xL in vascular smooth muscle cells in angiotensin II-induced hypertension**. *Bioengineered* (2022) **13** 1137-1148. PMID: 35258391
64. Liu H, Jia K, Ren Z. **PRMT5 critically mediates TMAO-induced inflammatory response in vascular smooth muscle cells**. *Cell Death Dis* (2022) **13** 299. PMID: 35379776
65. Jiang Z, Huang J, You L. **Protective effects of BP-1-102 against intracranial aneurysms-induced impairments in mice**. *J Drug Target* (2021) **29** 974-982. PMID: 33682559
66. Hernanz R, Martínez-Revelles S, Palacios R. **Toll-like receptor 4 contributes to vascular remodelling and endothelial dysfunction in angiotensin II-induced hypertension**. *Br J Pharmacol* (2015) **172** 3159-3176. PMID: 25712370
67. Njau F, Haller H. **Calcium dobesilate modulates PKCδ-NADPH oxidase- MAPK-NF-κB signaling pathway to reduce CD14, TLR4, and MMP9 expression during monocyte-to-macrophage differentiation: potential therapeutic implications for atherosclerosis**. *Antioxidants (Basel)* (2021) **10** 1798. PMID: 34829669
68. El Benna J, Faust RP, Johnson JL. **Phosphorylation of the respiratory burst oxidase subunit p47phox as determined by two-dimensional phosphopeptide mapping. Phosphorylation by protein kinase C, protein kinase A, and a mitogen-activated protein kinase**. *J Biol Chem* (1996) **271** 6374-6378. PMID: 8626435
69. Ruan H, Wang L, Wang J. **Sika deer antler protein against Acetaminophen-induced oxidative stress and apoptosis in HK-2 cells via activating Nrf2/keap1/HO-1 pathway**. *J Food Biochem* (2019) **43** e13067. PMID: 31599006
70. Sun H, Zhu X, Zhou Y. **C1q/TNF-related protein-9 ameliorates Ox-LDL-induced endothelial dysfunction via PGC-1α/AMPK-mediated antioxidant enzyme induction**. *Int J Mol Sci* (2017) **18** 1097. PMID: 28587104
71. Kansanen E, Kuosmanen SM, Leinonen H. **The Keap1-Nrf2 pathway: mechanisms of activation and dysregulation in cancer**. *Redox Biol* (2013) **1** 45-49. PMID: 24024136
72. Pillai R, Hayashi M, Zavitsanou AM. **NRF2: kEAPing tumors protected**. *Cancer Discov* (2022) **12** 625-643. PMID: 35101864
73. Wang Y, Liang Y, Vanhoutte PM. **SIRT1 and AMPK in regulating mammalian senescence: a critical review and a working model**. *FEBS Lett* (2011) **585** 986-994. PMID: 21130086
74. Zhao J, He X, Zuo M. **Anagliptin prevented interleukin 1β (IL-1β)-induced cellular senescence in vascular smooth muscle cells through increasing the expression of sirtuin1 (SIRT1)**. *Bioengineered* (2021) **12** 3968-3977. PMID: 34288819
75. Zhao ZY, Shi ZY, Zhang ZZ. **Anti-hypertensive and endothelia protective effects of Fufang Qima capsule on primary hypertension via adiponectin/adenosine monophosphate activated protein kinase pathway**. *J Tradit Chin Med = Chung i tsa chih ying wen pan* (2021) **41** 919-926. PMID: 34939388
76. Ho L, Hossen N, Nguyen T. **Epigenetic mechanisms as emerging therapeutic targets and microfluidic chips application in pulmonary arterial hypertension**. *Biomedicines* (2022) **10** 170. PMID: 35052850
77. Sun HJ, Xiong SP, Cao X. **Polysulfide-mediated sulfhydration of SIRT1 prevents diabetic nephropathy by suppressing phosphorylation and acetylation of p65 NF-κB and STAT3**. *Redox Biol* (2021) **38** 101813. PMID: 33279869
78. Lai CH, Chang CW, Lee FT. **Targeting vascular smooth muscle cell dysfunction with xanthine derivative KMUP-3 inhibits abdominal aortic aneurysm in mice**. *Atherosclerosis* (2020) **297** 16-24. PMID: 32059119
79. Huang S, You S, Qian J. **Myeloid differentiation 2 deficiency attenuates AngII-induced arterial vascular oxidative stress, inflammation, and remodeling**. *Aging (Albany NY)* (2021) **13** 4409-4427. PMID: 33495414
80. Toniolo A, Warden EA, Nassi A. **Regulation of SIRT1 in vascular smooth muscle cells from streptozotocin-diabetic rats**. *PloS one* (2013) **8** e65666. PMID: 23734259
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